增加交易策略、交易指标、量化库代码等文件夹
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'''逐行解释代码：
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1.导入所需的模块和库，包括 time、table（来自 matplotlib.pyplot）、pandas、numpy、numba 和 operator。
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2.定义了一个名为 process 的函数，用于处理买卖盘的字典数据。
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3.定义了一个名为 data 的函数，用于读取并处理 tick 数据，生成分钟级别的 bar 数据。
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4.定义了一个名为 orderflow_df_new 的函数，用于处理 tick 数据和分钟级别的 bar 数据，生成订单流数据。
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5.定义了一个名为 GetOrderFlow_dj 的函数，用于计算订单流的指标(堆积)。
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6.定义了一个名为 back_data 的函数，用于保存回测数据。
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7.在 if __name__ == "__main__": 下，首先调用 data() 函数获取 tick 数据和分钟级别的 bar 数据。
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然后调用 orderflow_df_new() 函数，传入 tick 数据和 bar 数据，生成订单流数据 ofdata。
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打印输出 ofdata。
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8.调用 back_data() 函数，将订单流数据保存为回测数据。
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打印输出 "done"，表示程序执行完毕。
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总体而言，该代码的功能是从 tick 数据中生成分钟级别的 bar 数据，然后根据 bar 数据计算订单流，并将订单流数据保存为回测数据。
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#公众号：松鼠Quant
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#主页：www.quant789.com
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#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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# 使用前注意事项：
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1、修改read_csv对应的文件地址
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2、修改resample对应的转化周期
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3、修改folder_path、to_csv对应的保存路径
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'''
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import time
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from matplotlib.pyplot import table
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from datetime import timedelta,datetime
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import pandas as pd
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import numpy as np
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from numba import *
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from numba import cuda
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import operator
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import os
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import chardet
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def process(bidDict,askDict):
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bidDictResult,askDictResult = {},{}
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sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
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#print('bidDict:',list(bidDict.keys()))
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#print('askDict:',list(askDict.keys()))
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#print('sList:',sList)
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#240884432
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for s in sList:
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if s in bidDict:
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bidDictResult[s] = bidDict[s]
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else:
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bidDictResult[s] = 0
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if s in askDict:
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askDictResult[s] = askDict[s]
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else:
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askDictResult[s] = 0
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return bidDictResult,askDictResult
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def dataload(data,cycle):
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#日期修正
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# data['业务日期'] = data['业务日期'].dt.strftime('%Y-%m-%d')
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# data['datetime'] = data['业务日期'] + ' '+data['最后修改时间'].dt.time.astype(str) + '.' + data['最后修改毫秒'].astype(str)
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# # 将 'datetime' 列的数据类型更改为 datetime 格式
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data['datetime'] = pd.to_datetime(data['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
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# 如果需要，可以将 datetime 列格式化为字符串
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#data['formatted_date'] = data['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')
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#计算瞬时成交量
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# data['volume'] = data['数量'] - data['数量'].shift(1)
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data['volume'] = data['volume'].fillna(0)
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#整理好要用的tick数据元素
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tickdata =pd.DataFrame({'datetime':data['datetime'],'symbol':data['symbol'],'lastprice':data['lastprice'],
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'volume':data['volume'],'bid_p':data['bid_p'],'bid_v':data['bid_v'],'ask_p':data['ask_p'],'ask_v':data['ask_v']})
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#tickdata['datetime'] = pd.to_datetime(tickdata['datetime'])
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tickdata['open'] = tickdata['lastprice']
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tickdata['high'] = tickdata['lastprice']
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tickdata['low'] = tickdata['lastprice']
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tickdata['close'] = tickdata['lastprice']
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tickdata['starttime'] = tickdata['datetime']
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# # 找到满足条件的行的索引
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# condition = tickdata['datetime'].dt.time == pd.to_datetime('22:59:59').time()
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# indexes_to_update = tickdata.index[condition]
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# # 遍历索引，将不一致的日期更新为上一行的日期
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# for idx in indexes_to_update:
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# if idx > 0:
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# tickdata.at[idx, 'datetime'] = tickdata.at[idx - 1, 'datetime'].replace(hour=22, minute=59, second=59)
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# 确保日期列按升序排序
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tickdata.sort_values(by='datetime', inplace=True)
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# rule = '1T
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bardata = tickdata.resample(on = 'datetime',rule = cycle,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
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#240884432
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bardata =bardata.dropna().reset_index(drop = True)
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return tickdata,bardata
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#公众号：松鼠Quant
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#主页：www.quant789.com
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#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
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#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
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def orderflow_df_new(df_tick,df_min):
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df_of=pd.DataFrame({})
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t1 = time.time()
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startArray = pd.to_datetime(df_min['starttime']).values
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voluememin= df_min['volume'].values
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highs=df_min['high'].values
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lows=df_min['low'].values
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opens=df_min['open'].values
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closes=df_min['close'].values
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endArray = pd.to_datetime(df_min['datetime']).values
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tTickArray = pd.to_datetime(df_tick['datetime']).values
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bp1TickArray = df_tick['bid_p'].values
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ap1TickArray = df_tick['ask_p'].values
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lastTickArray = df_tick['lastprice'].values
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volumeTickArray = df_tick['volume'].values
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symbolarray = df_tick['symbol'].values
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indexFinal = 0
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for index,tEnd in enumerate(endArray):
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start = startArray[index]
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bidDict = {}
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askDict = {}
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bar_vol=voluememin[index]
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bar_close=closes[index]
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bar_open=opens[index]
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bar_low=lows[index]
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bar_high=highs[index]
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bar_symbol=symbolarray[index]
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dt=endArray[index]
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for indexTick in range(indexFinal,len(df_tick)):
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if tTickArray[indexTick] > tEnd:
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break
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elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] <= tEnd):
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if indexTick==0:
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Bp = round(bp1TickArray[indexTick],2)
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Ap = round(ap1TickArray[indexTick],2)
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else:
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Bp = round(bp1TickArray[indexTick - 1],2)
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Ap = round(ap1TickArray[indexTick - 1],2)
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LastPrice = round(lastTickArray[indexTick],2)
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Volume = volumeTickArray[indexTick]
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if LastPrice >= Ap:
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if LastPrice in askDict.keys():
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askDict[LastPrice] += Volume
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else:
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askDict[LastPrice] = Volume
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if LastPrice <= Bp:
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if LastPrice in bidDict.keys():
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bidDict[LastPrice] += Volume
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else:
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bidDict[LastPrice] = Volume
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indexFinal = indexTick
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bidDictResult,askDictResult = process(bidDict,askDict)
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bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
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askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
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prinslist=list(bidDictResult.keys())
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asklist=list(askDictResult.values())
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bidlist=list(bidDictResult.values())
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delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
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df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
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df['symbol']=bar_symbol
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df['datetime']=dt
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df['delta']=str(delta)
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df['close']=bar_close
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df['open']=bar_open
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df['high']=bar_high
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df['low']=bar_low
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df['volume']=bar_vol
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# 过滤'volume'列小于等于0的行
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df = df[df['volume'] > 0]
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# 重新排序DataFrame，按照'datetime'列进行升序排序
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df = df.sort_values(by='datetime', ascending=True)
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# 重新设置索引，以便索引能够正确对齐
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df = df.reset_index(drop=True)
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#df['ticktime']=tTickArray[indexTick]
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df['dj']=GetOrderFlow_dj(df)
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#print(df)
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df_of = pd.concat([df_of, df], ignore_index=True)
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print(time.time() - t1)
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return df_of
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def GetOrderFlow_dj(kData):
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itemAskBG=['rgb(0,255,255)', 'rgb(255,0,255)', "rgb(255,182,193)"] # 买盘背景色
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itemBidBG=['rgb(173,255,47)', 'rgb(255,127,80)', "rgb(32,178,170)"] # 卖盘背景色
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Config={
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'Value1':3,
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'Value2':3,
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'Value3':3,
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'Value4':True,
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}
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aryData=kData
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djcout=0
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for index,row in aryData.iterrows():
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kItem=aryData.iloc[index]
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high=kItem['high']
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low=kItem['low']
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close=kItem['close']
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open=kItem['open']
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dtime=kItem['datetime']
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price_s=kItem['price']
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Ask_s=kItem['Ask']
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Bid_s=kItem['Bid']
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delta=kItem['delta']
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price_s=price_s
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Ask_s=Ask_s
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Bid_s=Bid_s
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gj=0
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xq=0
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gxx=0
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xxx=0
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for i in np.arange (0, len(price_s),1) :
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duiji={
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'price':0,
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'time':0,
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'longshort':0,
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'cout':0,
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'color':'blue'
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}
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if i==0 :
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delta=delta
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order= {
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"Price":price_s[i],
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"Bid":{ "Value":Bid_s[i]},
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"Ask":{ "Value":Ask_s[i]}
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}
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if i>=0 and i<len(price_s)-1:
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if (order["Bid"]["Value"]>Ask_s[i+1]*int(Config['Value1'])):
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order["Bid"]["Color"]=itemAskBG[1]
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gxx+=1
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gj+=1
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if gj>=int(Config['Value2']) and Config['Value4']==True:
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duiji['price']=price_s[i]
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duiji['time']=dtime
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duiji['longshort']=-1
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duiji['cout']=gj
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duiji['color']='rgba(0,139,0,0.45)'#绿色
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if float(duiji['price'])>0:
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djcout+=-1
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else :
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gj=0
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if i>=1 and i<=len(price_s)-1:
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if (order["Ask"]["Value"]>Bid_s[i-1]*int(Config['Value1'])):
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xq+=1
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xxx+=1
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order["Ask"]["Color"]=itemBidBG[1]
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if xq>=int(Config['Value2']) and Config['Value4']==True:
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duiji['price']=price_s[i]
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duiji['time']=dtime
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duiji['longshort']=1
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duiji['cout']=xq
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duiji['color']='rgba(255,0,0,0.45)' #红色
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if float(duiji['price'])>0:
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djcout+=1
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else :
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xq=0
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return djcout
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def back_data(df,csv_path):
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# 创建新的DataFrame并填充需要的列
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new_df = pd.DataFrame()
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new_df['datetime'] = pd.to_datetime(df['datetime'], format='%Y/%m/%d %H:%M')
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new_df['close'] = df['close']
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new_df['open'] = df['open']
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new_df['high'] = df['high']
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new_df['low'] = df['low']
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new_df['volume'] = df['volume']
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new_df['sig'] = df['dj']
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new_df['symbol'] = df['symbol']
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new_df['delta'] = df['delta']
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new_df.to_csv(csv_path,index=False)
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#new_df.to_csv(f'{sym}back_ofdata_dj.csv',index=False)
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def ofdata_dj(file, cycle):
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print("file:", file)
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csv_df = pd.DataFrame()
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dir = os.getcwd()
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fileNum_errors = 0
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try:
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# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
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csv_df = pd.read_csv(file,encoding='GBK',parse_dates=['datetime'])
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except:
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file_path = os.path.join(dir, file)
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fileNum_errors += 1
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with open(file_path, 'rb') as file:
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data = file.read()
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# 使用chardet检测编码
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detected_encoding = chardet.detect(data)['encoding']
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# print("%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(file,detected_encoding,fileNum_errors))
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print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors))
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with open('output_error.txt', 'a') as f:
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print("%s:%s当前文件不为gbk格式,其文件格式为%s,需要转换为gbk格式,错误总数为%s"%(datetime.now().strftime('%Y-%m-%d %H:%M:%S'),file_path,detected_encoding,fileNum_errors), file = f)
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# print(csv_df)
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tick,bar=dataload(csv_df,cycle)
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ofdata = orderflow_df_new(tick,bar)
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print(ofdata)
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code_value = csv_df.loc[0, 'main_contract']# csv_df['main_contract'].keys
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return code_value, ofdata
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@@ -0,0 +1,319 @@
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'''逐行解释代码：
|
||||
|
||||
1.导入所需的模块和库，包括 time、table（来自 matplotlib.pyplot）、pandas、numpy、numba 和 operator。
|
||||
|
||||
2.定义了一个名为 process 的函数，用于处理买卖盘的字典数据。
|
||||
|
||||
3.定义了一个名为 data 的函数，用于读取并处理 tick 数据，生成分钟级别的 bar 数据。
|
||||
|
||||
4.定义了一个名为 orderflow_df_new 的函数，用于处理 tick 数据和分钟级别的 bar 数据，生成订单流数据。
|
||||
|
||||
5.定义了一个名为 GetOrderFlow_dj 的函数，用于计算订单流的指标(堆积)。
|
||||
|
||||
6.定义了一个名为 back_data 的函数，用于保存回测数据。
|
||||
|
||||
7.在 if __name__ == "__main__": 下，首先调用 data() 函数获取 tick 数据和分钟级别的 bar 数据。
|
||||
|
||||
然后调用 orderflow_df_new() 函数，传入 tick 数据和 bar 数据，生成订单流数据 ofdata。
|
||||
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打印输出 ofdata。
|
||||
|
||||
8.调用 back_data() 函数，将订单流数据保存为回测数据。
|
||||
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打印输出 "done"，表示程序执行完毕。
|
||||
|
||||
总体而言，该代码的功能是从 tick 数据中生成分钟级别的 bar 数据，然后根据 bar 数据计算订单流，并将订单流数据保存为回测数据。
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
# 使用前注意事项：
|
||||
1、修改read_csv对应的文件地址
|
||||
2、修改resample对应的转化周期
|
||||
3、修改folder_path、to_csv对应的保存路径
|
||||
'''
|
||||
|
||||
import time
|
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from matplotlib.pyplot import table
|
||||
from datetime import timedelta
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import pandas as pd
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import numpy as np
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from numba import *
|
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from numba import cuda
|
||||
import operator
|
||||
|
||||
import os
|
||||
|
||||
|
||||
|
||||
def process(bidDict,askDict):
|
||||
bidDictResult,askDictResult = {},{}
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
#print('bidDict:',list(bidDict.keys()))
|
||||
#print('askDict:',list(askDict.keys()))
|
||||
#print('sList:',sList)
|
||||
#240884432
|
||||
for s in sList:
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||||
if s in bidDict:
|
||||
bidDictResult[s] = bidDict[s]
|
||||
else:
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bidDictResult[s] = 0
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if s in askDict:
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askDictResult[s] = askDict[s]
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else:
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askDictResult[s] = 0
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return bidDictResult,askDictResult
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||||
|
||||
|
||||
def dataload(data):
|
||||
#日期修正
|
||||
# data['业务日期'] = data['业务日期'].dt.strftime('%Y-%m-%d')
|
||||
# data['datetime'] = data['业务日期'] + ' '+data['最后修改时间'].dt.time.astype(str) + '.' + data['最后修改毫秒'].astype(str)
|
||||
# # 将 'datetime' 列的数据类型更改为 datetime 格式
|
||||
data['datetime'] = pd.to_datetime(data['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
|
||||
# 如果需要，可以将 datetime 列格式化为字符串
|
||||
#data['formatted_date'] = data['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')
|
||||
#计算瞬时成交量
|
||||
# data['volume'] = data['数量'] - data['数量'].shift(1)
|
||||
data['volume'] = data['volume'].fillna(0)
|
||||
#整理好要用的tick数据元素
|
||||
tickdata =pd.DataFrame({'datetime':data['datetime'],'symbol':data['symbol'],'lastprice':data['lastprice'],
|
||||
'volume':data['volume'],'bid_p':data['bid_p'],'bid_v':data['bid_v'],'ask_p':data['ask_p'],'ask_v':data['ask_v']})
|
||||
#tickdata['datetime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
|
||||
# # 找到满足条件的行的索引
|
||||
# condition = tickdata['datetime'].dt.time == pd.to_datetime('22:59:59').time()
|
||||
# indexes_to_update = tickdata.index[condition]
|
||||
|
||||
# # 遍历索引，将不一致的日期更新为上一行的日期
|
||||
# for idx in indexes_to_update:
|
||||
# if idx > 0:
|
||||
# tickdata.at[idx, 'datetime'] = tickdata.at[idx - 1, 'datetime'].replace(hour=22, minute=59, second=59)
|
||||
|
||||
# 确保日期列按升序排序
|
||||
tickdata.sort_values(by='datetime', inplace=True)
|
||||
|
||||
bardata = tickdata.resample(on = 'datetime',rule = '1T',label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
#240884432
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
return tickdata,bardata
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(df_tick,df_min):
|
||||
df_of=pd.DataFrame({})
|
||||
t1 = time.time()
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
endArray = pd.to_datetime(df_min['datetime']).values
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
dt=endArray[index]
|
||||
for indexTick in range(indexFinal,len(df_tick)):
|
||||
if tTickArray[indexTick] > tEnd:
|
||||
break
|
||||
elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] <= tEnd):
|
||||
if indexTick==0:
|
||||
Bp = round(bp1TickArray[indexTick],2)
|
||||
Ap = round(ap1TickArray[indexTick],2)
|
||||
else:
|
||||
Bp = round(bp1TickArray[indexTick - 1],2)
|
||||
Ap = round(ap1TickArray[indexTick - 1],2)
|
||||
LastPrice = round(lastTickArray[indexTick],2)
|
||||
Volume = volumeTickArray[indexTick]
|
||||
if LastPrice >= Ap:
|
||||
if LastPrice in askDict.keys():
|
||||
askDict[LastPrice] += Volume
|
||||
else:
|
||||
askDict[LastPrice] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if LastPrice in bidDict.keys():
|
||||
bidDict[LastPrice] += Volume
|
||||
else:
|
||||
bidDict[LastPrice] = Volume
|
||||
indexFinal = indexTick
|
||||
|
||||
bidDictResult,askDictResult = process(bidDict,askDict)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
# 过滤'volume'列小于等于0的行
|
||||
df = df[df['volume'] > 0]
|
||||
# 重新排序DataFrame，按照'datetime'列进行升序排序
|
||||
df = df.sort_values(by='datetime', ascending=True)
|
||||
# 重新设置索引，以便索引能够正确对齐
|
||||
df = df.reset_index(drop=True)
|
||||
#df['ticktime']=tTickArray[indexTick]
|
||||
df['dj']=GetOrderFlow_dj(df)
|
||||
#print(df)
|
||||
df_of = pd.concat([df_of, df], ignore_index=True)
|
||||
print(time.time() - t1)
|
||||
return df_of
|
||||
|
||||
def GetOrderFlow_dj(kData):
|
||||
itemAskBG=['rgb(0,255,255)', 'rgb(255,0,255)', "rgb(255,182,193)"] # 买盘背景色
|
||||
itemBidBG=['rgb(173,255,47)', 'rgb(255,127,80)', "rgb(32,178,170)"] # 卖盘背景色
|
||||
Config={
|
||||
'Value1':3,
|
||||
'Value2':3,
|
||||
'Value3':3,
|
||||
'Value4':True,
|
||||
}
|
||||
aryData=kData
|
||||
djcout=0
|
||||
for index,row in aryData.iterrows():
|
||||
kItem=aryData.iloc[index]
|
||||
high=kItem['high']
|
||||
low=kItem['low']
|
||||
close=kItem['close']
|
||||
open=kItem['open']
|
||||
dtime=kItem['datetime']
|
||||
price_s=kItem['price']
|
||||
Ask_s=kItem['Ask']
|
||||
Bid_s=kItem['Bid']
|
||||
delta=kItem['delta']
|
||||
|
||||
price_s=price_s
|
||||
Ask_s=Ask_s
|
||||
Bid_s=Bid_s
|
||||
|
||||
gj=0
|
||||
xq=0
|
||||
gxx=0
|
||||
xxx=0
|
||||
for i in np.arange (0, len(price_s),1) :
|
||||
duiji={
|
||||
'price':0,
|
||||
'time':0,
|
||||
'longshort':0,
|
||||
'cout':0,
|
||||
'color':'blue'
|
||||
}
|
||||
if i==0 :
|
||||
delta=delta
|
||||
|
||||
order= {
|
||||
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
if i>=0 and i<len(price_s)-1:
|
||||
if (order["Bid"]["Value"]>Ask_s[i+1]*int(Config['Value1'])):
|
||||
order["Bid"]["Color"]=itemAskBG[1]
|
||||
gxx+=1
|
||||
gj+=1
|
||||
if gj>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=-1
|
||||
duiji['cout']=gj
|
||||
duiji['color']='rgba(0,139,0,0.45)'#绿色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=-1
|
||||
else :
|
||||
gj=0
|
||||
if i>=1 and i<=len(price_s)-1:
|
||||
if (order["Ask"]["Value"]>Bid_s[i-1]*int(Config['Value1'])):
|
||||
xq+=1
|
||||
xxx+=1
|
||||
order["Ask"]["Color"]=itemBidBG[1]
|
||||
if xq>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=1
|
||||
duiji['cout']=xq
|
||||
duiji['color']='rgba(255,0,0,0.45)' #红色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=1
|
||||
else :
|
||||
xq=0
|
||||
return djcout
|
||||
|
||||
def back_data(df):
|
||||
# 创建新的DataFrame并填充需要的列
|
||||
new_df = pd.DataFrame()
|
||||
new_df['datetime'] = pd.to_datetime(df['datetime'], format='%Y/%m/%d %H:%M')
|
||||
new_df['close'] = df['close']
|
||||
new_df['open'] = df['open']
|
||||
new_df['high'] = df['high']
|
||||
new_df['low'] = df['low']
|
||||
new_df['volume'] = df['volume']
|
||||
new_df['sig'] = df['dj']
|
||||
new_df['symbol'] = df['symbol']
|
||||
new_df['delta'] = df['delta']
|
||||
new_df.to_csv(f'./rb888_rs_2022_back_ofdata_dj.csv',index=False)
|
||||
#new_df.to_csv(f'{sym}back_ofdata_dj.csv',index=False)
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
data=pd.read_csv('D:/data_transfer/data_rs_merged/上期所/rb888/rb888_rs_2022.csv',encoding='GBK',parse_dates=['datetime']) # ['业务日期','最后修改时间']
|
||||
print(data)
|
||||
tick,bar=dataload(data)
|
||||
ofdata = orderflow_df_new(tick,bar)
|
||||
print(ofdata)
|
||||
#保存orderflow数据
|
||||
folder_path = 'D:/of_data/tick生成的OF数据/data_rs_merged/上期所/rb888/'
|
||||
if not os.path.exists(folder_path):
|
||||
# os.mkdir(folder_path)
|
||||
os.makedirs(folder_path)
|
||||
# 获取当前工作目录
|
||||
current_directory = os.getcwd()
|
||||
print("当前工作目录：", current_directory)
|
||||
# 设置新的工作目录
|
||||
os.chdir(folder_path)
|
||||
# 验证新的工作目录
|
||||
updated_directory = os.getcwd()
|
||||
print("已更改为新的工作目录：", updated_directory)
|
||||
|
||||
|
||||
ofdata.to_csv('./rb888_rs_2022_ofdata_dj.csv')
|
||||
#保存回测数据
|
||||
back_data(ofdata)
|
||||
print('done')
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,226 @@
|
||||
datetime,close,open,high,low,volume,sig,symbol,delta
|
||||
2023-01-03 09:01:00,4070.0,4090.0,4090.0,4056.0,55742.0,0,rb2305,-12329.0
|
||||
2023-01-03 09:02:00,4056.0,4068.0,4069.0,4054.0,37763.0,0,rb2305,3143.0
|
||||
2023-01-03 09:03:00,4052.0,4055.0,4056.0,4046.0,35473.0,0,rb2305,-1952.0
|
||||
2023-01-03 09:04:00,4037.0,4051.0,4051.0,4033.0,46025.0,0,rb2305,89.0
|
||||
2023-01-03 09:05:00,4029.0,4037.0,4037.0,4028.0,39521.0,0,rb2305,-4223.0
|
||||
2023-01-03 09:06:00,4033.0,4029.0,4035.0,4024.0,35130.0,0,rb2305,9520.0
|
||||
2023-01-03 09:07:00,4040.0,4033.0,4042.0,4029.0,23920.0,0,rb2305,4720.0
|
||||
2023-01-03 09:08:00,4042.0,4041.0,4044.0,4037.0,18135.0,0,rb2305,1739.0
|
||||
2023-01-03 09:09:00,4042.0,4041.0,4046.0,4040.0,19528.0,0,rb2305,-86.0
|
||||
2023-01-03 09:10:00,4035.0,4042.0,4043.0,4035.0,13389.0,0,rb2305,-1531.0
|
||||
2023-01-03 09:11:00,4029.0,4034.0,4036.0,4028.0,20563.0,0,rb2305,-2435.0
|
||||
2023-01-03 09:12:00,4031.0,4029.0,4033.0,4029.0,16455.0,0,rb2305,6633.0
|
||||
2023-01-03 09:13:00,4030.0,4030.0,4034.0,4028.0,13479.0,0,rb2305,4027.0
|
||||
2023-01-03 09:14:00,4030.0,4030.0,4032.0,4028.0,17070.0,0,rb2305,-1711.0
|
||||
2023-01-03 09:15:00,4028.0,4030.0,4033.0,4028.0,12241.0,0,rb2305,2226.0
|
||||
2023-01-03 09:16:00,4028.0,4027.0,4031.0,4027.0,11094.0,0,rb2305,-544.0
|
||||
2023-01-03 09:17:00,4020.0,4029.0,4029.0,4019.0,23338.0,-1,rb2305,-5895.0
|
||||
2023-01-03 09:18:00,4018.0,4020.0,4024.0,4018.0,15721.0,0,rb2305,-302.0
|
||||
2023-01-03 09:19:00,4019.0,4018.0,4022.0,4017.0,10202.0,0,rb2305,1162.0
|
||||
2023-01-03 09:20:00,4022.0,4019.0,4022.0,4019.0,9270.0,1,rb2305,4734.0
|
||||
2023-01-03 09:21:00,4019.0,4020.0,4025.0,4018.0,13402.0,0,rb2305,2638.0
|
||||
2023-01-03 09:22:00,4023.0,4018.0,4024.0,4018.0,8214.0,0,rb2305,2843.0
|
||||
2023-01-03 09:23:00,4023.0,4023.0,4025.0,4022.0,9371.0,0,rb2305,3814.0
|
||||
2023-01-03 09:24:00,4023.0,4023.0,4024.0,4019.0,8897.0,0,rb2305,-1819.0
|
||||
2023-01-03 09:25:00,4022.0,4022.0,4024.0,4021.0,7895.0,0,rb2305,-162.0
|
||||
2023-01-03 09:26:00,4019.0,4022.0,4023.0,4016.0,10157.0,0,rb2305,-137.0
|
||||
2023-01-03 09:27:00,4025.0,4018.0,4026.0,4017.0,10624.0,0,rb2305,5036.0
|
||||
2023-01-03 09:28:00,4028.0,4025.0,4029.0,4023.0,10447.0,2,rb2305,2908.0
|
||||
2023-01-03 09:29:00,4030.0,4028.0,4032.0,4025.0,13354.0,0,rb2305,-1638.0
|
||||
2023-01-03 09:30:00,4036.0,4030.0,4038.0,4030.0,16303.0,0,rb2305,1827.0
|
||||
2023-01-03 09:31:00,4044.0,4037.0,4046.0,4035.0,24117.0,0,rb2305,-2499.0
|
||||
2023-01-03 09:32:00,4042.0,4043.0,4045.0,4041.0,8941.0,0,rb2305,-1775.0
|
||||
2023-01-03 09:33:00,4048.0,4042.0,4049.0,4042.0,12987.0,0,rb2305,1611.0
|
||||
2023-01-03 09:34:00,4047.0,4048.0,4049.0,4044.0,7839.0,0,rb2305,-283.0
|
||||
2023-01-03 09:35:00,4046.0,4046.0,4049.0,4044.0,8124.0,0,rb2305,-2421.0
|
||||
2023-01-03 09:36:00,4043.0,4046.0,4047.0,4042.0,7150.0,0,rb2305,-1262.0
|
||||
2023-01-03 09:37:00,4040.0,4042.0,4043.0,4039.0,6684.0,0,rb2305,-2299.0
|
||||
2023-01-03 09:38:00,4043.0,4041.0,4044.0,4040.0,3798.0,0,rb2305,362.0
|
||||
2023-01-03 09:39:00,4040.0,4043.0,4045.0,4038.0,7079.0,0,rb2305,-1865.0
|
||||
2023-01-03 09:40:00,4042.0,4039.0,4043.0,4038.0,3672.0,0,rb2305,979.0
|
||||
2023-01-03 09:41:00,4042.0,4042.0,4044.0,4040.0,5490.0,0,rb2305,1949.0
|
||||
2023-01-03 09:42:00,4042.0,4042.0,4044.0,4040.0,3555.0,0,rb2305,312.0
|
||||
2023-01-03 09:43:00,4045.0,4042.0,4045.0,4039.0,3854.0,0,rb2305,-1107.0
|
||||
2023-01-03 09:44:00,4042.0,4044.0,4047.0,4041.0,3984.0,0,rb2305,1618.0
|
||||
2023-01-03 09:45:00,4043.0,4041.0,4044.0,4041.0,2361.0,0,rb2305,-284.0
|
||||
2023-01-03 09:46:00,4045.0,4043.0,4047.0,4041.0,3712.0,0,rb2305,-81.0
|
||||
2023-01-03 09:47:00,4040.0,4045.0,4045.0,4040.0,2886.0,0,rb2305,-148.0
|
||||
2023-01-03 09:48:00,4035.0,4040.0,4041.0,4033.0,10250.0,-2,rb2305,-4437.0
|
||||
2023-01-03 09:49:00,4036.0,4034.0,4037.0,4033.0,3654.0,0,rb2305,6.0
|
||||
2023-01-03 09:50:00,4035.0,4035.0,4037.0,4034.0,3020.0,0,rb2305,118.0
|
||||
2023-01-03 09:51:00,4038.0,4036.0,4039.0,4036.0,3751.0,0,rb2305,1299.0
|
||||
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2023-01-03 14:35:00,4064.0,4065.0,4066.0,4064.0,1958.0,0,rb2305,132.0
|
||||
2023-01-03 14:36:00,4066.0,4064.0,4066.0,4062.0,2530.0,0,rb2305,-220.0
|
||||
2023-01-03 14:37:00,4062.0,4065.0,4066.0,4062.0,1994.0,0,rb2305,-666.0
|
||||
2023-01-03 14:38:00,4063.0,4063.0,4064.0,4062.0,1849.0,0,rb2305,-345.0
|
||||
2023-01-03 14:39:00,4063.0,4063.0,4065.0,4062.0,1307.0,0,rb2305,-271.0
|
||||
2023-01-03 14:40:00,4061.0,4062.0,4063.0,4061.0,1785.0,0,rb2305,164.0
|
||||
2023-01-03 14:41:00,4062.0,4061.0,4063.0,4061.0,1324.0,0,rb2305,238.0
|
||||
2023-01-03 14:42:00,4065.0,4062.0,4066.0,4061.0,1905.0,0,rb2305,500.0
|
||||
2023-01-03 14:43:00,4064.0,4065.0,4066.0,4064.0,1131.0,0,rb2305,-63.0
|
||||
2023-01-03 14:44:00,4062.0,4065.0,4066.0,4061.0,1773.0,0,rb2305,-427.0
|
||||
2023-01-03 14:45:00,4063.0,4062.0,4064.0,4062.0,700.0,0,rb2305,84.0
|
||||
2023-01-03 14:46:00,4061.0,4064.0,4065.0,4060.0,3722.0,0,rb2305,-1686.0
|
||||
2023-01-03 14:47:00,4058.0,4061.0,4061.0,4057.0,6062.0,0,rb2305,-1636.0
|
||||
2023-01-03 14:48:00,4057.0,4057.0,4058.0,4056.0,3102.0,0,rb2305,-846.0
|
||||
2023-01-03 14:49:00,4059.0,4057.0,4060.0,4056.0,2569.0,0,rb2305,154.0
|
||||
2023-01-03 14:50:00,4058.0,4058.0,4060.0,4057.0,1609.0,0,rb2305,-199.0
|
||||
2023-01-03 14:51:00,4057.0,4058.0,4060.0,4057.0,2286.0,0,rb2305,-441.0
|
||||
2023-01-03 14:52:00,4059.0,4057.0,4060.0,4057.0,2336.0,0,rb2305,-461.0
|
||||
2023-01-03 14:53:00,4059.0,4060.0,4060.0,4058.0,3043.0,0,rb2305,-843.0
|
||||
2023-01-03 14:54:00,4058.0,4059.0,4060.0,4057.0,1460.0,0,rb2305,-30.0
|
||||
2023-01-03 14:55:00,4058.0,4058.0,4059.0,4057.0,2081.0,0,rb2305,-225.0
|
||||
2023-01-03 14:56:00,4057.0,4058.0,4060.0,4057.0,3029.0,0,rb2305,-544.0
|
||||
2023-01-03 14:57:00,4059.0,4058.0,4060.0,4057.0,4039.0,0,rb2305,-1145.0
|
||||
2023-01-03 14:58:00,4061.0,4059.0,4061.0,4058.0,4940.0,0,rb2305,157.0
|
||||
2023-01-03 14:59:00,4063.0,4060.0,4064.0,4060.0,6425.0,0,rb2305,930.0
|
||||
2023-01-03 15:00:00,4063.0,4063.0,4064.0,4061.0,6581.0,0,rb2305,-2157.0
|
||||
|
@@ -0,0 +1,226 @@
|
||||
,price,Ask,Bid,symbol,datetime,delta,close,open,high,low,volume,dj
|
||||
0,"[4056.0, 4057.0, 4058.0, 4059.0, 4060.0, 4061.0, 4062.0, 4063.0, 4064.0, 4065.0, 4066.0, 4067.0, 4068.0, 4069.0, 4070.0, 4071.0, 4072.0, 4073.0, 4074.0, 4078.0, 4081.0, 4090.0]","[0, 0, 4574.0, 0, 1946.0, 330.0, 0, 467.0, 1910.0, 1881.0, 492.0, 504.0, 1094.0, 511.0, 3759.0, 2243.0, 1339.0, 446.0, 127.0, 0, 0, 0]","[2219.0, 1212.0, 0, 1095.0, 953.0, 812.0, 1156.0, 1702.0, 1193.0, 642.0, 2150.0, 1119.0, 985.0, 2139.0, 1927.0, 2644.0, 2720.0, 4267.0, 0, 1662.0, 1519.0, 1836.0]",rb2305,2023-01-03 09:01:00,-12329.0,4070.0,4090.0,4090.0,4056.0,55742.0,0
|
||||
1,"[4054.0, 4055.0, 4056.0, 4057.0, 4058.0, 4059.0, 4060.0, 4061.0, 4062.0, 4063.0, 4064.0, 4065.0, 4066.0, 4067.0, 4068.0, 4069.0]","[507.0, 0, 2896.0, 4249.0, 4348.0, 2734.0, 1108.0, 0, 507.0, 662.0, 286.0, 1097.0, 623.0, 752.0, 0, 231.0]","[1990.0, 246.0, 3662.0, 2049.0, 2456.0, 527.0, 1056.0, 323.0, 1981.0, 606.0, 259.0, 521.0, 0, 723.0, 458.0, 0]",rb2305,2023-01-03 09:02:00,3143.0,4056.0,4068.0,4069.0,4054.0,37763.0,0
|
||||
2,"[4046.0, 4047.0, 4048.0, 4049.0, 4050.0, 4051.0, 4052.0, 4053.0, 4054.0, 4055.0, 4056.0]","[0, 2179.0, 353.0, 1352.0, 1889.0, 388.0, 3919.0, 1451.0, 1826.0, 1780.0, 1221.0]","[1131.0, 338.0, 1222.0, 2522.0, 3443.0, 1898.0, 3251.0, 1232.0, 684.0, 2589.0, 0]",rb2305,2023-01-03 09:03:00,-1952.0,4052.0,4055.0,4056.0,4046.0,35473.0,0
|
||||
3,"[4033.0, 4034.0, 4035.0, 4036.0, 4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0, 4049.0, 4050.0, 4051.0]","[0, 749.0, 2253.0, 4202.0, 1733.0, 2596.0, 505.0, 1185.0, 129.0, 490.0, 1229.0, 373.0, 2846.0, 2451.0, 1013.0, 702.0, 145.0, 0, 0]","[300.0, 1487.0, 2859.0, 1452.0, 722.0, 0, 1610.0, 1239.0, 1336.0, 0, 302.0, 401.0, 1572.0, 2695.0, 1579.0, 745.0, 0, 3089.0, 1124.0]",rb2305,2023-01-03 09:04:00,89.0,4037.0,4051.0,4051.0,4033.0,46025.0,0
|
||||
4,"[4028.0, 4029.0, 4030.0, 4031.0, 4032.0, 4033.0, 4034.0, 4035.0, 4036.0, 4037.0]","[0, 1539.0, 930.0, 7269.0, 1853.0, 629.0, 2545.0, 1754.0, 243.0, 461.0]","[345.0, 2979.0, 4554.0, 3548.0, 2134.0, 2465.0, 3241.0, 1138.0, 1042.0, 0]",rb2305,2023-01-03 09:05:00,-4223.0,4029.0,4037.0,4037.0,4028.0,39521.0,0
|
||||
5,"[4024.0, 4025.0, 4026.0, 4027.0, 4028.0, 4029.0, 4030.0, 4031.0, 4032.0, 4033.0, 4034.0, 4035.0]","[1088.0, 1058.0, 4856.0, 518.0, 468.0, 2328.0, 1249.0, 2934.0, 1260.0, 2482.0, 3144.0, 821.0]","[2659.0, 928.0, 745.0, 533.0, 668.0, 2919.0, 537.0, 839.0, 599.0, 1639.0, 620.0, 0]",rb2305,2023-01-03 09:06:00,9520.0,4033.0,4029.0,4035.0,4024.0,35130.0,0
|
||||
6,"[4029.0, 4030.0, 4031.0, 4032.0, 4033.0, 4034.0, 4035.0, 4036.0, 4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0]","[424.0, 274.0, 1083.0, 496.0, 217.0, 508.0, 541.0, 408.0, 1005.0, 1987.0, 588.0, 2465.0, 2722.0, 1602.0]","[316.0, 740.0, 0, 875.0, 274.0, 351.0, 108.0, 144.0, 896.0, 1723.0, 2092.0, 1034.0, 1047.0, 0]",rb2305,2023-01-03 09:07:00,4720.0,4040.0,4033.0,4042.0,4029.0,23920.0,0
|
||||
7,"[4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 388.0, 806.0, 1077.0, 687.0, 2958.0, 2656.0, 1333.0]","[596.0, 296.0, 668.0, 1046.0, 1275.0, 2620.0, 1665.0, 0]",rb2305,2023-01-03 09:08:00,1739.0,4042.0,4041.0,4044.0,4037.0,18135.0,0
|
||||
8,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0, 4045.0, 4046.0]","[0, 420.0, 3391.0, 2666.0, 1971.0, 0, 1050.0]","[160.0, 2828.0, 3527.0, 2663.0, 340.0, 66.0, 0]",rb2305,2023-01-03 09:09:00,-86.0,4042.0,4041.0,4046.0,4040.0,19528.0,0
|
||||
9,"[4035.0, 4036.0, 4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0, 4043.0]","[0, 77.0, 61.0, 570.0, 480.0, 2377.0, 1319.0, 882.0, 163.0]","[215.0, 799.0, 41.0, 571.0, 1804.0, 2051.0, 139.0, 1840.0, 0]",rb2305,2023-01-03 09:10:00,-1531.0,4035.0,4042.0,4043.0,4035.0,13389.0,0
|
||||
10,"[4028.0, 4029.0, 4030.0, 4031.0, 4032.0, 4033.0, 4034.0, 4035.0, 4036.0]","[0, 3085.0, 1881.0, 765.0, 992.0, 192.0, 318.0, 1159.0, 672.0]","[1209.0, 4291.0, 985.0, 1617.0, 0, 189.0, 1544.0, 1664.0, 0]",rb2305,2023-01-03 09:11:00,-2435.0,4029.0,4034.0,4036.0,4028.0,20563.0,0
|
||||
11,"[4029.0, 4030.0, 4031.0, 4032.0, 4033.0]","[106.0, 930.0, 3851.0, 2200.0, 4345.0]","[173.0, 1410.0, 1544.0, 1580.0, 92.0]",rb2305,2023-01-03 09:12:00,6633.0,4031.0,4029.0,4033.0,4029.0,16455.0,0
|
||||
12,"[4028.0, 4029.0, 4030.0, 4031.0, 4032.0, 4033.0, 4034.0]","[0, 416.0, 1922.0, 2289.0, 427.0, 746.0, 2873.0]","[796.0, 917.0, 1201.0, 448.0, 913.0, 371.0, 0]",rb2305,2023-01-03 09:13:00,4027.0,4030.0,4030.0,4034.0,4028.0,13479.0,0
|
||||
13,"[4028.0, 4029.0, 4030.0, 4031.0, 4032.0]","[453.0, 2276.0, 3085.0, 1534.0, 313.0]","[2559.0, 3045.0, 2642.0, 1126.0, 0]",rb2305,2023-01-03 09:14:00,-1711.0,4030.0,4030.0,4032.0,4028.0,17070.0,0
|
||||
14,"[4028.0, 4029.0, 4030.0, 4031.0, 4032.0, 4033.0]","[0, 1481.0, 870.0, 3154.0, 1392.0, 326.0]","[371.0, 1010.0, 1212.0, 2198.0, 206.0, 0]",rb2305,2023-01-03 09:15:00,2226.0,4028.0,4030.0,4033.0,4028.0,12241.0,0
|
||||
15,"[4027.0, 4028.0, 4029.0, 4030.0, 4031.0]","[0, 0, 1444.0, 3494.0, 212.0]","[1395.0, 438.0, 2810.0, 1051.0, 0]",rb2305,2023-01-03 09:16:00,-544.0,4028.0,4027.0,4031.0,4027.0,11094.0,0
|
||||
16,"[4019.0, 4020.0, 4021.0, 4022.0, 4024.0, 4025.0, 4026.0, 4027.0, 4028.0, 4029.0]","[0, 669.0, 2293.0, 2244.0, 0, 158.0, 34.0, 1023.0, 559.0, 1631.0]","[268.0, 4242.0, 2684.0, 908.0, 1931.0, 2282.0, 792.0, 839.0, 560.0, 0]",rb2305,2023-01-03 09:17:00,-5895.0,4020.0,4029.0,4029.0,4019.0,23338.0,-1
|
||||
17,"[4018.0, 4019.0, 4020.0, 4021.0, 4022.0, 4023.0, 4024.0]","[0, 1490.0, 3966.0, 387.0, 1139.0, 574.0, 79.0]","[828.0, 1176.0, 2539.0, 1808.0, 1446.0, 140.0, 0]",rb2305,2023-01-03 09:18:00,-302.0,4018.0,4020.0,4024.0,4018.0,15721.0,0
|
||||
18,"[4017.0, 4018.0, 4019.0, 4020.0, 4021.0, 4022.0]","[0, 992.0, 917.0, 3045.0, 600.0, 128.0]","[155.0, 575.0, 1813.0, 1916.0, 61.0, 0]",rb2305,2023-01-03 09:19:00,1162.0,4019.0,4018.0,4022.0,4017.0,10202.0,0
|
||||
19,"[4019.0, 4020.0, 4021.0, 4022.0]","[0, 817.0, 778.0, 5309.0]","[230.0, 194.0, 1289.0, 457.0]",rb2305,2023-01-03 09:20:00,4734.0,4022.0,4019.0,4022.0,4019.0,9270.0,1
|
||||
20,"[4018.0, 4019.0, 4020.0, 4021.0, 4022.0, 4023.0, 4024.0, 4025.0]","[0, 1673.0, 922.0, 1102.0, 489.0, 823.0, 2493.0, 485.0]","[300.0, 2194.0, 910.0, 138.0, 1335.0, 280.0, 192.0, 0]",rb2305,2023-01-03 09:21:00,2638.0,4019.0,4020.0,4025.0,4018.0,13402.0,0
|
||||
21,"[4018.0, 4019.0, 4020.0, 4021.0, 4022.0, 4023.0, 4024.0]","[345.0, 1213.0, 342.0, 391.0, 1052.0, 1344.0, 801.0]","[987.0, 350.0, 264.0, 211.0, 210.0, 623.0, 0]",rb2305,2023-01-03 09:22:00,2843.0,4023.0,4018.0,4024.0,4018.0,8214.0,0
|
||||
22,"[4022.0, 4023.0, 4024.0, 4025.0]","[279.0, 3904.0, 1843.0, 554.0]","[313.0, 2437.0, 16.0, 0]",rb2305,2023-01-03 09:23:00,3814.0,4023.0,4023.0,4025.0,4022.0,9371.0,0
|
||||
23,"[4019.0, 4020.0, 4021.0, 4022.0, 4023.0, 4024.0]","[0, 677.0, 927.0, 1563.0, 193.0, 141.0]","[1746.0, 1829.0, 1018.0, 643.0, 84.0, 0]",rb2305,2023-01-03 09:24:00,-1819.0,4023.0,4023.0,4024.0,4019.0,8897.0,0
|
||||
24,"[4021.0, 4022.0, 4023.0, 4024.0]","[0, 920.0, 2814.0, 89.0]","[481.0, 1938.0, 1566.0, 0]",rb2305,2023-01-03 09:25:00,-162.0,4022.0,4022.0,4024.0,4021.0,7895.0,0
|
||||
25,"[4016.0, 4017.0, 4018.0, 4019.0, 4020.0, 4021.0, 4022.0, 4023.0]","[0, 389.0, 895.0, 1837.0, 867.0, 615.0, 331.0, 76.0]","[94.0, 1294.0, 1065.0, 1545.0, 451.0, 698.0, 0, 0]",rb2305,2023-01-03 09:26:00,-137.0,4019.0,4022.0,4023.0,4016.0,10157.0,0
|
||||
26,"[4017.0, 4018.0, 4019.0, 4020.0, 4021.0, 4022.0, 4023.0, 4024.0, 4025.0, 4026.0]","[0, 113.0, 230.0, 634.0, 345.0, 1042.0, 935.0, 2519.0, 1147.0, 855.0]","[26.0, 207.0, 49.0, 704.0, 10.0, 664.0, 503.0, 0, 621.0, 0]",rb2305,2023-01-03 09:27:00,5036.0,4025.0,4018.0,4026.0,4017.0,10624.0,0
|
||||
27,"[4023.0, 4024.0, 4025.0, 4026.0, 4027.0, 4028.0, 4029.0]","[0, 1120.0, 962.0, 632.0, 1021.0, 694.0, 2228.0]","[248.0, 2150.0, 56.0, 251.0, 221.0, 738.0, 85.0]",rb2305,2023-01-03 09:28:00,2908.0,4028.0,4025.0,4029.0,4023.0,10447.0,2
|
||||
28,"[4025.0, 4026.0, 4027.0, 4028.0, 4029.0, 4030.0, 4031.0, 4032.0]","[0, 210.0, 1000.0, 1094.0, 1923.0, 1149.0, 385.0, 97.0]","[41.0, 517.0, 1255.0, 2642.0, 2365.0, 576.0, 100.0, 0]",rb2305,2023-01-03 09:29:00,-1638.0,4030.0,4028.0,4032.0,4025.0,13354.0,0
|
||||
29,"[4030.0, 4031.0, 4032.0, 4033.0, 4034.0, 4035.0, 4036.0, 4037.0, 4038.0]","[0, 615.0, 723.0, 1070.0, 1354.0, 1237.0, 1186.0, 1076.0, 1728.0]","[476.0, 1192.0, 33.0, 400.0, 1208.0, 616.0, 499.0, 1789.0, 949.0]",rb2305,2023-01-03 09:30:00,1827.0,4036.0,4030.0,4038.0,4030.0,16303.0,0
|
||||
30,"[4035.0, 4036.0, 4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0, 4043.0, 4044.0, 4045.0, 4046.0]","[170.0, 326.0, 1343.0, 202.0, 330.0, 924.0, 790.0, 944.0, 1868.0, 1646.0, 1694.0, 480.0]","[198.0, 1110.0, 1607.0, 1973.0, 1266.0, 0, 519.0, 2448.0, 723.0, 1990.0, 1382.0, 0]",rb2305,2023-01-03 09:31:00,-2499.0,4044.0,4037.0,4046.0,4035.0,24117.0,0
|
||||
31,"[4041.0, 4042.0, 4043.0, 4044.0, 4045.0]","[0, 1651.0, 1386.0, 519.0, 27.0]","[413.0, 2423.0, 1884.0, 638.0, 0]",rb2305,2023-01-03 09:32:00,-1775.0,4042.0,4043.0,4045.0,4041.0,8941.0,0
|
||||
32,"[4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0, 4049.0]","[0, 704.0, 1668.0, 841.0, 1207.0, 425.0, 1431.0, 1023.0]","[633.0, 622.0, 1308.0, 466.0, 149.0, 708.0, 1439.0, 363.0]",rb2305,2023-01-03 09:33:00,1611.0,4048.0,4042.0,4049.0,4042.0,12987.0,0
|
||||
33,"[4044.0, 4045.0, 4046.0, 4047.0, 4048.0, 4049.0]","[222.0, 391.0, 711.0, 951.0, 999.0, 485.0]","[644.0, 83.0, 1029.0, 1471.0, 815.0, 0]",rb2305,2023-01-03 09:34:00,-283.0,4047.0,4048.0,4049.0,4044.0,7839.0,0
|
||||
34,"[4044.0, 4045.0, 4046.0, 4047.0, 4048.0, 4049.0]","[0, 35.0, 577.0, 871.0, 825.0, 521.0]","[246.0, 549.0, 1136.0, 1555.0, 1588.0, 176.0]",rb2305,2023-01-03 09:35:00,-2421.0,4046.0,4046.0,4049.0,4044.0,8124.0,0
|
||||
35,"[4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0]","[0, 839.0, 856.0, 563.0, 33.0, 633.0]","[21.0, 1460.0, 1241.0, 762.0, 517.0, 185.0]",rb2305,2023-01-03 09:36:00,-1262.0,4043.0,4046.0,4047.0,4042.0,7150.0,0
|
||||
36,"[4039.0, 4040.0, 4041.0, 4042.0, 4043.0]","[0, 358.0, 418.0, 892.0, 517.0]","[459.0, 1296.0, 1667.0, 1062.0, 0]",rb2305,2023-01-03 09:37:00,-2299.0,4040.0,4042.0,4043.0,4039.0,6684.0,0
|
||||
37,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 596.0, 565.0, 886.0, 33.0]","[317.0, 471.0, 750.0, 180.0, 0]",rb2305,2023-01-03 09:38:00,362.0,4043.0,4041.0,4044.0,4040.0,3798.0,0
|
||||
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50,"[4036.0, 4037.0, 4038.0, 4039.0]","[301.0, 1003.0, 647.0, 574.0]","[643.0, 230.0, 353.0, 0]",rb2305,2023-01-03 09:51:00,1299.0,4038.0,4036.0,4039.0,4036.0,3751.0,0
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55,"[4040.0, 4041.0, 4042.0]","[89.0, 1063.0, 27.0]","[710.0, 90.0, 0]",rb2305,2023-01-03 09:56:00,379.0,4040.0,4040.0,4042.0,4040.0,1979.0,0
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56,"[4037.0, 4038.0, 4039.0, 4040.0, 4041.0]","[0, 153.0, 246.0, 111.0, 94.0]","[320.0, 493.0, 436.0, 168.0, 0]",rb2305,2023-01-03 09:57:00,-813.0,4039.0,4041.0,4041.0,4037.0,2022.0,0
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57,"[4039.0, 4040.0, 4041.0, 4042.0, 4043.0]","[31.0, 343.0, 546.0, 493.0, 124.0]","[220.0, 196.0, 97.0, 232.0, 0]",rb2305,2023-01-03 09:58:00,792.0,4042.0,4039.0,4043.0,4039.0,2282.0,0
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58,"[4040.0, 4041.0, 4042.0]","[203.0, 416.0, 16.0]","[876.0, 205.0, 0]",rb2305,2023-01-03 09:59:00,-446.0,4040.0,4041.0,4042.0,4040.0,1716.0,0
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60,"[4037.0, 4038.0, 4039.0, 4040.0, 4041.0, 4042.0]","[0, 214.0, 128.0, 315.0, 171.0, 315.0]","[381.0, 116.0, 399.0, 329.0, 191.0, 54.0]",rb2305,2023-01-03 10:01:00,-327.0,4042.0,4037.0,4042.0,4037.0,2613.0,0
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61,"[4041.0, 4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0]","[0, 253.0, 219.0, 860.0, 163.0, 1596.0, 961.0]","[33.0, 124.0, 10.0, 650.0, 849.0, 670.0, 0]",rb2305,2023-01-03 10:02:00,1716.0,4047.0,4042.0,4047.0,4041.0,6388.0,0
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62,"[4046.0, 4047.0, 4048.0, 4049.0]","[0, 388.0, 1591.0, 819.0]","[214.0, 889.0, 745.0, 0]",rb2305,2023-01-03 10:03:00,950.0,4049.0,4047.0,4049.0,4046.0,4646.0,0
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63,"[4047.0, 4048.0, 4049.0]","[125.0, 517.0, 547.0]","[772.0, 1185.0, 0]",rb2305,2023-01-03 10:04:00,-768.0,4048.0,4048.0,4049.0,4047.0,3146.0,0
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64,"[4047.0, 4048.0, 4049.0, 4050.0]","[0, 878.0, 928.0, 1822.0]","[225.0, 557.0, 683.0, 0]",rb2305,2023-01-03 10:05:00,2163.0,4049.0,4048.0,4050.0,4047.0,5110.0,0
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65,"[4048.0, 4049.0, 4050.0]","[0, 730.0, 2592.0]","[4.0, 2145.0, 133.0]",rb2305,2023-01-03 10:06:00,1040.0,4049.0,4049.0,4050.0,4048.0,5620.0,0
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66,"[4048.0, 4049.0, 4050.0, 4051.0]","[0, 565.0, 1368.0, 1.0]","[140.0, 871.0, 240.0, 0]",rb2305,2023-01-03 10:07:00,683.0,4050.0,4049.0,4051.0,4048.0,3200.0,0
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67,"[4050.0, 4051.0, 4052.0, 4053.0, 4054.0]","[0, 881.0, 309.0, 745.0, 2677.0]","[163.0, 526.0, 320.0, 1749.0, 362.0]",rb2305,2023-01-03 10:08:00,1492.0,4053.0,4050.0,4054.0,4050.0,7732.0,0
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68,"[4051.0, 4052.0, 4053.0, 4054.0, 4055.0, 4056.0]","[0, 149.0, 309.0, 851.0, 2299.0, 95.0]","[22.0, 414.0, 868.0, 3139.0, 476.0, 0]",rb2305,2023-01-03 10:09:00,-1216.0,4051.0,4054.0,4056.0,4051.0,8622.0,0
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69,"[4049.0, 4050.0, 4051.0, 4052.0, 4053.0]","[0, 420.0, 1022.0, 429.0, 146.0]","[37.0, 1183.0, 518.0, 202.0, 0]",rb2305,2023-01-03 10:10:00,77.0,4050.0,4052.0,4053.0,4049.0,3967.0,0
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71,"[4040.0, 4041.0, 4042.0, 4043.0]","[84.0, 1011.0, 654.0, 200.0]","[1028.0, 1743.0, 636.0, 0]",rb2305,2023-01-03 10:12:00,-1458.0,4040.0,4042.0,4043.0,4040.0,5360.0,0
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72,"[4040.0, 4041.0, 4042.0]","[718.0, 1622.0, 191.0]","[1174.0, 479.0, 173.0]",rb2305,2023-01-03 10:13:00,705.0,4042.0,4041.0,4042.0,4040.0,4357.0,0
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73,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 777.0, 1578.0, 27.0]","[161.0, 258.0, 424.0, 0]",rb2305,2023-01-03 10:14:00,1539.0,4044.0,4042.0,4044.0,4041.0,3225.0,0
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74,"[4043.0, 4044.0, 4045.0]","[0, 795.0, 698.0]","[192.0, 154.0, 0]",rb2305,2023-01-03 10:15:00,1147.0,4044.0,4044.0,4045.0,4043.0,1839.0,0
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75,"[4049.0, 4050.0, 4051.0, 4052.0, 4053.0]","[360.0, 1451.0, 2605.0, 1062.0, 585.0]","[733.0, 1633.0, 1351.0, 821.0, 0]",rb2305,2023-01-03 10:31:00,1525.0,4051.0,4051.0,4053.0,4049.0,11318.0,0
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76,"[4049.0, 4050.0, 4051.0, 4052.0]","[0, 452.0, 564.0, 195.0]","[106.0, 1270.0, 315.0, 0]",rb2305,2023-01-03 10:32:00,-480.0,4050.0,4051.0,4052.0,4049.0,2903.0,0
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77,"[4048.0, 4049.0, 4050.0, 4051.0, 4052.0]","[0, 141.0, 153.0, 410.0, 51.0]","[225.0, 508.0, 1086.0, 177.0, 0]",rb2305,2023-01-03 10:33:00,-1241.0,4048.0,4051.0,4052.0,4048.0,2751.0,0
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78,"[4048.0, 4049.0, 4050.0, 4051.0, 4052.0]","[0, 231.0, 699.0, 961.0, 52.0]","[25.0, 400.0, 376.0, 343.0, 0]",rb2305,2023-01-03 10:34:00,799.0,4051.0,4048.0,4052.0,4048.0,3113.0,0
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79,"[4050.0, 4051.0, 4052.0, 4053.0]","[0, 249.0, 800.0, 169.0]","[69.0, 438.0, 377.0, 0]",rb2305,2023-01-03 10:35:00,334.0,4050.0,4052.0,4053.0,4050.0,2107.0,0
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80,"[4048.0, 4049.0, 4050.0, 4051.0, 4052.0]","[0, 405.0, 952.0, 357.0, 7.0]","[162.0, 603.0, 339.0, 23.0, 0]",rb2305,2023-01-03 10:36:00,594.0,4050.0,4051.0,4052.0,4048.0,2850.0,0
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81,"[4048.0, 4049.0, 4050.0]","[0, 435.0, 590.0]","[375.0, 671.0, 61.0]",rb2305,2023-01-03 10:37:00,-82.0,4049.0,4050.0,4050.0,4048.0,2133.0,0
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82,"[4048.0, 4049.0, 4050.0, 4051.0, 4052.0, 4053.0]","[0, 275.0, 187.0, 545.0, 394.0, 144.0]","[2.0, 92.0, 325.0, 0, 149.0, 0]",rb2305,2023-01-03 10:38:00,977.0,4052.0,4049.0,4053.0,4048.0,2113.0,0
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83,"[4047.0, 4048.0, 4049.0, 4050.0, 4051.0, 4052.0]","[0, 0, 340.0, 75.0, 70.0, 160.0]","[272.0, 401.0, 83.0, 101.0, 74.0, 0]",rb2305,2023-01-03 10:39:00,-286.0,4047.0,4052.0,4052.0,4047.0,1595.0,0
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84,"[4046.0, 4047.0, 4048.0, 4049.0, 4050.0]","[0, 128.0, 113.0, 299.0, 439.0]","[29.0, 365.0, 26.0, 195.0, 93.0]",rb2305,2023-01-03 10:40:00,271.0,4049.0,4046.0,4050.0,4046.0,1699.0,0
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85,"[4047.0, 4048.0, 4049.0, 4050.0]","[0, 57.0, 427.0, 93.0]","[43.0, 226.0, 509.0, 42.0]",rb2305,2023-01-03 10:41:00,-243.0,4048.0,4050.0,4050.0,4047.0,1397.0,0
|
||||
86,"[4048.0, 4049.0, 4050.0, 4051.0]","[0, 1272.0, 485.0, 200.0]","[114.0, 876.0, 113.0, 0]",rb2305,2023-01-03 10:42:00,854.0,4049.0,4048.0,4051.0,4048.0,3077.0,0
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87,"[4045.0, 4046.0, 4047.0, 4048.0, 4049.0]","[0, 431.0, 489.0, 446.0, 8.0]","[350.0, 1094.0, 639.0, 144.0, 0]",rb2305,2023-01-03 10:43:00,-853.0,4046.0,4049.0,4049.0,4045.0,3601.0,0
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88,"[4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[0, 135.0, 249.0, 206.0, 97.0]","[168.0, 1176.0, 249.0, 306.0, 0]",rb2305,2023-01-03 10:44:00,-1212.0,4046.0,4046.0,4048.0,4044.0,2586.0,0
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89,"[4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[0, 321.0, 342.0, 160.0, 122.0]","[85.0, 339.0, 150.0, 132.0, 0]",rb2305,2023-01-03 10:45:00,239.0,4046.0,4046.0,4048.0,4044.0,1653.0,0
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90,"[4042.0, 4043.0, 4044.0, 4045.0, 4046.0]","[0, 382.0, 195.0, 263.0, 241.0]","[28.0, 866.0, 454.0, 513.0, 182.0]",rb2305,2023-01-03 10:46:00,-962.0,4044.0,4046.0,4046.0,4042.0,3142.0,0
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91,"[4041.0, 4042.0, 4043.0, 4044.0]","[142.0, 243.0, 789.0, 139.0]","[1100.0, 1793.0, 645.0, 0]",rb2305,2023-01-03 10:47:00,-2225.0,4042.0,4043.0,4044.0,4041.0,4860.0,0
|
||||
92,"[4040.0, 4041.0, 4042.0, 4043.0]","[0, 486.0, 537.0, 116.0]","[191.0, 704.0, 106.0, 0]",rb2305,2023-01-03 10:48:00,138.0,4042.0,4041.0,4043.0,4040.0,2140.0,0
|
||||
93,"[4040.0, 4041.0, 4042.0, 4043.0]","[98.0, 562.0, 1419.0, 87.0]","[1350.0, 917.0, 250.0, 0]",rb2305,2023-01-03 10:49:00,-351.0,4042.0,4041.0,4043.0,4040.0,4683.0,0
|
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94,"[4038.0, 4039.0, 4040.0, 4041.0, 4042.0]","[0, 427.0, 806.0, 501.0, 286.0]","[106.0, 469.0, 330.0, 69.0, 0]",rb2305,2023-01-03 10:50:00,1046.0,4041.0,4042.0,4042.0,4038.0,2994.0,0
|
||||
95,"[4040.0, 4041.0, 4042.0, 4043.0]","[0, 495.0, 709.0, 1108.0]","[81.0, 266.0, 374.0, 67.0]",rb2305,2023-01-03 10:51:00,1524.0,4041.0,4041.0,4043.0,4040.0,3100.0,0
|
||||
96,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 164.0, 802.0, 30.0]","[1.0, 1156.0, 42.0, 0]",rb2305,2023-01-03 10:52:00,-203.0,4042.0,4042.0,4044.0,4041.0,2195.0,0
|
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97,"[4041.0, 4042.0, 4043.0]","[0, 614.0, 99.0]","[330.0, 263.0, 0]",rb2305,2023-01-03 10:53:00,120.0,4041.0,4042.0,4043.0,4041.0,1310.0,0
|
||||
98,"[4039.0, 4040.0, 4041.0, 4042.0]","[0, 239.0, 210.0, 27.0]","[206.0, 693.0, 197.0, 0]",rb2305,2023-01-03 10:54:00,-620.0,4040.0,4041.0,4042.0,4039.0,1576.0,0
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||||
99,"[4039.0, 4040.0, 4041.0]","[0, 456.0, 208.0]","[160.0, 299.0, 0]",rb2305,2023-01-03 10:55:00,205.0,4039.0,4041.0,4041.0,4039.0,1123.0,0
|
||||
100,"[4039.0, 4040.0, 4041.0]","[0, 281.0, 472.0]","[196.0, 176.0, 0]",rb2305,2023-01-03 10:56:00,381.0,4041.0,4039.0,4041.0,4039.0,1125.0,0
|
||||
101,"[4041.0, 4042.0]","[0, 919.0]","[570.0, 165.0]",rb2305,2023-01-03 10:57:00,184.0,4042.0,4041.0,4042.0,4041.0,1654.0,0
|
||||
102,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 16.0, 968.0, 86.0]","[279.0, 161.0, 304.0, 0]",rb2305,2023-01-03 10:58:00,326.0,4043.0,4041.0,4044.0,4041.0,1824.0,0
|
||||
103,"[4039.0, 4040.0, 4041.0, 4042.0, 4043.0]","[0, 46.0, 368.0, 60.0, 49.0]","[119.0, 454.0, 235.0, 120.0, 0]",rb2305,2023-01-03 10:59:00,-405.0,4040.0,4043.0,4043.0,4039.0,1451.0,0
|
||||
104,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 480.0, 88.0, 326.0, 414.0]","[571.0, 319.0, 238.0, 194.0, 0]",rb2305,2023-01-03 11:00:00,-14.0,4043.0,4040.0,4044.0,4040.0,2698.0,0
|
||||
105,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 335.0, 250.0, 165.0]","[75.0, 691.0, 293.0, 76.0]",rb2305,2023-01-03 11:01:00,-385.0,4041.0,4042.0,4044.0,4041.0,1899.0,0
|
||||
106,"[4037.0, 4038.0, 4039.0, 4040.0, 4041.0]","[0, 1136.0, 534.0, 10.0, 40.0]","[610.0, 1841.0, 751.0, 624.0, 210.0]",rb2305,2023-01-03 11:02:00,-2316.0,4038.0,4041.0,4041.0,4037.0,5970.0,-1
|
||||
107,"[4037.0, 4038.0, 4039.0, 4040.0]","[0, 276.0, 315.0, 246.0]","[287.0, 159.0, 173.0, 0]",rb2305,2023-01-03 11:03:00,218.0,4038.0,4039.0,4040.0,4037.0,1485.0,0
|
||||
108,"[4038.0, 4039.0, 4040.0, 4041.0]","[0, 556.0, 455.0, 198.0]","[209.0, 145.0, 239.0, 0]",rb2305,2023-01-03 11:04:00,616.0,4041.0,4039.0,4041.0,4038.0,1802.0,0
|
||||
109,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 411.0, 181.0, 387.0, 7.0]","[23.0, 147.0, 172.0, 101.0, 0]",rb2305,2023-01-03 11:05:00,543.0,4043.0,4041.0,4044.0,4040.0,1429.0,0
|
||||
110,"[4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[78.0, 74.0, 992.0, 523.0, 508.0, 611.0]","[11.0, 135.0, 112.0, 580.0, 1048.0, 116.0]",rb2305,2023-01-03 11:06:00,784.0,4046.0,4043.0,4048.0,4043.0,4905.0,1
|
||||
111,"[4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[31.0, 338.0, 64.0, 637.0, 234.0, 179.0]","[388.0, 180.0, 427.0, 359.0, 187.0, 0]",rb2305,2023-01-03 11:07:00,-58.0,4045.0,4046.0,4048.0,4043.0,3025.0,0
|
||||
112,"[4043.0, 4044.0, 4045.0]","[0, 179.0, 395.0]","[31.0, 515.0, 0]",rb2305,2023-01-03 11:08:00,28.0,4044.0,4044.0,4045.0,4043.0,1120.0,0
|
||||
113,"[4043.0, 4044.0, 4045.0]","[0, 150.0, 259.0]","[41.0, 260.0, 0]",rb2305,2023-01-03 11:09:00,108.0,4044.0,4045.0,4045.0,4043.0,710.0,0
|
||||
114,"[4042.0, 4043.0, 4044.0, 4045.0]","[0, 33.0, 83.0, 386.0]","[3.0, 392.0, 193.0, 0]",rb2305,2023-01-03 11:10:00,-86.0,4042.0,4044.0,4045.0,4042.0,1090.0,0
|
||||
115,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 295.0, 273.0, 354.0, 77.0]","[216.0, 501.0, 470.0, 549.0, 0]",rb2305,2023-01-03 11:11:00,-737.0,4042.0,4042.0,4044.0,4040.0,2735.0,0
|
||||
116,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0]","[0, 172.0, 113.0, 144.0, 41.0]","[149.0, 439.0, 240.0, 46.0, 0]",rb2305,2023-01-03 11:12:00,-404.0,4040.0,4042.0,4044.0,4040.0,1344.0,0
|
||||
117,"[4040.0, 4041.0, 4042.0, 4043.0, 4044.0, 4045.0, 4046.0]","[0, 176.0, 66.0, 97.0, 76.0, 432.0, 120.0]","[20.0, 63.0, 21.0, 6.0, 287.0, 99.0, 0]",rb2305,2023-01-03 11:13:00,471.0,4045.0,4040.0,4046.0,4040.0,1463.0,0
|
||||
118,"[4044.0, 4045.0, 4046.0, 4047.0]","[0, 0, 760.0, 15.0]","[16.0, 528.0, 116.0, 0]",rb2305,2023-01-03 11:14:00,115.0,4045.0,4045.0,4047.0,4044.0,1437.0,0
|
||||
119,"[4044.0, 4045.0, 4046.0, 4047.0]","[0, 253.0, 420.0, 287.0]","[340.0, 784.0, 5.0, 120.0]",rb2305,2023-01-03 11:15:00,-289.0,4044.0,4045.0,4047.0,4044.0,2209.0,0
|
||||
120,"[4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[0, 0, 13.0, 191.0, 490.0, 466.0, 1.0]","[1.0, 149.0, 261.0, 453.0, 84.0, 68.0, 0]",rb2305,2023-01-03 11:16:00,145.0,4042.0,4044.0,4048.0,4042.0,2179.0,0
|
||||
121,"[4042.0, 4043.0, 4044.0]","[0, 298.0, 3.0]","[514.0, 79.0, 0]",rb2305,2023-01-03 11:17:00,-292.0,4042.0,4042.0,4044.0,4042.0,894.0,0
|
||||
122,"[4040.0, 4041.0, 4042.0, 4043.0]","[0, 52.0, 80.0, 27.0]","[144.0, 477.0, 445.0, 20.0]",rb2305,2023-01-03 11:18:00,-927.0,4040.0,4042.0,4043.0,4040.0,1246.0,0
|
||||
123,"[4040.0, 4041.0, 4042.0]","[0, 239.0, 290.0]","[560.0, 272.0, 0]",rb2305,2023-01-03 11:19:00,-303.0,4041.0,4041.0,4042.0,4040.0,1361.0,0
|
||||
124,"[4040.0, 4041.0, 4042.0, 4043.0]","[0, 877.0, 355.0, 39.0]","[56.0, 652.0, 41.0, 0]",rb2305,2023-01-03 11:20:00,522.0,4041.0,4041.0,4043.0,4040.0,2020.0,0
|
||||
125,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 729.0, 720.0, 12.0]","[259.0, 379.0, 904.0, 0]",rb2305,2023-01-03 11:21:00,-81.0,4043.0,4041.0,4044.0,4041.0,3003.0,0
|
||||
126,"[4041.0, 4042.0, 4043.0, 4044.0]","[0, 184.0, 25.0, 87.0]","[9.0, 148.0, 242.0, 0]",rb2305,2023-01-03 11:22:00,-103.0,4042.0,4043.0,4044.0,4041.0,695.0,0
|
||||
127,"[4042.0, 4043.0, 4044.0, 4045.0, 4046.0, 4047.0, 4048.0]","[0, 185.0, 187.0, 632.0, 440.0, 278.0, 883.0]","[6.0, 34.0, 226.0, 110.0, 122.0, 332.0, 0]",rb2305,2023-01-03 11:23:00,1775.0,4047.0,4042.0,4048.0,4042.0,3435.0,0
|
||||
128,"[4046.0, 4047.0, 4048.0, 4049.0, 4050.0, 4051.0]","[0, 352.0, 133.0, 1159.0, 854.0, 336.0]","[121.0, 0, 236.0, 811.0, 341.0, 0]",rb2305,2023-01-03 11:24:00,1325.0,4049.0,4046.0,4051.0,4046.0,4359.0,0
|
||||
129,"[4047.0, 4048.0, 4049.0]","[0, 389.0, 160.0]","[585.0, 179.0, 0]",rb2305,2023-01-03 11:25:00,-215.0,4047.0,4048.0,4049.0,4047.0,1313.0,0
|
||||
130,"[4045.0, 4046.0, 4047.0, 4048.0, 4049.0]","[0, 97.0, 218.0, 361.0, 445.0]","[39.0, 354.0, 335.0, 642.0, 287.0]",rb2305,2023-01-03 11:26:00,-536.0,4048.0,4048.0,4049.0,4045.0,2788.0,0
|
||||
131,"[4046.0, 4047.0, 4048.0, 4049.0]","[0, 254.0, 376.0, 191.0]","[36.0, 500.0, 489.0, 0]",rb2305,2023-01-03 11:27:00,-204.0,4047.0,4048.0,4049.0,4046.0,1846.0,0
|
||||
132,"[4045.0, 4046.0, 4047.0, 4048.0]","[0, 73.0, 428.0, 224.0]","[1.0, 625.0, 367.0, 0]",rb2305,2023-01-03 11:28:00,-268.0,4047.0,4047.0,4048.0,4045.0,1724.0,0
|
||||
133,"[4046.0, 4047.0, 4048.0, 4049.0, 4050.0, 4051.0]","[0, 224.0, 29.0, 328.0, 984.0, 32.0]","[11.0, 116.0, 27.0, 461.0, 427.0, 0]",rb2305,2023-01-03 11:29:00,555.0,4050.0,4046.0,4051.0,4046.0,2648.0,0
|
||||
134,"[4050.0, 4051.0, 4052.0, 4053.0, 4054.0]","[0, 243.0, 1082.0, 549.0, 164.0]","[370.0, 1147.0, 1641.0, 224.0, 0]",rb2305,2023-01-03 11:30:00,-1344.0,4051.0,4051.0,4054.0,4050.0,5436.0,0
|
||||
135,"[4049.0, 4050.0, 4051.0, 4052.0, 4053.0, 4055.0, 4056.0]","[75.0, 1368.0, 2486.0, 1094.0, 743.0, 3036.0, 470.0]","[1410.0, 1848.0, 987.0, 1977.0, 233.0, 0, 0]",rb2305,2023-01-03 13:31:00,2817.0,4053.0,4055.0,4056.0,4049.0,16041.0,0
|
||||
136,"[4049.0, 4050.0, 4051.0, 4052.0, 4053.0, 4054.0]","[0, 605.0, 433.0, 77.0, 644.0, 627.0]","[122.0, 1097.0, 139.0, 61.0, 364.0, 406.0]",rb2305,2023-01-03 13:32:00,197.0,4050.0,4052.0,4054.0,4049.0,4575.0,0
|
||||
137,"[4050.0, 4051.0, 4052.0, 4053.0, 4054.0]","[0, 553.0, 655.0, 518.0, 88.0]","[261.0, 418.0, 160.0, 567.0, 0]",rb2305,2023-01-03 13:33:00,408.0,4051.0,4050.0,4054.0,4050.0,3222.0,0
|
||||
138,"[4048.0, 4049.0, 4050.0, 4051.0]","[0, 347.0, 1036.0, 95.0]","[296.0, 709.0, 1331.0, 0]",rb2305,2023-01-03 13:34:00,-858.0,4050.0,4051.0,4051.0,4048.0,3830.0,0
|
||||
139,"[4046.0, 4047.0, 4048.0, 4049.0, 4050.0]","[0, 62.0, 371.0, 486.0, 199.0]","[18.0, 346.0, 136.0, 308.0, 0]",rb2305,2023-01-03 13:35:00,310.0,4047.0,4050.0,4050.0,4046.0,1934.0,0
|
||||
140,"[4045.0, 4046.0, 4047.0, 4048.0]","[0, 810.0, 967.0, 354.0]","[371.0, 778.0, 861.0, 0]",rb2305,2023-01-03 13:36:00,121.0,4046.0,4047.0,4048.0,4045.0,4141.0,0
|
||||
141,"[4047.0, 4048.0, 4049.0, 4050.0, 4051.0, 4052.0]","[103.0, 289.0, 393.0, 941.0, 565.0, 31.0]","[27.0, 56.0, 86.0, 566.0, 0, 0]",rb2305,2023-01-03 13:37:00,1587.0,4050.0,4047.0,4052.0,4047.0,3057.0,1
|
||||
142,"[4051.0, 4052.0, 4053.0, 4054.0]","[55.0, 589.0, 1023.0, 439.0]","[66.0, 481.0, 370.0, 67.0]",rb2305,2023-01-03 13:38:00,1122.0,4054.0,4051.0,4054.0,4051.0,3090.0,0
|
||||
143,"[4052.0, 4053.0, 4054.0, 4055.0, 4056.0]","[0, 34.0, 436.0, 1798.0, 209.0]","[18.0, 865.0, 767.0, 174.0, 0]",rb2305,2023-01-03 13:39:00,653.0,4055.0,4054.0,4056.0,4052.0,4301.0,0
|
||||
144,"[4055.0, 4056.0, 4057.0, 4058.0, 4059.0, 4060.0]","[0, 538.0, 769.0, 1806.0, 2601.0, 131.0]","[130.0, 0, 175.0, 2129.0, 103.0, 0]",rb2305,2023-01-03 13:40:00,3308.0,4059.0,4055.0,4060.0,4055.0,8489.0,1
|
||||
145,"[4057.0, 4058.0, 4059.0, 4060.0, 4061.0, 4062.0, 4063.0]","[0, 371.0, 1499.0, 1585.0, 2378.0, 824.0, 533.0]","[168.0, 492.0, 967.0, 0, 739.0, 623.0, 0]",rb2305,2023-01-03 13:41:00,4201.0,4062.0,4059.0,4063.0,4057.0,10590.0,0
|
||||
146,"[4062.0, 4063.0, 4064.0]","[135.0, 1155.0, 863.0]","[850.0, 1459.0, 0]",rb2305,2023-01-03 13:42:00,-156.0,4064.0,4062.0,4064.0,4062.0,4483.0,0
|
||||
147,"[4061.0, 4062.0, 4063.0, 4064.0, 4065.0]","[0, 256.0, 104.0, 749.0, 310.0]","[483.0, 790.0, 197.0, 337.0, 0]",rb2305,2023-01-03 13:43:00,-388.0,4062.0,4064.0,4065.0,4061.0,3226.0,0
|
||||
148,"[4061.0, 4062.0, 4063.0]","[20.0, 852.0, 283.0]","[971.0, 261.0, 0]",rb2305,2023-01-03 13:44:00,-77.0,4062.0,4062.0,4063.0,4061.0,2446.0,0
|
||||
149,"[4062.0, 4063.0, 4064.0]","[0, 1033.0, 604.0]","[599.0, 829.0, 0]",rb2305,2023-01-03 13:45:00,209.0,4064.0,4063.0,4064.0,4062.0,3071.0,0
|
||||
150,"[4059.0, 4060.0, 4061.0, 4062.0, 4063.0, 4064.0, 4065.0]","[124.0, 322.0, 183.0, 274.0, 24.0, 378.0, 157.0]","[1342.0, 605.0, 538.0, 248.0, 459.0, 16.0, 0]",rb2305,2023-01-03 13:46:00,-1746.0,4060.0,4064.0,4065.0,4059.0,4674.0,0
|
||||
151,"[4058.0, 4059.0, 4060.0, 4061.0]","[0, 176.0, 476.0, 43.0]","[96.0, 833.0, 292.0, 0]",rb2305,2023-01-03 13:47:00,-526.0,4060.0,4060.0,4061.0,4058.0,1916.0,0
|
||||
152,"[4059.0, 4060.0, 4061.0, 4062.0]","[0, 272.0, 459.0, 61.0]","[91.0, 595.0, 554.0, 0]",rb2305,2023-01-03 13:48:00,-448.0,4062.0,4059.0,4062.0,4059.0,2074.0,0
|
||||
153,"[4061.0, 4062.0, 4063.0]","[0, 774.0, 432.0]","[254.0, 678.0, 0]",rb2305,2023-01-03 13:49:00,274.0,4062.0,4062.0,4063.0,4061.0,2138.0,0
|
||||
154,"[4062.0, 4063.0, 4064.0]","[128.0, 399.0, 550.0]","[702.0, 901.0, 0]",rb2305,2023-01-03 13:50:00,-526.0,4063.0,4063.0,4064.0,4062.0,2680.0,0
|
||||
155,"[4061.0, 4062.0, 4063.0, 4064.0]","[0, 185.0, 475.0, 97.0]","[361.0, 1119.0, 43.0, 0]",rb2305,2023-01-03 13:51:00,-766.0,4062.0,4064.0,4064.0,4061.0,2280.0,0
|
||||
156,"[4061.0, 4062.0, 4063.0]","[0, 124.0, 592.0]","[523.0, 670.0, 0]",rb2305,2023-01-03 13:52:00,-477.0,4061.0,4063.0,4063.0,4061.0,1909.0,0
|
||||
157,"[4060.0, 4061.0, 4062.0]","[0, 118.0, 463.0]","[897.0, 1056.0, 0]",rb2305,2023-01-03 13:53:00,-1372.0,4060.0,4061.0,4062.0,4060.0,2534.0,0
|
||||
158,"[4058.0, 4059.0, 4060.0, 4061.0]","[0, 388.0, 148.0, 377.0]","[354.0, 476.0, 645.0, 4.0]",rb2305,2023-01-03 13:54:00,-566.0,4059.0,4061.0,4061.0,4058.0,2392.0,0
|
||||
159,"[4058.0, 4059.0, 4060.0]","[0, 725.0, 305.0]","[234.0, 854.0, 0]",rb2305,2023-01-03 13:55:00,-58.0,4060.0,4059.0,4060.0,4058.0,2118.0,0
|
||||
160,"[4059.0, 4060.0, 4061.0]","[0, 669.0, 48.0]","[400.0, 118.0, 0]",rb2305,2023-01-03 13:56:00,199.0,4060.0,4059.0,4061.0,4059.0,1244.0,0
|
||||
161,"[4057.0, 4058.0, 4059.0, 4060.0, 4061.0]","[0, 337.0, 87.0, 66.0, 5.0]","[391.0, 1409.0, 460.0, 240.0, 0]",rb2305,2023-01-03 13:57:00,-2005.0,4058.0,4060.0,4061.0,4057.0,3063.0,-1
|
||||
162,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 159.0, 380.0, 83.0]","[77.0, 444.0, 243.0, 0]",rb2305,2023-01-03 13:58:00,-142.0,4058.0,4059.0,4060.0,4057.0,1386.0,0
|
||||
163,"[4058.0, 4059.0, 4060.0]","[0, 488.0, 43.0]","[879.0, 179.0, 0]",rb2305,2023-01-03 13:59:00,-527.0,4060.0,4058.0,4060.0,4058.0,1593.0,0
|
||||
164,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 124.0, 619.0, 35.0]","[64.0, 468.0, 316.0, 0]",rb2305,2023-01-03 14:00:00,-70.0,4058.0,4060.0,4060.0,4057.0,1626.0,0
|
||||
165,"[4058.0, 4059.0, 4060.0]","[0, 463.0, 616.0]","[60.0, 710.0, 57.0]",rb2305,2023-01-03 14:01:00,252.0,4060.0,4058.0,4060.0,4058.0,1907.0,0
|
||||
166,"[4058.0, 4059.0, 4060.0, 4061.0, 4062.0, 4063.0]","[0, 118.0, 172.0, 799.0, 422.0, 48.0]","[8.0, 307.0, 206.0, 547.0, 59.0, 0]",rb2305,2023-01-03 14:02:00,432.0,4059.0,4060.0,4063.0,4058.0,2686.0,0
|
||||
167,"[4058.0, 4059.0, 4060.0]","[0, 705.0, 4.0]","[191.0, 88.0, 0]",rb2305,2023-01-03 14:03:00,430.0,4058.0,4059.0,4060.0,4058.0,1019.0,0
|
||||
168,"[4058.0, 4059.0, 4060.0, 4061.0]","[0, 180.0, 396.0, 48.0]","[72.0, 249.0, 49.0, 0]",rb2305,2023-01-03 14:04:00,254.0,4061.0,4059.0,4061.0,4058.0,1000.0,0
|
||||
169,"[4061.0, 4062.0, 4063.0]","[354.0, 314.0, 729.0]","[50.0, 1146.0, 0]",rb2305,2023-01-03 14:05:00,201.0,4063.0,4061.0,4063.0,4061.0,2593.0,0
|
||||
170,"[4060.0, 4061.0, 4062.0, 4063.0]","[0, 71.0, 66.0, 350.0]","[1.0, 210.0, 378.0, 0]",rb2305,2023-01-03 14:06:00,-102.0,4061.0,4063.0,4063.0,4060.0,1076.0,0
|
||||
171,"[4057.0, 4058.0, 4059.0, 4060.0, 4061.0]","[0, 3.0, 104.0, 158.0, 234.0]","[54.0, 588.0, 194.0, 327.0, 0]",rb2305,2023-01-03 14:07:00,-664.0,4057.0,4060.0,4061.0,4057.0,1662.0,0
|
||||
172,"[4056.0, 4057.0, 4058.0, 4059.0, 4060.0]","[0, 10.0, 281.0, 112.0, 124.0]","[424.0, 434.0, 471.0, 168.0, 0]",rb2305,2023-01-03 14:08:00,-970.0,4059.0,4058.0,4060.0,4056.0,2024.0,0
|
||||
173,"[4059.0, 4060.0, 4061.0]","[0, 294.0, 51.0]","[483.0, 312.0, 0]",rb2305,2023-01-03 14:09:00,-450.0,4060.0,4060.0,4061.0,4059.0,1140.0,0
|
||||
174,"[4059.0, 4060.0, 4061.0]","[0, 255.0, 156.0]","[34.0, 574.0, 0]",rb2305,2023-01-03 14:10:00,-197.0,4061.0,4060.0,4061.0,4059.0,1021.0,0
|
||||
175,"[4060.0, 4061.0, 4062.0, 4063.0]","[0, 694.0, 75.0, 48.0]","[303.0, 401.0, 0, 0]",rb2305,2023-01-03 14:11:00,113.0,4063.0,4061.0,4063.0,4060.0,1537.0,0
|
||||
176,"[4061.0, 4062.0, 4063.0]","[0, 409.0, 12.0]","[222.0, 155.0, 0]",rb2305,2023-01-03 14:12:00,44.0,4062.0,4063.0,4063.0,4061.0,798.0,0
|
||||
177,"[4060.0, 4061.0, 4062.0, 4063.0]","[0, 291.0, 850.0, 19.0]","[159.0, 508.0, 147.0, 0]",rb2305,2023-01-03 14:13:00,346.0,4063.0,4061.0,4063.0,4060.0,1974.0,0
|
||||
178,"[4062.0, 4063.0, 4064.0, 4065.0, 4066.0, 4067.0, 4068.0]","[0, 630.0, 1021.0, 2734.0, 720.0, 2722.0, 361.0]","[53.0, 38.0, 329.0, 47.0, 1155.0, 742.0, 0]",rb2305,2023-01-03 14:14:00,5824.0,4065.0,4062.0,4068.0,4062.0,10552.0,2
|
||||
179,"[4063.0, 4064.0, 4065.0, 4066.0, 4067.0]","[0, 153.0, 474.0, 343.0, 246.0]","[25.0, 610.0, 1245.0, 398.0, 0]",rb2305,2023-01-03 14:15:00,-1062.0,4064.0,4066.0,4067.0,4063.0,3494.0,0
|
||||
180,"[4065.0, 4066.0, 4067.0]","[0, 387.0, 835.0]","[295.0, 339.0, 0]",rb2305,2023-01-03 14:16:00,588.0,4066.0,4066.0,4067.0,4065.0,1856.0,0
|
||||
181,"[4064.0, 4065.0, 4066.0, 4067.0]","[0, 632.0, 420.0, 104.0]","[171.0, 568.0, 94.0, 0]",rb2305,2023-01-03 14:17:00,323.0,4064.0,4067.0,4067.0,4064.0,1989.0,0
|
||||
182,"[4064.0, 4065.0, 4066.0, 4067.0]","[0, 365.0, 323.0, 148.0]","[364.0, 116.0, 266.0, 0]",rb2305,2023-01-03 14:18:00,90.0,4066.0,4064.0,4067.0,4064.0,1582.0,0
|
||||
183,"[4066.0, 4067.0, 4068.0]","[0, 346.0, 625.0]","[192.0, 727.0, 0]",rb2305,2023-01-03 14:19:00,52.0,4067.0,4067.0,4068.0,4066.0,1890.0,0
|
||||
184,"[4067.0, 4068.0, 4069.0, 4070.0, 4071.0]","[0, 684.0, 1145.0, 2860.0, 1251.0]","[228.0, 655.0, 1025.0, 658.0, 0]",rb2305,2023-01-03 14:20:00,3374.0,4069.0,4067.0,4071.0,4067.0,8506.0,0
|
||||
185,"[4068.0, 4069.0, 4070.0]","[0, 514.0, 1050.0]","[18.0, 929.0, 0]",rb2305,2023-01-03 14:21:00,617.0,4069.0,4069.0,4070.0,4068.0,2515.0,0
|
||||
186,"[4066.0, 4067.0, 4068.0, 4069.0, 4070.0]","[0, 108.0, 140.0, 206.0, 165.0]","[506.0, 671.0, 535.0, 475.0, 0]",rb2305,2023-01-03 14:22:00,-1568.0,4066.0,4069.0,4070.0,4066.0,2806.0,0
|
||||
187,"[4065.0, 4066.0, 4067.0, 4068.0]","[0, 101.0, 676.0, 699.0]","[403.0, 660.0, 282.0, 0]",rb2305,2023-01-03 14:23:00,131.0,4066.0,4067.0,4068.0,4065.0,2845.0,0
|
||||
188,"[4066.0, 4067.0, 4068.0]","[0, 506.0, 83.0]","[391.0, 117.0, 0]",rb2305,2023-01-03 14:24:00,81.0,4068.0,4066.0,4068.0,4066.0,1107.0,0
|
||||
189,"[4066.0, 4067.0, 4068.0, 4069.0, 4070.0]","[0, 35.0, 212.0, 313.0, 711.0]","[65.0, 176.0, 59.0, 658.0, 0]",rb2305,2023-01-03 14:25:00,313.0,4069.0,4068.0,4070.0,4066.0,2229.0,0
|
||||
190,"[4068.0, 4069.0, 4070.0, 4071.0]","[0, 321.0, 471.0, 697.0]","[416.0, 565.0, 394.0, 0]",rb2305,2023-01-03 14:26:00,114.0,4069.0,4069.0,4071.0,4068.0,2890.0,0
|
||||
191,"[4068.0, 4069.0, 4070.0, 4071.0]","[0, 1030.0, 1301.0, 0]","[477.0, 1347.0, 1315.0, 5.0]",rb2305,2023-01-03 14:27:00,-813.0,4070.0,4070.0,4071.0,4068.0,5475.0,0
|
||||
192,"[4069.0, 4070.0, 4071.0, 4072.0, 4073.0, 4074.0]","[0, 168.0, 179.0, 551.0, 758.0, 612.0]","[233.0, 361.0, 396.0, 954.0, 397.0, 0]",rb2305,2023-01-03 14:28:00,-73.0,4073.0,4070.0,4074.0,4069.0,4610.0,0
|
||||
193,"[4072.0, 4073.0, 4074.0]","[0, 828.0, 47.0]","[1213.0, 311.0, 0]",rb2305,2023-01-03 14:29:00,-649.0,4073.0,4072.0,4074.0,4072.0,2399.0,0
|
||||
194,"[4071.0, 4072.0, 4073.0]","[110.0, 425.0, 102.0]","[954.0, 259.0, 0]",rb2305,2023-01-03 14:30:00,-576.0,4072.0,4073.0,4073.0,4071.0,1854.0,0
|
||||
195,"[4069.0, 4070.0, 4071.0, 4072.0]","[0, 395.0, 75.0, 601.0]","[981.0, 771.0, 247.0, 0]",rb2305,2023-01-03 14:31:00,-928.0,4069.0,4072.0,4072.0,4069.0,3070.0,0
|
||||
196,"[4063.0, 4064.0, 4065.0, 4066.0, 4067.0, 4068.0, 4069.0, 4070.0]","[311.0, 423.0, 193.0, 299.0, 163.0, 0, 0, 130.0]","[1247.0, 518.0, 938.0, 567.0, 209.0, 598.0, 130.0, 0]",rb2305,2023-01-03 14:32:00,-2688.0,4063.0,4070.0,4070.0,4063.0,5785.0,-2
|
||||
197,"[4060.0, 4061.0, 4062.0, 4063.0, 4064.0]","[0, 998.0, 1264.0, 375.0, 12.0]","[1497.0, 1296.0, 773.0, 476.0, 0]",rb2305,2023-01-03 14:33:00,-1393.0,4062.0,4063.0,4064.0,4060.0,6702.0,0
|
||||
198,"[4062.0, 4063.0, 4064.0, 4065.0]","[0, 212.0, 656.0, 554.0]","[94.0, 282.0, 59.0, 0]",rb2305,2023-01-03 14:34:00,987.0,4065.0,4062.0,4065.0,4062.0,1857.0,0
|
||||
199,"[4064.0, 4065.0, 4066.0]","[0, 775.0, 270.0]","[491.0, 422.0, 0]",rb2305,2023-01-03 14:35:00,132.0,4064.0,4065.0,4066.0,4064.0,1958.0,0
|
||||
200,"[4062.0, 4063.0, 4064.0, 4065.0, 4066.0]","[0, 125.0, 182.0, 737.0, 111.0]","[160.0, 637.0, 139.0, 439.0, 0]",rb2305,2023-01-03 14:36:00,-220.0,4066.0,4064.0,4066.0,4062.0,2530.0,0
|
||||
201,"[4062.0, 4063.0, 4064.0, 4065.0, 4066.0]","[0, 110.0, 209.0, 209.0, 121.0]","[150.0, 391.0, 440.0, 334.0, 0]",rb2305,2023-01-03 14:37:00,-666.0,4062.0,4065.0,4066.0,4062.0,1994.0,0
|
||||
202,"[4062.0, 4063.0, 4064.0]","[32.0, 512.0, 208.0]","[897.0, 200.0, 0]",rb2305,2023-01-03 14:38:00,-345.0,4063.0,4063.0,4064.0,4062.0,1849.0,0
|
||||
203,"[4062.0, 4063.0, 4064.0, 4065.0]","[0, 186.0, 216.0, 116.0]","[227.0, 361.0, 201.0, 0]",rb2305,2023-01-03 14:39:00,-271.0,4063.0,4063.0,4065.0,4062.0,1307.0,0
|
||||
204,"[4061.0, 4062.0, 4063.0]","[0, 911.0, 62.0]","[139.0, 670.0, 0]",rb2305,2023-01-03 14:40:00,164.0,4061.0,4062.0,4063.0,4061.0,1785.0,0
|
||||
205,"[4061.0, 4062.0, 4063.0]","[0, 754.0, 26.0]","[188.0, 354.0, 0]",rb2305,2023-01-03 14:41:00,238.0,4062.0,4061.0,4063.0,4061.0,1324.0,0
|
||||
206,"[4061.0, 4062.0, 4063.0, 4064.0, 4065.0, 4066.0]","[0, 27.0, 416.0, 395.0, 323.0, 36.0]","[4.0, 94.0, 248.0, 334.0, 17.0, 0]",rb2305,2023-01-03 14:42:00,500.0,4065.0,4062.0,4066.0,4061.0,1905.0,0
|
||||
207,"[4064.0, 4065.0, 4066.0]","[0, 123.0, 411.0]","[154.0, 443.0, 0]",rb2305,2023-01-03 14:43:00,-63.0,4064.0,4065.0,4066.0,4064.0,1131.0,0
|
||||
208,"[4061.0, 4062.0, 4063.0, 4064.0, 4065.0, 4066.0]","[0, 192.0, 132.0, 35.0, 313.0, 1.0]","[58.0, 442.0, 154.0, 303.0, 143.0, 0]",rb2305,2023-01-03 14:44:00,-427.0,4062.0,4065.0,4066.0,4061.0,1773.0,0
|
||||
209,"[4062.0, 4063.0, 4064.0]","[0, 103.0, 289.0]","[12.0, 296.0, 0]",rb2305,2023-01-03 14:45:00,84.0,4063.0,4062.0,4064.0,4062.0,700.0,0
|
||||
210,"[4060.0, 4061.0, 4062.0, 4063.0, 4064.0, 4065.0]","[0, 75.0, 62.0, 237.0, 192.0, 446.0]","[629.0, 383.0, 247.0, 339.0, 487.0, 613.0]",rb2305,2023-01-03 14:46:00,-1686.0,4061.0,4064.0,4065.0,4060.0,3722.0,0
|
||||
211,"[4057.0, 4058.0, 4059.0, 4060.0, 4061.0]","[0, 1482.0, 143.0, 476.0, 112.0]","[1399.0, 1246.0, 53.0, 930.0, 221.0]",rb2305,2023-01-03 14:47:00,-1636.0,4058.0,4061.0,4061.0,4057.0,6062.0,0
|
||||
212,"[4056.0, 4057.0, 4058.0]","[0, 828.0, 300.0]","[271.0, 1703.0, 0]",rb2305,2023-01-03 14:48:00,-846.0,4057.0,4057.0,4058.0,4056.0,3102.0,0
|
||||
213,"[4056.0, 4057.0, 4058.0, 4059.0, 4060.0]","[0, 354.0, 506.0, 331.0, 167.0]","[513.0, 424.0, 170.0, 97.0, 0]",rb2305,2023-01-03 14:49:00,154.0,4059.0,4057.0,4060.0,4056.0,2569.0,0
|
||||
214,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 262.0, 218.0, 223.0]","[296.0, 559.0, 47.0, 0]",rb2305,2023-01-03 14:50:00,-199.0,4058.0,4058.0,4060.0,4057.0,1609.0,0
|
||||
215,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 458.0, 240.0, 224.0]","[805.0, 352.0, 206.0, 0]",rb2305,2023-01-03 14:51:00,-441.0,4057.0,4058.0,4060.0,4057.0,2286.0,0
|
||||
216,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 352.0, 579.0, 5.0]","[513.0, 802.0, 82.0, 0]",rb2305,2023-01-03 14:52:00,-461.0,4059.0,4057.0,4060.0,4057.0,2336.0,0
|
||||
217,"[4058.0, 4059.0, 4060.0]","[44.0, 526.0, 530.0]","[65.0, 1878.0, 0]",rb2305,2023-01-03 14:53:00,-843.0,4059.0,4060.0,4060.0,4058.0,3043.0,0
|
||||
218,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 169.0, 469.0, 77.0]","[54.0, 662.0, 29.0, 0]",rb2305,2023-01-03 14:54:00,-30.0,4058.0,4059.0,4060.0,4057.0,1460.0,0
|
||||
219,"[4057.0, 4058.0, 4059.0]","[0, 881.0, 47.0]","[850.0, 303.0, 0]",rb2305,2023-01-03 14:55:00,-225.0,4058.0,4058.0,4059.0,4057.0,2081.0,0
|
||||
220,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 226.0, 741.0, 268.0]","[97.0, 999.0, 683.0, 0]",rb2305,2023-01-03 14:56:00,-544.0,4057.0,4058.0,4060.0,4057.0,3029.0,0
|
||||
221,"[4057.0, 4058.0, 4059.0, 4060.0]","[0, 94.0, 714.0, 561.0]","[457.0, 925.0, 1132.0, 0]",rb2305,2023-01-03 14:57:00,-1145.0,4059.0,4058.0,4060.0,4057.0,4039.0,0
|
||||
222,"[4058.0, 4059.0, 4060.0, 4061.0]","[0, 1138.0, 1039.0, 368.0]","[540.0, 1425.0, 423.0, 0]",rb2305,2023-01-03 14:58:00,157.0,4061.0,4059.0,4061.0,4058.0,4940.0,0
|
||||
223,"[4060.0, 4061.0, 4062.0, 4063.0, 4064.0]","[0, 189.0, 1559.0, 1671.0, 222.0]","[212.0, 518.0, 1450.0, 531.0, 0]",rb2305,2023-01-03 14:59:00,930.0,4063.0,4060.0,4064.0,4060.0,6425.0,0
|
||||
224,"[4061.0, 4062.0, 4063.0, 4064.0]","[0, 323.0, 687.0, 1202.0]","[272.0, 2372.0, 1725.0, 0]",rb2305,2023-01-03 15:00:00,-2157.0,4063.0,4063.0,4064.0,4061.0,6581.0,0
|
||||
|
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
Binary file not shown.
+297
@@ -0,0 +1,297 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
|
||||
# import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
import talib as tb
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_bop = []
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
# position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_open_ma.append(self.open[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_bop = tb.BOP(
|
||||
np.array(self.renei_open_ma),
|
||||
np.array(self.renei_high_ma),
|
||||
np.array(self.renei_low_ma),
|
||||
np.array(self.rinei_ma),
|
||||
)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg is False:
|
||||
t3 = tb.T3(np.array(self.deltas_list))
|
||||
t3_cum = tb.T3(np.array(self.delta_cumsum))
|
||||
# 开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
# 开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
开多组合 = (
|
||||
self.reniei_bop[-1] > 0
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > t3[-1] # self.params.delta
|
||||
and self.delta_cumsum[-1] > t3_cum[-1] # self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_bop[-1] < 0
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < t3[-1] # -self.params.delta
|
||||
and self.delta_cumsum[-1] < t3_cum[-1] # -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2020, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(300000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file = params
|
||||
return evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj_new.csv"
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji = np.arange(1, 4, 1)
|
||||
cout_delta = np.arange(100000, 200000, 100000) # (500, 3500, 500)
|
||||
delta = np.arange(100000, 200000, 100000) # (500, 3500, 500)
|
||||
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta))
|
||||
combinations = [(tsp, fslp, d, cd, dl, csv_file) for tsp, fslp, d, cd, dl in combinations]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}")
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
# trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta
|
||||
# IM
|
||||
# 5M：(0.01, 0.02, 2, 700000, 500000)
|
||||
# 1M：(0.005, 0.02, 3, 100000, 200000)
|
||||
# IF
|
||||
# 5M：(0.005, 0.02, 1, 100000, 400000)
|
||||
+286
@@ -0,0 +1,286 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
|
||||
import talib as tb
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_sar = []
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_sar = tb.SAR(np.array(self.renei_high_ma), np.array(self.renei_low_ma), 0.02, 0.2)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg == False:
|
||||
# 开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
# 开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
开多组合 = (
|
||||
self.reniei_sar[-1] > self.closes[0]
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > self.params.delta
|
||||
and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_sar[-1] < self.closes[0]
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < -self.params.delta
|
||||
and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2020, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(300000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file = params
|
||||
return evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj.csv"
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji = np.arange(1, 4, 1)
|
||||
cout_delta = np.arange(100000, 800000, 100000) # (500, 3500, 500)
|
||||
delta = np.arange(100000, 800000, 100000) # (500, 3500, 500)
|
||||
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta))
|
||||
combinations = [(tsp, fslp, d, cd, dl, csv_file) for tsp, fslp, d, cd, dl in combinations]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}")
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
# trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta
|
||||
# IM
|
||||
# 5M：(0.01, 0.02, 2, 700000, 500000)
|
||||
# 1M：(0.005, 0.02, 3, 100000, 200000)
|
||||
# IF
|
||||
# 5M：(0.005, 0.02, 1, 100000, 400000)
|
||||
+382
@@ -0,0 +1,382 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
|
||||
# import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
import talib as tb
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_indicator = []
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
# position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if (
|
||||
clearing_time1_start <= current_time <= clearing_time1_end
|
||||
and not clearing_executed
|
||||
):
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif (
|
||||
clearing_time2_start <= current_time <= clearing_time2_end
|
||||
and not clearing_executed
|
||||
):
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_open_ma.append(self.open[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_indicator = tb.AROONOSC(
|
||||
np.array(self.renei_high_ma),
|
||||
np.array(self.renei_low_ma),
|
||||
self.params.delta,
|
||||
)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(
|
||||
self.low[0], self.long_trailing_stop_price
|
||||
)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(
|
||||
self.high[0], self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (
|
||||
1 + self.trailing_stop_percent
|
||||
)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg is False:
|
||||
# t3 = tb.T3(np.array(self.deltas_list))
|
||||
# t3_cum = tb.T3(np.array(self.delta_cumsum))
|
||||
# 开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
# 开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
开多组合 = (
|
||||
self.reniei_indicator[-1] > 0
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0]
|
||||
> max(self.deltas_list[-self.params.cout_delta : -1])
|
||||
and self.delta_cumsum[-1]
|
||||
> max(self.delta_cumsum[-self.params.cout_delta : -1])
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_indicator[-1] < 0
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0]
|
||||
< min(self.deltas_list[-self.params.cout_delta : -1])
|
||||
and self.delta_cumsum[-1]
|
||||
< min(self.delta_cumsum[-self.params.cout_delta : -1])
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=1,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=1,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file
|
||||
):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2020, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(300000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (
|
||||
trailing_stop_percent,
|
||||
fixed_stop_loss_percent,
|
||||
duiji,
|
||||
cout_delta,
|
||||
delta,
|
||||
)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
(
|
||||
trailing_stop_percent,
|
||||
fixed_stop_loss_percent,
|
||||
duiji,
|
||||
cout_delta,
|
||||
delta,
|
||||
csv_file,
|
||||
) = params
|
||||
return evaluate_strategy(
|
||||
trailing_stop_percent,
|
||||
fixed_stop_loss_percent,
|
||||
duiji,
|
||||
cout_delta,
|
||||
delta,
|
||||
csv_file,
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"E:\of_data\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj.csv"
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji = np.arange(0, 4, 1)
|
||||
cout_delta = np.arange(5, 20, 5) # (500, 3500, 500)
|
||||
delta = np.arange(20, 120, 10) # (500, 3500, 500)
|
||||
|
||||
combinations = list(
|
||||
itertools.product(
|
||||
trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta
|
||||
)
|
||||
)
|
||||
combinations = [
|
||||
(tsp, fslp, d, cd, dl, csv_file) for tsp, fslp, d, cd, dl in combinations
|
||||
]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(
|
||||
f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}"
|
||||
)
|
||||
|
||||
print(
|
||||
f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%"
|
||||
)
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
# trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta
|
||||
# IM
|
||||
# 5M：(0.01, 0.02, 2, 700000, 500000)
|
||||
# 1M：(0.005, 0.02, 3, 100000, 200000)
|
||||
# IF
|
||||
# 5M：(0.005, 0.02, 1, 100000, 400000)
|
||||
@@ -0,0 +1,553 @@
|
||||
"""
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
|
||||
6.使用前事项：
|
||||
1、主程序中修改ofdata_dj文件地址、png_filepath地址
|
||||
2、修改clearing_time2_start、clearing_time2_stop
|
||||
3、修改交易参数:lots、跟踪止损百分、固定止损百分比、duiji、cout_delta、delta
|
||||
4、修改资金参数:初始资金;回测参数：回测时间段、佣金、单边保证金、手续费;
|
||||
"""
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
import os
|
||||
|
||||
import talib as tb # jerom注释： 增加talib库
|
||||
|
||||
# import time
|
||||
# import matplotlib.pyplot as plt
|
||||
|
||||
手续费汇总 = 0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ("sig", "delta")
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.005), # 跟踪止盈百分比
|
||||
("fixed_stop_loss_percent", 0.02), # 固定止损百分比
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1 # 下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta = self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos = 0
|
||||
print(line_aliases)
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
# 240884432
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_bop = []
|
||||
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=["datetime", "high", "low", "close", "open", "delta", "delta_cumsum"])
|
||||
|
||||
self.trader_df = pd.DataFrame(columns=["open", "high", "low", "close", "volume", "openInterest", "delta"])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
"""可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等"""
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name, # 品种名称
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
def next(self):
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
# bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
# 时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
# 更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
# print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_open_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.renei_open_ma.append(self.open[0])
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_bop = tb.BOP(
|
||||
np.array(self.renei_open_ma),
|
||||
np.array(self.renei_high_ma),
|
||||
np.array(self.renei_low_ma),
|
||||
np.array(self.rinei_ma),
|
||||
)
|
||||
# self.delta_cumsum=[]
|
||||
# self.deltas_list=[]
|
||||
# print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
# 过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
# print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
self.low[0] if self.long_trailing_stop_price < self.low[0] else self.long_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
self.high[0] if self.high[0] < self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"--多头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
self.low[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"--空头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
self.high[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"--多头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"--空头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
#
|
||||
if run_kg is False: #
|
||||
# ————jerome注释：增加Boll指标测试
|
||||
upper, middle, lower = tb.BBANDS(np.array(self.deltas_list), timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
|
||||
upper_cum, middle_cum, lower_cum = tb.BBANDS(
|
||||
np.array(self.delta_cumsum), timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
|
||||
)
|
||||
# 增加PPO指标测试
|
||||
# ppo = tb.PPO(np.array(self.deltas_list))
|
||||
# PPO_cum = tb.PPO(np.array(self.delta_cumsum))
|
||||
# 增加MOM指标测试
|
||||
# mom = tb.MOM(np.array(self.deltas_list), 30)
|
||||
# mom_cum = tb.MOM(np.array(self.delta_cumsum), 30)
|
||||
# 增加 MACDFIX指标测试
|
||||
# macdfix = tb.MACDFIX(np.array(self.deltas_list), 9)
|
||||
# macdfix_cum = tb.MACDFIX(np.array(self.delta_cumsum), 9)
|
||||
# 增加 CMO指标测试
|
||||
# cmo = tb.CMO(np.array(self.deltas_list))
|
||||
# cmo_cum = tb.CMO(np.array(self.delta_cumsum))
|
||||
# 增加 APO指标测试
|
||||
# apo = tb.APO(np.array(self.deltas_list))
|
||||
# apo_cum = tb.APO(np.array(self.delta_cumsum))
|
||||
# 增加 WMA指标测试
|
||||
# wma = tb.WMA(np.array(self.deltas_list))
|
||||
# wma_cum = tb.WMA(np.array(self.delta_cumsum))
|
||||
# 增加 WMA指标测试
|
||||
# ema = tb.EMA(np.array(self.deltas_list))
|
||||
# ema_cum = tb.EMA(np.array(self.delta_cumsum))
|
||||
# 增加 T3指标测试(效果非常好)
|
||||
# t3 = tb.T3(np.array(self.deltas_list))
|
||||
# t3_cum = tb.T3(np.array(self.delta_cumsum))
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
# jerome注释：self.signal[0] >1 1为堆积信号
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>middle[-1] and self.delta_cumsum[-1]>middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<middle[-1] and self.delta_cumsum[-1]<middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
开多组合 = (
|
||||
self.reniei_bop[-1] > 0
|
||||
and self.signal[0] > 2
|
||||
and self.data.delta[0] > max(self.deltas_list[-30:-1], default=0) # 1.1 * middle[-1]
|
||||
and self.delta_cumsum[-1] > max(self.delta_cumsum[-31:-2], default=0) # 1.1 * middle_cum[-1]
|
||||
# and apo[-1] > 10
|
||||
# and apo_cum[-1] > 10
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_bop[-1] < 0
|
||||
and self.signal[0] < -2
|
||||
and self.data.delta[0] < min(self.deltas_list[-30:-1], default=0) # 0.9 * middle[-1]
|
||||
and self.delta_cumsum[-1] < min(self.delta_cumsum[-31:-2], default=0) # 0.9 * middle_cum[-1]
|
||||
# and apo[-1] < -10
|
||||
# and apo_cum[-1] < -10
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > 2
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -2
|
||||
if self.pos != 1: #
|
||||
if 平多条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合: #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos != -1: #
|
||||
if 平空条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
# 数据
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IF888\IF888_rs_2022_5T_back_ofdata_dj.csv" #
|
||||
png_filepath = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IF888\部分回测报告"
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2022, 1, 1),
|
||||
todate=datetime(2022, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
# cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
|
||||
# cerebro.addanalyzer(bt.analyzers.sharpe, __name_= "sharpe")
|
||||
初始资金 = 300000
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
# 手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300) # 回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()["total"]["total"]
|
||||
winning_trades = analyzer.trades.get_analysis()["won"]["total"]
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print("回测报告:")
|
||||
print("期初权益", 初始资金)
|
||||
print("期末权益", 初始资金 + round(total_profit))
|
||||
print("盈亏额", round(total_profit))
|
||||
print("最大回撤率,", round(analyzer.drawdown.get_analysis()["drawdown"], 2), "%")
|
||||
print("胜率,", round(win_rate * 100, 2), "%")
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print("总手续费+滑点,", 手续费汇总)
|
||||
手续费汇总 = 0
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] + "ss" + "_plot.png"
|
||||
# plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
if not os.path.exists(png_filepath):
|
||||
# os.mkdir(png_filepath)
|
||||
os.makedirs(png_filepath)
|
||||
plot_path = os.path.join(png_filepath, plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
|
||||
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1,514 @@
|
||||
"""
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
|
||||
6.使用前事项：
|
||||
1、主程序中修改ofdata_dj文件地址、png_filepath地址
|
||||
2、修改clearing_time2_start、clearing_time2_stop
|
||||
3、修改交易参数:lots、跟踪止损百分、固定止损百分比、duiji、cout_delta、delta
|
||||
4、修改资金参数:初始资金;回测参数：回测时间段、佣金、单边保证金、手续费;
|
||||
"""
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
import talib as tb # jerom注释： 增加talib库
|
||||
|
||||
手续费汇总 = 0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ("sig", "delta")
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.010), # 跟踪止盈百分比
|
||||
("fixed_stop_loss_percent", 0.02), # 固定止损百分比
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1 # 下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta = self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos = 0
|
||||
print(line_aliases)
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
# 240884432
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_sar = []
|
||||
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=["datetime", "high", "low", "close", "open", "delta", "delta_cumsum"])
|
||||
|
||||
self.trader_df = pd.DataFrame(columns=["open", "high", "low", "close", "volume", "openInterest", "delta"])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
"""可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等"""
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name, # 品种名称
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
def next(self):
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
# bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
# 时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
# 更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
# print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_sar = tb.SAR(np.array(self.renei_high_ma), np.array(self.renei_low_ma), 0.02, 0.2)
|
||||
# self.delta_cumsum=[]
|
||||
# self.deltas_list=[]
|
||||
# print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
# 过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
# print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
self.low[0] if self.long_trailing_stop_price < self.low[0] else self.long_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
self.high[0] if self.high[0] < self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"--多头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
self.low[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"--空头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
self.high[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"--多头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"--空头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
#
|
||||
if run_kg == False: #
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
upper, middle, lower = tb.BBANDS(np.array(self.deltas_list), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
|
||||
upper_cum, middle_cum, lower_cum = tb.BBANDS(
|
||||
np.array(self.delta_cumsum), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0
|
||||
)
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
# jerome注释：self.signal[0] >1 1为堆积信号
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>middle[-1] and self.delta_cumsum[-1]>middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<middle[-1] and self.delta_cumsum[-1]<middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
开多组合 = (
|
||||
self.reniei_sar[-1] > self.closes[0]
|
||||
and self.signal[0] > 3
|
||||
and self.data.delta[0] > 700000
|
||||
and self.delta_cumsum[-1] > 700000
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_sar[-1] < self.closes[0]
|
||||
and self.signal[0] < -3
|
||||
and self.data.delta[0] < -700000
|
||||
and self.delta_cumsum[-1] < -700000
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > 3
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -3
|
||||
if self.pos != 1: #
|
||||
if 平多条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合: #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos != -1: #
|
||||
if 平空条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
# 数据
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2022_5T_back_ofdata_dj.csv" #
|
||||
png_filepath = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\部分回测报告"
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2022, 1, 1),
|
||||
todate=datetime(2022, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
# cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
|
||||
# cerebro.addanalyzer(bt.analyzers.sharpe, __name_= "sharpe")
|
||||
初始资金 = 300000
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
# 手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300) # 回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()["total"]["total"]
|
||||
winning_trades = analyzer.trades.get_analysis()["won"]["total"]
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print("回测报告:")
|
||||
print("期初权益", 初始资金)
|
||||
print("期末权益", 初始资金 + round(total_profit))
|
||||
print("盈亏额", round(total_profit))
|
||||
print("最大回撤率,", round(analyzer.drawdown.get_analysis()["drawdown"], 2), "%")
|
||||
print("胜率,", round(win_rate * 100, 2), "%")
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print("总手续费+滑点,", 手续费汇总)
|
||||
手续费汇总 = 0
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] + "ss" + "_plot.png"
|
||||
# plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
if not os.path.exists(png_filepath):
|
||||
# os.mkdir(png_filepath)
|
||||
os.makedirs(png_filepath)
|
||||
plot_path = os.path.join(png_filepath, plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
|
||||
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1,679 @@
|
||||
"""
|
||||
以下是代码的详细说明：
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
|
||||
6.使用前事项：
|
||||
1、主程序中修改ofdata_dj文件地址、png_filepath地址
|
||||
2、修改clearing_time2_start、clearing_time2_stop
|
||||
3、修改交易参数:lots、跟踪止损百分、固定止损百分比、duiji、cout_delta、delta
|
||||
4、修改资金参数:初始资金;回测参数：回测时间段、佣金、单边保证金、手续费;
|
||||
"""
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import os
|
||||
import matplotlib.pyplot as plt
|
||||
import talib as tb # jerom注释： 增加talib库
|
||||
|
||||
# import akshare as ak
|
||||
|
||||
# 下面是需要设置的参数.其他需要设置参数在函数中：每日重置数据时间、回测开始时间、结束时间、初始资金、手续费，单手保证金，合约倍数
|
||||
手续费汇总 = 0
|
||||
trailing_stop_value = 0.02
|
||||
fixed_stop_loss_value = 0.01
|
||||
deltas_windows = 240
|
||||
deltas_cum_windows = 240
|
||||
duji_value = 2
|
||||
csv_file_path = r"E:\of_data\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj.csv"
|
||||
png_file_path = (
|
||||
r"E:\of_data\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\部分回测报告"
|
||||
)
|
||||
start_date = datetime(2023, 1, 1)
|
||||
end_date = datetime(2023, 12, 31)
|
||||
回测资金 = 3000000
|
||||
单手手续费 = 60
|
||||
单手保证金 = 15000
|
||||
交易乘率 = 300
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ("sig", "delta")
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
# jerome注释增加
|
||||
def ultimate_smoother(price, period):
|
||||
# 初始化变量
|
||||
a1 = np.exp(-1.414 * np.pi / period)
|
||||
b1 = 2 * a1 * np.cos(1.414 * 180 / period)
|
||||
c2 = b1
|
||||
c3 = -a1 * a1
|
||||
c1 = (1 + c2 - c3) / 4
|
||||
|
||||
# 准备输出结果的序列
|
||||
us = np.zeros(len(price))
|
||||
|
||||
# 计算 Ultimate Smoother
|
||||
for i in range(len(price)):
|
||||
if i < 4:
|
||||
us[i] = price[i]
|
||||
else:
|
||||
us[i] = (
|
||||
(1 - c1) * price[i]
|
||||
+ (2 * c1 - c2) * price[i - 1]
|
||||
- (c1 + c3) * price[i - 2]
|
||||
+ c2 * us[i - 1]
|
||||
+ c3 * us[i - 2]
|
||||
)
|
||||
|
||||
return us
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", trailing_stop_value), # 跟踪止盈百分比
|
||||
("fixed_stop_loss_percent", fixed_stop_loss_value), # 固定止损百分比
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1 # 下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta = self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos = 0
|
||||
print(line_aliases)
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
|
||||
self.ultimate_smoother_value = []
|
||||
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.rinei_open_ma = []
|
||||
self.rinei_high_ma = []
|
||||
self.rinei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.rinei_T3 = []
|
||||
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.duiji_list = []
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(
|
||||
columns=[
|
||||
"datetime",
|
||||
"high",
|
||||
"low",
|
||||
"close",
|
||||
"open",
|
||||
"delta",
|
||||
"delta_cumsum",
|
||||
]
|
||||
)
|
||||
|
||||
self.trader_df = pd.DataFrame(
|
||||
columns=["open", "high", "low", "close", "volume", "openInterest", "delta"]
|
||||
)
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
"""可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等"""
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name, # 品种名称
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
def next(self):
|
||||
# bar线计数初始化
|
||||
self.barN += 1
|
||||
# position = self.getposition(self.datas[0]).size
|
||||
# 时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
# 更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
# print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if (
|
||||
clearing_time1_start <= current_time <= clearing_time1_end
|
||||
and not clearing_executed
|
||||
):
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.rinei_open_ma = []
|
||||
self.rinei_high_ma = []
|
||||
self.rinei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
self.duiji_list = []
|
||||
elif (
|
||||
clearing_time2_start <= current_time <= clearing_time2_end
|
||||
and not clearing_executed
|
||||
):
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.rinei_open_ma = []
|
||||
self.rinei_high_ma = []
|
||||
self.rinei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
self.duiji_list = []
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_open_ma.append(self.open[0])
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_high_ma.append(self.high[0])
|
||||
self.rinei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
# self.rinei_T3 = tb.HT_TRENDLINE(np.array(self.rinei_ma),timeperiod=5)
|
||||
self.rinei_T3 = tb.T3(
|
||||
np.array(self.rinei_ma), 3
|
||||
) # , timeperiod=5, vfactor=0.7
|
||||
# self.riniei_bop = tb.BOP(
|
||||
# np.array(self.rinei_open_ma),
|
||||
# np.array(self.rinei_high_ma),
|
||||
# np.array(self.rinei_low_ma),
|
||||
# np.array(self.rinei_ma),
|
||||
# )
|
||||
# self.delta_cumsum=[]
|
||||
# self.deltas_list=[]
|
||||
# print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
# 过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
# print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
self.low[0]
|
||||
if self.long_trailing_stop_price < self.low[0]
|
||||
else self.long_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
self.high[0]
|
||||
if self.high[0] < self.short_trailing_stop_price
|
||||
else self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
# self.out_long = self.long_trailing_stop_price * (
|
||||
# 1 - self.trailing_stop_percent)
|
||||
# self.out_short = self.short_trailing_stop_price * (
|
||||
# 1 + self.trailing_stop_percent)
|
||||
self.ultimate_smoother_value = ultimate_smoother(self.closes, 20)
|
||||
self.out_long = self.ultimate_smoother_value[-1]
|
||||
self.out_short = self.ultimate_smoother_value[-1]
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"--多头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
self.low[0],
|
||||
)
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"--空头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
self.high[0],
|
||||
)
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"--多头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"--空头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=self.Lots,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list[-20:]))
|
||||
self.duiji_list.append(self.data.sig[0])
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
#
|
||||
if run_kg is False:
|
||||
# ————jerome注释：增加指标测试
|
||||
# 增加Boll指标测试
|
||||
# upper, middle, lower = tb.BBANDS(np.array(self.deltas_list), timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
|
||||
# upper_cum, middle_cum, lower_cum = tb.BBANDS(
|
||||
# np.array(self.delta_cumsum), timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
|
||||
# )
|
||||
# 增加PPO指标测试
|
||||
# ppo = tb.PPO(np.array(self.deltas_list))
|
||||
# PPO_cum = tb.PPO(np.array(self.delta_cumsum))
|
||||
# 增加MOM指标测试
|
||||
# mom = tb.MOM(np.array(self.deltas_list), 30)
|
||||
# mom_cum = tb.MOM(np.array(self.delta_cumsum), 30)
|
||||
# 增加 MACDFIX指标测试
|
||||
# macdfix = tb.MACDFIX(np.array(self.deltas_list), 9)
|
||||
# macdfix_cum = tb.MACDFIX(np.array(self.delta_cumsum), 9)
|
||||
# 增加 CMO指标测试
|
||||
# cmo = tb.CMO(np.array(self.deltas_list))
|
||||
# cmo_cum = tb.CMO(np.array(self.delta_cumsum))
|
||||
# 增加 APO指标测试
|
||||
# apo = tb.APO(np.array(self.deltas_list))
|
||||
# apo_cum = tb.APO(np.array(self.delta_cumsum))
|
||||
# 增加 WMA指标测试
|
||||
# wma = tb.WMA(np.array(self.deltas_list))
|
||||
# wma_cum = tb.WMA(np.array(self.delta_cumsum))
|
||||
# 增加 WMA指标测试
|
||||
# ema = tb.EMA(np.array(self.deltas_list))
|
||||
# ema_cum = tb.EMA(np.array(self.delta_cumsum))
|
||||
# 增加 T3指标测试(效果非常好)
|
||||
t3 = tb.T3(np.array(self.deltas_list))
|
||||
t3_cum = tb.T3(np.array(self.delta_cumsum))
|
||||
# futures_main_sina_hist = ak.futures_main_sina(
|
||||
# symbol="IM0", start_date=start_date, end_date="20231231"
|
||||
# )
|
||||
# futures_main_sina_hist["5day_ma"] = (
|
||||
# futures_main_sina_hist["收盘价"].rolling(window=5).mean()
|
||||
# )
|
||||
# ——jerome注释：self.signal[0] >1 1为堆积信号
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>middle[-1] and self.delta_cumsum[-1]>middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<middle[-1] and self.delta_cumsum[-1]<middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
开多组合 = (
|
||||
self.closes[0] > self.rinei_T3[-1]
|
||||
# futures_main_sina_hist["收盘价"].iloc[0]
|
||||
# > futures_main_sina_hist["5day_ma"].iloc[-1]
|
||||
# self.riniei_bop[-1] > 0
|
||||
and self.signal[0]
|
||||
> max(self.duiji_list[-deltas_windows:-1], default=0) # duji_value
|
||||
and self.data.delta[0] > t3[-1] # max(
|
||||
# self.deltas_list[-deltas_windows:-1], default=0
|
||||
# ) # 1.1 * middle[-1]
|
||||
and self.delta_cumsum[-1] > t3_cum[-1] # max(
|
||||
# self.delta_cumsum[-deltas_cum_windows - 1 : -2], default=0
|
||||
# ) # 1.1 * middle_cum[-1]
|
||||
# and apo[-1] > 10
|
||||
# and apo_cum[-1] > 10
|
||||
)
|
||||
开空组合 = (
|
||||
self.closes[0] < self.rinei_T3[-1]
|
||||
# self.riniei_bop[-1] < 0
|
||||
# futures_main_sina_hist["收盘价"].iloc[0]
|
||||
# < futures_main_sina_hist["5day_ma"].iloc[-1]
|
||||
and self.signal[0]
|
||||
< min(self.duiji_list[-deltas_windows:-1], default=0) # -duji_value
|
||||
and self.data.delta[0] < t3[-1] # min(
|
||||
# self.deltas_list[-deltas_windows:-1], default=0
|
||||
# ) # 0.9 * middle[-1]
|
||||
and self.delta_cumsum[-1] < t3_cum[-1] # min(
|
||||
# self.delta_cumsum[-deltas_cum_windows - 1 : -2], default=0
|
||||
# ) # 0.9 * middle_cum[-1]
|
||||
# and apo[-1] < -10
|
||||
# and apo_cum[-1] < -10
|
||||
)
|
||||
平空条件 = self.pos < 0 and self.signal[0] > max(
|
||||
self.duiji_list[-deltas_windows:-1], default=0
|
||||
) # duji_value
|
||||
平多条件 = self.pos > 0 and self.signal[0] < min(
|
||||
self.duiji_list[-deltas_windows:-1], default=0
|
||||
) # -duji_value
|
||||
if self.pos != 1: #
|
||||
if 平空条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合: #
|
||||
self.buy(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=1,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos != -1: #
|
||||
if 平多条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(
|
||||
data=self.data,
|
||||
price=self.data.close[0],
|
||||
size=1,
|
||||
exectype=bt.Order.Market,
|
||||
)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
# 数据
|
||||
csv_file = csv_file_path
|
||||
png_filepath = png_file_path
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=start_date, # datetime(2023, 1, 1),
|
||||
todate=end_date, # datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
# cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
|
||||
# cerebro.addanalyzer(bt.analyzers.sharpe, __name_= "sharpe")
|
||||
初始资金 = 回测资金
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
# 手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(
|
||||
commission=单手手续费, margin=单手保证金, mult=交易乘率
|
||||
) # 回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()["total"]["total"]
|
||||
winning_trades = analyzer.trades.get_analysis()["won"]["total"]
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print("回测报告:")
|
||||
print("期初权益", 初始资金)
|
||||
print("期末权益", 初始资金 + round(total_profit))
|
||||
print("盈亏额", round(total_profit))
|
||||
print("最大回撤率,", round(analyzer.drawdown.get_analysis()["drawdown"], 2), "%")
|
||||
print("胜率,", round(win_rate * 100, 2), "%")
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print("总手续费+滑点,", 手续费汇总)
|
||||
手续费汇总 = 0
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] + "ss" + "_plot.png"
|
||||
# plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
if not os.path.exists(png_filepath):
|
||||
# os.mkdir(png_filepath)
|
||||
os.makedirs(png_filepath)
|
||||
plot_path = os.path.join(png_filepath, plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
@@ -0,0 +1,406 @@
|
||||
'''
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
使用 matplotlib 绘制回测结果
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
import itertools
|
||||
from scipy.optimize import brute
|
||||
|
||||
|
||||
手续费汇总=0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ('sig','delta')
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (('sig',6),('delta', 8))
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
('trailing_stop_percent', 0.02), # 跟踪止盈百分比
|
||||
('fixed_stop_loss_percent', 0.01), # 固定止损百分比
|
||||
('duiji', 1), # 堆积
|
||||
('cout_delta', 1), # 日累计delta
|
||||
('delta', 1), # delta单bar
|
||||
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots=1 #下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta= self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos=0
|
||||
print(line_aliases)
|
||||
self.high=self.datas[0].high
|
||||
self.low=self.datas[0].low
|
||||
self.closes=self.datas[0].close
|
||||
self.open=self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price=0
|
||||
self.sl_shor_price=0
|
||||
self.out_long=0
|
||||
self.out_short=0
|
||||
self.rinei_ma=[]
|
||||
self.rinei_mean=0
|
||||
|
||||
self.datetime_list= []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum=[]
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=['datetime', 'high', 'low', 'close', 'open', 'delta', 'delta_cumsum'])
|
||||
|
||||
self.trader_df=pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume', 'openInterest','delta'])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
'''可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等'''
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print('%s, %s' % (dt.isoformat(), txt))
|
||||
|
||||
# def notify_order(self, order):
|
||||
# # 未被处理的订单
|
||||
# if order.status in [order.Submitted, order.Accepted]:
|
||||
# return
|
||||
# # 已经处理的订单
|
||||
# if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
# global 手续费汇总
|
||||
# if order.isbuy():
|
||||
# 手续费汇总 +=order.executed.comm
|
||||
# self.log(
|
||||
# 'BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
# (order.ref, # 订单编号
|
||||
# order.executed.price, # 成交价
|
||||
# order.executed.comm, # 佣金
|
||||
# order.executed.size, # 成交量
|
||||
# order.data._name,# 品种名称
|
||||
# 手续费汇总))
|
||||
|
||||
# else: # Sell
|
||||
# 手续费汇总 +=order.executed.comm
|
||||
# self.log('SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
# (order.ref,
|
||||
# order.executed.price,
|
||||
# order.executed.comm,
|
||||
# order.executed.size,
|
||||
# order.data._name,
|
||||
# 手续费汇总))
|
||||
|
||||
def next(self):
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
#时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
#更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
#print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
#self.delta_cumsum=[]
|
||||
#self.deltas_list=[]
|
||||
#print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg=每日重置数据()
|
||||
|
||||
#过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0 :
|
||||
return
|
||||
#print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price >0 and self.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = self.low[0] if self.long_trailing_stop_price<self.low[0] else self.long_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price >0 and self.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = self.high[0] if self.high[0] <self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long >0:
|
||||
if self.low[0] < self.out_long and self.pos>0 and self.sl_long_price>0 and self.low[0]>self.sl_long_price:
|
||||
#print('--多头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position','TR',self.out_long,'low', self.low[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long=0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short>0:
|
||||
if self.high[0] > self.out_short and self.pos<0 and self.sl_shor_price>0 and self.high[0]<self.sl_shor_price:
|
||||
#print('--空头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position: ','TR',self.out_short,'high', self.high[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and self.closes[0] < self.fixed_stop_loss_L:
|
||||
#print('--多头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and self.closes[0] > self.fixed_stop_loss_S:
|
||||
#print('--空头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots,exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
|
||||
#
|
||||
if run_kg==False : #
|
||||
#print(self.delta_cumsum)
|
||||
开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >self.params.duiji and self.data.delta[0]>self.params.delta and self.delta_cumsum[-1]>self.params.cout_delta
|
||||
开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-self.params.duiji and self.data.delta[0]<-self.params.delta and self.delta_cumsum[-1]<-self.params.cout_delta
|
||||
平多条件=self.pos<0 and self.signal[0] >self.params.duiji
|
||||
平空条件=self.pos>0 and self.signal[0] <-self.params.duiji
|
||||
if self.pos !=1 : #
|
||||
if 平多条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合 : #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=1
|
||||
self.long_trailing_stop_price=self.low[0]
|
||||
self.sl_long_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos !=-1 : #
|
||||
if 平空条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=-1
|
||||
self.short_trailing_stop_price=self.high[0]
|
||||
self.sl_shor_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
#数据
|
||||
csv_file='E:/of_data/tick生成的OF数据/data_rs_merged/上期所/ag888/ag888_rs_2023_5M_back_ofdata_dj.csv' #
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023,1,1),
|
||||
todate=datetime(2023,12,29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat='%Y-%m-%d %H:%M:%S',
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8
|
||||
)
|
||||
|
||||
# 评估函数，输入参数，返回评估函数值，这里是总市值，要求最大化
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent,duiji,cout_delta,delta):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
cerebro.adddata(data) # 确保你有一个有效的数据源
|
||||
cerebro.broker.setcash(10000.0)
|
||||
#手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=5000.0,mult=10)#回测参数
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue()
|
||||
|
||||
# 创建参数网格
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji= np.arange(1, 3, 1) #
|
||||
cout_delta= np.arange(500, 3500, 500)
|
||||
delta=np.arange(500, 3500, 500)
|
||||
|
||||
# 生成所有参数组合
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents,duiji,cout_delta,delta))
|
||||
|
||||
# 评估所有参数组合并找到最佳参数
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
for combo in combinations:
|
||||
value = evaluate_strategy(*combo)
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = combo
|
||||
print(f'combo: {combo},best_value: {best_value},best_parameters: {best_parameters}')
|
||||
|
||||
# 打印最佳参数组合
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1,218 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import itertools
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ('sig', 'delta')
|
||||
params = (('sig', 6), ('delta', 8))
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
('trailing_stop_percent', 0.02),
|
||||
('fixed_stop_loss_percent', 0.01),
|
||||
('duiji', 1),
|
||||
('cout_delta', 1),
|
||||
('delta', 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print('%s, %s' % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if self.out_long > 0 and self.low[0] < self.out_long and self.pos > 0 and self.sl_long_price > 0 and self.low[0] > self.sl_long_price:
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0 and self.high[0] > self.out_short and self.pos < 0 and self.sl_shor_price > 0 and self.high[0] < self.sl_shor_price:
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.sl_long_price > 0 and self.fixed_stop_loss_L > 0 and self.pos > 0 and self.closes[0] < self.fixed_stop_loss_L:
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.sl_shor_price > 0 and self.fixed_stop_loss_S > 0 and self.pos < 0 and self.closes[0] > self.fixed_stop_loss_S:
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg == False:
|
||||
开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
if __name__ == "__main__":
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat='%Y-%m-%d %H:%M:%S',
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(10000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=5000.0, mult=10)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue()
|
||||
|
||||
csv_file = 'E:/of_data/tick生成的OF数据/data_rs_merged/上期所/ag888/ag888_rs_2023_5M_back_ofdata_dj.csv'
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji = np.arange(1, 3, 1)
|
||||
cout_delta = np.arange(500, 3500, 500)
|
||||
delta = np.arange(500, 3500, 500)
|
||||
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta))
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for combo in combinations:
|
||||
value = evaluate_strategy(*combo)
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = combo
|
||||
print(f'combo: {combo}, best_value: {best_value}, best_parameters: {best_parameters}')
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
@@ -0,0 +1,302 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime, time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
from deap import base, creator, tools, algorithms
|
||||
import random
|
||||
|
||||
|
||||
# 自定义CSV数据类
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
# 自定义策略类
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.01),
|
||||
("fixed_stop_loss_percent", 0.005),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
# 初始化变量
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
# position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def reset_daily_data():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(22, 55)
|
||||
clearing_time2_end = s_time(23, 0)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = reset_daily_data()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if not run_kg:
|
||||
开多组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] > self.rinei_mean
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > self.params.delta
|
||||
and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] < self.rinei_mean
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < -self.params.delta
|
||||
and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
# 评估策略函数
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(500000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue()
|
||||
|
||||
|
||||
# 遗传算法优化部分
|
||||
def optimize_with_ga(csv_file):
|
||||
# 创建适应度和个体
|
||||
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
|
||||
creator.create("Individual", list, fitness=creator.FitnessMax)
|
||||
|
||||
# 定义个体生成和变异操作
|
||||
toolbox = base.Toolbox()
|
||||
toolbox.register("attr_trailing_stop_percent", random.uniform, 0.002, 0.01)
|
||||
toolbox.register("attr_fixed_stop_loss_percent", random.uniform, 0.002, 0.01)
|
||||
toolbox.register("attr_duiji", random.randint, 3, 5)
|
||||
toolbox.register("attr_cout_delta", random.randint, 20, 400)
|
||||
toolbox.register("attr_delta", random.randint, 20, 400)
|
||||
|
||||
toolbox.register(
|
||||
"individual",
|
||||
tools.initCycle,
|
||||
creator.Individual,
|
||||
(
|
||||
toolbox.attr_trailing_stop_percent,
|
||||
toolbox.attr_fixed_stop_loss_percent,
|
||||
toolbox.attr_duiji,
|
||||
toolbox.attr_cout_delta,
|
||||
toolbox.attr_delta,
|
||||
),
|
||||
)
|
||||
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
|
||||
|
||||
# 定义评价函数
|
||||
def evaluate(individual):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta = individual
|
||||
result = evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file)
|
||||
if isinstance(result, complex):
|
||||
print(f"Complex result encountered: {result}")
|
||||
return (result,)
|
||||
|
||||
toolbox.register("evaluate", evaluate)
|
||||
toolbox.register("mate", tools.cxBlend, alpha=0.5)
|
||||
toolbox.register(
|
||||
"mutate",
|
||||
tools.mutPolynomialBounded,
|
||||
low=[0.002, 0.002, 3, 20, 20],
|
||||
up=[0.01, 0.01, 5, 400, 400],
|
||||
eta=0.1,
|
||||
indpb=0.2,
|
||||
)
|
||||
toolbox.register("select", tools.selTournament, tournsize=3)
|
||||
|
||||
# 遗传算法主循环
|
||||
population = toolbox.population(n=50)
|
||||
ngen, cxpb, mutpb = 40, 0.5, 0.2
|
||||
algorithms.eaSimple(population, toolbox, cxpb, mutpb, ngen, verbose=True)
|
||||
|
||||
# 获取最佳个体
|
||||
best_individual = tools.selBest(population, 1)[0]
|
||||
return best_individual
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj.csv"
|
||||
|
||||
best_parameters = optimize_with_ga(csv_file)
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"对冲 {best_parameters[2]}, 策略 {best_parameters[3]}, 增量 {best_parameters[4]}")
|
||||
+270
@@ -0,0 +1,270 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(22, 55)
|
||||
clearing_time2_end = s_time(23, 00)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg == False:
|
||||
开多组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] > self.rinei_mean
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > self.params.delta
|
||||
and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] < self.rinei_mean
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < -self.params.delta
|
||||
and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(500000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file = params
|
||||
return evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = (
|
||||
r"E:\of_data\主力连续\tick生成的OF数据(1M)\data_rs_merged\中金所\IF888\IF888_rs_2023_1T_back_ofdata_dj.csv"
|
||||
)
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.020, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.040, 0.01)
|
||||
duiji = np.arange(1, 3, 1)
|
||||
cout_delta = np.arange(20, 220, 20) # (500, 3500, 500)
|
||||
delta = np.arange(20, 220, 20) # (500, 3500, 500)
|
||||
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta))
|
||||
combinations = [(tsp, fslp, d, cd, dl, csv_file) for tsp, fslp, d, cd, dl in combinations]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}")
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
+291
@@ -0,0 +1,291 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
|
||||
import talib as tb
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_aroon_up = []
|
||||
self.reniei_aroon_down = []
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_aroon_up, self.reniei_aroon_down = tb.AROON(
|
||||
np.array(self.renei_high_ma), np.array(self.renei_low_ma), 14
|
||||
)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg == False:
|
||||
# 开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
# 开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
开多组合 = (
|
||||
# self.reniei_aroon_up[-1] > 50
|
||||
self.reniei_aroon_up[-1] > self.reniei_aroon_down[-1]
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > self.params.delta
|
||||
and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
# self.reniei_aroon_down[-1] > 50
|
||||
self.reniei_aroon_up[-1] < self.reniei_aroon_down[-1]
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < -self.params.delta
|
||||
and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2020, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(300000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file = params
|
||||
return evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, csv_file)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(5M)\data_rs_merged\中金所\IM888\IM888_rs_2023_5T_back_ofdata_dj.csv"
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji = np.arange(1, 4, 1)
|
||||
cout_delta = np.arange(100000, 800000, 100000) # (500, 3500, 500)
|
||||
delta = np.arange(100000, 800000, 100000) # (500, 3500, 500)
|
||||
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta))
|
||||
combinations = [(tsp, fslp, d, cd, dl, csv_file) for tsp, fslp, d, cd, dl in combinations]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}")
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
# trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta
|
||||
# IM
|
||||
# 5M：(0.01, 0.02, 2, 700000, 500000)
|
||||
# 1M：(0.005, 0.02, 3, 100000, 200000)
|
||||
# IF
|
||||
# 5M：(0.005, 0.02, 1, 100000, 400000)
|
||||
+307
@@ -0,0 +1,307 @@
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import itertools
|
||||
import multiprocessing
|
||||
|
||||
import talib as tb
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
lines = ("sig", "delta")
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.02),
|
||||
("fixed_stop_loss_percent", 0.01),
|
||||
("duiji", 1),
|
||||
("cout_delta", 1),
|
||||
("delta", 1),
|
||||
("aroon_timeperiod", 14),
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1
|
||||
self.signal = self.datas[0].sig
|
||||
self.delta = self.datas[0].delta
|
||||
self.pos = 0
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_aroon_up = []
|
||||
self.reniei_aroon_down = []
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
self.barN = 0
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def next(self):
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
def 每日重置数据():
|
||||
current_time = dt.time()
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_aroon_up, self.reniei_aroon_down = tb.AROON(
|
||||
np.array(self.renei_high_ma), np.array(self.renei_low_ma), self.params.aroon_timeperiod
|
||||
)
|
||||
clearing_executed = False
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
self.long_trailing_stop_price = max(self.low[0], self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
self.short_trailing_stop_price = min(self.high[0], self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
|
||||
if (
|
||||
self.out_long > 0
|
||||
and self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if (
|
||||
self.out_short > 0
|
||||
and self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
if run_kg == False:
|
||||
# 开多组合 = self.rinei_mean > 0 and self.closes[0] > self.rinei_mean and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
# 开空组合 = self.rinei_mean > 0 and self.closes[0] < self.rinei_mean and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
开多组合 = (
|
||||
# self.reniei_aroon_up[-1] > 50
|
||||
self.reniei_aroon_up[-1] > self.reniei_aroon_down[-1]
|
||||
and self.signal[0] > self.params.duiji
|
||||
and self.data.delta[0] > self.params.delta
|
||||
and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
)
|
||||
开空组合 = (
|
||||
# self.reniei_aroon_down[-1] > 50
|
||||
self.reniei_aroon_up[-1] < self.reniei_aroon_down[-1]
|
||||
and self.signal[0] < -self.params.duiji
|
||||
and self.data.delta[0] < -self.params.delta
|
||||
and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > self.params.duiji
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -self.params.duiji
|
||||
|
||||
if self.pos != 1:
|
||||
if 平多条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合:
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
|
||||
if self.pos != -1:
|
||||
if 平空条件:
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合:
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
|
||||
|
||||
def evaluate_strategy(
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, aroon_timeperiod, csv_file
|
||||
):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(
|
||||
MyStrategy_固定止损_跟踪止盈,
|
||||
trailing_stop_percent=trailing_stop_percent,
|
||||
fixed_stop_loss_percent=fixed_stop_loss_percent,
|
||||
duiji=duiji,
|
||||
cout_delta=cout_delta,
|
||||
delta=delta,
|
||||
aroon_timeperiod=aroon_timeperiod,
|
||||
)
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2022, 1, 1),
|
||||
todate=datetime(2022, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
cerebro.adddata(data)
|
||||
cerebro.broker.setcash(300000.0)
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300)
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue(), (
|
||||
trailing_stop_percent,
|
||||
fixed_stop_loss_percent,
|
||||
duiji,
|
||||
cout_delta,
|
||||
delta,
|
||||
aroon_timeperiod,
|
||||
)
|
||||
|
||||
|
||||
def run_backtest(params):
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, aroon_timeperiod, csv_file = params
|
||||
return evaluate_strategy(
|
||||
trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta, aroon_timeperiod, csv_file
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(2M)\data_rs_merged\中金所\IM888\IM888_rs_2022_2T_back_ofdata_dj_new.csv"
|
||||
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.030, 0.01)
|
||||
duiji = np.arange(2, 4, 1)
|
||||
cout_delta = np.arange(500000, 4000000, 500000) # (500, 3500, 500)
|
||||
delta = np.arange(500000, 4000000, 500000) # (500, 3500, 500)
|
||||
aroon_timeperiod = np.arange(4, 20, 4)
|
||||
|
||||
combinations = list(
|
||||
itertools.product(trailing_stop_percents, fixed_stop_loss_percents, duiji, cout_delta, delta, aroon_timeperiod)
|
||||
)
|
||||
combinations = [(tsp, fslp, d, cd, dl, d2, csv_file) for tsp, fslp, d, cd, dl, d2 in combinations]
|
||||
|
||||
with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
|
||||
results = pool.map(run_backtest, combinations)
|
||||
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
|
||||
for value, params in results:
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = params
|
||||
print(f"combo: {params}, value: {value}, best_value: {best_value}, best_parameters: {best_parameters}")
|
||||
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
# trailing_stop_percent, fixed_stop_loss_percent, duiji, cout_delta, delta
|
||||
# IM
|
||||
# 5M：(0.01, 0.02, 2, 700000, 500000)
|
||||
# 1M：(0.005, 0.02, 3, 100000, 200000)
|
||||
# IF
|
||||
# 5M：(0.005, 0.02, 1, 100000, 400000)
|
||||
@@ -0,0 +1,422 @@
|
||||
'''
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
使用 matplotlib 绘制回测结果
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
import itertools
|
||||
from scipy.optimize import brute
|
||||
|
||||
import talib as tb
|
||||
|
||||
|
||||
手续费汇总=0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ('sig','delta')
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (('sig',6),('delta', 8))
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
('trailing_stop_percent', 0.02), # 跟踪止盈百分比
|
||||
('fixed_stop_loss_percent', 0.01), # 固定止损百分比
|
||||
('duiji', 1), # 堆积
|
||||
('cout_delta', 1), # 日累计delta
|
||||
('delta', 1), # delta单bar
|
||||
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots=1 #下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta= self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos=0
|
||||
print(line_aliases)
|
||||
self.high=self.datas[0].high
|
||||
self.low=self.datas[0].low
|
||||
self.closes=self.datas[0].close
|
||||
self.open=self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price=0
|
||||
self.sl_shor_price=0
|
||||
self.out_long=0
|
||||
self.out_short=0
|
||||
self.rinei_ma=[]
|
||||
self.rinei_mean=0
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.reniei_aroon_up = []
|
||||
self.reniei_aroon_down = []
|
||||
|
||||
self.datetime_list= []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum=[]
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=['datetime', 'high', 'low', 'close', 'open', 'delta', 'delta_cumsum'])
|
||||
|
||||
self.trader_df=pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume', 'openInterest','delta'])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
'''可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等'''
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print('%s, %s' % (dt.isoformat(), txt))
|
||||
|
||||
# def notify_order(self, order):
|
||||
# # 未被处理的订单
|
||||
# if order.status in [order.Submitted, order.Accepted]:
|
||||
# return
|
||||
# # 已经处理的订单
|
||||
# if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
# global 手续费汇总
|
||||
# if order.isbuy():
|
||||
# 手续费汇总 +=order.executed.comm
|
||||
# self.log(
|
||||
# 'BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
# (order.ref, # 订单编号
|
||||
# order.executed.price, # 成交价
|
||||
# order.executed.comm, # 佣金
|
||||
# order.executed.size, # 成交量
|
||||
# order.data._name,# 品种名称
|
||||
# 手续费汇总))
|
||||
|
||||
# else: # Sell
|
||||
# 手续费汇总 +=order.executed.comm
|
||||
# self.log('SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
# (order.ref,
|
||||
# order.executed.price,
|
||||
# order.executed.comm,
|
||||
# order.executed.size,
|
||||
# order.data._name,
|
||||
# 手续费汇总))
|
||||
|
||||
def next(self):
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
#时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
#更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
#print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_aroon_up, self.reniei_aroon_down = tb.AROON(np.array(self.renei_high_ma), np.array(self.renei_low_ma), 14)
|
||||
#self.delta_cumsum=[]
|
||||
#self.deltas_list=[]
|
||||
#print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg=每日重置数据()
|
||||
|
||||
#过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0 :
|
||||
return
|
||||
#print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price >0 and self.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = self.low[0] if self.long_trailing_stop_price<self.low[0] else self.long_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price >0 and self.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = self.high[0] if self.high[0] <self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long >0:
|
||||
if self.low[0] < self.out_long and self.pos>0 and self.sl_long_price>0 and self.low[0]>self.sl_long_price:
|
||||
#print('--多头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position','TR',self.out_long,'low', self.low[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long=0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short>0:
|
||||
if self.high[0] > self.out_short and self.pos<0 and self.sl_shor_price>0 and self.high[0]<self.sl_shor_price:
|
||||
#print('--空头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position: ','TR',self.out_short,'high', self.high[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and self.closes[0] < self.fixed_stop_loss_L:
|
||||
#print('--多头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and self.closes[0] > self.fixed_stop_loss_S:
|
||||
#print('--空头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots,exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
|
||||
#
|
||||
if run_kg==False : #
|
||||
#print(self.delta_cumsum)
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >self.params.duiji and self.data.delta[0]>self.params.delta and self.delta_cumsum[-1]>self.params.cout_delta
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-self.params.duiji and self.data.delta[0]<-self.params.delta and self.delta_cumsum[-1]<-self.params.cout_delta
|
||||
开多组合 = self.reniei_aroon_up[-1] > 50 and self.reniei_aroon_up[-1] > self.reniei_aroon_down[-1] and self.signal[0] > self.params.duiji and self.data.delta[0] > self.params.delta and self.delta_cumsum[-1] > self.params.cout_delta
|
||||
开空组合 = self.reniei_aroon_down[-1] > 50 and self.reniei_aroon_up[-1] < self.reniei_aroon_down[-1] and self.signal[0] < -self.params.duiji and self.data.delta[0] < -self.params.delta and self.delta_cumsum[-1] < -self.params.cout_delta
|
||||
平多条件=self.pos<0 and self.signal[0] >self.params.duiji
|
||||
平空条件=self.pos>0 and self.signal[0] <-self.params.duiji
|
||||
if self.pos !=1 : #
|
||||
if 平多条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合 : #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=1
|
||||
self.long_trailing_stop_price=self.low[0]
|
||||
self.sl_long_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos !=-1 : #
|
||||
if 平空条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=-1
|
||||
self.short_trailing_stop_price=self.high[0]
|
||||
self.sl_shor_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
#数据
|
||||
csv_file='E:/of_data/主力连续/tick生成的OF数据(1M)/data_rs_merged/上期所/ag888/ag888_rs_2023_1T_back_ofdata_dj.csv' #
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023,1,1),
|
||||
todate=datetime(2023,12,29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat='%Y-%m-%d %H:%M:%S',
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8
|
||||
)
|
||||
|
||||
# 评估函数，输入参数，返回评估函数值，这里是总市值，要求最大化
|
||||
def evaluate_strategy(trailing_stop_percent, fixed_stop_loss_percent,duiji,cout_delta,delta):
|
||||
cerebro = bt.Cerebro()
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
cerebro.adddata(data) # 确保你有一个有效的数据源
|
||||
cerebro.broker.setcash(10000.0)
|
||||
#手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=5000.0,mult=10)#回测参数
|
||||
cerebro.run()
|
||||
return cerebro.broker.getvalue()
|
||||
|
||||
# 创建参数网格
|
||||
trailing_stop_percents = np.arange(0.005, 0.025, 0.005)
|
||||
fixed_stop_loss_percents = np.arange(0.01, 0.050, 0.01)
|
||||
duiji= np.arange(1, 3, 1) #
|
||||
cout_delta= np.arange(500, 3500, 500)
|
||||
delta=np.arange(500, 3500, 500)
|
||||
|
||||
# 生成所有参数组合
|
||||
combinations = list(itertools.product(trailing_stop_percents, fixed_stop_loss_percents,duiji,cout_delta,delta))
|
||||
|
||||
# 评估所有参数组合并找到最佳参数
|
||||
best_value = 0
|
||||
best_parameters = None
|
||||
for combo in combinations:
|
||||
value = evaluate_strategy(*combo)
|
||||
if value > best_value:
|
||||
best_value = value
|
||||
best_parameters = combo
|
||||
print(f'combo: {combo},best_value: {best_value},best_parameters: {best_parameters}')
|
||||
|
||||
# 打印最佳参数组合
|
||||
print(f"最佳参数组合: 跟踪止损百分比 {best_parameters[0]}%, 固定止损百分比 {best_parameters[1]}%")
|
||||
print(f"最大市值: {best_value}")
|
||||
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1,489 @@
|
||||
"""
|
||||
以下是代码的详细说明：
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
|
||||
6.使用前事项：
|
||||
1、主程序中修改ofdata_dj文件地址、png_filepath地址
|
||||
2、修改clearing_time2_start、clearing_time2_stop
|
||||
3、修改交易参数:lots、跟踪止损百分、固定止损百分比、duiji、cout_delta、delta
|
||||
4、修改资金参数:初始资金;回测参数：回测时间段、佣金、单边保证金、手续费;
|
||||
"""
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
import talib as tb # jerom注释： 增加talib库
|
||||
|
||||
手续费汇总 = 0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ("sig", "delta")
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.01), # 跟踪止盈百分比
|
||||
("fixed_stop_loss_percent", 0.01), # 固定止损百分比
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1 # 下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta = self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos = 0
|
||||
print(line_aliases)
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
# 240884432
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.rinei_mean = 0
|
||||
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=["datetime", "high", "low", "close", "open", "delta", "delta_cumsum"])
|
||||
|
||||
self.trader_df = pd.DataFrame(columns=["open", "high", "low", "close", "volume", "openInterest", "delta"])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
"""可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等"""
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name, # 品种名称
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
def next(self):
|
||||
# bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
# 时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
# 更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
# print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
# self.delta_cumsum=[]
|
||||
# self.deltas_list=[]
|
||||
# print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
# 过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
# print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
self.low[0] if self.long_trailing_stop_price < self.low[0] else self.long_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
self.high[0] if self.high[0] < self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"--多头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
self.low[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"--空头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
self.high[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"--多头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"--空头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
#
|
||||
if run_kg == False: #
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
upper, middle, lower = tb.BBANDS(np.array(self.deltas_list), timeperiod=14, nbdevup=2, nbdevdn=2, matype=0)
|
||||
upper_cum, middle_cum, lower_cum = tb.BBANDS(
|
||||
np.array(self.delta_cumsum), timeperiod=14, nbdevup=2, nbdevdn=2, matype=0
|
||||
)
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
# jerome注释：self.signal[0] >1 1为堆积信号
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>middle[-1] and self.delta_cumsum[-1]>middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<middle[-1] and self.delta_cumsum[-1]<middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
# 平多条件=self.pos<0 and self.signal[0] >1
|
||||
# 平空条件=self.pos>0 and self.signal[0] <-1
|
||||
开多组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] > self.rinei_mean
|
||||
and self.signal[0] > 2
|
||||
and self.data.delta[0] > 600
|
||||
and self.delta_cumsum[-1] > 200
|
||||
) # jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
开空组合 = (
|
||||
self.rinei_mean > 0
|
||||
and self.closes[0] < self.rinei_mean
|
||||
and self.signal[0] < -2
|
||||
and self.data.delta[0] < -600
|
||||
and self.delta_cumsum[-1] < -200
|
||||
) # jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
平多条件 = self.pos < 0 and self.signal[0] > 2
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -2
|
||||
if self.pos != 1: #
|
||||
if 平多条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合: #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos != -1: #
|
||||
if 平空条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
# 数据
|
||||
csv_file = (
|
||||
r"E:\of_data\主力连续\tick生成的OF数据(1M)\data_rs_merged\中金所\IM888\IM888_rs_2023_1T_back_ofdata_dj.csv"
|
||||
)
|
||||
png_filepath = r"E:\of_data\主力连续\tick生成的OF数据(1M)\data_rs_merged\中金所\IM888\部分回测报告"
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023, 1, 1),
|
||||
todate=datetime(2023, 12, 29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
# cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
|
||||
初始资金 = 300000
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
# 手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=30, margin=180000.0, mult=300) # 回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()["total"]["total"]
|
||||
winning_trades = analyzer.trades.get_analysis()["won"]["total"]
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print("回测报告:")
|
||||
print("期初权益", 初始资金)
|
||||
print("期末权益", 初始资金 + round(total_profit))
|
||||
print("盈亏额", round(total_profit))
|
||||
print("最大回撤率,", round(analyzer.drawdown.get_analysis()["drawdown"], 2), "%")
|
||||
print("胜率,", round(win_rate * 100, 2), "%")
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print("总手续费+滑点,", 手续费汇总)
|
||||
手续费汇总 = 0
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] + "ss" + "_plot.png"
|
||||
# plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
if not os.path.exists(png_filepath):
|
||||
# os.mkdir(png_filepath)
|
||||
os.makedirs(png_filepath)
|
||||
plot_path = os.path.join(png_filepath, plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
@@ -0,0 +1,523 @@
|
||||
"""
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
|
||||
6.使用前事项：
|
||||
1、主程序中修改ofdata_dj文件地址、png_filepath地址
|
||||
2、修改clearing_time2_start、clearing_time2_stop
|
||||
3、修改交易参数:lots、跟踪止损百分、固定止损百分比、duiji、cout_delta、delta
|
||||
4、修改资金参数:初始资金;回测参数：回测时间段、佣金、单边保证金、手续费;
|
||||
"""
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
import talib as tb # jerom注释： 增加talib库
|
||||
|
||||
手续费汇总 = 0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ("sig", "delta")
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (("sig", 6), ("delta", 8))
|
||||
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
("trailing_stop_percent", 0.005), # 跟踪止盈百分比
|
||||
("fixed_stop_loss_percent", 0.01), # 固定止损百分比
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots = 1 # 下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta = self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos = 0
|
||||
print(line_aliases)
|
||||
self.high = self.datas[0].high
|
||||
self.low = self.datas[0].low
|
||||
self.closes = self.datas[0].close
|
||||
self.open = self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
# 240884432
|
||||
self.out_short = 0
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.rinei_mean = 0
|
||||
self.reniei_aroon_up = []
|
||||
self.reniei_aroon_down = []
|
||||
|
||||
self.datetime_list = []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum = []
|
||||
|
||||
self.barN = 0
|
||||
self.df = pd.DataFrame(columns=["datetime", "high", "low", "close", "open", "delta", "delta_cumsum"])
|
||||
|
||||
self.trader_df = pd.DataFrame(columns=["open", "high", "low", "close", "volume", "openInterest", "delta"])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
"""可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等"""
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print("%s, %s" % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name, # 品种名称
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 += order.executed.comm
|
||||
self.log(
|
||||
"SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f"
|
||||
% (
|
||||
order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总,
|
||||
)
|
||||
)
|
||||
|
||||
def next(self):
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
# bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
# 时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
# 更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
# print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(2, 25)
|
||||
clearing_time2_end = s_time(2, 30)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed:
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma = []
|
||||
self.renei_high_ma = []
|
||||
self.renei_low_ma = []
|
||||
self.delta_cumsum = []
|
||||
self.deltas_list = []
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.renei_high_ma.append(self.high[0])
|
||||
self.renei_low_ma.append(self.low[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
self.reniei_aroon_up, self.reniei_aroon_down = tb.AROON(
|
||||
np.array(self.renei_high_ma), np.array(self.renei_low_ma), 10
|
||||
)
|
||||
# self.delta_cumsum=[]
|
||||
# self.deltas_list=[]
|
||||
# print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg = 每日重置数据()
|
||||
|
||||
# 过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0:
|
||||
return
|
||||
# print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
self.low[0] if self.long_trailing_stop_price < self.low[0] else self.long_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
self.high[0] if self.high[0] < self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short = self.short_trailing_stop_price * (1 + self.trailing_stop_percent)
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
self.low[0] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and self.low[0] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"--多头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
self.low[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
self.high[0] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and self.high[0] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"--空头止盈出场datetime+sig: ",
|
||||
dt,
|
||||
"Trailing stop triggered: Closing position: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
self.high[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and self.closes[0] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"--多头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and self.closes[0] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"--空头止损datetime+sig: ",
|
||||
dt,
|
||||
"Fixed stop loss triggered: Closing position",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
self.closes[0],
|
||||
)
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
#
|
||||
if run_kg is False: #
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
upper, middle, lower = tb.BBANDS(np.array(self.deltas_list), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
|
||||
upper_cum, middle_cum, lower_cum = tb.BBANDS(
|
||||
np.array(self.delta_cumsum), timeperiod=5, nbdevup=2, nbdevdn=2, matype=0
|
||||
)
|
||||
# ————jerome注释：增加Boll函数测试
|
||||
# jerome注释：self.signal[0] >1 1为堆积信号
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>middle[-1] and self.delta_cumsum[-1]>middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]>0 and self.delta_cumsum[-1]>2000
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<middle[-1] and self.delta_cumsum[-1]<middle_cum[-1]#jerome注释：self.signal[0] >1 1为堆积信号，and self.data.delta[0]<-0 and self.delta_cumsum[-1]<-2000
|
||||
开多组合 = (
|
||||
self.reniei_aroon_up[-1] > 50
|
||||
and self.reniei_aroon_up[-1] > self.reniei_aroon_down[-1]
|
||||
# self.rinei_mean > 0
|
||||
# and self.closes[0] > self.rinei_mean
|
||||
and self.signal[0] > 2
|
||||
and self.data.delta[0] > 500000
|
||||
and self.delta_cumsum[-1] > 500000
|
||||
)
|
||||
开空组合 = (
|
||||
self.reniei_aroon_down[-1] > 50
|
||||
and self.reniei_aroon_up[-1] < self.reniei_aroon_down[-1]
|
||||
# self.rinei_mean > 0
|
||||
# and self.closes[0] < self.rinei_mean
|
||||
and self.signal[0] < -2
|
||||
and self.data.delta[0] < -500000
|
||||
and self.delta_cumsum[-1] < -500000
|
||||
)
|
||||
平多条件 = self.pos < 0 and self.signal[0] > 2
|
||||
平空条件 = self.pos > 0 and self.signal[0] < -2
|
||||
if self.pos != 1: #
|
||||
if 平多条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合: #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = self.low[0]
|
||||
self.sl_long_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos != -1: #
|
||||
if 平空条件:
|
||||
# print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = self.high[0]
|
||||
self.sl_shor_price = self.data.open[0]
|
||||
# print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
# 版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
# 数据
|
||||
csv_file = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(2M)\data_rs_merged\中金所\IM888\IM888_rs_2022_2T_back_ofdata_dj_new.csv"
|
||||
png_filepath = r"D:\BaiduNetdiskDownload\主力连续\tick生成的OF数据(2M)\data_rs_merged\中金所\IM888\部分回测报告"
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2022, 1, 1),
|
||||
todate=datetime(2022, 12, 31),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat="%Y-%m-%d %H:%M:%S",
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8,
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
# cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
|
||||
# cerebro.addanalyzer(bt.analyzers.sharpe, __name_= "sharpe")
|
||||
初始资金 = 300000
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
# 手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=150000.0, mult=300) # 回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()["total"]["total"]
|
||||
winning_trades = analyzer.trades.get_analysis()["won"]["total"]
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print("回测报告:")
|
||||
print("期初权益", 初始资金)
|
||||
print("期末权益", 初始资金 + round(total_profit))
|
||||
print("盈亏额", round(total_profit))
|
||||
print("最大回撤率,", round(analyzer.drawdown.get_analysis()["drawdown"], 2), "%")
|
||||
print("胜率,", round(win_rate * 100, 2), "%")
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print("总手续费+滑点,", 手续费汇总)
|
||||
手续费汇总 = 0
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] + "ss" + "_plot.png"
|
||||
# plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
if not os.path.exists(png_filepath):
|
||||
# os.mkdir(png_filepath)
|
||||
os.makedirs(png_filepath)
|
||||
plot_path = os.path.join(png_filepath, plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
|
||||
|
||||
# 公众号：松鼠Quant
|
||||
# 主页：www.quant789.com
|
||||
# 本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1 @@
|
||||
IF2023:combo: (0.008, 0.008, 5, 200, 200), value: 433379.9999999999, best_value: 563755.9999999976, best_parameters: (0.002, 0.004, 3, 0, 0)
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 111 KiB |
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|
After Width: | Height: | Size: 231 KiB |
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|
After Width: | Height: | Size: 21 KiB |
@@ -0,0 +1,2 @@
|
||||
@echo off
|
||||
for /f "tokens=2 delims=," %%a in ('tasklist /v /fo csv ^| findstr /i "real.py"') do taskkill /pid %%~a
|
||||
@@ -0,0 +1,74 @@
|
||||
'''
|
||||
Author: zhoujie2104231 zhoujie@me.com
|
||||
Date: 2024-03-02 16:20:40
|
||||
LastEditors: zhoujie2104231 zhoujie@me.com
|
||||
LastEditTime: 2024-04-10 10:02:34
|
||||
Description:
|
||||
|
||||
'''
|
||||
import subprocess
|
||||
import schedule
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
import os
|
||||
|
||||
# 获取当前工作目录
|
||||
current_directory = os.getcwd()
|
||||
print("当前工作目录：", current_directory)
|
||||
# 设置新的工作目录
|
||||
new_directory = "C:/real_trader"
|
||||
os.chdir(new_directory)
|
||||
# 验证新的工作目录
|
||||
updated_directory = os.getcwd()
|
||||
print("已更改为新的工作目录：", updated_directory)
|
||||
|
||||
|
||||
# 定义要启动的文件['real-a.py','real-ag.py','real-rb.py','real-hc.py']
|
||||
files_to_run = ['real.py']
|
||||
|
||||
|
||||
def run_scripts():
|
||||
print("启动程序...")
|
||||
for file in files_to_run:
|
||||
time.sleep(1)
|
||||
# 使用subprocess模块运行命令
|
||||
subprocess.Popen(['start', 'cmd', '/k', 'python', file], shell=True)
|
||||
print(file)
|
||||
print(datetime.now(),'程序重新启动完成，等待明天关闭重启')
|
||||
|
||||
|
||||
def close_scripts():
|
||||
print("关闭程序...")
|
||||
# 通过创建一个包含关闭指定窗口命令的批处理文件来关闭CMD窗口
|
||||
def close_specific_cmd_window(cmd_window_title):
|
||||
with open("close_cmd_window.bat", "w") as batch_file:
|
||||
batch_file.write(f'@echo off\nfor /f "tokens=2 delims=," %%a in (\'tasklist /v /fo csv ^| findstr /i "{cmd_window_title}"\') do taskkill /pid %%~a')
|
||||
|
||||
# 运行批处理文件
|
||||
subprocess.run("close_cmd_window.bat", shell=True)
|
||||
|
||||
|
||||
# 循环关闭所有脚本对应的CMD窗口
|
||||
for title in files_to_run:
|
||||
close_specific_cmd_window(title)
|
||||
print(datetime.now(),'已关闭程序，等待重新运行程序')
|
||||
|
||||
|
||||
|
||||
# 设置定时任务，关闭程序
|
||||
schedule.every().day.at("15:30").do(close_scripts)
|
||||
schedule.every().day.at("03:00").do(close_scripts)
|
||||
|
||||
# 设置定时任务，启动程序
|
||||
schedule.every().day.at("08:55").do(run_scripts)
|
||||
schedule.every().day.at("20:55").do(run_scripts)
|
||||
|
||||
|
||||
|
||||
# 保持脚本运行，等待定时任务触发
|
||||
#240884432
|
||||
while True:
|
||||
schedule.run_pending()
|
||||
time.sleep(1)
|
||||
#240884432
|
||||
@@ -0,0 +1,895 @@
|
||||
'''
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
'''
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
# 加入邮件通知
|
||||
import smtplib
|
||||
from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件
|
||||
from email.mime.multipart import MIMEMultipart # 导入 MIMEMultipart 类发送带有附件的邮件
|
||||
from email.mime.application import MIMEApplication # 导入 MIMEApplication 类发送二进制附件
|
||||
|
||||
## 配置邮件信息
|
||||
receivers = ["240884432@qq.com"] # 设置邮件接收人地址
|
||||
subject = "订单流策略交易信号" # 设置邮件主题
|
||||
#text = " " # 设置邮件正文
|
||||
# file_path = "test.txt" # 设置邮件附件文件路径
|
||||
|
||||
## 配置邮件服务器信息
|
||||
smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
|
||||
smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
|
||||
sender = "240884432@qq.com" # 设置发送邮件的邮箱地址
|
||||
username = "240884432@qq.com" # 设置发送邮件的邮箱用户名
|
||||
password = "osjyjmbqrzxtbjbf" #zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trade_dfs = {} #pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={}
|
||||
|
||||
# 日盘商品期货交易品种
|
||||
commodity_day_dict = {'bb': s_time(15,00), 'jd': s_time(15,00), 'lh': s_time(15,00), 'l': s_time(15,00), 'fb': s_time(15,00), 'ec': s_time(15,00),
|
||||
'AP': s_time(15,00), 'CJ': s_time(15,00), 'JR': s_time(15,00), 'LR': s_time(15,00), 'RS': s_time(15,00), 'PK': s_time(15,00),
|
||||
'PM': s_time(15,00), 'PX': s_time(15,00), 'RI': s_time(15,00), 'SF': s_time(15,00), 'SM': s_time(15,00), 'UR': s_time(15,00),
|
||||
'WH': s_time(15,00), 'ao': s_time(15,00), 'br': s_time(15,00), 'wr': s_time(15,00),}
|
||||
|
||||
# 夜盘商品期货交易品种
|
||||
commodity_night_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0), 'SH': s_time(23,00)}
|
||||
|
||||
# 金融期货交易品种
|
||||
financial_time_dict = {'IH': s_time(15,00), 'IF': s_time(15,00), 'IC': s_time(15,00), 'IM': s_time(15,00),'T': s_time(15,15), 'TS': s_time(15,15),
|
||||
'TF': s_time(15,15), 'TL': s_time(15,15)}
|
||||
|
||||
fees_df = pd.read_csv('./futures_fees_info.csv', header = 0, usecols= [1, 4, 17, 19, 25],names=['合约', '合约乘数', '做多保证金率', '做空保证金率', '品种代码'])
|
||||
contacts_df = pd.read_csv('./main_contacts.csv', header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
class ParamObj:
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
symbol = None #合约名称
|
||||
Lots = None #下单手数
|
||||
py = None #设置委托价格的偏移，更加容易促成成交
|
||||
trailing_stop_percent = None #跟踪出场参数
|
||||
fixed_stop_loss_percent = None #固定出场参数
|
||||
dj_X = None #开仓的堆积参数
|
||||
delta = None #开仓的delta参数
|
||||
sum_delta = None #开仓的delta累积参数
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
|
||||
# 策略需要用到的变量
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
short_trailing_stop_price = 0
|
||||
long_trailing_stop_price = 0
|
||||
sl_long_price = 0
|
||||
sl_shor_price = 0
|
||||
out_long = 0
|
||||
out_short = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
def __init__(self, symbol, Lots, py, trailing_stop_percent, fixed_stop_loss_percent, dj_X, delta, sum_delta,失衡,堆积,周期):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.trailing_stop_percent = trailing_stop_percent
|
||||
self.fixed_stop_loss_percent = fixed_stop_loss_percent
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
# 将df数据合并到trader_df中
|
||||
# if symbol not in trade_dfs.keys():
|
||||
# trade_df = pd.DataFrame({})
|
||||
# else:
|
||||
# trade_df = trade_dfs[symbol]
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
|
||||
#print(trader_df)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i < len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
#读取保存的数据
|
||||
def read_to_csv(self,symbol):
|
||||
# 文件夹路径和文件路径
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
param = self.param_dict[symbol]
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
|
||||
# 如果文件夹不存在则创建
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
|
||||
# 读取保留的模型数据CSV文件
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and param.kgdata==True:
|
||||
# 选择最后一行数据
|
||||
row = df.iloc[-1]
|
||||
|
||||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||||
param.pos = int(row['pos'])
|
||||
param.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
param.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
param.sl_long_price = float(row['sl_long_price'])
|
||||
param.sl_shor_price = float(row['sl_shor_price'])
|
||||
# param.out_long = int(row['out_long'])
|
||||
# param.out_short = int(row['out_short'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
param.kgdata=False
|
||||
else:
|
||||
pass
|
||||
#print("没有找到历史交易数据文件", file_path)
|
||||
#如果没有找到CSV，则初始化变量
|
||||
|
||||
pass
|
||||
|
||||
#保存数据
|
||||
def save_to_csv(self,symbol):
|
||||
param = self.param_dict[symbol]
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 创建DataFrame
|
||||
|
||||
data = {
|
||||
'datetime': [trade_dfs[symbol]['datetime'].iloc[-1]],
|
||||
'pos': [param.pos],
|
||||
'short_trailing_stop_price': [param.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [param.long_trailing_stop_price],
|
||||
'sl_long_price': [param.sl_long_price],
|
||||
'sl_shor_price': [param.sl_shor_price],
|
||||
# 'out_long': [param.out_long],
|
||||
# 'out_short': [param.out_short]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 将DataFrame保存到CSV文件
|
||||
df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False)
|
||||
|
||||
#每日收盘重置数据
|
||||
def day_data_reset(self, symbol):
|
||||
param = self.param_dict[symbol]
|
||||
sec = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围（日盘收盘）
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
param.clearing_executed = False
|
||||
# 检查当前时间第一个操作的时间范围内
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True)#清除当天的行情数据
|
||||
|
||||
# 检查当前时间是否在第二个操作的时间范围内(夜盘收盘)
|
||||
elif sec in commodity_night_dict.keys():
|
||||
clearing_time2_start = commodity_night_dict[sec]
|
||||
clearing_time2_end = s_time(clearing_time2_start.hour, clearing_time2_start.minute+15)
|
||||
if clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True) #清除当天的行情数据
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
return param.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
data = symbol_queue.get(block=True, timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset(instrument_id)
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
#新K线开始，启动交易程序 and 保存行情数据
|
||||
self.read_to_csv(instrument_id)
|
||||
# size = symbol_queue.qsize()
|
||||
# if size > 2:
|
||||
# print(f'！！！！！当前{instrument_id}共享队列长度为：',size)
|
||||
if len(trade_df)>param.cont_df:
|
||||
# 检查文件是否存在
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
trade_df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个DataFrame
|
||||
trade_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if param.long_trailing_stop_price >0 and param.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',param.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
param.long_trailing_stop_price = trade_df['low'].iloc[-1] if param.long_trailing_stop_price<trade_df['low'].iloc[-1] else param.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',param.long_trailing_stop_price)
|
||||
if param.short_trailing_stop_price >0 and param.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',param.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
param.short_trailing_stop_price = trade_df['high'].iloc[-1] if trade_df['high'].iloc[-1] <param.short_trailing_stop_price else param.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',param.short_trailing_stop_price)
|
||||
|
||||
param.out_long=param.long_trailing_stop_price * (1 - param.trailing_stop_percent)
|
||||
param.out_short=param.short_trailing_stop_price*(1 + param.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',param.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',param.out_long)
|
||||
# 跟踪出场
|
||||
if param.out_long >0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——多头止盈——','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
if trade_df['low'].iloc[-1] < param.out_long and param.pos>0 and param.sl_long_price>0 and trade_df['low'].iloc[-1]>param.sl_long_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'多头止盈','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.out_long=0
|
||||
param.sl_long_price=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if param.out_short>0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
if trade_df['high'].iloc[-1] > param.out_short and param.pos<0 and param.sl_shor_price>0 and trade_df['high'].iloc[-1]<param.sl_shor_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'空头止盈: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
fixed_stop_loss_L = param.sl_long_price * (1 - param.fixed_stop_loss_percent)
|
||||
if param.pos>0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_long_price>0 and fixed_stop_loss_L>0 and param.pos > 0 and trade_df['close'].iloc[-1] < fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.sl_long_price=0
|
||||
param.out_long = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
fixed_stop_loss_S = param.sl_shor_price * (1 + param.fixed_stop_loss_percent)
|
||||
if param.pos<0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_shor_price>0 and fixed_stop_loss_S>0 and param.pos < 0 and trade_df['close'].iloc[-1] > fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
param.out_short = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#日均线
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
|
||||
#小于日均线
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
|
||||
平多条件=trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
平空条件=trade_df['dj'].iloc[-1]>param.dj_X
|
||||
#开仓
|
||||
#多头开仓条件
|
||||
if param.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.sl_shor_price=0
|
||||
param.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"平空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平空的平仓价格为{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=1
|
||||
param.long_trailing_stop_price=data['AskPrice1']
|
||||
param.sl_long_price=data['AskPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"开多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 多头开仓的开仓价格{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-param.py)
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.long_trailing_stop_price=0
|
||||
param.sl_long_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#发送邮件
|
||||
text = f"平多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos==0 and 开空条件 :
|
||||
print('开空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1'])
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=-1
|
||||
param.short_trailing_stop_price=data['BidPrice1']
|
||||
param.sl_shor_price=data['BidPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"开空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 空头开仓的开仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
print(trade_df)
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
# print(f"当前合约队列为空，等待新数据插入。")
|
||||
pass
|
||||
|
||||
# 将CTP推送的行情数据分发给对应线程队列去执行
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False) # 往对应合约队列中插入行情
|
||||
# print(f"{instrument_id}合约数据插入。")
|
||||
except queue.Full:
|
||||
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(20) #为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],)) # 为每个合约单独创建一个线程计算开仓逻辑
|
||||
threads.append(t)
|
||||
t.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
# 发送邮件
|
||||
def send_mail(text):
|
||||
msg = MIMEMultipart()
|
||||
msg["From"] = sender
|
||||
msg["To"] = ";".join(receivers)
|
||||
msg["Subject"] = subject
|
||||
msg.attach(MIMEText(text, "plain", "utf-8"))
|
||||
smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
|
||||
smtp.login(username, password)
|
||||
smtp.sendmail(sender, receivers, msg.as_string())
|
||||
smtp.quit()
|
||||
|
||||
# 获取主力连续代码
|
||||
def get_main_contact_on_time(main_symbol_code,contacts_df):
|
||||
main_symbol = contacts_df[contacts_df['品种代码'] == main_symbol_code]['主连代码'].iloc[0]
|
||||
print("最终使用的主连代码：",main_symbol)
|
||||
return main_symbol
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
sb_1 = get_main_contact_on_time('IH', contacts_df)
|
||||
sb_2 = get_main_contact_on_time('ag', contacts_df)
|
||||
sb_3 = get_main_contact_on_time('eb', contacts_df)
|
||||
sb_4 = get_main_contact_on_time('si', contacts_df)
|
||||
sb_5 = get_main_contact_on_time('sc', contacts_df)
|
||||
sb_6 = get_main_contact_on_time('SA', contacts_df)
|
||||
sb_7 = get_main_contact_on_time('rb', contacts_df)
|
||||
sb_8 = get_main_contact_on_time('ru', contacts_df)
|
||||
sb_9 = get_main_contact_on_time('ni', contacts_df)
|
||||
sb_10 = get_main_contact_on_time('m', contacts_df)
|
||||
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
param_dict = {}
|
||||
param_dict[sb_1] = ParamObj(symbol=sb_1, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_2] = ParamObj(symbol=sb_2, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_3] = ParamObj(symbol=sb_4, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_4] = ParamObj(symbol=sb_4, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_5] = ParamObj(symbol=sb_5, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_6] = ParamObj(symbol=sb_6, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_7] = ParamObj(symbol=sb_7, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_8] = ParamObj(symbol=sb_8, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_9] = ParamObj(symbol=sb_9, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict[sb_10] = ParamObj(symbol=sb_10, Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
|
||||
# param_dict['ag2408'] = ParamObj(symbol='ag2408', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=500,sum_delta=1000,失衡=3,堆积=3,周期='5T')
|
||||
# param_dict['j2405'] = ParamObj(symbol='j2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['TA405'] = ParamObj(symbol='TA405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['au2406'] = ParamObj(symbol='au2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['sc2405'] = ParamObj(symbol='sc2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['bc2406'] = ParamObj(symbol='bc2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['lu2406'] = ParamObj(symbol='lu2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
# future_account = get_simulate_account(
|
||||
# investor_id='135858', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
# password='Zj82334475', # simnow密码
|
||||
# server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
||||
# subscribe_list=list(param_dict.keys()), # 合约列表
|
||||
# )
|
||||
|
||||
#实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
future_account = FutureAccount(
|
||||
broker_id='8888', # 期货公司BrokerID
|
||||
server_dict={'TDServer': "103.140.14.210:43205", 'MDServer': '103.140.14.210:43173'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
reserve_server_dict={}, # 备用服务器地址
|
||||
investor_id='******', # 账户
|
||||
password='******', # 密码
|
||||
app_id='vntech_vnpy_2.0', # 认证使用AppID
|
||||
auth_code='N46EKN6TJ9U7V06V', # 认证使用授权码
|
||||
subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
||||
md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
)
|
||||
|
||||
if datetime.time == time(15, 5):
|
||||
df =
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,4 @@
|
||||
@echo off
|
||||
taskkill /im python.exe /f
|
||||
taskkill /im cmd.exe /f
|
||||
exit
|
||||
@@ -0,0 +1,4 @@
|
||||
@echo off
|
||||
set python_path=C:\veighna_studio\python.exe
|
||||
start python D:\real_test\on_time.py
|
||||
exit
|
||||
@@ -0,0 +1,8 @@
|
||||
echo oft
|
||||
w32tm /config /manualpeerlist:"time.nist.gov"/syncfromflags:manual /reliable:yes /update
|
||||
w32tm /resync
|
||||
w32tm /resync
|
||||
w32tm /config /manualpeerlist:"time.windows.com"/syncfromflags:manual /reliable:yes /update
|
||||
w32tm /resync
|
||||
w32tm /resync
|
||||
echo同步结束！
|
||||
@@ -0,0 +1,5 @@
|
||||
[tool.ruff]
|
||||
line-length = 120 # 设置行长度为120
|
||||
|
||||
[tool.ruff.select]
|
||||
E501 = "ignore" # 忽略行长度限制错误
|
||||
@@ -0,0 +1,588 @@
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
tickdatadict = {}
|
||||
quotedict = {}
|
||||
ofdatadict = {}
|
||||
trade_dfs = {}
|
||||
previous_volume = {}
|
||||
tsymbollist={}
|
||||
clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0)}
|
||||
|
||||
class ParamObj:
|
||||
symbol = None
|
||||
Lots = None
|
||||
py = None
|
||||
trailing_stop_percent = None
|
||||
fixed_stop_loss_percent = None
|
||||
dj_X = None
|
||||
delta = None
|
||||
sum_delta = None
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
short_trailing_stop_price = 0
|
||||
long_trailing_stop_price = 0
|
||||
sl_long_price = 0
|
||||
sl_shor_price = 0
|
||||
out_long = 0
|
||||
out_short = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
def __init__(self, symbol, Lots, py, trailing_stop_percent, fixed_stop_loss_percent, dj_X, delta, sum_delta,失衡,堆积,周期):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.trailing_stop_percent = trailing_stop_percent
|
||||
self.fixed_stop_loss_percent = fixed_stop_loss_percent
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode()
|
||||
ActionDay = data['ActionDay'].decode()
|
||||
update_time = data['UpdateTime'].decode()
|
||||
update_millisec = str(data['UpdateMillisec'])
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec
|
||||
tick = {
|
||||
'symbol': instrument_id,
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
'price': float(data['LastPrice']),
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0,
|
||||
'bid_p': float(data['BidPrice1']),
|
||||
'bid_v': int(data['BidVolume1']),
|
||||
'ask_p': float(data['AskPrice1']),
|
||||
'ask_v': int(data['AskVolume1']),
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']),
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']),
|
||||
'TradingDay': data['TradingDay'].decode(),
|
||||
'cum_volume': int(data['Volume']),
|
||||
'cum_amount': float(data['Turnover']),
|
||||
'cum_position': int(data['OpenInterest']),
|
||||
}
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
return bidDictResult, askDictResult
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
endArray = df_min['bartime'].values
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
if i == 0:
|
||||
delta = delta
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
if i >= 1 and i < len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
return djcout
|
||||
|
||||
def read_to_csv(self,symbol):
|
||||
param = self.param_dict[symbol]
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and param.kgdata==True:
|
||||
row = df.iloc[-1]
|
||||
param.pos = int(row['pos'])
|
||||
param.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
param.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
param.sl_long_price = float(row['sl_long_price'])
|
||||
param.sl_shor_price = float(row['sl_shor_price'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
param.kgdata=False
|
||||
else:
|
||||
pass
|
||||
pass
|
||||
|
||||
def save_to_csv(self,symbol):
|
||||
param = self.param_dict[symbol]
|
||||
data = {
|
||||
'datetime': [trade_dfs[symbol]['datetime'].iloc[-1]],
|
||||
'pos': [param.pos],
|
||||
'short_trailing_stop_price': [param.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [param.long_trailing_stop_price],
|
||||
'sl_long_price': [param.sl_long_price],
|
||||
'sl_shor_price': [param.sl_shor_price],
|
||||
}
|
||||
df = pd.DataFrame(data)
|
||||
df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False)
|
||||
|
||||
def day_data_reset(self, symbol):
|
||||
param = self.param_dict[symbol]
|
||||
sec = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
current_time = datetime.now().time()
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
param.clearing_executed = False
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
param.clearing_executed = True
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True)
|
||||
elif sec in clearing_time_dict.keys():
|
||||
clearing_time2_start = clearing_time_dict[sec]
|
||||
clearing_time2_end = s_time(clearing_time2_start.hour, clearing_time2_start.minute+15)
|
||||
if clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
param.clearing_executed = True
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True)
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
return param.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
data = symbol_queue.get(block=True, timeout=5)
|
||||
instrument_id = data['InstrumentID'].decode()
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset(instrument_id)
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
self.read_to_csv(instrument_id)
|
||||
if len(trade_df)>param.cont_df:
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
trade_df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
trade_df.to_csv(csv_file_path, index=False)
|
||||
if param.long_trailing_stop_price >0 and param.pos>0:
|
||||
param.long_trailing_stop_price = trade_df['low'].iloc[-1] if param.long_trailing_stop_price<trade_df['low'].iloc[-1] else param.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.short_trailing_stop_price >0 and param.pos<0:
|
||||
param.short_trailing_stop_price = trade_df['high'].iloc[-1] if trade_df['high'].iloc[-1] <param.short_trailing_stop_price else param.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
param.out_long=param.long_trailing_stop_price * (1 - param.trailing_stop_percent)
|
||||
param.out_short=param.short_trailing_stop_price*(1 + param.trailing_stop_percent)
|
||||
if param.out_long >0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——多头止盈——','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
if trade_df['low'].iloc[-1] < param.out_long and param.pos>0 and param.sl_long_price>0 and trade_df['low'].iloc[-1]>param.sl_long_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'多头止盈','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.out_long=0
|
||||
param.sl_long_price=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.out_short>0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
if trade_df['high'].iloc[-1] > param.out_short and param.pos<0 and param.sl_shor_price>0 and trade_df['high'].iloc[-1]<param.sl_shor_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'空头止盈: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
fixed_stop_loss_L = param.sl_long_price * (1 - param.fixed_stop_loss_percent)
|
||||
if param.pos>0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_long_price>0 and fixed_stop_loss_L>0 and param.pos > 0 and trade_df['close'].iloc[-1] < fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.sl_long_price=0
|
||||
param.out_long = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
fixed_stop_loss_S = param.sl_shor_price * (1 + param.fixed_stop_loss_percent)
|
||||
if param.pos<0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_shor_price>0 and fixed_stop_loss_S>0 and param.pos < 0 and trade_df['close'].iloc[-1] > fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
param.out_short = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
平多条件=trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
平空条件=trade_df['dj'].iloc[-1]>param.dj_X
|
||||
if param.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.sl_shor_price=0
|
||||
param.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=1
|
||||
param.long_trailing_stop_price=data['AskPrice1']
|
||||
param.sl_long_price=data['AskPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-param.py)
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.long_trailing_stop_price=0
|
||||
param.sl_long_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.pos==0 and 开空条件 :
|
||||
print('开空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1'])
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=-1
|
||||
param.short_trailing_stop_price=data['BidPrice1']
|
||||
param.sl_shor_price=data['BidPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
print(trade_df)
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
pass
|
||||
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode()
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False)
|
||||
except queue.Full:
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(20)
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],))
|
||||
threads.append(t)
|
||||
t.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
param_dict = {}
|
||||
param_dict['rb2410'] = ParamObj(symbol='rb2410', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
future_account = get_simulate_account(
|
||||
investor_id='***',
|
||||
password='***',
|
||||
server_name='***',
|
||||
subscribe_list=list(param_dict.keys())
|
||||
)
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
share_queue = Queue(maxsize=200)
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue,
|
||||
future_account.td_flow_path
|
||||
))
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,53 @@
|
||||
import subprocess
|
||||
import schedule
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
# 定义要启动的文件
|
||||
files_to_run = ['实盘运行版本.py']
|
||||
|
||||
|
||||
def run_scripts():
|
||||
print("启动程序...")
|
||||
for file in files_to_run:
|
||||
time.sleep(1)
|
||||
# 使用subprocess模块运行命令
|
||||
subprocess.Popen(['start', 'cmd', '/k', 'python', file], shell=True)
|
||||
print(file)
|
||||
print(datetime.now(),'程序重新启动完成，等待明天关闭重启')
|
||||
|
||||
|
||||
def close_scripts():
|
||||
print("关闭程序...")
|
||||
# 通过创建一个包含关闭指定窗口命令的批处理文件来关闭CMD窗口
|
||||
def close_specific_cmd_window(cmd_window_title):
|
||||
with open("close_cmd_window.bat", "w") as batch_file:
|
||||
batch_file.write(f'@echo off\nfor /f "tokens=2 delims=," %%a in (\'tasklist /v /fo csv ^| findstr /i "{cmd_window_title}"\') do taskkill /pid %%~a')
|
||||
|
||||
# 运行批处理文件
|
||||
subprocess.run("close_cmd_window.bat", shell=True)
|
||||
|
||||
|
||||
# 循环关闭所有脚本对应的CMD窗口
|
||||
for title in files_to_run:
|
||||
close_specific_cmd_window(title)
|
||||
print(datetime.now(),'已关闭程序，等待重新运行程序')
|
||||
|
||||
|
||||
|
||||
# 设置定时任务，关闭程序
|
||||
schedule.every().day.at("15:30").do(close_scripts)
|
||||
schedule.every().day.at("03:00").do(close_scripts)
|
||||
|
||||
# 设置定时任务，启动程序
|
||||
schedule.every().day.at("08:55").do(run_scripts)
|
||||
schedule.every().day.at("20:55").do(run_scripts)
|
||||
|
||||
|
||||
|
||||
# 保持脚本运行，等待定时任务触发
|
||||
#240884432
|
||||
while True:
|
||||
schedule.run_pending()
|
||||
time.sleep(1)
|
||||
#240884432
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,840 @@
|
||||
'''
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trade_dfs = {} #pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={}
|
||||
|
||||
clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
class ParamObj:
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
symbol = None #合约名称
|
||||
Lots = None #下单手数
|
||||
py = None #设置委托价格的偏移，更加容易促成成交
|
||||
trailing_stop_percent = None #跟踪出场参数
|
||||
fixed_stop_loss_percent = None #固定出场参数
|
||||
dj_X = None #开仓的堆积参数
|
||||
delta = None #开仓的delta参数
|
||||
sum_delta = None #开仓的delta累积参数
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
|
||||
# 策略需要用到的变量
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
short_trailing_stop_price = 0
|
||||
long_trailing_stop_price = 0
|
||||
sl_long_price = 0
|
||||
sl_shor_price = 0
|
||||
out_long = 0
|
||||
out_short = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
def __init__(self, symbol, Lots, py, trailing_stop_percent, fixed_stop_loss_percent, dj_X, delta, sum_delta,失衡,堆积,周期):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.trailing_stop_percent = trailing_stop_percent
|
||||
self.fixed_stop_loss_percent = fixed_stop_loss_percent
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
# 将df数据合并到trader_df中
|
||||
# if symbol not in trade_dfs.keys():
|
||||
# trade_df = pd.DataFrame({})
|
||||
# else:
|
||||
# trade_df = trade_dfs[symbol]
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
|
||||
#print(trader_df)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i <= len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
#读取保存的数据
|
||||
def read_to_csv(self,symbol):
|
||||
# 文件夹路径和文件路径
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
param = self.param_dict[symbol]
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
|
||||
# 如果文件夹不存在则创建
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
|
||||
# 读取保留的模型数据CSV文件
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and param.kgdata==True:
|
||||
# 选择最后一行数据
|
||||
row = df.iloc[-1]
|
||||
|
||||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||||
param.pos = int(row['pos'])
|
||||
param.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
param.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
param.sl_long_price = float(row['sl_long_price'])
|
||||
param.sl_shor_price = float(row['sl_shor_price'])
|
||||
# param.out_long = int(row['out_long'])
|
||||
# param.out_short = int(row['out_short'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
param.kgdata=False
|
||||
else:
|
||||
pass
|
||||
#print("没有找到历史交易数据文件", file_path)
|
||||
#如果没有找到CSV，则初始化变量
|
||||
|
||||
pass
|
||||
|
||||
#保存数据
|
||||
def save_to_csv(self,symbol):
|
||||
param = self.param_dict[symbol]
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 创建DataFrame
|
||||
|
||||
data = {
|
||||
'datetime': [trade_dfs[symbol]['datetime'].iloc[-1]],
|
||||
'pos': [param.pos],
|
||||
'short_trailing_stop_price': [param.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [param.long_trailing_stop_price],
|
||||
'sl_long_price': [param.sl_long_price],
|
||||
'sl_shor_price': [param.sl_shor_price],
|
||||
# 'out_long': [param.out_long],
|
||||
# 'out_short': [param.out_short]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 将DataFrame保存到CSV文件
|
||||
df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False)
|
||||
|
||||
#每日收盘重置数据
|
||||
def day_data_reset(self, symbol):
|
||||
param = self.param_dict[symbol]
|
||||
sec = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围（日盘收盘）
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
param.clearing_executed = False
|
||||
# 检查当前时间第一个操作的时间范围内
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True)#清除当天的行情数据
|
||||
|
||||
# 检查当前时间是否在第二个操作的时间范围内(夜盘收盘)
|
||||
elif sec in clearing_time_dict.keys():
|
||||
clearing_time2_start = clearing_time_dict[sec]
|
||||
clearing_time2_end = s_time(clearing_time2_start.hour, clearing_time2_start.minute+15)
|
||||
if clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True) #清除当天的行情数据
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
return param.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
data = symbol_queue.get(block=True, timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset(instrument_id)
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
#新K线开始，启动交易程序 and 保存行情数据
|
||||
self.read_to_csv(instrument_id)
|
||||
# size = symbol_queue.qsize()
|
||||
# if size > 2:
|
||||
# print(f'！！！！！当前{instrument_id}共享队列长度为：',size)
|
||||
if len(trade_df)>param.cont_df:
|
||||
# 检查文件是否存在
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
trade_df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个DataFrame
|
||||
trade_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if param.long_trailing_stop_price >0 and param.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',param.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
param.long_trailing_stop_price = trade_df['low'].iloc[-1] if param.long_trailing_stop_price<trade_df['low'].iloc[-1] else param.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',param.long_trailing_stop_price)
|
||||
if param.short_trailing_stop_price >0 and param.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',param.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
param.short_trailing_stop_price = trade_df['high'].iloc[-1] if trade_df['high'].iloc[-1] <param.short_trailing_stop_price else param.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',param.short_trailing_stop_price)
|
||||
|
||||
param.out_long=param.long_trailing_stop_price * (1 - param.trailing_stop_percent)
|
||||
param.out_short=param.short_trailing_stop_price*(1 + param.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',param.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',param.out_long)
|
||||
# 跟踪出场
|
||||
if param.out_long >0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——多头止盈——','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
if trade_df['low'].iloc[-1] < param.out_long and param.pos>0 and param.sl_long_price>0 and trade_df['low'].iloc[-1]>param.sl_long_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'多头止盈','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.out_long=0
|
||||
param.sl_long_price=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if param.out_short>0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
if trade_df['high'].iloc[-1] > param.out_short and param.pos<0 and param.sl_shor_price>0 and trade_df['high'].iloc[-1]<param.sl_shor_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'空头止盈: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
fixed_stop_loss_L = param.sl_long_price * (1 - param.fixed_stop_loss_percent)
|
||||
if param.pos>0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_long_price>0 and fixed_stop_loss_L>0 and param.pos > 0 and trade_df['close'].iloc[-1] < fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.sl_long_price=0
|
||||
param.out_long = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
fixed_stop_loss_S = param.sl_shor_price * (1 + param.fixed_stop_loss_percent)
|
||||
if param.pos<0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_shor_price>0 and fixed_stop_loss_S>0 and param.pos < 0 and trade_df['close'].iloc[-1] > fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
param.out_short = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#日均线
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
|
||||
#小于日均线
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
|
||||
平多条件=trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
平空条件=trade_df['dj'].iloc[-1]>param.dj_X
|
||||
#开仓
|
||||
#多头开仓条件
|
||||
if param.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.sl_shor_price=0
|
||||
param.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=1
|
||||
param.long_trailing_stop_price=data['AskPrice1']
|
||||
param.sl_long_price=data['AskPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if param.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-param.py)
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.long_trailing_stop_price=0
|
||||
param.sl_long_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
if param.pos==0 and 开空条件 :
|
||||
print('开空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1'])
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=-1
|
||||
param.short_trailing_stop_price=data['BidPrice1']
|
||||
param.sl_shor_price=data['BidPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
print(trade_df)
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
# print(f"当前合约队列为空，等待新数据插入。")
|
||||
pass
|
||||
|
||||
# 将CTP推送的行情数据分发给对应线程队列去执行
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False) # 往对应合约队列中插入行情
|
||||
# print(f"{instrument_id}合约数据插入。")
|
||||
except queue.Full:
|
||||
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(10) #为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],)) # 为每个合约单独创建一个线程计算开仓逻辑
|
||||
threads.append(t)
|
||||
t.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
param_dict = {}
|
||||
param_dict['rb2405'] = ParamObj(symbol='rb2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['ni2405'] = ParamObj(symbol='ni2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['j2405'] = ParamObj(symbol='j2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['TA405'] = ParamObj(symbol='TA405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['au2406'] = ParamObj(symbol='au2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['sc2405'] = ParamObj(symbol='sc2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['bc2405'] = ParamObj(symbol='bc2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
param_dict['lu2405'] = ParamObj(symbol='lu2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
future_account = get_simulate_account(
|
||||
investor_id='', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
password='', # simnow密码
|
||||
server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
||||
subscribe_list=list(param_dict.keys()), # 合约列表
|
||||
)
|
||||
|
||||
#实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
# future_account = FutureAccount(
|
||||
# broker_id='', # 期货公司BrokerID
|
||||
# server_dict={'TDServer': "ip:port", 'MDServer': 'ip:port'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
# reserve_server_dict={}, # 备用服务器地址
|
||||
# investor_id='', # 账户
|
||||
# password='', # 密码
|
||||
# app_id='simnow_client_test', # 认证使用AppID
|
||||
# auth_code='0000000000000000', # 认证使用授权码
|
||||
# subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
||||
# md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
# td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
# )
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
定时启动可以每日避开收盘时间，
|
||||
|
||||
只要设置好实盘运行版本.py里的账户，品种，以及程序参数，可以使用定时启动，每日无人监管。
|
||||
@@ -0,0 +1,928 @@
|
||||
"""
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
"""
|
||||
|
||||
# from multiprocessing import Process, Queue
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
# import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trader_df = pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist = {}
|
||||
|
||||
|
||||
def tickcome(md_queue):
|
||||
global previous_volume
|
||||
|
||||
data = md_queue
|
||||
instrument_id = data["InstrumentID"] # 品种代码
|
||||
# 将 ActionDay 转换为日期字符串
|
||||
action_day = pd.to_datetime(data["ActionDay"]).strftime("%Y-%m-%d")
|
||||
# 从 UpdateTime 中提取时间部分
|
||||
update_time = pd.to_datetime(data["UpdateTime"]).strftime("%H:%M:%S")
|
||||
# 组合时间字符串
|
||||
created_at = f"{action_day} {update_time}.{data['UpdateMillisec']:03d}"
|
||||
# created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
"symbol": instrument_id, # 品种代码和交易所ID
|
||||
"created_at": datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
# 'created_at': created_at, # 创建时间
|
||||
"price": float(data["LastPrice"]), # 最新价
|
||||
"last_volume": int(data["Volume"]) - previous_volume.get(instrument_id, 0)
|
||||
if previous_volume.get(instrument_id, 0) != 0
|
||||
else 0, # 瞬时成交量
|
||||
"bid_p": float(data["BidPrice1"]), # 买价
|
||||
"bid_v": int(data["BidVolume1"]), # 买量
|
||||
"ask_p": float(data["AskPrice1"]), # 卖价
|
||||
"ask_v": int(data["AskVolume1"]), # 卖量
|
||||
"UpperLimitPrice": float(data["UpperLimitPrice"]), # 涨停价
|
||||
"LowerLimitPrice": float(data["LowerLimitPrice"]), # 跌停价
|
||||
"TradingDay": data["TradingDay"], # 交易日日期
|
||||
"cum_volume": int(data["Volume"]), # 最新总成交量
|
||||
"cum_amount": float(data["Turnover"]), # 最新总成交额
|
||||
"cum_position": int(data["OpenInterest"]), # 合约持仓量
|
||||
}
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data["Volume"])
|
||||
if tick["last_volume"] > 0:
|
||||
# print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
on_tick(tick)
|
||||
|
||||
|
||||
def can_time(hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
|
||||
def on_tick(tick):
|
||||
# tm = can_time(tick["created_at"].hour, tick["created_at"].minute)
|
||||
# print(tick['symbol'])
|
||||
# print(1)
|
||||
# if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick["last_volume"] == 0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick = str(tick["created_at"]).replace("+08:00", "")
|
||||
tsymbol = tick["symbol"]
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
tsymbollist[tsymbol] = tick
|
||||
bid_p = quotes["bid_p"]
|
||||
ask_p = quotes["ask_p"]
|
||||
bid_v = quotes["bid_v"]
|
||||
ask_v = quotes["ask_v"]
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes = tsymbollist[tsymbol]
|
||||
bid_p = rquotes["bid_p"]
|
||||
ask_p = rquotes["ask_p"]
|
||||
bid_v = rquotes["bid_v"]
|
||||
ask_v = rquotes["ask_v"]
|
||||
tsymbollist[tsymbol] = tick
|
||||
tick_dt = pd.DataFrame(
|
||||
{
|
||||
"datetime": timetick,
|
||||
"symbol": tick["symbol"],
|
||||
"mainsym": tick["symbol"].rstrip("0123456789").upper(),
|
||||
"lastprice": tick["price"],
|
||||
"vol": tick["last_volume"],
|
||||
"bid_p": bid_p,
|
||||
"ask_p": ask_p,
|
||||
"bid_v": bid_v,
|
||||
"ask_v": ask_v,
|
||||
},
|
||||
index=[0],
|
||||
)
|
||||
sym = tick_dt["symbol"][0]
|
||||
# print(tick_dt)
|
||||
tickdata(tick_dt, sym)
|
||||
|
||||
|
||||
def data_of(df):
|
||||
global trader_df
|
||||
# 将df数据合并到trader_df中
|
||||
trader_df = pd.concat([trader_df, df], ignore_index=True)
|
||||
# print('trader_df: ', len(trader_df))
|
||||
# print(trader_df)
|
||||
|
||||
|
||||
def process(bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic["bidDictResult"]
|
||||
askDictResult = dic["askDictResult"]
|
||||
except Exception:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {"bidDictResult": bidDictResult, "askDictResult": askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
|
||||
|
||||
def tickdata(df, symbol):
|
||||
tickdata = pd.DataFrame(
|
||||
{
|
||||
"datetime": df["datetime"],
|
||||
"symbol": df["symbol"],
|
||||
"lastprice": df["lastprice"],
|
||||
"volume": df["vol"],
|
||||
"bid_p": df["bid_p"],
|
||||
"bid_v": df["bid_v"],
|
||||
"ask_p": df["ask_p"],
|
||||
"ask_v": df["ask_v"],
|
||||
}
|
||||
)
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf = tickdatadict[symbol]
|
||||
rdftm = pd.to_datetime(rdf["bartime"][0]).strftime("%Y-%m-%d %H:%M:%S")
|
||||
now = str(tickdata["datetime"][0])
|
||||
if now > rdftm:
|
||||
try:
|
||||
oo = ofdatadict[symbol]
|
||||
data_of(oo)
|
||||
# print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print("rdftm捕获到异常", e)
|
||||
tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
|
||||
tickdata["open"] = tickdata["lastprice"]
|
||||
tickdata["high"] = tickdata["lastprice"]
|
||||
tickdata["low"] = tickdata["lastprice"]
|
||||
tickdata["close"] = tickdata["lastprice"]
|
||||
tickdata["starttime"] = tickdata["datetime"]
|
||||
else:
|
||||
tickdata["bartime"] = rdf["bartime"]
|
||||
tickdata["open"] = rdf["open"]
|
||||
tickdata["high"] = max(tickdata["lastprice"].values, rdf["high"].values)
|
||||
tickdata["low"] = min(tickdata["lastprice"].values, rdf["low"].values)
|
||||
tickdata["close"] = tickdata["lastprice"]
|
||||
tickdata["volume"] = df["vol"] + rdf["volume"].values
|
||||
tickdata["starttime"] = rdf["starttime"]
|
||||
else:
|
||||
print("新bar的第一个tick进入")
|
||||
tickdata["bartime"] = pd.to_datetime(tickdata["datetime"])
|
||||
tickdata["open"] = tickdata["lastprice"]
|
||||
tickdata["high"] = tickdata["lastprice"]
|
||||
tickdata["low"] = tickdata["lastprice"]
|
||||
tickdata["close"] = tickdata["lastprice"]
|
||||
tickdata["starttime"] = tickdata["datetime"]
|
||||
except IOError as e:
|
||||
print("捕获到异常", e)
|
||||
|
||||
tickdata["bartime"] = pd.to_datetime(tickdata["bartime"])
|
||||
bardata = (
|
||||
tickdata.resample(on="bartime", rule="1T", label="right", closed="right")
|
||||
.agg(
|
||||
{
|
||||
"starttime": "first",
|
||||
"symbol": "last",
|
||||
"open": "first",
|
||||
"high": "max",
|
||||
"low": "min",
|
||||
"close": "last",
|
||||
"volume": "sum",
|
||||
}
|
||||
)
|
||||
.reset_index(drop=False)
|
||||
)
|
||||
bardata = bardata.dropna().reset_index(drop=True)
|
||||
bardata["bartime"] = pd.to_datetime(bardata["bartime"][0]).strftime(
|
||||
"%Y-%m-%d %H:%M:%S"
|
||||
)
|
||||
tickdatadict[symbol] = bardata
|
||||
tickdata["volume"] = df["vol"].values
|
||||
# print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
orderflow_df_new(tickdata, bardata, symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
|
||||
def orderflow_df_new(df_tick, df_min, symbol):
|
||||
# startArray = pd.to_datetime(df_min["starttime"]).values
|
||||
voluememin = df_min["volume"].values
|
||||
highs = df_min["high"].values
|
||||
lows = df_min["low"].values
|
||||
opens = df_min["open"].values
|
||||
closes = df_min["close"].values
|
||||
# endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min["bartime"].values
|
||||
# print(endArray)
|
||||
# deltaArray = np.zeros((len(endArray),))
|
||||
# tTickArray = pd.to_datetime(df_tick["datetime"]).values
|
||||
bp1TickArray = df_tick["bid_p"].values
|
||||
ap1TickArray = df_tick["ask_p"].values
|
||||
lastTickArray = df_tick["lastprice"].values
|
||||
volumeTickArray = df_tick["volume"].values
|
||||
symbolarray = df_tick["symbol"].values
|
||||
# indexFinal = 0
|
||||
for index, tEnd in enumerate(endArray):
|
||||
dt = endArray[index]
|
||||
# start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol = voluememin[index]
|
||||
bar_close = closes[index]
|
||||
bar_open = opens[index]
|
||||
bar_low = lows[index]
|
||||
bar_high = highs[index]
|
||||
bar_symbol = symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0], 4)
|
||||
Ap = round(ap1TickArray[0], 4)
|
||||
LastPrice = round(lastTickArray[0], 4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult, askDictResult = process(bidDict, askDict, symbol)
|
||||
bidDictResult = dict(sorted(bidDictResult.items(), key=operator.itemgetter(0)))
|
||||
askDictResult = dict(sorted(askDictResult.items(), key=operator.itemgetter(0)))
|
||||
prinslist = list(bidDictResult.keys())
|
||||
asklist = list(askDictResult.values())
|
||||
bidlist = list(bidDictResult.values())
|
||||
delta = sum(askDictResult.values()) - sum(bidDictResult.values())
|
||||
# print(prinslist,asklist,bidlist)
|
||||
# print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"price": pd.Series([prinslist]),
|
||||
"Ask": pd.Series([asklist]),
|
||||
"Bid": pd.Series([bidlist]),
|
||||
}
|
||||
)
|
||||
# df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df["symbol"] = bar_symbol
|
||||
df["datetime"] = dt
|
||||
df["delta"] = str(delta)
|
||||
df["close"] = bar_close
|
||||
df["open"] = bar_open
|
||||
df["high"] = bar_high
|
||||
df["low"] = bar_low
|
||||
df["volume"] = bar_vol
|
||||
# df['ticktime']=tTickArray[0]
|
||||
df["dj"] = GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol] = df
|
||||
|
||||
|
||||
def GetOrderFlow_dj(kData):
|
||||
Config = {
|
||||
"Value1": 3,
|
||||
"Value2": 3,
|
||||
"Value3": 3,
|
||||
"Value4": True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
# high = kItem["high"]
|
||||
# low = kItem["low"]
|
||||
# close = kItem["close"]
|
||||
# open = kItem["open"]
|
||||
dtime = kItem["datetime"]
|
||||
price_s = kItem["price"]
|
||||
Ask_s = kItem["Ask"]
|
||||
Bid_s = kItem["Bid"]
|
||||
delta = kItem["delta"]
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
"price": 0,
|
||||
"time": 0,
|
||||
"longshort": 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order = {
|
||||
"Price": price_s[i],
|
||||
"Bid": {"Value": Bid_s[i]},
|
||||
"Ask": {"Value": Ask_s[i]},
|
||||
}
|
||||
# 空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if order["Bid"]["Value"] > Ask_s[i + 1] * int(Config["Value1"]):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config["Value2"]) and Config["Value4"] is True:
|
||||
duiji["price"] = price_s[i]
|
||||
duiji["time"] = dtime
|
||||
duiji["longshort"] = -1
|
||||
if float(duiji["price"]) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
# 多头堆积
|
||||
if i >= 1 and i <= len(price_s) - 1:
|
||||
if order["Ask"]["Value"] > Bid_s[i - 1] * int(Config["Value1"]):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config["Value2"]) and Config["Value4"] is True:
|
||||
duiji["price"] = price_s[i]
|
||||
duiji["time"] = dtime
|
||||
duiji["longshort"] = 1
|
||||
if float(duiji["price"]) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
|
||||
# 交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
class 专享08of:
|
||||
def __init__(self):
|
||||
self.py = 5 # 设置委托价格的偏移，更加容易促成成交。仅螺纹，其他品种根据最小点波动，自己设置
|
||||
self.cont_df = 0
|
||||
self.trailing_stop_percent = 0.02 # 跟踪出场参数
|
||||
self.fixed_stop_loss_percent = 0.01 # 固定出场参数
|
||||
self.dj_X = 1 # 开仓的堆积参数
|
||||
|
||||
self.pos = 0
|
||||
self.Lots = 1 # 下单手数
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_long = 0
|
||||
self.out_short = 0
|
||||
self.clearing_executed = False
|
||||
self.kgdata = True
|
||||
|
||||
# 读取保存的数据
|
||||
def read_to_csv(self, symbol):
|
||||
# 文件夹路径和文件路径
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
symbol = "".join(re.findall("[a-zA-Z]", str(symbol)))
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}traderdata.csv")
|
||||
# 如果文件夹不存在则创建
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
|
||||
# 读取保留的模型数据CSV文件
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and self.kgdata is True:
|
||||
# 选择最后一行数据
|
||||
row = df.iloc[-1]
|
||||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||||
self.pos = int(row["pos"])
|
||||
self.short_trailing_stop_price = float(row["short_trailing_stop_price"])
|
||||
self.long_trailing_stop_price = float(row["long_trailing_stop_price"])
|
||||
self.sl_long_price = float(row["sl_long_price"])
|
||||
self.sl_shor_price = float(row["sl_shor_price"])
|
||||
# self.out_long = int(row['out_long'])
|
||||
# self.out_short = int(row['out_short'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
self.kgdata = False
|
||||
else:
|
||||
pass
|
||||
# print("没有找到历史交易数据文件", file_path)
|
||||
# 如果没有找到CSV，则初始化变量
|
||||
|
||||
pass
|
||||
|
||||
# 保存数据
|
||||
def save_to_csv(self, symbol):
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
symbol = "".join(re.findall("[a-zA-Z]", str(symbol)))
|
||||
# 创建DataFrame
|
||||
data = {
|
||||
"datetime": [trader_df["datetime"].iloc[-1]],
|
||||
"pos": [self.pos],
|
||||
"short_trailing_stop_price": [self.short_trailing_stop_price],
|
||||
"long_trailing_stop_price": [self.long_trailing_stop_price],
|
||||
"sl_long_price": [self.sl_long_price],
|
||||
"sl_shor_price": [self.sl_shor_price],
|
||||
# 'out_long': [self.out_long],
|
||||
# 'out_short': [self.out_short]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 将DataFrame保存到CSV文件
|
||||
df.to_csv(f"traderdata/{str(symbol)}traderdata.csv", index=False)
|
||||
|
||||
# 每日收盘重置数据
|
||||
def day_data_reset(self):
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围
|
||||
clearing_time1_start = s_time(15, 00)
|
||||
clearing_time1_end = s_time(15, 15)
|
||||
|
||||
# 第二时间范围
|
||||
clearing_time2_start = s_time(23, 0)
|
||||
clearing_time2_end = s_time(23, 15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
self.clearing_executed = False
|
||||
# 检查当前时间第一个操作的时间范围内
|
||||
if (
|
||||
clearing_time1_start <= current_time <= clearing_time1_end
|
||||
and not self.clearing_executed
|
||||
):
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.index, inplace=True) # 清除当天的行情数据
|
||||
|
||||
# 检查当前时间是否在第二个操作的时间范围内
|
||||
elif (
|
||||
clearing_time2_start <= current_time <= clearing_time2_end
|
||||
and not self.clearing_executed
|
||||
):
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.index, inplace=True) # 清除当天的行情数据
|
||||
else:
|
||||
self.clearing_executed = False
|
||||
pass
|
||||
return self.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def Join(self, tickdata):
|
||||
data = tickdata
|
||||
# print(tickdata)
|
||||
instrument_id = data["InstrumentID"] # 品种代码
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset()
|
||||
tickcome(data)
|
||||
# 新K线开始，启动交易程序 and 保存行情数据
|
||||
if len(trader_df) > self.cont_df:
|
||||
# 检查文件是否存在
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
trader_df.tail(1).to_csv(
|
||||
csv_file_path, mode="a", header=False, index=False
|
||||
)
|
||||
else:
|
||||
# 创建新文件并保存整个DataFrame
|
||||
trader_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if self.long_trailing_stop_price > 0 and self.pos > 0:
|
||||
# print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = (
|
||||
trader_df["low"].iloc[-1]
|
||||
if self.long_trailing_stop_price < trader_df["low"].iloc[-1]
|
||||
else self.long_trailing_stop_price
|
||||
)
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price > 0 and self.pos < 0:
|
||||
# print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = (
|
||||
trader_df["high"].iloc[-1]
|
||||
if trader_df["high"].iloc[-1] < self.short_trailing_stop_price
|
||||
else self.short_trailing_stop_price
|
||||
)
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long = self.long_trailing_stop_price * (
|
||||
1 - self.trailing_stop_percent
|
||||
)
|
||||
self.out_short = self.short_trailing_stop_price * (
|
||||
1 + self.trailing_stop_percent
|
||||
)
|
||||
# print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
# print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long > 0:
|
||||
if (
|
||||
trader_df["low"].iloc[-1] < self.out_long
|
||||
and self.pos > 0
|
||||
and self.sl_long_price > 0
|
||||
and trader_df["low"].iloc[-1] > self.sl_long_price
|
||||
):
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"多头止盈",
|
||||
"TR",
|
||||
self.out_long,
|
||||
"low",
|
||||
trader_df["low"].iloc[-1],
|
||||
)
|
||||
# 平多
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"3",
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.out_long = 0
|
||||
self.sl_long_price = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if self.out_short > 0:
|
||||
if (
|
||||
trader_df["high"].iloc[-1] > self.out_short
|
||||
and self.pos < 0
|
||||
and self.sl_shor_price > 0
|
||||
and trader_df["high"].iloc[-1] < self.sl_shor_price
|
||||
):
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"空头止盈: ",
|
||||
"TR",
|
||||
self.out_short,
|
||||
"high",
|
||||
trader_df["high"].iloc[-1],
|
||||
)
|
||||
# 平空
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"3",
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_shor = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (
|
||||
1 - self.fixed_stop_loss_percent
|
||||
)
|
||||
if (
|
||||
self.sl_long_price > 0
|
||||
and self.fixed_stop_loss_L > 0
|
||||
and self.pos > 0
|
||||
and trader_df["close"].iloc[-1] < self.fixed_stop_loss_L
|
||||
):
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"多头止损",
|
||||
"SL",
|
||||
self.fixed_stop_loss_L,
|
||||
"close",
|
||||
trader_df["close"].iloc[-1],
|
||||
)
|
||||
# 平多
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"3",
|
||||
)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (
|
||||
1 + self.fixed_stop_loss_percent
|
||||
)
|
||||
if (
|
||||
self.sl_shor_price > 0
|
||||
and self.fixed_stop_loss_S > 0
|
||||
and self.pos < 0
|
||||
and trader_df["close"].iloc[-1] > self.fixed_stop_loss_S
|
||||
):
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"空头止损",
|
||||
"SL",
|
||||
self.fixed_stop_loss_S,
|
||||
"close",
|
||||
trader_df["close"].iloc[-1],
|
||||
)
|
||||
# 平空
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"3",
|
||||
)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price = 0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 日均线
|
||||
trader_df["dayma"] = trader_df["close"].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trader_df["delta"] = trader_df["delta"].astype(float)
|
||||
trader_df["delta累计"] = trader_df["delta"].cumsum()
|
||||
|
||||
# 大于日均线
|
||||
开多1 = (
|
||||
trader_df["dayma"].iloc[-1] > 0
|
||||
and trader_df["close"].iloc[-1] > trader_df["dayma"].iloc[-1]
|
||||
)
|
||||
|
||||
# 累计多空净量大于X
|
||||
开多4 = (
|
||||
trader_df["delta累计"].iloc[-1] > 2000
|
||||
and trader_df["delta"].iloc[-1] > 1500
|
||||
)
|
||||
|
||||
# 小于日均线
|
||||
开空1 = (
|
||||
trader_df["dayma"].iloc[-1] > 0
|
||||
and trader_df["close"].iloc[-1] < trader_df["dayma"].iloc[-1]
|
||||
)
|
||||
|
||||
# 累计多空净量小于X
|
||||
开空4 = (
|
||||
trader_df["delta累计"].iloc[-1] < -2000
|
||||
and trader_df["delta"].iloc[-1] < -1500
|
||||
)
|
||||
|
||||
开多组合 = 开多1 and 开多4 and trader_df["dj"].iloc[-1] > self.dj_X
|
||||
开空条件 = 开空1 and 开空4 and trader_df["dj"].iloc[-1] < -self.dj_X
|
||||
|
||||
平多条件 = trader_df["dj"].iloc[-1] < -self.dj_X
|
||||
平空条件 = trader_df["dj"].iloc[-1] > self.dj_X
|
||||
# 开仓
|
||||
# 多头开仓条件
|
||||
if self.pos < 0 and 平空条件:
|
||||
print(
|
||||
"平空： ",
|
||||
"ExchangeID: ",
|
||||
data["ExchangeID"],
|
||||
"InstrumentID",
|
||||
data["InstrumentID"],
|
||||
"AskPrice1",
|
||||
data["AskPrice1"] + self.py,
|
||||
)
|
||||
# 平空
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"3",
|
||||
)
|
||||
self.pos = 0
|
||||
self.sl_shor_price = 0
|
||||
self.short_trailing_stop_price = 0
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"反手平空：",
|
||||
"平仓价格：",
|
||||
data["AskPrice1"] + self.py,
|
||||
"堆积数：",
|
||||
trader_df["dj"].iloc[-1],
|
||||
)
|
||||
self.save_to_csv(instrument_id)
|
||||
if self.pos == 0 and 开多组合:
|
||||
print(
|
||||
"开多： ",
|
||||
"ExchangeID: ",
|
||||
data["ExchangeID"],
|
||||
"InstrumentID",
|
||||
data["InstrumentID"],
|
||||
"AskPrice1",
|
||||
data["AskPrice1"] + self.py,
|
||||
)
|
||||
# 开多
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["AskPrice1"] + self.py,
|
||||
self.Lots,
|
||||
b"0",
|
||||
b"0",
|
||||
)
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"多头开仓",
|
||||
"开仓价格：",
|
||||
data["AskPrice1"] + self.py,
|
||||
"堆积数：",
|
||||
trader_df["dj"].iloc[-1],
|
||||
)
|
||||
self.pos = 1
|
||||
self.long_trailing_stop_price = data["AskPrice1"]
|
||||
self.sl_long_price = data["AskPrice1"]
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if self.pos > 0 and 平多条件:
|
||||
print(
|
||||
"平多： ",
|
||||
"ExchangeID: ",
|
||||
data["ExchangeID"],
|
||||
"InstrumentID",
|
||||
data["InstrumentID"],
|
||||
"BidPrice1",
|
||||
data["BidPrice1"] - self.py,
|
||||
)
|
||||
# 平多
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"1",
|
||||
)
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"3",
|
||||
)
|
||||
self.pos = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price = 0
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"反手平多",
|
||||
"平仓价格：",
|
||||
data["BidPrice1"] - self.py,
|
||||
"堆积数：",
|
||||
trader_df["dj"].iloc[-1],
|
||||
)
|
||||
self.save_to_csv(instrument_id)
|
||||
if self.pos == 0 and 开空条件:
|
||||
print(
|
||||
"开空： ",
|
||||
"ExchangeID: ",
|
||||
data["ExchangeID"],
|
||||
"InstrumentID",
|
||||
data["InstrumentID"],
|
||||
"BidPrice1",
|
||||
data["BidPrice1"],
|
||||
)
|
||||
# 开空
|
||||
self.insert_order(
|
||||
data["ExchangeID"],
|
||||
data["InstrumentID"],
|
||||
data["BidPrice1"] - self.py,
|
||||
self.Lots,
|
||||
b"1",
|
||||
b"0",
|
||||
)
|
||||
print(
|
||||
"datetime+sig: ",
|
||||
trader_df["datetime"].iloc[-1],
|
||||
"空头开仓",
|
||||
"开仓价格：",
|
||||
data["BidPrice1"] - self.py,
|
||||
"堆积数：",
|
||||
trader_df["dj"].iloc[-1],
|
||||
)
|
||||
self.pos = -1
|
||||
self.short_trailing_stop_price = data["BidPrice1"]
|
||||
self.sl_shor_price = data["BidPrice1"]
|
||||
self.save_to_csv(instrument_id)
|
||||
# print(trader_df)
|
||||
self.cont_df = len(trader_df)
|
||||
@@ -0,0 +1,279 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
|
||||
# from datetime import datetime
|
||||
import gzip
|
||||
import numpy as np
|
||||
import os
|
||||
import io
|
||||
from 专享08策略 import 专享08of # 导入您的 MyTrader 类
|
||||
|
||||
|
||||
class BacktestEngine:
|
||||
def __init__(self, trader_class, initial_capital=1000000):
|
||||
self.trader = trader_class()
|
||||
self.initial_capital = initial_capital
|
||||
self.equity_curve = []
|
||||
self.positions = {} # {instrument_id: {'long': {'today': 0, 'yesterday': 0}, 'short': {'today': 0, 'yesterday': 0}}}
|
||||
self.cash = initial_capital
|
||||
self.current_date = None
|
||||
|
||||
def run(self, data, start=0, end=None, start_date=None, end_date=None):
|
||||
for i, (_, row) in enumerate(data.iloc[start:end].iterrows()):
|
||||
tick = row.to_dict()
|
||||
|
||||
action_day = pd.to_datetime(tick["ActionDay"]).strftime("%Y-%m-%d")
|
||||
update_time = pd.to_datetime(tick["UpdateTime"]).strftime("%H:%M:%S")
|
||||
created_at = f"{action_day} {update_time}.{tick['UpdateMillisec']:03d}"
|
||||
|
||||
current_date = pd.to_datetime(created_at)
|
||||
|
||||
if start_date is not None and current_date < start_date:
|
||||
continue
|
||||
if end_date is not None and current_date > end_date:
|
||||
break
|
||||
|
||||
tick_date = pd.to_datetime(created_at).date()
|
||||
if self.current_date is None or tick_date != self.current_date:
|
||||
self.update_positions_day()
|
||||
self.current_date = tick_date
|
||||
|
||||
self.trader.Join(tickdata=tick)
|
||||
self.update_account(created_at, tick["LastPrice"], tick["InstrumentID"])
|
||||
|
||||
def update_positions_day(self):
|
||||
for position in self.positions.values():
|
||||
position["long"]["yesterday"] += position["long"]["today"]
|
||||
position["long"]["today"] = 0
|
||||
position["short"]["yesterday"] += position["short"]["today"]
|
||||
position["short"]["today"] = 0
|
||||
|
||||
def update_account(self, datetime, last_price, instrument_id):
|
||||
position_value = 0
|
||||
for inst, pos in self.positions.items():
|
||||
price = (
|
||||
last_price
|
||||
if inst == instrument_id
|
||||
else self.positions[inst]["last_price"]
|
||||
)
|
||||
long_value = (pos["long"]["today"] + pos["long"]["yesterday"]) * price
|
||||
short_value = (pos["short"]["today"] + pos["short"]["yesterday"]) * price
|
||||
position_value += long_value - short_value
|
||||
current_equity = self.cash + position_value
|
||||
self.equity_curve.append((datetime, current_equity))
|
||||
|
||||
def mock_insert_order(
|
||||
self, exchange_id, instrument_id, price, volume, direction, offset
|
||||
):
|
||||
is_buy = direction == b"0"
|
||||
is_open = offset == b"0"
|
||||
is_close_today = offset == b"3"
|
||||
|
||||
if instrument_id not in self.positions:
|
||||
self.positions[instrument_id] = {
|
||||
"long": {"today": 0, "yesterday": 0},
|
||||
"short": {"today": 0, "yesterday": 0},
|
||||
"last_price": price,
|
||||
}
|
||||
|
||||
position = self.positions[instrument_id]
|
||||
|
||||
if is_open:
|
||||
if is_buy:
|
||||
position["long"]["today"] += volume
|
||||
self.cash -= price * volume
|
||||
else:
|
||||
position["short"]["today"] += volume
|
||||
self.cash += price * volume
|
||||
else: # close
|
||||
if is_buy: # buy to close short
|
||||
if is_close_today:
|
||||
position["short"]["today"] -= volume
|
||||
else:
|
||||
if position["short"]["yesterday"] >= volume:
|
||||
position["short"]["yesterday"] -= volume
|
||||
else:
|
||||
remaining = volume - position["short"]["yesterday"]
|
||||
position["short"]["yesterday"] = 0
|
||||
position["short"]["today"] -= remaining
|
||||
self.cash -= price * volume
|
||||
else: # sell to close long
|
||||
if is_close_today:
|
||||
position["long"]["today"] -= volume
|
||||
else:
|
||||
if position["long"]["yesterday"] >= volume:
|
||||
position["long"]["yesterday"] -= volume
|
||||
else:
|
||||
remaining = volume - position["long"]["yesterday"]
|
||||
position["long"]["yesterday"] = 0
|
||||
position["long"]["today"] -= remaining
|
||||
self.cash += price * volume
|
||||
|
||||
position["last_price"] = price
|
||||
|
||||
def calculate_performance(self):
|
||||
df = pd.DataFrame(self.equity_curve, columns=["time", "equity"])
|
||||
df["time"] = pd.to_datetime(df["time"])
|
||||
df.set_index("time", inplace=True)
|
||||
|
||||
if len(df) < 2:
|
||||
print("警告：回测数据点不足，无法计算性能指标。")
|
||||
return {
|
||||
"total_return": 0,
|
||||
"sharpe_ratio": 0,
|
||||
"max_drawdown": 0,
|
||||
"equity_curve": df,
|
||||
}
|
||||
|
||||
df["returns"] = df["equity"].pct_change()
|
||||
|
||||
total_return = (df["equity"].iloc[-1] - df["equity"].iloc[0]) / df[
|
||||
"equity"
|
||||
].iloc[0]
|
||||
sharpe_ratio = np.sqrt(len(df)) * df["returns"].mean() / df["returns"].std()
|
||||
|
||||
drawdown = df["equity"] / df["equity"].cummax() - 1
|
||||
max_drawdown = drawdown.min()
|
||||
|
||||
return {
|
||||
"total_return": total_return,
|
||||
"sharpe_ratio": sharpe_ratio,
|
||||
"max_drawdown": max_drawdown,
|
||||
"equity_curve": df,
|
||||
}
|
||||
|
||||
def plot_performance(self):
|
||||
performance = self.calculate_performance()
|
||||
equity_curve = performance["equity_curve"]
|
||||
|
||||
plt.figure(figsize=(12, 8))
|
||||
plt.plot(equity_curve.index, equity_curve["equity"])
|
||||
plt.title("Equity Curve")
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Equity")
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
|
||||
print(f"Total Return: {performance['total_return']:.6%}")
|
||||
print(f"Sharpe Ratio: {performance['sharpe_ratio']:.6f}")
|
||||
print(f"Max Drawdown: {performance['max_drawdown']:.6%}")
|
||||
|
||||
|
||||
# 定义中文表头到英文表头的映射
|
||||
header_mapping = {
|
||||
"交易日": "TradingDay",
|
||||
"合约代码": "InstrumentID",
|
||||
"交易所代码": "ExchangeID",
|
||||
"合约在交易所的代码": "ExchangeInstID",
|
||||
"最新价": "LastPrice",
|
||||
"上次结算价": "PreSettlementPrice",
|
||||
"昨收盘": "PreClosePrice",
|
||||
"昨持仓量": "PreOpenInterest",
|
||||
"今开盘": "OpenPrice",
|
||||
"最高价": "HighestPrice",
|
||||
"最低价": "LowestPrice",
|
||||
"数量": "Volume",
|
||||
"成交金额": "Turnover",
|
||||
"持仓量": "OpenInterest",
|
||||
"今收盘": "ClosePrice",
|
||||
"本次结算价": "SettlementPrice",
|
||||
"涨停板价": "UpperLimitPrice",
|
||||
"跌停板价": "LowerLimitPrice",
|
||||
"昨虚实度": "PreDelta",
|
||||
"今虚实度": "CurrDelta",
|
||||
"最后修改时间": "UpdateTime",
|
||||
"最后修改毫秒": "UpdateMillisec",
|
||||
"申买价一": "BidPrice1",
|
||||
"申买量一": "BidVolume1",
|
||||
"申卖价一": "AskPrice1",
|
||||
"申卖量一": "AskVolume1",
|
||||
"申买价二": "BidPrice2",
|
||||
"申买量二": "BidVolume2",
|
||||
"申卖价二": "AskPrice2",
|
||||
"申卖量二": "AskVolume2",
|
||||
"申买价三": "BidPrice3",
|
||||
"申买量三": "BidVolume3",
|
||||
"申卖价三": "AskPrice3",
|
||||
"申卖量三": "AskVolume3",
|
||||
"申买价四": "BidPrice4",
|
||||
"申买量四": "BidVolume4",
|
||||
"申卖价四": "AskPrice4",
|
||||
"申卖量四": "AskVolume4",
|
||||
"申买价五": "BidPrice5",
|
||||
"申买量五": "BidVolume5",
|
||||
"申卖价五": "AskPrice5",
|
||||
"申卖量五": "AskVolume5",
|
||||
"当日均价": "AveragePrice",
|
||||
"业务日期": "ActionDay",
|
||||
}
|
||||
|
||||
|
||||
def load_and_process_data(folder_path):
|
||||
dfs = []
|
||||
for filename in os.listdir(folder_path):
|
||||
file_path = os.path.join(folder_path, filename)
|
||||
|
||||
try:
|
||||
if filename.endswith(".gz"):
|
||||
# 处理 GZ 文件
|
||||
with gzip.open(file_path, "rt", encoding="gbk") as gz_file:
|
||||
csv_data = io.StringIO(gz_file.read())
|
||||
df = pd.read_csv(csv_data, parse_dates=["业务日期", "最后修改时间"])
|
||||
elif filename.endswith(".csv"):
|
||||
# 处理 CSV 文件
|
||||
df = pd.read_csv(
|
||||
file_path,
|
||||
encoding="utf-8",
|
||||
parse_dates=["业务日期", "最后修改时间"],
|
||||
)
|
||||
else:
|
||||
# 跳过非 GZ 和非 CSV 文件
|
||||
print(f"Skipping {filename}: not a GZ or CSV file")
|
||||
continue
|
||||
|
||||
# 重命名列
|
||||
df.rename(columns=header_mapping, inplace=True)
|
||||
|
||||
dfs.append(df)
|
||||
print(f"Successfully read {filename}")
|
||||
except Exception as e:
|
||||
print(f"Error reading {filename}: {str(e)}")
|
||||
print("Skipping this file.")
|
||||
continue
|
||||
|
||||
if dfs:
|
||||
data = pd.concat(dfs, ignore_index=True)
|
||||
data.sort_values(["ActionDay", "UpdateTime", "UpdateMillisec"], inplace=True)
|
||||
data = data.reset_index(drop=True) # 重置索引
|
||||
return data
|
||||
else:
|
||||
print("没有找到可读取的GZ或CSV文件")
|
||||
return None
|
||||
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
# 使用示例
|
||||
folder_path = "./回测数据" # 替换为您的数据文件夹路径
|
||||
# 读取，排序，合并tick数据
|
||||
data = load_and_process_data(folder_path)
|
||||
print(data)
|
||||
if data is not None:
|
||||
# 初始化回测引擎,设置策略和初始资金
|
||||
backtest = BacktestEngine(专享08of, initial_capital=10000)
|
||||
|
||||
# 替换MyTrader中的insert_order方法
|
||||
backtest.trader.insert_order = backtest.mock_insert_order
|
||||
|
||||
# # 运行回测
|
||||
# backtest.run(data)
|
||||
|
||||
backtest.run(
|
||||
data,
|
||||
# start=1000,
|
||||
# end=3000,
|
||||
# start_date=pd.to_datetime('2023-01-01'),
|
||||
# end_date=pd.to_datetime('2023-01-31')
|
||||
)
|
||||
# 显示回测结果
|
||||
backtest.plot_performance()
|
||||
@@ -0,0 +1,279 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
|
||||
# from datetime import datetime
|
||||
import gzip
|
||||
import numpy as np
|
||||
import os
|
||||
import io
|
||||
from 专享08策略 import 专享08of # 导入您的 MyTrader 类
|
||||
|
||||
|
||||
class BacktestEngine:
|
||||
def __init__(self, trader_class, initial_capital=1000000):
|
||||
self.trader = trader_class()
|
||||
self.initial_capital = initial_capital
|
||||
self.equity_curve = []
|
||||
self.positions = {} # {instrument_id: {'long': {'today': 0, 'yesterday': 0}, 'short': {'today': 0, 'yesterday': 0}}}
|
||||
self.cash = initial_capital
|
||||
self.current_date = None
|
||||
|
||||
def run(self, data, start=0, end=None, start_date=None, end_date=None):
|
||||
for i, (_, row) in enumerate(data.iloc[start:end].iterrows()):
|
||||
tick = row.to_dict()
|
||||
|
||||
action_day = pd.to_datetime(tick["ActionDay"]).strftime("%Y-%m-%d")
|
||||
update_time = pd.to_datetime(tick["UpdateTime"]).strftime("%H:%M:%S")
|
||||
created_at = f"{action_day} {update_time}.{tick['UpdateMillisec']:03d}"
|
||||
|
||||
current_date = pd.to_datetime(created_at)
|
||||
|
||||
if start_date is not None and current_date < start_date:
|
||||
continue
|
||||
if end_date is not None and current_date > end_date:
|
||||
break
|
||||
|
||||
tick_date = pd.to_datetime(created_at).date()
|
||||
if self.current_date is None or tick_date != self.current_date:
|
||||
self.update_positions_day()
|
||||
self.current_date = tick_date
|
||||
|
||||
self.trader.Join(tickdata=tick)
|
||||
self.update_account(created_at, tick["LastPrice"], tick["InstrumentID"])
|
||||
|
||||
def update_positions_day(self):
|
||||
for position in self.positions.values():
|
||||
position["long"]["yesterday"] += position["long"]["today"]
|
||||
position["long"]["today"] = 0
|
||||
position["short"]["yesterday"] += position["short"]["today"]
|
||||
position["short"]["today"] = 0
|
||||
|
||||
def update_account(self, datetime, last_price, instrument_id):
|
||||
position_value = 0
|
||||
for inst, pos in self.positions.items():
|
||||
price = (
|
||||
last_price
|
||||
if inst == instrument_id
|
||||
else self.positions[inst]["last_price"]
|
||||
)
|
||||
long_value = (pos["long"]["today"] + pos["long"]["yesterday"]) * price
|
||||
short_value = (pos["short"]["today"] + pos["short"]["yesterday"]) * price
|
||||
position_value += long_value - short_value
|
||||
current_equity = self.cash + position_value
|
||||
self.equity_curve.append((datetime, current_equity))
|
||||
|
||||
def mock_insert_order(
|
||||
self, exchange_id, instrument_id, price, volume, direction, offset
|
||||
):
|
||||
is_buy = direction == b"0"
|
||||
is_open = offset == b"0"
|
||||
is_close_today = offset == b"3"
|
||||
|
||||
if instrument_id not in self.positions:
|
||||
self.positions[instrument_id] = {
|
||||
"long": {"today": 0, "yesterday": 0},
|
||||
"short": {"today": 0, "yesterday": 0},
|
||||
"last_price": price,
|
||||
}
|
||||
|
||||
position = self.positions[instrument_id]
|
||||
|
||||
if is_open:
|
||||
if is_buy:
|
||||
position["long"]["today"] += volume
|
||||
self.cash -= price * volume
|
||||
else:
|
||||
position["short"]["today"] += volume
|
||||
self.cash += price * volume
|
||||
else: # close
|
||||
if is_buy: # buy to close short
|
||||
if is_close_today:
|
||||
position["short"]["today"] -= volume
|
||||
else:
|
||||
if position["short"]["yesterday"] >= volume:
|
||||
position["short"]["yesterday"] -= volume
|
||||
else:
|
||||
remaining = volume - position["short"]["yesterday"]
|
||||
position["short"]["yesterday"] = 0
|
||||
position["short"]["today"] -= remaining
|
||||
self.cash -= price * volume
|
||||
else: # sell to close long
|
||||
if is_close_today:
|
||||
position["long"]["today"] -= volume
|
||||
else:
|
||||
if position["long"]["yesterday"] >= volume:
|
||||
position["long"]["yesterday"] -= volume
|
||||
else:
|
||||
remaining = volume - position["long"]["yesterday"]
|
||||
position["long"]["yesterday"] = 0
|
||||
position["long"]["today"] -= remaining
|
||||
self.cash += price * volume
|
||||
|
||||
position["last_price"] = price
|
||||
|
||||
def calculate_performance(self):
|
||||
df = pd.DataFrame(self.equity_curve, columns=["time", "equity"])
|
||||
df["time"] = pd.to_datetime(df["time"])
|
||||
df.set_index("time", inplace=True)
|
||||
|
||||
if len(df) < 2:
|
||||
print("警告：回测数据点不足，无法计算性能指标。")
|
||||
return {
|
||||
"total_return": 0,
|
||||
"sharpe_ratio": 0,
|
||||
"max_drawdown": 0,
|
||||
"equity_curve": df,
|
||||
}
|
||||
|
||||
df["returns"] = df["equity"].pct_change()
|
||||
|
||||
total_return = (df["equity"].iloc[-1] - df["equity"].iloc[0]) / df[
|
||||
"equity"
|
||||
].iloc[0]
|
||||
sharpe_ratio = np.sqrt(len(df)) * df["returns"].mean() / df["returns"].std()
|
||||
|
||||
drawdown = df["equity"] / df["equity"].cummax() - 1
|
||||
max_drawdown = drawdown.min()
|
||||
|
||||
return {
|
||||
"total_return": total_return,
|
||||
"sharpe_ratio": sharpe_ratio,
|
||||
"max_drawdown": max_drawdown,
|
||||
"equity_curve": df,
|
||||
}
|
||||
|
||||
def plot_performance(self):
|
||||
performance = self.calculate_performance()
|
||||
equity_curve = performance["equity_curve"]
|
||||
|
||||
plt.figure(figsize=(12, 8))
|
||||
plt.plot(equity_curve.index, equity_curve["equity"])
|
||||
plt.title("Equity Curve")
|
||||
plt.xlabel("Time")
|
||||
plt.ylabel("Equity")
|
||||
plt.grid(True)
|
||||
plt.show()
|
||||
|
||||
print(f"Total Return: {performance['total_return']:.6%}")
|
||||
print(f"Sharpe Ratio: {performance['sharpe_ratio']:.6f}")
|
||||
print(f"Max Drawdown: {performance['max_drawdown']:.6%}")
|
||||
|
||||
|
||||
# 定义中文表头到英文表头的映射
|
||||
header_mapping = {
|
||||
"交易日": "TradingDay",
|
||||
"合约代码": "InstrumentID",
|
||||
"交易所代码": "ExchangeID",
|
||||
"合约在交易所的代码": "ExchangeInstID",
|
||||
"最新价": "LastPrice",
|
||||
"上次结算价": "PreSettlementPrice",
|
||||
"昨收盘": "PreClosePrice",
|
||||
"昨持仓量": "PreOpenInterest",
|
||||
"今开盘": "OpenPrice",
|
||||
"最高价": "HighestPrice",
|
||||
"最低价": "LowestPrice",
|
||||
"数量": "Volume",
|
||||
"成交金额": "Turnover",
|
||||
"持仓量": "OpenInterest",
|
||||
"今收盘": "ClosePrice",
|
||||
"本次结算价": "SettlementPrice",
|
||||
"涨停板价": "UpperLimitPrice",
|
||||
"跌停板价": "LowerLimitPrice",
|
||||
"昨虚实度": "PreDelta",
|
||||
"今虚实度": "CurrDelta",
|
||||
"最后修改时间": "UpdateTime",
|
||||
"最后修改毫秒": "UpdateMillisec",
|
||||
"申买价一": "BidPrice1",
|
||||
"申买量一": "BidVolume1",
|
||||
"申卖价一": "AskPrice1",
|
||||
"申卖量一": "AskVolume1",
|
||||
"申买价二": "BidPrice2",
|
||||
"申买量二": "BidVolume2",
|
||||
"申卖价二": "AskPrice2",
|
||||
"申卖量二": "AskVolume2",
|
||||
"申买价三": "BidPrice3",
|
||||
"申买量三": "BidVolume3",
|
||||
"申卖价三": "AskPrice3",
|
||||
"申卖量三": "AskVolume3",
|
||||
"申买价四": "BidPrice4",
|
||||
"申买量四": "BidVolume4",
|
||||
"申卖价四": "AskPrice4",
|
||||
"申卖量四": "AskVolume4",
|
||||
"申买价五": "BidPrice5",
|
||||
"申买量五": "BidVolume5",
|
||||
"申卖价五": "AskPrice5",
|
||||
"申卖量五": "AskVolume5",
|
||||
"当日均价": "AveragePrice",
|
||||
"业务日期": "ActionDay",
|
||||
}
|
||||
|
||||
|
||||
def load_and_process_data(folder_path):
|
||||
dfs = []
|
||||
for filename in os.listdir(folder_path):
|
||||
file_path = os.path.join(folder_path, filename)
|
||||
|
||||
try:
|
||||
if filename.endswith(".gz"):
|
||||
# 处理 GZ 文件
|
||||
with gzip.open(file_path, "rt", encoding="gbk") as gz_file:
|
||||
csv_data = io.StringIO(gz_file.read())
|
||||
df = pd.read_csv(csv_data, parse_dates=["业务日期", "最后修改时间"])
|
||||
elif filename.endswith(".csv"):
|
||||
# 处理 CSV 文件
|
||||
df = pd.read_csv(
|
||||
file_path,
|
||||
encoding="utf-8",
|
||||
parse_dates=["业务日期", "最后修改时间"],
|
||||
)
|
||||
else:
|
||||
# 跳过非 GZ 和非 CSV 文件
|
||||
print(f"Skipping {filename}: not a GZ or CSV file")
|
||||
continue
|
||||
|
||||
# 重命名列
|
||||
df.rename(columns=header_mapping, inplace=True)
|
||||
|
||||
dfs.append(df)
|
||||
print(f"Successfully read {filename}")
|
||||
except Exception as e:
|
||||
print(f"Error reading {filename}: {str(e)}")
|
||||
print("Skipping this file.")
|
||||
continue
|
||||
|
||||
if dfs:
|
||||
data = pd.concat(dfs, ignore_index=True)
|
||||
data.sort_values(["ActionDay", "UpdateTime", "UpdateMillisec"], inplace=True)
|
||||
data = data.reset_index(drop=True) # 重置索引
|
||||
return data
|
||||
else:
|
||||
print("没有找到可读取的GZ或CSV文件")
|
||||
return None
|
||||
|
||||
|
||||
# 使用示例
|
||||
if __name__ == "__main__":
|
||||
# 使用示例
|
||||
folder_path = "./回测数据" # 替换为您的数据文件夹路径
|
||||
# 读取，排序，合并tick数据
|
||||
data = load_and_process_data(folder_path)
|
||||
print(data)
|
||||
if data is not None:
|
||||
# 初始化回测引擎,设置策略和初始资金
|
||||
backtest = BacktestEngine(专享08of, initial_capital=10000)
|
||||
|
||||
# 替换MyTrader中的insert_order方法
|
||||
backtest.trader.insert_order = backtest.mock_insert_order
|
||||
|
||||
# # 运行回测
|
||||
# backtest.run(data)
|
||||
|
||||
backtest.run(
|
||||
data,
|
||||
# start=1000,
|
||||
# end=3000,
|
||||
# start_date=pd.to_datetime('2023-01-01'),
|
||||
# end_date=pd.to_datetime('2023-01-31')
|
||||
)
|
||||
# 显示回测结果
|
||||
backtest.plot_performance()
|
||||
@@ -0,0 +1,95 @@
|
||||
import os
|
||||
from chardet.universaldetector import UniversalDetector
|
||||
import chardet
|
||||
|
||||
|
||||
def get_filelist(path):
|
||||
"""
|
||||
获取路径下所有csv文件的路径列表
|
||||
"""
|
||||
Filelist = []
|
||||
for home, dirs, files in os.walk(path):
|
||||
for filename in files:
|
||||
if ".csv" in filename:
|
||||
Filelist.append(os.path.join(home, filename))
|
||||
return Filelist
|
||||
|
||||
|
||||
def read_file(file):
|
||||
"""
|
||||
逐个读取文件的内容
|
||||
"""
|
||||
with open(file, 'rb') as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
def get_encode_info(file):
|
||||
"""
|
||||
逐个读取文件的编码方式
|
||||
"""
|
||||
with open(file, 'rb') as f:
|
||||
# data = f.read()
|
||||
# detected_encoding = chardet.detect(data)['encoding']
|
||||
detector = UniversalDetector()
|
||||
for line in f.readlines():
|
||||
detector.feed(line)
|
||||
if detector.done:
|
||||
break
|
||||
detector.close()
|
||||
# return detected_encoding
|
||||
return detector.result['encoding']
|
||||
# return detected_encoding
|
||||
|
||||
|
||||
def convert_encode2gbk(file, original_encode, des_encode):
|
||||
"""
|
||||
将文件的编码方式转换为gbk，并写入原先的文件中。
|
||||
"""
|
||||
file_content = read_file(file)
|
||||
file_decode = file_content.decode(original_encode, 'ignore')
|
||||
file_encode = file_decode.encode(des_encode)
|
||||
with open(file, 'wb') as f:
|
||||
f.write(file_encode)
|
||||
|
||||
|
||||
def read_and_convert(path):
|
||||
"""
|
||||
读取文件并转换
|
||||
"""
|
||||
Filelist = get_filelist(path=path)
|
||||
fileNum= 0
|
||||
for filename in Filelist:
|
||||
try:
|
||||
file_content = read_file(filename)
|
||||
print("filename:", filename)
|
||||
encode_info = get_encode_info(filename)
|
||||
print("encode_info", encode_info)
|
||||
if encode_info != 'gbk':
|
||||
fileNum +=1
|
||||
convert_encode2gbk(filename, encode_info, 'gbk')
|
||||
print('成功转换 %s 个文件 %s '%(fileNum,filename))
|
||||
except BaseException:
|
||||
print(filename,'存在问题，请检查！')
|
||||
|
||||
|
||||
def recheck_again(path):
|
||||
"""
|
||||
再次判断文件是否为gbk
|
||||
"""
|
||||
print('---------------------以下文件仍存在问题---------------------')
|
||||
Filelist = get_filelist(path)
|
||||
for filename in Filelist:
|
||||
encode_info_ch = get_encode_info(filename)
|
||||
if encode_info_ch != 'gbk':
|
||||
print(filename,'的编码方式是：',encode_info_ch)
|
||||
|
||||
|
||||
print('--------------------------检查结束--------------------------')
|
||||
# if __name__ == "__main__":
|
||||
# """
|
||||
# 输入文件路径
|
||||
# """
|
||||
# path = r"D:\data"
|
||||
# read_and_convert(path)
|
||||
# recheck_again(path)
|
||||
# print('转换结束！')
|
||||
@@ -0,0 +1,515 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2d85dda4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"from file_format_conversion import file_format_conversion\n",
|
||||
"from ffc import read_and_convert, recheck_again"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fe51b707",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"new_directory = \"D:/data_all/doing\" # \"E:/data/大商所/test\"\n",
|
||||
"os.chdir(new_directory) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d2694b45",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"read_and_convert(new_directory)\n",
|
||||
"recheck_again(new_directory)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f6ba7f98",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 文件格式检查并转换替换\n",
|
||||
"for root, dirs, files in os.walk('.'):\n",
|
||||
" if len(dirs) > 0:\n",
|
||||
" for dir in dirs:\n",
|
||||
" # 获取二级子文件夹中的所有 CSV 文件\n",
|
||||
" all_csv_files = [os.path.join(dir, file) for file in os.listdir(dir) if file.endswith('.csv')]\n",
|
||||
"\n",
|
||||
" sp_old_chars = ['_2019', '_2020', '_2021']\n",
|
||||
" for sp_old_char in sp_old_chars:\n",
|
||||
" csv_old_files = [sp_file for sp_file in all_csv_files if sp_old_char in sp_file]\n",
|
||||
" if len(csv_old_files) > 0:\n",
|
||||
" old_df, old_code_value = file_format_conversion(csv_old_files, sp_old_char)\n",
|
||||
"\n",
|
||||
" # sp_new_chars = ['_2022', '_2023']\n",
|
||||
" # for sp_new_char in sp_new_chars:\n",
|
||||
" # csv_new_files = [sp_file for sp_file in all_csv_files if sp_new_char in sp_file]\n",
|
||||
" # if len(csv_new_files) > 0:\n",
|
||||
" # new_df, new_code_value = file_format_conversion(csv_new_files, sp_new_char)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d9c211c7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import chardet\n",
|
||||
"csv_file = \"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\"\n",
|
||||
"with open(csv_file, 'rb') as f:\n",
|
||||
" data = f.read()\n",
|
||||
"\n",
|
||||
"detected_encoding = chardet.detect(data)['encoding']\n",
|
||||
"print(\"当前文件编码格式:\", detected_encoding)\n",
|
||||
"\n",
|
||||
"if detected_encoding and detected_encoding != 'gbk':\n",
|
||||
" print(\"当前文件不为gbk格式:\", csv_file)\n",
|
||||
" print(\"当前文件编码格式:\", detected_encoding)\n",
|
||||
" # convert_csv_to_gbk(csv_file,detected_encoding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e5cf73aa",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"# 读取 ISO-8859-1 编码的 CSV 文件\n",
|
||||
"df = pd.read_csv('D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv', encoding='iso-8859-1', error_bad_lines= False)\n",
|
||||
"\n",
|
||||
"# # 将数据框转换为 GBK 编码\n",
|
||||
"# df = df.to_csv('D:/data_all/doing/bu主力连续/bu主力连续_20190530_bak.csv', index=False, encoding='gbk')\n",
|
||||
"\n",
|
||||
"# # 替换原始 CSV 文件\n",
|
||||
"# os.replace('D:/data_all/doing/bu主力连续/bu主力连续_20190530_bak.csv', 'D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv')\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3d81f4d0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "714d5286",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = df.to_csv('D:/data_all/doing/bu主力连续/bu主力连续_20190530_bak.csv', index=False, encoding='gbk')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3d4ff2c6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"f.close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ebc8f7ab",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"csv_file = \"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\"\n",
|
||||
"csv_df = pd.read_csv(csv_file,encoding=\"ISO-8859-1\")#detected_encoding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1beee390",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import codecs\n",
|
||||
" \n",
|
||||
"def convert_encoding(input_str, input_encoding='iso-8859-1', output_encoding='gbk'):\n",
|
||||
" return codecs.encode(input_str, output_encoding, input_encoding)\n",
|
||||
" \n",
|
||||
"input_str = csv_df\n",
|
||||
"converted_str = convert_encoding(input_str, 'iso-8859-1', 'gbk')\n",
|
||||
"print(converted_str)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d8d36cfc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import csv\n",
|
||||
"import codecs\n",
|
||||
"from csv import reader\n",
|
||||
"from csv import writer\n",
|
||||
" \n",
|
||||
"# 指定原始文件和目标文件路径\n",
|
||||
"input_file_path = 'D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv'\n",
|
||||
"output_file_path = 'D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv'\n",
|
||||
" \n",
|
||||
"# 打开原始文件和目标文件\n",
|
||||
"with open(input_file_path, 'r', encoding='iso-8859-1') as input_file, \\\n",
|
||||
" codecs.open(output_file_path, 'w', 'gbk') as output_file:\n",
|
||||
" # 创建读取器和写入器\n",
|
||||
" input_reader = reader(input_file)\n",
|
||||
" output_writer = writer(output_file)\n",
|
||||
" \n",
|
||||
" # 读取并写入数据\n",
|
||||
" for row in input_reader:\n",
|
||||
" output_writer.writerow(row)\n",
|
||||
" \n",
|
||||
"print(f'文件编码从ISO-8859-1转换为GBK，已保存为 {output_file_path}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7c814155",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import csv\n",
|
||||
"import codecs\n",
|
||||
"import os\n",
|
||||
" \n",
|
||||
"input_file_name = 'D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv'\n",
|
||||
"output_file_name = 'D:/data_all/doing/bu主力连续/bu主力连续_20190530_bak.csv'\n",
|
||||
" \n",
|
||||
"# 打开原始ISO-8859-1编码的CSV文件\n",
|
||||
"with codecs.open(input_file_name, 'r', 'iso-8859-1') as input_file:\n",
|
||||
" reader = csv.reader(input_file)\n",
|
||||
" # 打开目标GBK编码的CSV文件进行写入\n",
|
||||
" with open(output_file_name, 'w', newline='', encoding='utf-8') as output_file:\n",
|
||||
" writer = csv.writer(output_file)\n",
|
||||
" for row in reader:\n",
|
||||
" writer.writerow(row)\n",
|
||||
" \n",
|
||||
"# 删除原始文件\n",
|
||||
"os.remove(input_file_name)\n",
|
||||
"# 重命名新文件为原始文件名\n",
|
||||
"os.rename(output_file_name, input_file_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "fb75d9a0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv(\"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\",encoding=\"gbk\")\n",
|
||||
"\n",
|
||||
"# df.to_csv(\"D:/data_all/doing/bu主力连续/bu主力连续_20190530_bak.csv\", encoding=\"gbk\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b9687682",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4e300950",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(csv_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ddf98c52",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.remove(csv_file)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "364add14",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import csv\n",
|
||||
"import codecs\n",
|
||||
" \n",
|
||||
"# # 输入输出文件路径\n",
|
||||
"# input_file_path = 'utf8_file.csv'\n",
|
||||
"# output_file_path = 'gbk_file.csv'\n",
|
||||
" \n",
|
||||
"# 打开UTF-8编码的CSV文件进行读取\n",
|
||||
"with open(csv_file, 'r', encoding= detected_encoding) as input_file:\n",
|
||||
" reader = csv.reader(csv_file)\n",
|
||||
" os.remove(csv_file)\n",
|
||||
" # 使用codecs打开GBK编码的CSV文件进行写入\n",
|
||||
" with codecs.open(csv_file, 'w', encoding='gbk') as output_file:\n",
|
||||
" writer = csv.writer(csv_file)\n",
|
||||
" for row in reader:\n",
|
||||
" writer.writerow(row)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f6f546c6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"csv_df.to_csv(csv_file, index=False, encoding='gbk')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a028b88e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 列错读取错误文件\n",
|
||||
"当前读取文件读取错误： bu主力连续\\bu主力连续_20190530.csv\n",
|
||||
"当前读取文件读取错误： bu主力连续\\bu主力连续_20190701.csv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "200715a8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_csv(\n",
|
||||
" \"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\", \n",
|
||||
" header=0,\n",
|
||||
" # usecols=[ 1, 2, 3, 7, 12, 13, 14, 15],\n",
|
||||
" # names=[\n",
|
||||
" # \"合约代码\",\n",
|
||||
" # \"时间\",\n",
|
||||
" # \"最新\",\n",
|
||||
" # \"成交量\",\n",
|
||||
" # \"买一价\",\n",
|
||||
" # \"卖一价\",\n",
|
||||
" # \"买一量\",\n",
|
||||
" # \"卖一量\",\n",
|
||||
" # ],\n",
|
||||
" encoding='ISO-8859-1',#ISO-8859-1\n",
|
||||
" # skiprows=0,\n",
|
||||
" # parse_dates=['时间']\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a8de54b6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from chardet.universaldetector import UniversalDetector\n",
|
||||
"import chardet\n",
|
||||
"with open(\"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\", 'rb') as f:\n",
|
||||
" data = f.read()\n",
|
||||
" detected_encoding = chardet.detect(data)['encoding']\n",
|
||||
"print(detected_encoding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"id": "d874e66b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd \n",
|
||||
"import chardet \n",
|
||||
"# 读取CSV文件 \n",
|
||||
"df = pd.read_csv('D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv',encoding=\"ISO-8859-1\", error_bad_lines=False, warn_bad_lines=True) \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"id": "55c69d0a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CSV文件字符编码为: ISO-8859-1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 检测CSV文件字符编码 \n",
|
||||
"with open('D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv', 'rb') as f: \n",
|
||||
" result = chardet.detect(f.read()) \n",
|
||||
" encoding = result['encoding'] \n",
|
||||
" print('CSV文件字符编码为:', encoding) \n",
|
||||
" # 转换CSV文件编码格式 \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"id": "bee6800a",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CSV文件已转换为GBK编码格式\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"if encoding != 'GBK': \n",
|
||||
" df.to_csv('D:/data_all/doing/bu主力连续/bu主力连续_20190530_gbk.csv', encoding='utf-8', index=False) \n",
|
||||
" print('CSV文件已转换为GBK编码格式') \n",
|
||||
"else: print('CSV文件无需转换')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3c61930c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#'gbk' codec can't decode byte 0xc3 in position 189076: illegal multibyte sequence\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1b22d988",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = df.sort_values(by = ['datetime'], ascending=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cc14352a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['datetime'] = sorted(df['datetime'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2dc49f0c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "befec0c4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import chardet"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6397fffb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 假设file_path是你要读取的文件路径\n",
|
||||
"with open(\"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\", 'rb') as file:\n",
|
||||
" data = file.read()\n",
|
||||
" \n",
|
||||
"# 使用chardet检测编码\n",
|
||||
"detected_encoding = chardet.detect(data)['encoding']\n",
|
||||
" \n",
|
||||
"# # 如果检测到的编码不是gbk，可以尝试转换编码后再读取\n",
|
||||
"# if detected_encoding and detected_encoding != 'gbk':\n",
|
||||
"# with open(file_path, 'rb') as file:\n",
|
||||
"# data = file.read().decode(detected_encoding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c2433c60",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(detected_encoding)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,66 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
# from datetime import time as s_time
|
||||
# from datetime import datetime
|
||||
import chardet
|
||||
import csv
|
||||
|
||||
# has_common_keys(commodity_day_dict, commodity_night_dict,financial_time_dict)
|
||||
|
||||
# import chardet
|
||||
|
||||
# # 假设file_path是你要读取的文件路径
|
||||
# with open(file_path, 'rb') as file:
|
||||
# data = file.read()
|
||||
|
||||
# # 使用chardet检测编码
|
||||
# detected_encoding = chardet.detect(data)['encoding']
|
||||
|
||||
# # 如果检测到的编码不是gbk，可以尝试转换编码后再读取
|
||||
# if detected_encoding and detected_encoding != 'gbk':
|
||||
# with open(file_path, 'rb') as file:
|
||||
# data = file.read().decode(detected_encoding)
|
||||
|
||||
|
||||
def file_format_conversion(all_csv_files, sp_char):
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
merged_df = pd.DataFrame()
|
||||
dir = os.getcwd()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for csv_file in csv_files:
|
||||
|
||||
# file_path = os.path.join(dir, file)
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
with open(csv_file, 'rb') as f:
|
||||
data = f.read()
|
||||
|
||||
detected_encoding = chardet.detect(data)['encoding']
|
||||
|
||||
if detected_encoding and detected_encoding != 'gbk':
|
||||
print("当前文件不为gbk格式:", csv_file)
|
||||
convert_csv_to_gbk(csv_file,detected_encoding)
|
||||
|
||||
# with open(file_path, 'rb') as file:
|
||||
# data = file.read().decode(detected_encoding)
|
||||
|
||||
# 定义一个函数来处理单个CSV文件
|
||||
def convert_csv_to_gbk(csv_file,encoding_type):
|
||||
# 读取CSV文件，以UTF-8格式
|
||||
with open(csv_file, 'r', encoding=encoding_type) as f:
|
||||
reader = csv.reader(f)
|
||||
rows = list(reader)
|
||||
|
||||
# 将读取的内容写入新的CSV文件，以GBK格式
|
||||
with open(csv_file, 'w', newline='', encoding='gbk') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerows(rows)
|
||||
|
||||
# 删除原始CSV文件
|
||||
os.remove(csv_file)
|
||||
# 将转换后的文件重命名为原始文件名
|
||||
new_file = csv_file.replace('.csv', '_gbk.csv')
|
||||
os.rename(new_file, csv_file)
|
||||
print("当前文件已经转换为gbk格式:", csv_file)
|
||||
@@ -0,0 +1,351 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "2d85dda4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"'''\n",
|
||||
"Author: zhoujie2104231 zhoujie@me.com\n",
|
||||
"Date: 2024-04-07 19:26:52\n",
|
||||
"LastEditors: zhoujie2104231 zhoujie@me.com\n",
|
||||
"LastEditTime: 2024-04-07 20:56:21\n",
|
||||
"FilePath: \"/Gitee_Code/trading_strategy/SS_Code\\SF08\\使用文档\\数据转换最终版/merged_by_year.ipynb\"\n",
|
||||
"Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE\n",
|
||||
"'''\n",
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"from merged_tickdata import merged_old_tickdata, merged_new_tickdata, all_dict\n",
|
||||
"# from merged_tickdata_tmp import merged_old_tickdata, merged_new_tickdata, all_dict"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "fe51b707",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"new_directory = \"D:/data_all/doing\" # \"E:/data/大商所/test\"\n",
|
||||
"os.chdir(new_directory) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "3356d8ff",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"csv_files: ['bu主力连续\\\\bu主力连续_20190703.csv', 'bu主力连续\\\\bu主力连续_20190528.csv', 'bu主力连续\\\\bu主力连续_20190529.csv', 'bu主力连续\\\\bu主力连续_20190530.csv', 'bu主力连续\\\\bu主力连续_20190531.csv', 'bu主力连续\\\\bu主力连续_20190603.csv', 'bu主力连续\\\\bu主力连续_20190604.csv', 'bu主力连续\\\\bu主力连续_20190605.csv', 'bu主力连续\\\\bu主力连续_20190606.csv', 'bu主力连续\\\\bu主力连续_20190610.csv', 'bu主力连续\\\\bu主力连续_20190611.csv', 'bu主力连续\\\\bu主力连续_20190612.csv', 'bu主力连续\\\\bu主力连续_20190613.csv', 'bu主力连续\\\\bu主力连续_20190614.csv', 'bu主力连续\\\\bu主力连续_20190617.csv', 'bu主力连续\\\\bu主力连续_20190618.csv', 'bu主力连续\\\\bu主力连续_20190619.csv', 'bu主力连续\\\\bu主力连续_20190620.csv', 'bu主力连续\\\\bu主力连续_20190621.csv', 'bu主力连续\\\\bu主力连续_20190624.csv', 'bu主力连续\\\\bu主力连续_20190625.csv', 'bu主力连续\\\\bu主力连续_20190626.csv', 'bu主力连续\\\\bu主力连续_20190627.csv', 'bu主力连续\\\\bu主力连续_20190628.csv', 'bu主力连续\\\\bu主力连续_20190701.csv', 'bu主力连续\\\\bu主力连续_20190702.csv']\n",
|
||||
"当前读取文件读取错误： <_io.BufferedReader name='D:\\\\data_all\\\\doing\\\\bu主力连续\\\\bu主力连续_20190530.csv'>\n",
|
||||
"当前读取文件正确解码格式 ISO-8859-1\n",
|
||||
"当前读取文件读取错误： <_io.BufferedReader name='D:\\\\data_all\\\\doing\\\\bu主力连续\\\\bu主力连续_20190701.csv'>\n",
|
||||
"当前读取文件正确解码格式 ISO-8859-1\n",
|
||||
"code_value characters: bu888\n",
|
||||
"按照夜盘截止交易时间为23:00筛选商品期货品种\n",
|
||||
"bu888_2019数据生成成功!\n",
|
||||
"bu888_2019.CSV文件合并成功！\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for root, dirs, files in os.walk('.'):\n",
|
||||
" if len(dirs) > 0:\n",
|
||||
" for dir in dirs:\n",
|
||||
" # 获取二级子文件夹中的所有 CSV 文件\n",
|
||||
" all_csv_files = [os.path.join(dir, file) for file in os.listdir(dir) if file.endswith('.csv')]\n",
|
||||
"\n",
|
||||
" sp_old_chars = ['_2019', '_2020', '_2021']\n",
|
||||
" for sp_old_char in sp_old_chars:\n",
|
||||
" csv_old_files = [sp_file for sp_file in all_csv_files if sp_old_char in sp_file]\n",
|
||||
" if len(csv_old_files) > 0:\n",
|
||||
" old_df, old_code_value = merged_old_tickdata(csv_old_files, sp_old_char)\n",
|
||||
" folder_path = str('D:/data_merged/上期所/%s'%(old_code_value))\n",
|
||||
"\n",
|
||||
" if not os.path.exists(folder_path):\n",
|
||||
" os.makedirs(folder_path)\n",
|
||||
" \n",
|
||||
" old_df.to_csv('%s/%s%s.csv'%(folder_path,old_code_value,sp_old_char), index=False)\n",
|
||||
" print(\"%s%s.CSV文件合并成功！\"%(old_code_value,sp_old_char))\n",
|
||||
"\n",
|
||||
" sp_new_chars = ['_2022', '_2023']\n",
|
||||
" for sp_new_char in sp_new_chars:\n",
|
||||
" csv_new_files = [sp_file for sp_file in all_csv_files if sp_new_char in sp_file]\n",
|
||||
" if len(csv_new_files) > 0:\n",
|
||||
" new_df, new_code_value = merged_new_tickdata(csv_new_files, sp_new_char)\n",
|
||||
" new_df.head()\n",
|
||||
" folder_path = str('D:/data_merged/上期所/%s'%(new_code_value))\n",
|
||||
"\n",
|
||||
" if not os.path.exists(folder_path):\n",
|
||||
" os.makedirs(folder_path)\n",
|
||||
" \n",
|
||||
" new_df.to_csv('%s/%s%s.csv'%(folder_path,new_code_value,sp_new_char), index=False)\n",
|
||||
" print(\"%s%s.CSV文件合并成功！\"%(new_code_value,sp_new_char))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a028b88e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 列错读取错误文件\n",
|
||||
"当前读取文件读取错误： bu主力连续\\bu主力连续_20190530.csv\n",
|
||||
"当前读取文件读取错误： bu主力连续\\bu主力连续_20190701.csv"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "200715a8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.read_csv(\n",
|
||||
" \"D:/data_merged/上期所/bu888/bu888_2019.csv\", \n",
|
||||
" header=0,\n",
|
||||
" # usecols=[ 1, 2, 3, 7, 12, 13, 14, 15],\n",
|
||||
" # names=[\n",
|
||||
" # \"合约代码\",\n",
|
||||
" # \"时间\",\n",
|
||||
" # \"最新\",\n",
|
||||
" # \"成交量\",\n",
|
||||
" # \"买一价\",\n",
|
||||
" # \"卖一价\",\n",
|
||||
" # \"买一量\",\n",
|
||||
" # \"卖一量\",\n",
|
||||
" # ],\n",
|
||||
" encoding='gbk',#ISO-8859-1\n",
|
||||
" # skiprows=0,\n",
|
||||
" # parse_dates=['时间']\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "3c61930c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>main_contract</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>datetime</th>\n",
|
||||
" <th>lastprice</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>bid_p</th>\n",
|
||||
" <th>ask_p</th>\n",
|
||||
" <th>bid_v</th>\n",
|
||||
" <th>ask_v</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>bu888</td>\n",
|
||||
" <td>bu1912</td>\n",
|
||||
" <td>2019-05-27 21:00:00.500</td>\n",
|
||||
" <td>3252.0</td>\n",
|
||||
" <td>734</td>\n",
|
||||
" <td>3252.0</td>\n",
|
||||
" <td>3256.0</td>\n",
|
||||
" <td>288</td>\n",
|
||||
" <td>15</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>bu888</td>\n",
|
||||
" <td>bu1912</td>\n",
|
||||
" <td>2019-05-27 21:00:01.000</td>\n",
|
||||
" <td>3250.0</td>\n",
|
||||
" <td>3240</td>\n",
|
||||
" <td>3250.0</td>\n",
|
||||
" <td>3252.0</td>\n",
|
||||
" <td>43</td>\n",
|
||||
" <td>38</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>bu888</td>\n",
|
||||
" <td>bu1912</td>\n",
|
||||
" <td>2019-05-27 21:00:01.500</td>\n",
|
||||
" <td>3250.0</td>\n",
|
||||
" <td>2226</td>\n",
|
||||
" <td>3250.0</td>\n",
|
||||
" <td>3252.0</td>\n",
|
||||
" <td>15</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>bu888</td>\n",
|
||||
" <td>bu1912</td>\n",
|
||||
" <td>2019-05-27 21:00:02.000</td>\n",
|
||||
" <td>3248.0</td>\n",
|
||||
" <td>962</td>\n",
|
||||
" <td>3246.0</td>\n",
|
||||
" <td>3248.0</td>\n",
|
||||
" <td>110</td>\n",
|
||||
" <td>8</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>bu888</td>\n",
|
||||
" <td>bu1912</td>\n",
|
||||
" <td>2019-05-27 21:00:02.500</td>\n",
|
||||
" <td>3248.0</td>\n",
|
||||
" <td>1178</td>\n",
|
||||
" <td>3248.0</td>\n",
|
||||
" <td>3250.0</td>\n",
|
||||
" <td>10</td>\n",
|
||||
" <td>110</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" main_contract symbol datetime lastprice volume bid_p \\\n",
|
||||
"0 bu888 bu1912 2019-05-27 21:00:00.500 3252.0 734 3252.0 \n",
|
||||
"1 bu888 bu1912 2019-05-27 21:00:01.000 3250.0 3240 3250.0 \n",
|
||||
"2 bu888 bu1912 2019-05-27 21:00:01.500 3250.0 2226 3250.0 \n",
|
||||
"3 bu888 bu1912 2019-05-27 21:00:02.000 3248.0 962 3246.0 \n",
|
||||
"4 bu888 bu1912 2019-05-27 21:00:02.500 3248.0 1178 3248.0 \n",
|
||||
"\n",
|
||||
" ask_p bid_v ask_v \n",
|
||||
"0 3256.0 288 15 \n",
|
||||
"1 3252.0 43 38 \n",
|
||||
"2 3252.0 15 19 \n",
|
||||
"3 3248.0 110 8 \n",
|
||||
"4 3250.0 10 110 "
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#'gbk' codec can't decode byte 0xc3 in position 189076: illegal multibyte sequence\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1b22d988",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = df.sort_values(by = ['datetime'], ascending=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "cc14352a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['datetime'] = sorted(df['datetime'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2dc49f0c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "befec0c4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import chardet"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6397fffb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 假设file_path是你要读取的文件路径\n",
|
||||
"with open(\"D:/data_all/doing/bu主力连续/bu主力连续_20190530.csv\", 'rb') as file:\n",
|
||||
" data = file.read()\n",
|
||||
" \n",
|
||||
"# 使用chardet检测编码\n",
|
||||
"detected_encoding = chardet.detect(data)['encoding']\n",
|
||||
" \n",
|
||||
"# # 如果检测到的编码不是gbk，可以尝试转换编码后再读取\n",
|
||||
"# if detected_encoding and detected_encoding != 'gbk':\n",
|
||||
"# with open(file_path, 'rb') as file:\n",
|
||||
"# data = file.read().decode(detected_encoding)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c2433c60",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(detected_encoding)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,335 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
from datetime import time as s_time
|
||||
from datetime import datetime
|
||||
import chardet
|
||||
|
||||
# 日盘商品期货交易品种
|
||||
commodity_day_dict = {'bb': s_time(15,00), 'jd': s_time(15,00), 'lh': s_time(15,00), 'l': s_time(15,00), 'fb': s_time(15,00), 'ec': s_time(15,00),
|
||||
'AP': s_time(15,00), 'CJ': s_time(15,00), 'JR': s_time(15,00), 'LR': s_time(15,00), 'RS': s_time(15,00), 'PK': s_time(15,00),
|
||||
'PM': s_time(15,00), 'PX': s_time(15,00), 'RI': s_time(15,00), 'ao': s_time(15,00), 'br': s_time(15,00), 'wr': s_time(15,00),}
|
||||
|
||||
# 夜盘商品期货交易品种
|
||||
commodity_night_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
# 金融期货交易品种
|
||||
financial_time_dict = {'IH': s_time(15,00), 'IF': s_time(15,00), 'IC': s_time(15,00), 'IM': s_time(15,00),'T': s_time(15,00), 'TS': s_time(15,00),
|
||||
'TF': s_time(15,00), 'TL': s_time(15,00)}
|
||||
|
||||
# 所有已列入的筛选品种
|
||||
all_dict = {k: v for d in [commodity_day_dict, commodity_night_dict, financial_time_dict] for k, v in d.items()}
|
||||
|
||||
# def has_common_keys(*dicts):
|
||||
# keys_union = set().union(*dicts) # 计算所有字典键的并集
|
||||
# keys_intersection = set().intersection(*dicts) # 计算所有字典键的交集
|
||||
# return len(keys_intersection) > 0
|
||||
|
||||
# has_common_keys(commodity_day_dict, commodity_night_dict,financial_time_dict)
|
||||
|
||||
# import chardet
|
||||
|
||||
# # 假设file_path是你要读取的文件路径
|
||||
# with open(file_path, 'rb') as file:
|
||||
# data = file.read()
|
||||
|
||||
# # 使用chardet检测编码
|
||||
# detected_encoding = chardet.detect(data)['encoding']
|
||||
|
||||
# # 如果检测到的编码不是gbk，可以尝试转换编码后再读取
|
||||
# if detected_encoding and detected_encoding != 'gbk':
|
||||
# with open(file_path, 'rb') as file:
|
||||
# data = file.read().decode(detected_encoding)
|
||||
|
||||
def split_alpha_numeric(string):
|
||||
alpha_chars = ""
|
||||
numeric_chars = ""
|
||||
for char in string:
|
||||
if char.isalpha():
|
||||
alpha_chars += char
|
||||
elif char.isdigit():
|
||||
numeric_chars += char
|
||||
return alpha_chars, numeric_chars
|
||||
|
||||
def find_files(all_csv_files):
|
||||
all_csv_files = sorted(all_csv_files)
|
||||
sp_old_chars = ['_2019','_2020','_2021']
|
||||
sp_old_chars = sorted(sp_old_chars)
|
||||
sp_new_chars = ['_2022','_2023']
|
||||
sp_new_chars = sorted(sp_new_chars)
|
||||
csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]
|
||||
csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]
|
||||
|
||||
return csv_old_files, csv_new_files
|
||||
|
||||
def merged_old_tickdata(all_csv_files, sp_char):
|
||||
merged_up_df = pd.DataFrame()
|
||||
merged_up_df = merged_old_unprocessed_tickdata(all_csv_files, sp_char)
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_up_df.loc[0,'合约代码'])
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
merged_up_df.insert(loc=0,column="统一代码", value=code_value)
|
||||
|
||||
while alpha_chars not in all_dict.keys():
|
||||
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
|
||||
continue
|
||||
|
||||
# merged_df['时间'] = pd.to_datetime(merged_df['时间'])
|
||||
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['时间'],'lastprice':merged_df['最新'],'volume':merged_df['成交量'],
|
||||
'bid_p':merged_df['买一价'],'ask_p':merged_df['卖一价'],'bid_v':merged_df['买一量'],'ask_v':merged_df['卖一量']})
|
||||
|
||||
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
|
||||
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
|
||||
del merged_df['tmp_time']
|
||||
|
||||
|
||||
if alpha_chars in financial_time_dict.keys():
|
||||
drop_index1 = pd.DataFrame().index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照中金所交易时间筛选金融期货品种")
|
||||
# else:
|
||||
elif alpha_chars in commodity_night_dict.keys():
|
||||
if commodity_night_dict[alpha_chars] == s_time(23,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(1,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(2,30):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
|
||||
else:
|
||||
print("夜盘截止交易时间未设置或者设置错误！！！")
|
||||
elif alpha_chars in commodity_day_dict.keys():
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照无夜盘筛选商品期货品种")
|
||||
else:
|
||||
print("%s期货品种未列入筛选条件中!!!"%(code_value))
|
||||
# 清理不在交易时间段的数据
|
||||
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index2, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index3, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index4, axis=0, inplace=True)
|
||||
|
||||
del merged_df['time']
|
||||
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], ascending=True)
|
||||
# merged_df['datetime'] = pd.to_datetime(merged_df['datetime'])
|
||||
merged_df['datetime'] = sorted(merged_df['datetime'])
|
||||
print("%s%s数据生成成功!"%(code_value,sp_char))
|
||||
|
||||
return merged_df, code_value
|
||||
|
||||
def merged_new_tickdata(all_csv_files, sp_char):
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
dir = os.getcwd()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
try:
|
||||
df = pd.read_csv(
|
||||
file,
|
||||
header=0,
|
||||
usecols=[0, 1, 4, 11, 20, 21, 22, 23, 24, 25, 43],
|
||||
names=[
|
||||
"交易日",
|
||||
"合约代码",
|
||||
"最新价",
|
||||
"数量",
|
||||
"最后修改时间",
|
||||
"最后修改毫秒",
|
||||
"申买价一",
|
||||
"申买量一",
|
||||
"申卖价一",
|
||||
"申卖量一",
|
||||
"业务日期",
|
||||
],
|
||||
encoding='gbk',
|
||||
# skiprows=0,
|
||||
parse_dates=['业务日期','最后修改时间','最后修改毫秒'])#注意此处增加的排序，为了后面按时间排序
|
||||
except:
|
||||
# 假设file_path是你要读取的文件路径
|
||||
file_path = os.path.join(dir, file)
|
||||
with open(file_path, 'rb') as file:
|
||||
data = file.read()
|
||||
|
||||
# 使用chardet检测编码
|
||||
detected_encoding = chardet.detect(data)['encoding']
|
||||
print("当前读取文件读取错误：", file)
|
||||
print("当前读取文件正确解码格式", detected_encoding)
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset = merged_df.columns.tolist(), inplace=True)
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
#print("合约代码:", merged_df["合约代码"])
|
||||
|
||||
# 插入新的数据
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
|
||||
# print("Alphabetical characters:", alpha_chars)
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
merged_df.insert(loc=1, column="统一代码", value=code_value)
|
||||
|
||||
while alpha_chars not in all_dict.keys():
|
||||
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
|
||||
continue
|
||||
|
||||
#日期修正
|
||||
#merged_df['业务日期'] = pd.to_datetime(merged_df['业务日期'])
|
||||
merged_df['业务日期'] = merged_df['业务日期'].dt.strftime('%Y-%m-%d')
|
||||
merged_df['datetime'] = merged_df['业务日期'] + ' '+merged_df['最后修改时间'].dt.time.astype(str) + '.' + merged_df['最后修改毫秒'].astype(str)
|
||||
# 将'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
|
||||
merged_df['datetime'] = pd.to_datetime(merged_df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
|
||||
#计算瞬时成交量
|
||||
merged_df['volume'] = merged_df['数量'] - merged_df['数量'].shift(1)
|
||||
merged_df['volume'] = merged_df['volume'].fillna(0)
|
||||
|
||||
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['datetime'],'lastprice':merged_df['最新价'],'volume':merged_df['volume'],
|
||||
'bid_p':merged_df['申买价一'],'ask_p':merged_df['申卖价一'],'bid_v':merged_df['申买量一'],'ask_v':merged_df['申卖量一']})
|
||||
|
||||
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
|
||||
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
|
||||
del merged_df['tmp_time']
|
||||
|
||||
if alpha_chars in financial_time_dict.keys():
|
||||
drop_index1 = pd.DataFrame().index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照中金所交易时间筛选金融期货品种")
|
||||
# else:
|
||||
elif alpha_chars in commodity_night_dict.keys():
|
||||
if commodity_night_dict[alpha_chars] == s_time(23,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(1,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(2,30):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
|
||||
else:
|
||||
print("夜盘截止交易时间未设置或者设置错误！！！")
|
||||
elif alpha_chars in commodity_day_dict.keys():
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照无夜盘筛选商品期货品种")
|
||||
else:
|
||||
print("%s期货品种未列入筛选条件中!!!"%(code_value))
|
||||
|
||||
# 清理不在交易时间段的数据
|
||||
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index2, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index3, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index4, axis=0, inplace=True)
|
||||
|
||||
del merged_df['time']
|
||||
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], inplace=True)
|
||||
merged_df['datetime'] = sorted(merged_df['datetime'])
|
||||
print("%s%s数据生成成功!"%(code_value,sp_char))
|
||||
|
||||
|
||||
return merged_df, code_value
|
||||
|
||||
def merged_old_unprocessed_tickdata(all_csv_files, sp_char):
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
dir = os.getcwd()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
try:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
df = pd.read_csv(file, header=0, encoding='gbk')
|
||||
except:
|
||||
file_path = os.path.join(dir, file)
|
||||
with open(file_path, 'rb') as file:
|
||||
data = file.read()
|
||||
|
||||
# 使用chardet检测编码
|
||||
detected_encoding = chardet.detect(data)['encoding']
|
||||
print("当前读取文件读取错误：", file)
|
||||
print("当前读取文件正确解码格式", detected_encoding)
|
||||
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset=merged_df.columns.tolist(), inplace=True)
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
|
||||
# 插入新的数据
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
|
||||
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
merged_df.insert(loc=1,column="统一代码", value=code_value)
|
||||
|
||||
|
||||
# 将合并后的数据保存到csv文件中
|
||||
folder_path = "合成tick数据2019-2021"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('合成tick数据2019-2021')
|
||||
|
||||
# sorted_merged_df = merged_df.sort_values(by= ['业务日期','最后修改时间','最后修改毫秒'], ascending=[True, True, True])
|
||||
# sorted_merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False)
|
||||
|
||||
merged_df['时间'] = pd.to_datetime(merged_df['时间'])
|
||||
sorted_merged_df = merged_df.sort_values(by = ['时间'], ascending=True)
|
||||
sorted_merged_df.to_csv('./合成tick数据2019-2021/%s%s.csv'%(code_value,sp_char), index=False)
|
||||
del merged_df
|
||||
del sorted_merged_df
|
||||
#merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False) #数据按照时间排序，前面文件夹按照时间修改好了可以直接用这里
|
||||
|
||||
# 打印提示信息
|
||||
print("CSV文件合并成功！")
|
||||
@@ -0,0 +1,281 @@
|
||||
import pandas as pd
|
||||
import os
|
||||
from datetime import time as s_time
|
||||
from datetime import datetime
|
||||
|
||||
# 日盘商品期货交易品种
|
||||
commodity_day_dict = {'bb': s_time(15,00), 'jd': s_time(15,00), 'lh': s_time(15,00), 'l': s_time(15,00), 'fb': s_time(15,00),}
|
||||
|
||||
# 夜盘商品期货交易品种
|
||||
commodity_night_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
# 金融期货交易品种
|
||||
financial_time_dict = {'IH': s_time(15,00), 'IF': s_time(15,00), 'IC': s_time(15,00), 'IM': s_time(15,00),'T': s_time(15,00), 'TS': s_time(15,00),
|
||||
'TF': s_time(15,00), 'TL': s_time(15,00)}
|
||||
|
||||
# 所有已列入的筛选品种
|
||||
all_dict = {k: v for d in [commodity_day_dict, commodity_night_dict, financial_time_dict] for k, v in d.items()}
|
||||
|
||||
# def has_common_keys(*dicts):
|
||||
# keys_union = set().union(*dicts) # 计算所有字典键的并集
|
||||
# keys_intersection = set().intersection(*dicts) # 计算所有字典键的交集
|
||||
# return len(keys_intersection) > 0
|
||||
|
||||
# has_common_keys(commodity_day_dict, commodity_night_dict,financial_time_dict)
|
||||
|
||||
def split_alpha_numeric(string):
|
||||
alpha_chars = ""
|
||||
numeric_chars = ""
|
||||
for char in string:
|
||||
if char.isalpha():
|
||||
alpha_chars += char
|
||||
elif char.isdigit():
|
||||
numeric_chars += char
|
||||
return alpha_chars, numeric_chars
|
||||
|
||||
def find_files(all_csv_files):
|
||||
all_csv_files = sorted(all_csv_files)
|
||||
sp_old_chars = ['_2019','_2020','_2021']
|
||||
sp_old_chars = sorted(sp_old_chars)
|
||||
sp_new_chars = ['_2022','_2023']
|
||||
sp_new_chars = sorted(sp_new_chars)
|
||||
csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]
|
||||
csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]
|
||||
|
||||
return csv_old_files, csv_new_files
|
||||
|
||||
def merged_old_tickdata(all_csv_files, sp_char):
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
df = pd.read_csv(
|
||||
file,
|
||||
header=0,
|
||||
usecols=[ 1, 2, 3, 4, 8, 13, 14, 15, 16],
|
||||
names=[
|
||||
"统一代码",
|
||||
"合约代码",
|
||||
"时间",
|
||||
"最新",
|
||||
"成交量",
|
||||
"买一价",
|
||||
"卖一价",
|
||||
"买一量",
|
||||
"卖一量",
|
||||
],
|
||||
encoding='utf-8',
|
||||
# skiprows=0,
|
||||
parse_dates=['时间'])#注意此处增加的排序，为了后面按时间排序
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset = merged_df.columns.tolist(), inplace=True)
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
# print("合约代码:", merged_df["合约代码"])
|
||||
|
||||
# # 插入新的数据
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
|
||||
# print("Alphabetical characters:", alpha_chars)
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
# merged_df.insert(loc=0,column="统一代码", value=code_value)
|
||||
|
||||
while alpha_chars not in all_dict.keys():
|
||||
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
|
||||
continue
|
||||
|
||||
# merged_df['时间'] = pd.to_datetime(merged_df['时间'])
|
||||
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['时间'],'lastprice':merged_df['最新'],'volume':merged_df['成交量'],
|
||||
'bid_p':merged_df['买一价'],'ask_p':merged_df['卖一价'],'bid_v':merged_df['买一量'],'ask_v':merged_df['卖一量']})
|
||||
|
||||
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
|
||||
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
|
||||
del merged_df['tmp_time']
|
||||
|
||||
|
||||
if alpha_chars in financial_time_dict.keys():
|
||||
drop_index1 = pd.DataFrame().index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照中金所交易时间筛选金融期货品种")
|
||||
# else:
|
||||
elif alpha_chars in commodity_night_dict.keys():
|
||||
if commodity_night_dict[alpha_chars] == s_time(23,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(1,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(2,30):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
|
||||
else:
|
||||
print("夜盘截止交易时间未设置或者设置错误！！！")
|
||||
elif alpha_chars in commodity_day_dict.keys():
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照无夜盘筛选商品期货品种")
|
||||
else:
|
||||
print("%s期货品种未列入筛选条件中!!!"%(code_value))
|
||||
# 清理不在交易时间段的数据
|
||||
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index2, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index3, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index4, axis=0, inplace=True)
|
||||
|
||||
del merged_df['time']
|
||||
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], ascending=True)
|
||||
sorted(merged_df['datetime'])
|
||||
print("%s%s数据生成成功!"%(code_value,sp_char))
|
||||
|
||||
return merged_df, code_value
|
||||
|
||||
def merged_new_tickdata(all_csv_files, sp_char):
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
df = pd.read_csv(
|
||||
file,
|
||||
header=0,
|
||||
usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],
|
||||
names=[
|
||||
"交易日",
|
||||
"统一代码",
|
||||
"合约代码",
|
||||
"最新价",
|
||||
"数量",
|
||||
"最后修改时间",
|
||||
"最后修改毫秒",
|
||||
"申买价一",
|
||||
"申买量一",
|
||||
"申卖价一",
|
||||
"申卖量一",
|
||||
"业务日期",
|
||||
],
|
||||
encoding='utf-8',
|
||||
# skiprows=0,
|
||||
parse_dates=['业务日期','最后修改时间','最后修改毫秒'])#注意此处增加的排序，为了后面按时间排序
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset = merged_df.columns.tolist(), inplace=True)
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
#print("合约代码:", merged_df["合约代码"])
|
||||
|
||||
# 插入新的数据
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df.loc[0,'合约代码'])
|
||||
# print("Alphabetical characters:", alpha_chars)
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
# merged_df.insert(loc=1, column="统一代码", value=code_value)
|
||||
|
||||
while alpha_chars not in all_dict.keys():
|
||||
print("%s期货品种未列入所有筛选条件中!!!"%(code_value))
|
||||
continue
|
||||
|
||||
#日期修正
|
||||
#merged_df['业务日期'] = pd.to_datetime(merged_df['业务日期'])
|
||||
merged_df['业务日期'] = merged_df['业务日期'].dt.strftime('%Y-%m-%d')
|
||||
merged_df['datetime'] = merged_df['业务日期'] + ' '+merged_df['最后修改时间'].dt.time.astype(str) + '.' + merged_df['最后修改毫秒'].astype(str)
|
||||
# 将'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
|
||||
merged_df['datetime'] = pd.to_datetime(merged_df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
|
||||
#计算瞬时成交量
|
||||
merged_df['volume'] = merged_df['数量'] - merged_df['数量'].shift(1)
|
||||
merged_df['volume'] = merged_df['volume'].fillna(0)
|
||||
|
||||
merged_df =pd.DataFrame({'main_contract':merged_df['统一代码'],'symbol':merged_df['合约代码'],'datetime':merged_df['datetime'],'lastprice':merged_df['最新价'],'volume':merged_df['volume'],
|
||||
'bid_p':merged_df['申买价一'],'ask_p':merged_df['申卖价一'],'bid_v':merged_df['申买量一'],'ask_v':merged_df['申卖量一']})
|
||||
|
||||
merged_df['tmp_time'] = merged_df['datetime'].dt.strftime('%H:%M:%S.%f')
|
||||
merged_df['time'] = merged_df['tmp_time'].apply(lambda x: datetime.strptime(x, '%H:%M:%S.%f')).dt.time
|
||||
del merged_df['tmp_time']
|
||||
|
||||
if alpha_chars in financial_time_dict.keys():
|
||||
drop_index1 = pd.DataFrame().index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 0, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 30, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照中金所交易时间筛选金融期货品种")
|
||||
# else:
|
||||
elif alpha_chars in commodity_night_dict.keys():
|
||||
if commodity_night_dict[alpha_chars] == s_time(23,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(23, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为23:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(1,00):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(1, 0, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为1:00筛选商品期货品种")
|
||||
elif commodity_night_dict[alpha_chars] == s_time(2,30):
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) & (merged_df['time'] < s_time(21, 0, 0, 000000))].index
|
||||
drop_index4 = merged_df.loc[(merged_df['time'] > s_time(2, 30, 0, 000000)) & (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
print("按照夜盘截止交易时间为2:30筛选商品期货品种")
|
||||
else:
|
||||
print("夜盘截止交易时间未设置或者设置错误！！！")
|
||||
elif alpha_chars in commodity_day_dict.keys():
|
||||
drop_index1 = merged_df.loc[(merged_df['time'] > s_time(10, 15, 0, 000000)) & (merged_df['time'] < s_time(10, 30, 0, 000000))].index
|
||||
drop_index2 = merged_df.loc[(merged_df['time'] > s_time(11, 30, 0, 000000)) & (merged_df['time'] < s_time(13, 30, 0, 000000))].index
|
||||
drop_index3 = merged_df.loc[(merged_df['time'] > s_time(15, 0, 0, 000000)) | (merged_df['time'] < s_time(9, 0, 0, 000000))].index
|
||||
drop_index4 = pd.DataFrame().index
|
||||
print("按照无夜盘筛选商品期货品种")
|
||||
else:
|
||||
print("%s期货品种未列入筛选条件中!!!"%(code_value))
|
||||
|
||||
# 清理不在交易时间段的数据
|
||||
merged_df.drop(labels=drop_index1, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index2, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index3, axis=0, inplace=True)
|
||||
merged_df.drop(drop_index4, axis=0, inplace=True)
|
||||
|
||||
del merged_df['time']
|
||||
# sorted_merged_df = merged_df.sort_values(by = ['datetime'], inplace=True)
|
||||
sorted(merged_df['datetime'])
|
||||
print("%s%s数据生成成功!"%(code_value,sp_char))
|
||||
|
||||
|
||||
return merged_df, code_value
|
||||
@@ -0,0 +1,454 @@
|
||||
'''
|
||||
以下是代码的详细说明：
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
1.
|
||||
导入必要的模块和库：
|
||||
backtrader 用于回测功能
|
||||
datetime 用于处理日期和时间
|
||||
GenericCSVData 用于从CSV文件加载数据
|
||||
numpy 用于数值操作
|
||||
time 用于时间相关操作
|
||||
matplotlib.pyplot 用于绘图
|
||||
|
||||
2. 定义自定义手续费模板MyCommission
|
||||
继承自bt.CommInfoBase
|
||||
|
||||
3.
|
||||
定义自定义数据源类 GenericCSV_SIG：
|
||||
继承自 GenericCSVData，并添加了两个额外的行：'sig'和'delta'
|
||||
定义了参数 'sig'和'delta'
|
||||
|
||||
4.
|
||||
定义 MyStrategy_固定止损_跟踪止盈 类：
|
||||
继承自 bt.Strategy（backtrader的基础策略类）
|
||||
定义了两个参数：trailing_stop_percent 和 fixed_stop_loss_percent
|
||||
初始化策略并设置各种变量和指标
|
||||
实现了 next 方法，该方法在数据源的每个新的K线出现时被调用
|
||||
根据当前K线数据更新跟踪止盈价格
|
||||
实现了跟踪止盈出场和固定止损出场
|
||||
根据信号处理多头和空头仓位
|
||||
在策略执行过程中打印调试信息
|
||||
|
||||
5.
|
||||
if __name__ == "__main__": 代码块：
|
||||
使用 Cerebro 实例设置回测环境
|
||||
使用 GenericCSV_SIG 数据源从CSV文件加载数据
|
||||
将数据源和策略添加到 Cerebro 实例中
|
||||
添加观察者和分析器以评估性能
|
||||
设置初始资金和经纪人参数
|
||||
运行回测并获取结果
|
||||
打印回测报告，包括收益率、回撤、胜率和交易统计数据
|
||||
使用 matplotlib 绘制回测结果
|
||||
使用说明：使用前需要调整的相关参数如下
|
||||
1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
|
||||
2.MyStrategy_固定止损_跟踪止盈：可以修改跟踪百分比和移动周期均线。
|
||||
3.__init__函数中：可以修改lost手数，，
|
||||
4.next函数：一、修改清仓时间参数，每个品种不一致；二、window_size和window_size_delta的周期暂为10；三、修改“开多组合”和“开空组合”
|
||||
5.__main__函数:一、修改回测时间段fromdate和todate；二、根据交易平中设置初始资金、手续费，单手保证金，合约倍数；
|
||||
'''
|
||||
|
||||
# 需要进一步了解windows_size的计算规则，日线？
|
||||
|
||||
import backtrader as bt
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
from backtrader.feeds import GenericCSVData
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import time
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
|
||||
# 导入表头解决图标中中文显示问题
|
||||
from pylab import mpl
|
||||
|
||||
手续费汇总=0
|
||||
|
||||
|
||||
class GenericCSV_SIG(GenericCSVData):
|
||||
# 从基类继承，添加一个 'sig'，‘delta’行
|
||||
lines = ('sig','delta')
|
||||
# 添加参数为从基类继承的参数
|
||||
params = (('sig',6),('delta', 8))
|
||||
|
||||
class MyStrategy_固定止损_跟踪止盈(bt.Strategy):
|
||||
params = (
|
||||
('trailing_stop_percent', 0.02), # 跟踪止盈百分比
|
||||
('fixed_stop_loss_percent', 0.01), # 固定止损百分比
|
||||
|
||||
# ('sma1_period', 60), # 移动平均线周期
|
||||
# ('sma2_period',120),
|
||||
|
||||
|
||||
)
|
||||
|
||||
def __init__(self):
|
||||
self.Lots=1 #下单手数
|
||||
|
||||
self.signal = self.datas[0].sig # 使用sig字段作为策略的信号字段
|
||||
self.delta= self.datas[0].delta
|
||||
# 获取数据序列别名列表
|
||||
line_aliases = self.datas[0].getlinealiases()
|
||||
self.pos=0
|
||||
print(line_aliases)
|
||||
self.high=self.datas[0].high
|
||||
self.low=self.datas[0].low
|
||||
self.closes=self.datas[0].close
|
||||
self.open=self.datas[0].open
|
||||
self.trailing_stop_percent = self.params.trailing_stop_percent
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.fixed_stop_loss_percent = self.params.fixed_stop_loss_percent
|
||||
self.sl_long_price=0
|
||||
self.sl_shor_price=0
|
||||
#240884432
|
||||
self.out_long=0
|
||||
self.out_short=0
|
||||
self.rinei_ma=[]
|
||||
self.rinei_mean=0
|
||||
|
||||
self.datetime_list= []
|
||||
self.high_list = []
|
||||
self.low_list = []
|
||||
self.close_list = []
|
||||
self.opens_list = []
|
||||
self.deltas_list = []
|
||||
self.delta_cumsum=[]
|
||||
|
||||
self.barN = 0
|
||||
|
||||
# self.sma1 = bt.indicators.SimpleMovingAverage(
|
||||
# self.data, period=self.params.sma1_period
|
||||
# )
|
||||
# self.sma2 = bt.indicators.SimpleMovingAverage(
|
||||
# self.data, period=self.params.sma2_period
|
||||
# )
|
||||
|
||||
self.df = pd.DataFrame(columns=['datetime', 'high', 'low', 'close', 'open', 'delta', 'delta_cumsum'])
|
||||
|
||||
self.trader_df=pd.DataFrame(columns=['open', 'high', 'low', 'close', 'volume', 'openInterest','delta'])
|
||||
|
||||
def log(self, txt, dt=None):
|
||||
'''可选，构建策略打印日志的函数：可用于打印订单记录或交易记录等'''
|
||||
dt = dt or self.datas[0].datetime.date(0)
|
||||
print('%s, %s' % (dt.isoformat(), txt))
|
||||
|
||||
def notify_order(self, order):
|
||||
# 未被处理的订单
|
||||
if order.status in [order.Submitted, order.Accepted]:
|
||||
return
|
||||
# 已经处理的订单
|
||||
if order.status in [order.Completed, order.Canceled, order.Margin]:
|
||||
global 手续费汇总
|
||||
if order.isbuy():
|
||||
手续费汇总 +=order.executed.comm
|
||||
self.log(
|
||||
'BUY EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
(order.ref, # 订单编号
|
||||
order.executed.price, # 成交价
|
||||
order.executed.comm, # 佣金
|
||||
order.executed.size, # 成交量
|
||||
order.data._name,# 品种名称
|
||||
手续费汇总))
|
||||
|
||||
else: # Sell
|
||||
手续费汇总 +=order.executed.comm
|
||||
self.log('SELL EXECUTED, 订单编号:%.0f,成交价格: %.2f, 手续费滑点：%.2f, 成交量: %.2f, 品种: %s,手续费汇总：%.2f' %
|
||||
(order.ref,
|
||||
order.executed.price,
|
||||
order.executed.comm,
|
||||
order.executed.size,
|
||||
order.data._name,
|
||||
手续费汇总))
|
||||
|
||||
def next(self):
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#bar线计数初始化
|
||||
self.barN += 1
|
||||
position = self.getposition(self.datas[0]).size
|
||||
#时间轴
|
||||
dt = bt.num2date(self.data.datetime[0])
|
||||
|
||||
#更新跟踪止损价格
|
||||
def 每日重置数据():
|
||||
# 获取当前时间
|
||||
current_time = dt.time()
|
||||
#print(current_time)
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(22, 55)
|
||||
clearing_time2_end = s_time(23, 0)
|
||||
|
||||
# 创建一个标志变量
|
||||
clearing_executed = False
|
||||
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not clearing_executed :
|
||||
clearing_executed = True # 设置标志变量为已执行
|
||||
self.rinei_ma=[]
|
||||
self.delta_cumsum=[]
|
||||
self.deltas_list=[]
|
||||
# 如果不在任何时间范围内，可以执行其他操作
|
||||
else:
|
||||
self.rinei_ma.append(self.closes[0])
|
||||
self.rinei_mean = np.mean(self.rinei_ma)
|
||||
#self.delta_cumsum=[]
|
||||
#self.deltas_list=[]
|
||||
#print('rinei_ma',self.rinei_ma)
|
||||
clearing_executed = False
|
||||
pass
|
||||
|
||||
return clearing_executed
|
||||
|
||||
run_kg=每日重置数据()
|
||||
|
||||
#过滤成交量为0或小于0
|
||||
if self.data.volume[0] <= 0 :
|
||||
return
|
||||
#print(f'volume,{self.data.volume[0]}')
|
||||
if self.long_trailing_stop_price >0 and self.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = self.low[0] if self.long_trailing_stop_price<self.low[0] else self.long_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price >0 and self.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = self.high[0] if self.high[0] <self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long >0:
|
||||
if self.low[0] < self.out_long and self.pos>0 and self.sl_long_price>0 and self.low[0]>self.sl_long_price:
|
||||
print('--多头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position','TR',self.out_long,'low', self.low[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long=0
|
||||
self.pos = 0
|
||||
|
||||
if self.out_short>0:
|
||||
if self.high[0] > self.out_short and self.pos<0 and self.sl_shor_price>0 and self.high[0]<self.sl_shor_price:
|
||||
print('--空头止盈出场datetime+sig: ',dt,'Trailing stop triggered: Closing position: ','TR',self.out_short,'high', self.high[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
self.pos = 0
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and self.closes[0] < self.fixed_stop_loss_L:
|
||||
print('--多头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0],size=self.Lots, exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and self.closes[0] > self.fixed_stop_loss_S:
|
||||
print('--空头止损datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], size=self.Lots,exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
|
||||
|
||||
# 更新最高价和最低价的列表
|
||||
self.datetime_list.append(dt)
|
||||
self.high_list.append(self.data.high[0])
|
||||
self.low_list.append(self.data.low[0])
|
||||
self.close_list.append(self.data.close[0])
|
||||
self.opens_list.append(self.data.open[0])
|
||||
self.deltas_list.append(self.data.delta[0])
|
||||
# 计算delta累计
|
||||
self.delta_cumsum.append(sum(self.deltas_list))
|
||||
|
||||
|
||||
# 将当前行数据添加到 DataFrame
|
||||
# new_row = {
|
||||
# 'datetime': dt,
|
||||
# 'high': self.data.high[0],
|
||||
# 'low': self.data.low[0],
|
||||
# 'close': self.data.close[0],
|
||||
# 'open': self.data.open[0],
|
||||
# 'delta': self.data.delta[0],
|
||||
# 'delta_cumsum': sum(self.deltas_list)
|
||||
# }
|
||||
# # 使用pandas.concat代替append
|
||||
# self.df = pd.concat([self.df, pd.DataFrame([new_row])], ignore_index=True)
|
||||
|
||||
# # 检查文件是否存在
|
||||
# csv_file_path = f"output.csv"
|
||||
# if os.path.exists(csv_file_path):
|
||||
# # 仅保存最后一行数据
|
||||
# self.df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
# else:
|
||||
# # 创建新文件并保存整个DataFrame
|
||||
# self.df.to_csv(csv_file_path, index=False)
|
||||
|
||||
|
||||
#
|
||||
if run_kg==False : #
|
||||
# # 构建delta的正数和负数
|
||||
# positive_nums = [x for x in self.data.delta if x > 0]
|
||||
# negative_nums = [x for x in self.data.delta if x < 0]
|
||||
|
||||
# positive_sums = [x for x in self.delta_cumsum if x > 0]
|
||||
# negative_sums = [x for x in self.delta_cumsum if x < 0]
|
||||
# #
|
||||
# # 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>1000 and self.delta_cumsum[-1]>1500
|
||||
# # 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<-1000 and self.delta_cumsum[-1]<-1500
|
||||
# 开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] > 1 and self.data.delta[0]>max(self.data.delta[-60:-1]) #and self.delta_cumsum[-1] > np.max(self.delta_cumsum[-61:-2]) #np.mean(self.data.delta_cumsum[-61:-2])
|
||||
# 开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<min(self.data.delta[-60:-1]) #and self.delta_cumsum[-1] < np.min(self.delta_cumsum[-61:-2]) #np.mean(self.data.delta_cumsum[-61:-2])
|
||||
#print(self.delta_cumsum)
|
||||
开多组合= self.rinei_mean>0 and self.closes[0]>self.rinei_mean and self.signal[0] >1 and self.data.delta[0]>1500 and self.delta_cumsum[-1]>2000
|
||||
开空组合= self.rinei_mean>0 and self.closes[0]<self.rinei_mean and self.signal[0] <-1 and self.data.delta[0]<-1500 and self.delta_cumsum[-1]<-2000
|
||||
平多条件=self.pos<0 and self.signal[0] >1
|
||||
平空条件=self.pos>0 and self.signal[0] <-1
|
||||
if self.pos !=1 : #
|
||||
if 平多条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_S, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
if 开多组合 : #
|
||||
self.buy(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=1
|
||||
self.long_trailing_stop_price=self.low[0]
|
||||
self.sl_long_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存多头价格: ',self.long_trailing_stop_price)
|
||||
|
||||
if self.pos !=-1 : #
|
||||
if 平空条件:
|
||||
#print('datetime+sig: ', dt, 'Fixed stop loss triggered: Closing position', 'SL', self.fixed_stop_loss_L, 'close', self.closes[0])
|
||||
self.close(data=self.data, price=self.data.close[0], exectype=bt.Order.Market)
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
if 开空组合: #
|
||||
self.sell(data=self.data, price=self.data.close[0], size=1, exectype=bt.Order.Market)
|
||||
self.pos=-1
|
||||
self.short_trailing_stop_price=self.high[0]
|
||||
self.sl_shor_price=self.data.open[0]
|
||||
#print('datetime+sig: ',dt,' sig: ',self.signal[0],'保存空头价格: ',self.short_trailing_stop_price)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
# 创建Cerebro实例
|
||||
|
||||
cerebro = bt.Cerebro()
|
||||
#数据
|
||||
csv_file='./tick生成的OF数据-own/back_ofdata_dj.csv' #
|
||||
# 从CSV文件加载数据
|
||||
data = GenericCSV_SIG(
|
||||
dataname=csv_file,
|
||||
fromdate=datetime(2023,1,1),
|
||||
todate=datetime(2023,12,29),
|
||||
timeframe=bt.TimeFrame.Minutes,
|
||||
nullvalue=0.0,
|
||||
dtformat='%Y-%m-%d %H:%M:%S',
|
||||
datetime=0,
|
||||
high=3,
|
||||
low=4,
|
||||
open=2,
|
||||
close=1,
|
||||
volume=5,
|
||||
openinterest=None,
|
||||
sig=6,
|
||||
delta=8
|
||||
)
|
||||
|
||||
# 添加数据到Cerebro实例
|
||||
cerebro.adddata(data)
|
||||
|
||||
# 添加策略到Cerebro实例
|
||||
cerebro.addstrategy(MyStrategy_固定止损_跟踪止盈)
|
||||
|
||||
# 添加观察者和分析器到Cerebro实例
|
||||
#cerebro.addobserver(bt.observers.BuySell)
|
||||
cerebro.addobserver(bt.observers.Value)
|
||||
cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
|
||||
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
|
||||
cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
|
||||
初始资金=10000
|
||||
cerebro.broker.setcash(初始资金) # 设置初始资金
|
||||
|
||||
#手续费，单手保证金，合约倍数
|
||||
cerebro.broker.setcommission(commission=14, margin=5000.0,mult=10)#回测参数
|
||||
|
||||
# 运行回测
|
||||
result = cerebro.run()
|
||||
|
||||
# 获取策略分析器中的结果
|
||||
analyzer = result[0].analyzers
|
||||
total_trades = analyzer.trades.get_analysis()['total']['total']
|
||||
winning_trades = analyzer.trades.get_analysis()['won']['total']
|
||||
# 获取TradeAnalyzer分析器的结果
|
||||
trade_analyzer_result = analyzer.trades.get_analysis()
|
||||
# 获取总收益额
|
||||
total_profit = trade_analyzer_result.pnl.net.total
|
||||
|
||||
if total_trades > 0:
|
||||
win_rate = winning_trades / total_trades
|
||||
else:
|
||||
win_rate = 0.0
|
||||
# 打印回测报告
|
||||
print('回测报告:')
|
||||
print('期初权益', 初始资金)
|
||||
print('期末权益', 初始资金+round(total_profit))
|
||||
print('盈亏额', round(total_profit))
|
||||
print('最大回撤率,', round(analyzer.drawdown.get_analysis()['drawdown'],2),'%')
|
||||
print('胜率,', round(win_rate*100,2),'%')
|
||||
print("交易次数,", total_trades)
|
||||
print("盈利次数,", winning_trades)
|
||||
print("亏损次数,", total_trades - winning_trades)
|
||||
print('总手续费+滑点,', 手续费汇总)
|
||||
手续费汇总=0
|
||||
|
||||
# 设置中文显示
|
||||
mpl.rcParams["font.sans-serif"] = ["SimHei"]
|
||||
mpl.rcParams["axes.unicode_minus"] = False
|
||||
|
||||
# 保存回测图像文件
|
||||
plot = cerebro.plot()[0][0]
|
||||
plot_filename = os.path.splitext(os.path.basename(csv_file))[0] +'ss'+ '_plot.png'
|
||||
plot_path = os.path.join('部分回测报告', plot_filename)
|
||||
plot.savefig(plot_path)
|
||||
|
||||
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
@@ -0,0 +1,309 @@
|
||||
'''逐行解释代码：
|
||||
|
||||
1.导入所需的模块和库，包括 time、table（来自 matplotlib.pyplot）、pandas、numpy、numba 和 operator。
|
||||
|
||||
2.定义了一个名为 process 的函数，用于处理买卖盘的字典数据。
|
||||
|
||||
3.定义了一个名为 data 的函数，用于读取并处理 tick 数据，生成分钟级别的 bar 数据。
|
||||
|
||||
4.定义了一个名为 orderflow_df_new 的函数，用于处理 tick 数据和分钟级别的 bar 数据，生成订单流数据。
|
||||
|
||||
5.定义了一个名为 GetOrderFlow_dj 的函数，用于计算订单流的指标(堆积)。
|
||||
|
||||
6.定义了一个名为 back_data 的函数，用于保存回测数据。
|
||||
|
||||
7.在 if __name__ == "__main__": 下，首先调用 data() 函数获取 tick 数据和分钟级别的 bar 数据。
|
||||
|
||||
然后调用 orderflow_df_new() 函数，传入 tick 数据和 bar 数据，生成订单流数据 ofdata。
|
||||
|
||||
打印输出 ofdata。
|
||||
|
||||
8.调用 back_data() 函数，将订单流数据保存为回测数据。
|
||||
|
||||
打印输出 "done"，表示程序执行完毕。
|
||||
|
||||
总体而言，该代码的功能是从 tick 数据中生成分钟级别的 bar 数据，然后根据 bar 数据计算订单流，并将订单流数据保存为回测数据。
|
||||
使用说明：使用前需要调整的相关参数如下
|
||||
1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
|
||||
2.dataload函数：一、确定datetime函数和其他key值是否和现在的一致，不一致的修改；二、resample函数中rule的取样周期进行修改，默认为5T，即5分钟。
|
||||
3.back_data函数和main中需要注意修改相应的时间节点，将开盘的初始数据设置为0，
|
||||
4.如果生成的时间和实际时间相差8小时，可以调用timedelta函数修改
|
||||
'''
|
||||
# GetOrderFlow_dj函数需要进一步了解，先不修改
|
||||
|
||||
import time
|
||||
from matplotlib.pyplot import table
|
||||
from datetime import timedelta
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from numba import *
|
||||
from numba import cuda
|
||||
import operator
|
||||
|
||||
import os
|
||||
|
||||
# 对于含时区的datetime，可以通过timedelta来修改数据
|
||||
#from datetime import datetime, timedelta
|
||||
#os.environ['tz'] = 'Asia/ShangHai'
|
||||
#time.tzset()
|
||||
|
||||
def process(bidDict,askDict):
|
||||
bidDictResult,askDictResult = {},{}
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
#print('bidDict:',list(bidDict.keys()))
|
||||
#print('askDict:',list(askDict.keys()))
|
||||
#print('sList:',sList)
|
||||
#240884432
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
bidDictResult[s] = bidDict[s]
|
||||
else:
|
||||
bidDictResult[s] = 0
|
||||
if s in askDict:
|
||||
askDictResult[s] = askDict[s]
|
||||
else:
|
||||
askDictResult[s] = 0
|
||||
|
||||
return bidDictResult,askDictResult
|
||||
|
||||
|
||||
|
||||
|
||||
def dataload(data):
|
||||
#日期修正
|
||||
data['业务日期'] = data['业务日期'].dt.strftime('%Y-%m-%d')
|
||||
data['datetime'] = data['业务日期'] + ' '+data['最后修改时间'].dt.time.astype(str) + '.' + data['最后修改毫秒'].astype(str)
|
||||
# 将 'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
|
||||
data['datetime'] = pd.to_datetime(data['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
|
||||
# 如果需要，可以将 datetime 列格式化为字符串
|
||||
#data['formatted_date'] = data['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')
|
||||
#计算瞬时成交量
|
||||
data['volume'] = data['数量'] - data['数量'].shift(1)
|
||||
data['volume'] = data['volume'].fillna(0)
|
||||
#整理好要用的tick数据元素,具体按照数据的表头进行修改
|
||||
tickdata =pd.DataFrame({'datetime':data['datetime'],'symbol':data['合约代码'],'lastprice':data['最新价'],
|
||||
'volume':data['volume'],'bid_p':data['申买价一'],'bid_v':data['申买量一'],'ask_p':data['申卖价一'],'ask_v':data['申卖量一']})
|
||||
#tickdata['datetime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
|
||||
# # 找到满足条件的行的索引
|
||||
# condition = tickdata['datetime'].dt.time == pd.to_datetime('22:59:59').time()
|
||||
# indexes_to_update = tickdata.index[condition]
|
||||
|
||||
# # 遍历索引，将不一致的日期更新为上一行的日期
|
||||
# for idx in indexes_to_update:
|
||||
# if idx > 0:
|
||||
# tickdata.at[idx, 'datetime'] = tickdata.at[idx - 1, 'datetime'].replace(hour=22, minute=59, second=59)
|
||||
|
||||
# 确保日期列按升序排序
|
||||
tickdata.sort_values(by='datetime', inplace=True)
|
||||
# 时序重采样 https://zhuanlan.zhihu.com/p/70353374
|
||||
bardata = tickdata.resample(on = 'datetime',rule = '1T',label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
#240884432
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
return tickdata,bardata
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(df_tick,df_min):
|
||||
df_of=pd.DataFrame({})
|
||||
t1 = time.time()
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
endArray = pd.to_datetime(df_min['datetime']).values
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
dt=endArray[index]
|
||||
for indexTick in range(indexFinal,len(df_tick)):
|
||||
if tTickArray[indexTick] > tEnd:
|
||||
break
|
||||
elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] <= tEnd):
|
||||
if indexTick==0:
|
||||
Bp = round(bp1TickArray[indexTick],2)
|
||||
Ap = round(ap1TickArray[indexTick],2)
|
||||
else:
|
||||
Bp = round(bp1TickArray[indexTick - 1],2)
|
||||
Ap = round(ap1TickArray[indexTick - 1],2)
|
||||
LastPrice = round(lastTickArray[indexTick],2)
|
||||
Volume = volumeTickArray[indexTick]
|
||||
if LastPrice >= Ap:
|
||||
if LastPrice in askDict.keys():
|
||||
askDict[LastPrice] += Volume
|
||||
else:
|
||||
askDict[LastPrice] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if LastPrice in bidDict.keys():
|
||||
bidDict[LastPrice] += Volume
|
||||
else:
|
||||
bidDict[LastPrice] = Volume
|
||||
indexFinal = indexTick
|
||||
|
||||
bidDictResult,askDictResult = process(bidDict,askDict)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
# 过滤'volume'列小于等于0的行
|
||||
df = df[df['volume'] > 0]
|
||||
# 重新排序DataFrame，按照'datetime'列进行升序排序
|
||||
df = df.sort_values(by='datetime', ascending=True)
|
||||
# 重新设置索引，以便索引能够正确对齐
|
||||
df = df.reset_index(drop=True)
|
||||
#df['ticktime']=tTickArray[indexTick]
|
||||
df['dj']=GetOrderFlow_dj(df)
|
||||
#print(df)
|
||||
df_of = pd.concat([df_of, df], ignore_index=True)
|
||||
print(time.time() - t1)
|
||||
return df_of
|
||||
|
||||
def GetOrderFlow_dj(kData):
|
||||
itemAskBG=['rgb(0,255,255)', 'rgb(255,0,255)', "rgb(255,182,193)"] # 买盘背景色
|
||||
itemBidBG=['rgb(173,255,47)', 'rgb(255,127,80)', "rgb(32,178,170)"] # 卖盘背景色
|
||||
Config={
|
||||
'Value1':3,
|
||||
'Value2':3,
|
||||
'Value3':3,
|
||||
'Value4':True,
|
||||
}
|
||||
aryData=kData
|
||||
djcout=0
|
||||
for index,row in aryData.iterrows():
|
||||
kItem=aryData.iloc[index]
|
||||
high=kItem['high']
|
||||
low=kItem['low']
|
||||
close=kItem['close']
|
||||
open=kItem['open']
|
||||
dtime=kItem['datetime']
|
||||
price_s=kItem['price']
|
||||
Ask_s=kItem['Ask']
|
||||
Bid_s=kItem['Bid']
|
||||
delta=kItem['delta']
|
||||
|
||||
price_s=price_s
|
||||
Ask_s=Ask_s
|
||||
Bid_s=Bid_s
|
||||
|
||||
gj=0
|
||||
xq=0
|
||||
gxx=0
|
||||
xxx=0
|
||||
for i in np.arange (0, len(price_s),1) :
|
||||
duiji={
|
||||
'price':0,
|
||||
'time':0,
|
||||
'longshort':0,
|
||||
'cout':0,
|
||||
'color':'blue'
|
||||
}
|
||||
if i==0 :
|
||||
delta=delta
|
||||
|
||||
order= {
|
||||
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
if i>=0 and i<len(price_s)-1:
|
||||
if (order["Bid"]["Value"]>Ask_s[i+1]*int(Config['Value1'])):
|
||||
order["Bid"]["Color"]=itemAskBG[1]
|
||||
gxx+=1
|
||||
gj+=1
|
||||
if gj>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=-1
|
||||
duiji['cout']=gj
|
||||
duiji['color']='rgba(0,139,0,0.45)'#绿色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=-1
|
||||
else :
|
||||
gj=0
|
||||
if i>=1 and i<len(price_s)-1:
|
||||
if (order["Ask"]["Value"]>Bid_s[i-1]*int(Config['Value1'])):
|
||||
xq+=1
|
||||
xxx+=1
|
||||
order["Ask"]["Color"]=itemBidBG[1]
|
||||
if xq>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=1
|
||||
duiji['cout']=xq
|
||||
duiji['color']='rgba(255,0,0,0.45)' #红色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=1
|
||||
else :
|
||||
xq=0
|
||||
return djcout
|
||||
|
||||
def back_data(df):
|
||||
# 创建新的DataFrame并填充需要的列
|
||||
new_df = pd.DataFrame()
|
||||
new_df['datetime'] = pd.to_datetime(df['datetime'], format='%Y/%m/%d %H:%M')
|
||||
new_df['close'] = df['close']
|
||||
new_df['open'] = df['open']
|
||||
new_df['high'] = df['high']
|
||||
new_df['low'] = df['low']
|
||||
new_df['volume'] = df['volume']
|
||||
new_df['sig'] = df['dj']
|
||||
new_df['symbol'] = df['symbol']
|
||||
new_df['delta'] = df['delta']
|
||||
new_df.to_csv(f'./tick生成的OF数据/back_ofdata_dj.csv',index=False)
|
||||
#new_df.to_csv(f'{sym}back_ofdata_dj.csv',index=False)
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
data=pd.read_csv('rb主力连续_20230103.csv',encoding='GBK',parse_dates=['业务日期','最后修改时间']) #
|
||||
print(data)
|
||||
tick,bar=dataload(data)
|
||||
ofdata = orderflow_df_new(tick,bar)
|
||||
print(ofdata)
|
||||
#保存orderflow数据
|
||||
# os.mkdir('./tick生成的OF数据')，或者在to_csv中修改生成的文件名
|
||||
folder_path = "tick生成的OF数据"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('tick生成的OF数据')
|
||||
ofdata.to_csv('./tick生成的OF数据/ofdata_dj.csv')
|
||||
#保存回测数据
|
||||
back_data(ofdata)
|
||||
print('done')
|
||||
|
||||
@@ -0,0 +1,309 @@
|
||||
'''逐行解释代码：
|
||||
|
||||
1.导入所需的模块和库，包括 time、table（来自 matplotlib.pyplot）、pandas、numpy、numba 和 operator。
|
||||
|
||||
2.定义了一个名为 process 的函数，用于处理买卖盘的字典数据。
|
||||
|
||||
3.定义了一个名为 data 的函数，用于读取并处理 tick 数据，生成分钟级别的 bar 数据。
|
||||
|
||||
4.定义了一个名为 orderflow_df_new 的函数，用于处理 tick 数据和分钟级别的 bar 数据，生成订单流数据。
|
||||
|
||||
5.定义了一个名为 GetOrderFlow_dj 的函数，用于计算订单流的指标(堆积)。
|
||||
|
||||
6.定义了一个名为 back_data 的函数，用于保存回测数据。
|
||||
|
||||
7.在 if __name__ == "__main__": 下，首先调用 data() 函数获取 tick 数据和分钟级别的 bar 数据。
|
||||
|
||||
然后调用 orderflow_df_new() 函数，传入 tick 数据和 bar 数据，生成订单流数据 ofdata。
|
||||
|
||||
打印输出 ofdata。
|
||||
|
||||
8.调用 back_data() 函数，将订单流数据保存为回测数据。
|
||||
|
||||
打印输出 "done"，表示程序执行完毕。
|
||||
|
||||
总体而言，该代码的功能是从 tick 数据中生成分钟级别的 bar 数据，然后根据 bar 数据计算订单流，并将订单流数据保存为回测数据。
|
||||
使用说明：使用前需要调整的相关参数如下
|
||||
1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
|
||||
2.dataload函数：一、确定datetime函数和其他key值是否和现在的一致，不一致的修改；二、resample函数中rule的取样周期进行修改，默认为5T，即5分钟。
|
||||
3.back_data函数和main中需要注意修改相应的时间节点，将开盘的初始数据设置为0，
|
||||
4.如果生成的时间和实际时间相差8小时，可以调用timedelta函数修改
|
||||
'''
|
||||
# GetOrderFlow_dj函数需要进一步了解，先不修改
|
||||
|
||||
import time
|
||||
from matplotlib.pyplot import table
|
||||
from datetime import timedelta
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from numba import *
|
||||
from numba import cuda
|
||||
import operator
|
||||
|
||||
import os
|
||||
|
||||
# 对于含时区的datetime，可以通过timedelta来修改数据
|
||||
#from datetime import datetime, timedelta
|
||||
#os.environ['tz'] = 'Asia/ShangHai'
|
||||
#time.tzset()
|
||||
|
||||
def process(bidDict,askDict):
|
||||
bidDictResult,askDictResult = {},{}
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
#print('bidDict:',list(bidDict.keys()))
|
||||
#print('askDict:',list(askDict.keys()))
|
||||
#print('sList:',sList)
|
||||
#240884432
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
bidDictResult[s] = bidDict[s]
|
||||
else:
|
||||
bidDictResult[s] = 0
|
||||
if s in askDict:
|
||||
askDictResult[s] = askDict[s]
|
||||
else:
|
||||
askDictResult[s] = 0
|
||||
|
||||
return bidDictResult,askDictResult
|
||||
|
||||
|
||||
|
||||
|
||||
def dataload(data):
|
||||
#日期修正
|
||||
data['业务日期'] = data['业务日期'].dt.strftime('%Y-%m-%d')
|
||||
data['datetime'] = data['业务日期'] + ' '+data['最后修改时间'].dt.time.astype(str) + '.' + data['最后修改毫秒'].astype(str)
|
||||
# 将 'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理
|
||||
data['datetime'] = pd.to_datetime(data['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')
|
||||
# 如果需要，可以将 datetime 列格式化为字符串
|
||||
#data['formatted_date'] = data['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')
|
||||
#计算瞬时成交量
|
||||
data['volume'] = data['数量'] - data['数量'].shift(1)
|
||||
data['volume'] = data['volume'].fillna(0)
|
||||
#整理好要用的tick数据元素,具体按照数据的表头进行修改
|
||||
tickdata =pd.DataFrame({'datetime':data['datetime'],'symbol':data['合约代码'],'lastprice':data['最新价'],
|
||||
'volume':data['volume'],'bid_p':data['申买价一'],'bid_v':data['申买量一'],'ask_p':data['申卖价一'],'ask_v':data['申卖量一']})
|
||||
#tickdata['datetime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
|
||||
# # 找到满足条件的行的索引
|
||||
# condition = tickdata['datetime'].dt.time == pd.to_datetime('22:59:59').time()
|
||||
# indexes_to_update = tickdata.index[condition]
|
||||
|
||||
# # 遍历索引，将不一致的日期更新为上一行的日期
|
||||
# for idx in indexes_to_update:
|
||||
# if idx > 0:
|
||||
# tickdata.at[idx, 'datetime'] = tickdata.at[idx - 1, 'datetime'].replace(hour=22, minute=59, second=59)
|
||||
|
||||
# 确保日期列按升序排序
|
||||
tickdata.sort_values(by='datetime', inplace=True)
|
||||
# 时序重采样 https://zhuanlan.zhihu.com/p/70353374
|
||||
bardata = tickdata.resample(on = 'datetime',rule = '1T',label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
#240884432
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
return tickdata,bardata
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(df_tick,df_min):
|
||||
df_of=pd.DataFrame({})
|
||||
t1 = time.time()
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
endArray = pd.to_datetime(df_min['datetime']).values
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
dt=endArray[index]
|
||||
for indexTick in range(indexFinal,len(df_tick)):
|
||||
if tTickArray[indexTick] > tEnd:
|
||||
break
|
||||
elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] <= tEnd):
|
||||
if indexTick==0:
|
||||
Bp = round(bp1TickArray[indexTick],2)
|
||||
Ap = round(ap1TickArray[indexTick],2)
|
||||
else:
|
||||
Bp = round(bp1TickArray[indexTick - 1],2)
|
||||
Ap = round(ap1TickArray[indexTick - 1],2)
|
||||
LastPrice = round(lastTickArray[indexTick],2)
|
||||
Volume = volumeTickArray[indexTick]
|
||||
if LastPrice >= Ap:
|
||||
if LastPrice in askDict.keys():
|
||||
askDict[LastPrice] += Volume
|
||||
else:
|
||||
askDict[LastPrice] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if LastPrice in bidDict.keys():
|
||||
bidDict[LastPrice] += Volume
|
||||
else:
|
||||
bidDict[LastPrice] = Volume
|
||||
indexFinal = indexTick
|
||||
|
||||
bidDictResult,askDictResult = process(bidDict,askDict)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
# 过滤'volume'列小于等于0的行
|
||||
df = df[df['volume'] > 0]
|
||||
# 重新排序DataFrame，按照'datetime'列进行升序排序
|
||||
df = df.sort_values(by='datetime', ascending=True)
|
||||
# 重新设置索引，以便索引能够正确对齐
|
||||
df = df.reset_index(drop=True)
|
||||
#df['ticktime']=tTickArray[indexTick]
|
||||
df['dj']=GetOrderFlow_dj(df)
|
||||
#print(df)
|
||||
df_of = pd.concat([df_of, df], ignore_index=True)
|
||||
print(time.time() - t1)
|
||||
return df_of
|
||||
|
||||
def GetOrderFlow_dj(kData):
|
||||
itemAskBG=['rgb(0,255,255)', 'rgb(255,0,255)', "rgb(255,182,193)"] # 买盘背景色
|
||||
itemBidBG=['rgb(173,255,47)', 'rgb(255,127,80)', "rgb(32,178,170)"] # 卖盘背景色
|
||||
Config={
|
||||
'Value1':3,
|
||||
'Value2':3,
|
||||
'Value3':3,
|
||||
'Value4':True,
|
||||
}
|
||||
aryData=kData
|
||||
djcout=0
|
||||
for index,row in aryData.iterrows():
|
||||
kItem=aryData.iloc[index]
|
||||
high=kItem['high']
|
||||
low=kItem['low']
|
||||
close=kItem['close']
|
||||
open=kItem['open']
|
||||
dtime=kItem['datetime']
|
||||
price_s=kItem['price']
|
||||
Ask_s=kItem['Ask']
|
||||
Bid_s=kItem['Bid']
|
||||
delta=kItem['delta']
|
||||
|
||||
price_s=price_s
|
||||
Ask_s=Ask_s
|
||||
Bid_s=Bid_s
|
||||
|
||||
gj=0
|
||||
xq=0
|
||||
gxx=0
|
||||
xxx=0
|
||||
for i in np.arange (0, len(price_s),1) :
|
||||
duiji={
|
||||
'price':0,
|
||||
'time':0,
|
||||
'longshort':0,
|
||||
'cout':0,
|
||||
'color':'blue'
|
||||
}
|
||||
if i==0 :
|
||||
delta=delta
|
||||
|
||||
order= {
|
||||
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
if i>=0 and i<len(price_s)-1:
|
||||
if (order["Bid"]["Value"]>Ask_s[i+1]*int(Config['Value1'])):
|
||||
order["Bid"]["Color"]=itemAskBG[1]
|
||||
gxx+=1
|
||||
gj+=1
|
||||
if gj>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=-1
|
||||
duiji['cout']=gj
|
||||
duiji['color']='rgba(0,139,0,0.45)'#绿色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=-1
|
||||
else :
|
||||
gj=0
|
||||
if i>=1 and i<len(price_s)-1:
|
||||
if (order["Ask"]["Value"]>Bid_s[i-1]*int(Config['Value1'])):
|
||||
xq+=1
|
||||
xxx+=1
|
||||
order["Ask"]["Color"]=itemBidBG[1]
|
||||
if xq>=int(Config['Value2']) and Config['Value4']==True:
|
||||
duiji['price']=price_s[i]
|
||||
duiji['time']=dtime
|
||||
duiji['longshort']=1
|
||||
duiji['cout']=xq
|
||||
duiji['color']='rgba(255,0,0,0.45)' #红色
|
||||
if float(duiji['price'])>0:
|
||||
djcout+=1
|
||||
else :
|
||||
xq=0
|
||||
return djcout
|
||||
|
||||
def back_data(df):
|
||||
# 创建新的DataFrame并填充需要的列
|
||||
new_df = pd.DataFrame()
|
||||
new_df['datetime'] = pd.to_datetime(df['datetime'], format='%Y/%m/%d %H:%M')
|
||||
new_df['close'] = df['close']
|
||||
new_df['open'] = df['open']
|
||||
new_df['high'] = df['high']
|
||||
new_df['low'] = df['low']
|
||||
new_df['volume'] = df['volume']
|
||||
new_df['sig'] = df['dj']
|
||||
new_df['symbol'] = df['symbol']
|
||||
new_df['delta'] = df['delta']
|
||||
new_df.to_csv(f'./tick生成的OF数据/back_ofdata_dj.csv',index=False)
|
||||
#new_df.to_csv(f'{sym}back_ofdata_dj.csv',index=False)
|
||||
|
||||
if __name__ == "__main__":
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
data=pd.read_csv('rb主力连续_20230103.csv',encoding='GBK',parse_dates=['业务日期','最后修改时间']) #
|
||||
print(data)
|
||||
tick,bar=dataload(data)
|
||||
ofdata = orderflow_df_new(tick,bar)
|
||||
print(ofdata)
|
||||
#保存orderflow数据
|
||||
# os.mkdir('./tick生成的OF数据')，或者在to_csv中修改生成的文件名
|
||||
folder_path = "tick生成的OF数据"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('tick生成的OF数据')
|
||||
ofdata.to_csv('./tick生成的OF数据/ofdata_dj.csv')
|
||||
#保存回测数据
|
||||
back_data(ofdata)
|
||||
print('done')
|
||||
|
||||
@@ -0,0 +1,313 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"id": "30ee221d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"id": "c826c49d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv('E:/data/ag/tick生成的OF数据/back_ofdata_dj.csv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"id": "887ba88c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"delta_values = df['delta'].abs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"id": "a662ed7c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算正数部分的百分位数、中位数和标准差\n",
|
||||
"percentile = delta_values.quantile(0.70)\n",
|
||||
"median = delta_values.median()\n",
|
||||
"std = delta_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"id": "69d1e7cb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"全部数据绝对值的百分位：740.0\n",
|
||||
"全部数据绝对值的中位数：426.0\n",
|
||||
"全部数据绝对值的标准差：722.8068551884389\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 打印相关结果\n",
|
||||
"print(f\"全部数据绝对值的百分位：{percentile}\")\n",
|
||||
"print(f\"全部数据绝对值的中位数：{median}\")\n",
|
||||
"print(f\"全部数据绝对值的标准差：{std}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"id": "979f66c9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# positive_values = [x for x in delta_values if x > 0]\n",
|
||||
"# negative_values = [x for x in delta_values if x < 0]\n",
|
||||
"positive_values = df['delta'][df['delta'] > 0]\n",
|
||||
"negative_values = df['delta'][df['delta'] < 0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"id": "3f0334e8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算正数部分的百分位数、中位数和标准差\n",
|
||||
"positive_percentile = positive_values.quantile(0.70)\n",
|
||||
"positive_median = positive_values.median()\n",
|
||||
"positive_std = positive_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"id": "9daebce5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算负数部分的百分位数、中位数和标准差\n",
|
||||
"negative_percentile = negative_values.quantile(0.70)\n",
|
||||
"negative_median = negative_values.median()\n",
|
||||
"negative_std = negative_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"id": "0406696f",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"正数的百分位：1014.8000000000011\n",
|
||||
"正数的中位数：432.0\n",
|
||||
"正数的标准差：708.9579385007327\n",
|
||||
"负数的百分位：-998.0\n",
|
||||
"负数的中位数：-421.0\n",
|
||||
"负数的标准差：736.3260520762277\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 打印相关结果\n",
|
||||
"print(f\"正数的百分位：{positive_percentile}\")\n",
|
||||
"print(f\"正数的中位数：{positive_median}\")\n",
|
||||
"print(f\"正数的标准差：{positive_std}\")\n",
|
||||
"\n",
|
||||
"print(f\"负数的百分位：{negative_percentile}\")\n",
|
||||
"print(f\"负数的中位数：{negative_median}\")\n",
|
||||
"print(f\"负数的标准差：{negative_std}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bd9ed5a7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# positive_values.tail()\n",
|
||||
"# negative_values.tail()\n",
|
||||
"# pos_qua_nums = positive_values.iloc[-120:-1].quantile(0.95)\n",
|
||||
"# print(pos_qua_nums)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "39fe34e9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['delta_cumsum'] = df['delta'].cumsum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ea2ad82f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['delta'].head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f71b1dc3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['delta_cumsum'].head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "29cf2703",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"delta_cumsum_values = df['delta_cumsum']#.abs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7f8ff173",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算和值的正数部分的百分位数、中位数和标准差\n",
|
||||
"cumsum_percentile = delta_cumsum_values.quantile(0.95)\n",
|
||||
"cumsum_median = delta_cumsum_values.median()\n",
|
||||
"cumsum_std = delta_cumsum_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c2c57b8a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 打印相关结果\n",
|
||||
"print(f\"和值的全部数据绝对值的百分位：{cumsum_percentile}\")\n",
|
||||
"print(f\"和值的全部数据绝对值的中位数：{cumsum_median}\")\n",
|
||||
"print(f\"和值的全部数据绝对值的标准差：{cumsum_std}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "82860da9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"positive_cumsum_values = df['delta_cumsum'][df['delta_cumsum'] > 0]\n",
|
||||
"negative_cumsum_values = df['delta_cumsum'][df['delta_cumsum'] < 0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "56f9f922",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"positive_cumsum_values.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b8dcab86",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"negative_cumsum_values.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "db782086",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算正数部分的百分位数、中位数和标准差\n",
|
||||
"positive_cumsum_percentile = positive_cumsum_values.quantile(0.7)\n",
|
||||
"positive_cumsum_median = positive_cumsum_values.median()\n",
|
||||
"positive_cumsum_std = positive_cumsum_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2430d9b7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 计算负数部分的百分位数、中位数和标准差\n",
|
||||
"negative_cumsum_percentile = negative_cumsum_values.quantile(0.7)\n",
|
||||
"negative_cumsum_median = negative_cumsum_values.median()\n",
|
||||
"negative_cumsum_std = negative_cumsum_values.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "698948f6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 打印相关结果\n",
|
||||
"print(f\"和值的正数百分位：{positive_cumsum_percentile}\")\n",
|
||||
"print(f\"和值的正的中位数：{positive_cumsum_median}\")\n",
|
||||
"print(f\"和值的正数标准差：{positive_cumsum_std}\")\n",
|
||||
"\n",
|
||||
"print(f\"和值的负数百分位：{negative_cumsum_percentile}\")\n",
|
||||
"print(f\"和值的负数中位数：{negative_cumsum_median}\")\n",
|
||||
"print(f\"和值的负数标准差：{negative_cumsum_std}\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@@ -0,0 +1,892 @@
|
||||
'''
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
# 加入邮件通知
|
||||
import smtplib
|
||||
from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件
|
||||
from email.mime.multipart import MIMEMultipart # 导入 MIMEMultipart 类发送带有附件的邮件
|
||||
from email.mime.application import MIMEApplication # 导入 MIMEApplication 类发送二进制附件
|
||||
|
||||
## 配置邮件信息
|
||||
receivers = ["240884432@qq.com"] # 设置邮件接收人地址
|
||||
subject = "订单流策略交易信号" # 设置邮件主题
|
||||
#text = " " # 设置邮件正文
|
||||
# file_path = "test.txt" # 设置邮件附件文件路径
|
||||
|
||||
## 配置邮件服务器信息
|
||||
smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
|
||||
smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
|
||||
sender = "240884432@qq.com" # 设置发送邮件的邮箱地址
|
||||
username = "240884432@qq.com" # 设置发送邮件的邮箱用户名
|
||||
password = "ifjgwlnzdvrfbjgf" #zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trade_dfs = {} #pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={}
|
||||
|
||||
clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
|
||||
def send_mail(text):
|
||||
msg = MIMEMultipart()
|
||||
msg["From"] = sender
|
||||
msg["To"] = ";".join(receivers)
|
||||
msg["Subject"] = subject
|
||||
msg.attach(MIMEText(text, "plain", "utf-8"))
|
||||
smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
|
||||
smtp.login(username, password)
|
||||
smtp.sendmail(sender, receivers, msg.as_string())
|
||||
smtp.quit()
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
class ParamObj:
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
symbol = None #合约名称
|
||||
Lots = None #下单手数
|
||||
py = None #设置委托价格的偏移，更加容易促成成交
|
||||
trailing_stop_percent = None #跟踪出场参数
|
||||
fixed_stop_loss_percent = None #固定出场参数
|
||||
dj_X = None #开仓的堆积参数
|
||||
delta = None #开仓的delta参数
|
||||
sum_delta = None #开仓的delta累积参数
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
|
||||
# 策略需要用到的变量
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
short_trailing_stop_price = 0
|
||||
long_trailing_stop_price = 0
|
||||
sl_long_price = 0
|
||||
sl_shor_price = 0
|
||||
out_long = 0
|
||||
out_short = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
def __init__(self, symbol, Lots, py, trailing_stop_percent, fixed_stop_loss_percent, dj_X, delta, sum_delta,失衡,堆积,周期):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.trailing_stop_percent = trailing_stop_percent
|
||||
self.fixed_stop_loss_percent = fixed_stop_loss_percent
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
|
||||
|
||||
# 邮件通知模块
|
||||
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
# 将df数据合并到trader_df中
|
||||
# if symbol not in trade_dfs.keys():
|
||||
# trade_df = pd.DataFrame({})
|
||||
# else:
|
||||
# trade_df = trade_dfs[symbol]
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
|
||||
#print(trader_df)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i < len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
#读取保存的数据
|
||||
def read_to_csv(self,symbol):
|
||||
# 文件夹路径和文件路径
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
param = self.param_dict[symbol]
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}_traderdata.csv")
|
||||
# 如果文件夹不存在则创建
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
|
||||
# 读取保留的模型数据CSV文件
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and param.kgdata==True:
|
||||
# 选择最后一行数据
|
||||
row = df.iloc[-1]
|
||||
|
||||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||||
param.pos = int(row['pos'])
|
||||
param.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
param.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
param.sl_long_price = float(row['sl_long_price'])
|
||||
param.sl_shor_price = float(row['sl_shor_price'])
|
||||
# param.out_long = int(row['out_long'])
|
||||
# param.out_short = int(row['out_short'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
param.kgdata=False
|
||||
else:
|
||||
pass
|
||||
#print("没有找到历史交易数据文件", file_path)
|
||||
#如果没有找到CSV，则初始化变量
|
||||
|
||||
pass
|
||||
|
||||
#保存数据
|
||||
def save_to_csv(self,symbol):
|
||||
param = self.param_dict[symbol]
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
# symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 创建DataFrame
|
||||
|
||||
data = {
|
||||
'datetime': [trade_dfs[symbol]['datetime'].iloc[-1]],
|
||||
'pos': [param.pos],
|
||||
'short_trailing_stop_price': [param.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [param.long_trailing_stop_price],
|
||||
'sl_long_price': [param.sl_long_price],
|
||||
'sl_shor_price': [param.sl_shor_price],
|
||||
# 'out_long': [param.out_long],
|
||||
# 'out_short': [param.out_short]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 将DataFrame保存到CSV文件
|
||||
df.to_csv(f"traderdata/{str(symbol)}_traderdata.csv", index=False)
|
||||
|
||||
#每日收盘重置数据
|
||||
def day_data_reset(self, symbol):
|
||||
param = self.param_dict[symbol]
|
||||
sec = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围（日盘收盘）
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
param.clearing_executed = False
|
||||
# 检查当前时间第一个操作的时间范围内
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True)#清除当天的行情数据
|
||||
|
||||
# 检查当前时间是否在第二个操作的时间范围内(夜盘收盘)
|
||||
elif sec in clearing_time_dict.keys():
|
||||
clearing_time2_start = clearing_time_dict[sec]
|
||||
clearing_time2_end = s_time(clearing_time2_start.hour, clearing_time2_start.minute+15)
|
||||
if clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
trade_dfs[symbol].drop(trade_dfs[symbol].index,inplace=True) #清除当天的行情数据
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
return param.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
data = symbol_queue.get(block=True, timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset(instrument_id)
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
#新K线开始，启动交易程序 and 保存行情数据
|
||||
self.read_to_csv(instrument_id)
|
||||
# size = symbol_queue.qsize()
|
||||
# if size > 2:
|
||||
# print(f'！！！！！当前{instrument_id}共享队列长度为：',size)
|
||||
if len(trade_df)>param.cont_df:
|
||||
# 检查文件是否存在
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
trade_df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个DataFrame
|
||||
trade_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if param.long_trailing_stop_price >0 and param.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',param.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
param.long_trailing_stop_price = trade_df['low'].iloc[-1] if param.long_trailing_stop_price<trade_df['low'].iloc[-1] else param.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',param.long_trailing_stop_price)
|
||||
if param.short_trailing_stop_price >0 and param.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',param.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
param.short_trailing_stop_price = trade_df['high'].iloc[-1] if trade_df['high'].iloc[-1] <param.short_trailing_stop_price else param.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',param.short_trailing_stop_price)
|
||||
|
||||
param.out_long=param.long_trailing_stop_price * (1 - param.trailing_stop_percent)
|
||||
param.out_short=param.short_trailing_stop_price*(1 + param.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',param.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',param.out_long)
|
||||
# 跟踪出场
|
||||
if param.out_long >0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——多头止盈——','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
if trade_df['low'].iloc[-1] < param.out_long and param.pos>0 and param.sl_long_price>0 and trade_df['low'].iloc[-1]>param.sl_long_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'多头止盈','TR',param.out_long,'low', trade_df['low'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.out_long=0
|
||||
param.sl_long_price=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if param.out_short>0:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
if trade_df['high'].iloc[-1] > param.out_short and param.pos<0 and param.sl_shor_price>0 and trade_df['high'].iloc[-1]<param.sl_shor_price:
|
||||
print('datetime+sig: ',trade_df['datetime'].iloc[-1],'空头止盈: ','TR',param.out_short,'high', trade_df['high'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
fixed_stop_loss_L = param.sl_long_price * (1 - param.fixed_stop_loss_percent)
|
||||
if param.pos>0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_long_price>0 and fixed_stop_loss_L>0 and param.pos > 0 and trade_df['close'].iloc[-1] < fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头止损', 'SL', fixed_stop_loss_L, 'close', trade_df['close'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.long_trailing_stop_price = 0
|
||||
param.sl_long_price=0
|
||||
param.out_long = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
fixed_stop_loss_S = param.sl_shor_price * (1 + param.fixed_stop_loss_percent)
|
||||
if param.pos<0:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '预设——空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
if param.sl_shor_price>0 and fixed_stop_loss_S>0 and param.pos < 0 and trade_df['close'].iloc[-1] > fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头止损', 'SL', fixed_stop_loss_S, 'close', trade_df['close'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.short_trailing_stop_price = 0
|
||||
param.sl_shor_price=0
|
||||
param.out_short = 0
|
||||
param.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#日均线
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
|
||||
#小于日均线
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
|
||||
平多条件=trade_df['dj'].iloc[-1]<-param.dj_X
|
||||
平空条件=trade_df['dj'].iloc[-1]>param.dj_X
|
||||
#开仓
|
||||
#多头开仓条件
|
||||
if param.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.sl_shor_price=0
|
||||
param.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"平空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平空的平仓价格为{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+param.py)
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=1
|
||||
param.long_trailing_stop_price=data['AskPrice1']
|
||||
param.sl_long_price=data['AskPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"开多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 多头开仓的开仓价格{data['AskPrice1']+param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-param.py)
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.long_trailing_stop_price=0
|
||||
param.sl_long_price=0
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#发送邮件
|
||||
text = f"平多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
if param.pos==0 and 开空条件 :
|
||||
print('开空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1'])
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trade_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-param.py,'堆积数：', trade_df['dj'].iloc[-1])
|
||||
param.pos=-1
|
||||
param.short_trailing_stop_price=data['BidPrice1']
|
||||
param.sl_shor_price=data['BidPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"开空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trade_df['datetime'].iloc[-1]}, 空头开仓的开仓价格{data['BidPrice1']-param.py}, 交易手数位{param.Lots}"
|
||||
send_mail(text)
|
||||
|
||||
print(trade_df)
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
# print(f"当前合约队列为空，等待新数据插入。")
|
||||
pass
|
||||
|
||||
# 将CTP推送的行情数据分发给对应线程队列去执行
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False) # 往对应合约队列中插入行情
|
||||
# print(f"{instrument_id}合约数据插入。")
|
||||
except queue.Full:
|
||||
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(20) #为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],)) # 为每个合约单独创建一个线程计算开仓逻辑
|
||||
threads.append(t)
|
||||
t.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
param_dict = {}
|
||||
param_dict['rb2410'] = ParamObj(symbol='rb2410', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=1,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['ni2405'] = ParamObj(symbol='ni2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=1500,sum_delta=2000,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['j2405'] = ParamObj(symbol='j2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['TA405'] = ParamObj(symbol='TA405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['au2406'] = ParamObj(symbol='au2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['sc2405'] = ParamObj(symbol='sc2405', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['bc2406'] = ParamObj(symbol='bc2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
# param_dict['lu2406'] = ParamObj(symbol='lu2406', Lots=1, py=5, trailing_stop_percent=0.02, fixed_stop_loss_percent=0.01,dj_X=0,delta=15,sum_delta=20,失衡=3,堆积=3,周期='1T')
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
# future_account = get_simulate_account(
|
||||
# investor_id='135858', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
# password='Zj82334475', # simnow密码
|
||||
# server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
||||
# subscribe_list=list(param_dict.keys()), # 合约列表
|
||||
# )
|
||||
|
||||
#实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
future_account = FutureAccount(
|
||||
broker_id='8888', # 期货公司BrokerID
|
||||
server_dict={'TDServer': "103.140.14.210:43205", 'MDServer': '103.140.14.210:43173'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
reserve_server_dict={}, # 备用服务器地址
|
||||
investor_id='155878', # 账户
|
||||
password='Zj82334475', # 密码
|
||||
app_id='vntech_vnpy_2.0', # 认证使用AppID
|
||||
auth_code='N46EKN6TJ9U7V06V', # 认证使用授权码
|
||||
subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
||||
md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
)
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
@@ -0,0 +1,780 @@
|
||||
'''
|
||||
使用说明：使用前需要调整的相关参数如下
|
||||
1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
|
||||
2.配置邮件信息和参数。
|
||||
3.tickdata函数中：一、修改时间冲采样resample中rule周期5T为交易周期，
|
||||
4.GetOrderFlow_dj函数：一、堆积函数config参数暂时均为3
|
||||
5.MyTrader类:
|
||||
1) init函数初始化:委托价格的偏移、跟踪出场、固定出差参数、交易手数的设置；
|
||||
2) day_data_reset函数、每日收盘重置数据按照交易品种设置。
|
||||
3）Join函数：修改“开多组合”和“开空组合”
|
||||
6. __main__函数：设置交易账户变量
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
'''
|
||||
# 需要完善：__main__函数中手动设置subscribe_list变量，通过时间判断是否需要进行换月修改，并发送邮件通知
|
||||
|
||||
from multiprocessing import Process, Queue
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import re
|
||||
|
||||
# 加入邮件通知
|
||||
import smtplib
|
||||
from email.mime.text import MIMEText # 导入 MIMEText 类发送纯文本邮件
|
||||
from email.mime.multipart import MIMEMultipart # 导入 MIMEMultipart 类发送带有附件的邮件
|
||||
from email.mime.application import MIMEApplication # 导入 MIMEApplication 类发送二进制附件
|
||||
|
||||
## 配置邮件信息
|
||||
receivers = ["***@qq.com"] # 设置邮件接收人地址
|
||||
subject = "订单流策略交易信号" # 设置邮件主题
|
||||
#text = " " # 设置邮件正文
|
||||
# file_path = "test.txt" # 设置邮件附件文件路径
|
||||
|
||||
## 配置邮件服务器信息
|
||||
smtp_server = "smtp.qq.com" # 设置发送邮件的 SMTP 服务器地址
|
||||
smtp_port = 465 # 设置发送邮件的 SMTP 服务器端口号，一般为 25 端口 465
|
||||
sender = "***@qq.com" # 设置发送邮件的邮箱地址
|
||||
username = "***@qq.com" # 设置发送邮件的邮箱用户名
|
||||
password = "zrmpcgttataabhjh" #zrmpcgttataabhjh，设置发送邮件的邮箱密码或授权码
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trader_df = pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={}
|
||||
|
||||
# 邮件通知模块
|
||||
def send_mail(text):
|
||||
msg = MIMEMultipart()
|
||||
msg["From"] = sender
|
||||
msg["To"] = ";".join(receivers)
|
||||
msg["Subject"] = subject
|
||||
msg.attach(MIMEText(text, "plain", "utf-8"))
|
||||
smtp = smtplib.SMTP_SSL(smtp_server, smtp_port)
|
||||
smtp.login(username, password)
|
||||
smtp.sendmail(sender, receivers, msg.as_string())
|
||||
smtp.quit()
|
||||
|
||||
def tickcome(md_queue):
|
||||
global previous_volume
|
||||
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
on_tick(tick)
|
||||
|
||||
def can_time(hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(tick):
|
||||
|
||||
tm=can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
tickdata(tick_dt,sym)
|
||||
|
||||
# 这个函数的主要目的是将输入的买盘和卖盘字典合并、排序、累加，并将处理后的结果存储在一个全局字典quotedict中，同时返回这个结果。
|
||||
def data_of(df):
|
||||
global trader_df
|
||||
# 将df数据合并到trader_df中
|
||||
trader_df = pd.concat([trader_df, df], ignore_index=True)
|
||||
#print('trader_df: ', len(trader_df))
|
||||
#print(trader_df)
|
||||
|
||||
def process(bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
|
||||
def tickdata(df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
data_of(oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
bardata = tickdata.resample(on = 'bartime',rule = '1T',label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
def orderflow_df_new(df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def GetOrderFlow_dj(kData):
|
||||
Config = {
|
||||
'Value1': 3,
|
||||
'Value2': 3,
|
||||
'Value3': 3,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order = {
|
||||
"Price": price_s[i],
|
||||
"Bid": { "Value":Bid_s[i]},
|
||||
"Ask": { "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i < len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.py=5 #设置委托价格的偏移，更加容易促成成交。仅螺纹，其他品种根据最小点波动，自己设置
|
||||
self.cont_df=0
|
||||
self.trailing_stop_percent = 0.02 #跟踪出场参数
|
||||
self.fixed_stop_loss_percent = 0.01 #固定出场参数
|
||||
self.dj_X=1 #开仓的堆积参数
|
||||
|
||||
self.pos=0
|
||||
self.Lots=1
|
||||
self.short_trailing_stop_price = 0
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.sl_shor_price=0
|
||||
self.out_long=0
|
||||
self.out_short=0
|
||||
self.clearing_executed=False
|
||||
self.kgdata=True
|
||||
|
||||
|
||||
#读取保存的数据
|
||||
def read_to_csv(self,symbol):
|
||||
# 文件夹路径和文件路径
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
folder_path = "traderdata"
|
||||
file_path = os.path.join(folder_path, f"{str(symbol)}traderdata.csv")
|
||||
# 如果文件夹不存在则创建
|
||||
if not os.path.exists(folder_path):
|
||||
os.makedirs(folder_path)
|
||||
|
||||
# 读取保留的模型数据CSV文件
|
||||
if os.path.exists(file_path):
|
||||
df = pd.read_csv(file_path)
|
||||
if not df.empty and self.kgdata==True:
|
||||
# 选择最后一行数据
|
||||
row = df.iloc[-1]
|
||||
# 根据CSV文件的列名将数据赋值给相应的属性
|
||||
self.pos = int(row['pos'])
|
||||
self.short_trailing_stop_price = float(row['short_trailing_stop_price'])
|
||||
self.long_trailing_stop_price = float(row['long_trailing_stop_price'])
|
||||
self.sl_long_price = float(row['sl_long_price'])
|
||||
self.sl_shor_price = float(row['sl_shor_price'])
|
||||
# self.out_long = int(row['out_long'])
|
||||
# self.out_short = int(row['out_short'])
|
||||
print("找到历史交易数据文件,已经更新持仓，止损止盈数据", df.iloc[-1])
|
||||
self.kgdata=False
|
||||
else:
|
||||
pass
|
||||
#print("没有找到历史交易数据文件", file_path)
|
||||
#如果没有找到CSV，则初始化变量
|
||||
|
||||
pass
|
||||
|
||||
#保存数据
|
||||
def save_to_csv(self,symbol):
|
||||
# 使用正则表达式提取英文字母并重新赋值给symbol
|
||||
symbol = ''.join(re.findall('[a-zA-Z]', str(symbol)))
|
||||
# 创建DataFrame
|
||||
data = {
|
||||
'datetime': [trader_df['datetime'].iloc[-1]],
|
||||
'pos': [self.pos],
|
||||
'short_trailing_stop_price': [self.short_trailing_stop_price],
|
||||
'long_trailing_stop_price': [self.long_trailing_stop_price],
|
||||
'sl_long_price': [self.sl_long_price],
|
||||
'sl_shor_price': [self.sl_shor_price],
|
||||
# 'out_long': [self.out_long],
|
||||
# 'out_short': [self.out_short]
|
||||
}
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 将DataFrame保存到CSV文件
|
||||
df.to_csv(f"traderdata/{str(symbol)}traderdata.csv", index=False)
|
||||
|
||||
#每日收盘重置数据
|
||||
def day_data_reset(self):
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 第一时间范围
|
||||
clearing_time1_start = s_time(15,00)
|
||||
clearing_time1_end = s_time(15,15)
|
||||
|
||||
# 第二时间范围
|
||||
clearing_time2_start = s_time(23,0)
|
||||
clearing_time2_end = s_time(23,15)
|
||||
|
||||
# 创建一个标志变量，用于记录是否已经执行过
|
||||
self.clearing_executed = False
|
||||
# 检查当前时间第一个操作的时间范围内
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not self.clearing_executed :
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.index,inplace=True)#清除当天的行情数据
|
||||
|
||||
# 检查当前时间是否在第二个操作的时间范围内
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not self.clearing_executed :
|
||||
self.clearing_executed = True # 设置标志变量为已执行
|
||||
trader_df.drop(trader_df.index,inplace=True) #清除当天的行情数据
|
||||
else:
|
||||
self.clearing_executed = False
|
||||
pass
|
||||
return self.clearing_executed
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
def Join(self):
|
||||
data = None
|
||||
while True:
|
||||
if self.status == 0:
|
||||
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
self.read_to_csv(instrument_id)
|
||||
self.day_data_reset()
|
||||
tickcome(data)
|
||||
#新K线开始，启动交易程序 and 保存行情数据
|
||||
if len(trader_df)>self.cont_df:
|
||||
# 检查文件是否存在
|
||||
csv_file_path = f"traderdata/{instrument_id}_ofdata.csv"
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
trader_df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个DataFrame
|
||||
trader_df.to_csv(csv_file_path, index=False)
|
||||
|
||||
# 更新跟踪止损价格
|
||||
if self.long_trailing_stop_price >0 and self.pos>0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧多头出线',self.long_trailing_stop_price,'low',self.low[0])
|
||||
|
||||
self.long_trailing_stop_price = trader_df['low'].iloc[-1] if self.long_trailing_stop_price<trader_df['low'].iloc[-1] else self.long_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'多头出线',self.long_trailing_stop_price)
|
||||
if self.short_trailing_stop_price >0 and self.pos<0:
|
||||
|
||||
#print('datetime+sig: ',dt,'旧空头出线',self.short_trailing_stop_price,'high',self.high[0])
|
||||
|
||||
self.short_trailing_stop_price = trader_df['high'].iloc[-1] if trader_df['high'].iloc[-1] <self.short_trailing_stop_price else self.short_trailing_stop_price
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#print('datetime+sig: ',dt,'空头出线',self.short_trailing_stop_price)
|
||||
|
||||
self.out_long=self.long_trailing_stop_price * (1 - self.trailing_stop_percent)
|
||||
self.out_short=self.short_trailing_stop_price*(1 + self.trailing_stop_percent)
|
||||
#print('datetime+sig: ',dt,'空头出线',self.out_short)
|
||||
#print('datetime+sig: ',dt,'多头出线',self.out_long)
|
||||
# 跟踪出场
|
||||
if self.out_long >0:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'预设——多头止盈——','TR',self.out_long,'low', trader_df['low'].iloc[-1])
|
||||
if trader_df['low'].iloc[-1] < self.out_long and self.pos>0 and self.sl_long_price>0 and trader_df['low'].iloc[-1]>self.sl_long_price:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'多头止盈','TR',self.out_long,'low', trader_df['low'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.long_trailing_stop_price = 0
|
||||
self.out_long=0
|
||||
self.sl_long_price=0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
if self.out_short>0:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'预设——空头止盈——: ','TR',self.out_short,'high', trader_df['high'].iloc[-1])
|
||||
if trader_df['high'].iloc[-1] > self.out_short and self.pos<0 and self.sl_shor_price>0 and trader_df['high'].iloc[-1]<self.sl_shor_price:
|
||||
print('datetime+sig: ',trader_df['datetime'].iloc[-1],'空头止盈: ','TR',self.out_short,'high', trader_df['high'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_shor=0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 固定止损
|
||||
self.fixed_stop_loss_L = self.sl_long_price * (1 - self.fixed_stop_loss_percent)
|
||||
if self.pos>0:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '预设——多头止损', 'SL', self.fixed_stop_loss_L, 'close', trader_df['close'].iloc[-1])
|
||||
if self.sl_long_price>0 and self.fixed_stop_loss_L>0 and self.pos > 0 and trader_df['close'].iloc[-1] < self.fixed_stop_loss_L:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '多头止损', 'SL', self.fixed_stop_loss_L, 'close', trader_df['close'].iloc[-1])
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.long_trailing_stop_price = 0
|
||||
self.sl_long_price=0
|
||||
self.out_long = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
self.fixed_stop_loss_S = self.sl_shor_price * (1 + self.fixed_stop_loss_percent)
|
||||
if self.pos<0:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '预设——空头止损', 'SL', self.fixed_stop_loss_S, 'close', trader_df['close'].iloc[-1])
|
||||
if self.sl_shor_price>0 and self.fixed_stop_loss_S>0 and self.pos < 0 and trader_df['close'].iloc[-1] > self.fixed_stop_loss_S:
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '空头止损', 'SL', self.fixed_stop_loss_S, 'close', trader_df['close'].iloc[-1])
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.short_trailing_stop_price = 0
|
||||
self.sl_shor_price=0
|
||||
self.out_short = 0
|
||||
self.pos = 0
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#日均线
|
||||
trader_df['dayma']=trader_df['close'].mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trader_df['delta'] = trader_df['delta'].astype(float)
|
||||
trader_df['delta累计'] = trader_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trader_df['dayma'].iloc[-1] > 0 and trader_df['close'].iloc[-1] > trader_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trader_df['delta累计'].iloc[-1] > 2000 and trader_df['delta'].iloc[-1] > 1500
|
||||
|
||||
#小于日均线
|
||||
开空1=trader_df['dayma'].iloc[-1]>0 and trader_df['close'].iloc[-1] < trader_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trader_df['delta累计'].iloc[-1] < -2000 and trader_df['delta'].iloc[-1] < -1500
|
||||
|
||||
开多组合= 开多1 and 开多4 and trader_df['dj'].iloc[-1]>self.dj_X
|
||||
开空条件= 开空1 and 开空4 and trader_df['dj'].iloc[-1]<-self.dj_X
|
||||
|
||||
平多条件=trader_df['dj'].iloc[-1]<-self.dj_X
|
||||
平空条件=trader_df['dj'].iloc[-1]>self.dj_X
|
||||
#开仓
|
||||
#多头开仓条件
|
||||
if self.pos<0 and 平空条件 :
|
||||
print('平空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+self.py)
|
||||
#insert_order:买卖方向，开仓：0，平仓：1，强平：2，平今：3，平昨：4，强减：5，本地强平：6
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'3')
|
||||
self.pos=0
|
||||
self.sl_shor_price=0
|
||||
self.short_trailing_stop_price=0
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '反手平空：', '平仓价格：', data['AskPrice1']+self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#发送邮件
|
||||
text = f"平空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trader_df['datetime'].iloc[-1]}, 反手平空的平仓价格{data['AskPrice1']+self.py}"
|
||||
send_mail(text)
|
||||
if self.pos==0 and 开多组合:
|
||||
print('开多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'AskPrice1',data['AskPrice1']+self.py)
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+self.py,self.Lots,b'0',b'0')
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '多头开仓', '开仓价格：', data['AskPrice1']+self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.pos=1
|
||||
self.long_trailing_stop_price=data['AskPrice1']
|
||||
self.sl_long_price=data['AskPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#发送邮件
|
||||
text = f"开多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trader_df['datetime'].iloc[-1]}, 多头开仓的开仓价格{data['AskPrice1']+self.py}，预设——多头止盈——TR{self.out_long},多头止损SL{self.fixed_stop_loss_L}"
|
||||
send_mail(text)
|
||||
|
||||
|
||||
if self.pos>0 and 平多条件 :
|
||||
print('平多： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1']-self.py)
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'3')
|
||||
self.pos=0
|
||||
self.long_trailing_stop_price=0
|
||||
self.sl_long_price=0
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '反手平多', '平仓价格：', data['BidPrice1']-self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
#发送邮件
|
||||
text = f"平多交易： 交易品种为{data['InstrumentID']}, 交易时间为{trader_df['datetime'].iloc[-1]}, 反手平多的平仓价格{data['BidPrice1']-self.py}"
|
||||
send_mail(text)
|
||||
|
||||
if self.pos==0 and 开空条件 :
|
||||
print('开空： ','ExchangeID: ',data['ExchangeID'],'InstrumentID',data['InstrumentID'],'BidPrice1',data['BidPrice1'])
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-self.py,self.Lots,b'1',b'0')
|
||||
print('datetime+sig: ', trader_df['datetime'].iloc[-1], '空头开仓', '开仓价格：', data['BidPrice1']-self.py,'堆积数：', trader_df['dj'].iloc[-1])
|
||||
self.pos=-1
|
||||
self.short_trailing_stop_price=data['BidPrice1']
|
||||
self.sl_shor_price=data['BidPrice1']
|
||||
self.save_to_csv(instrument_id)
|
||||
|
||||
# 发送邮件
|
||||
text = f"开空交易： 交易品种为{data['InstrumentID']}, 交易时间为{trader_df['datetime'].iloc[-1]}, 空头开仓的开仓价格{data['BidPrice1']-self.py},预设——空头止盈——TR{self.out_short},空头止损{self.fixed_stop_loss_S}"
|
||||
send_mail(text)
|
||||
|
||||
print(trader_df)
|
||||
self.cont_df=len(trader_df)
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
|
||||
def run_trader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.Join()
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
future_account = get_simulate_account(
|
||||
investor_id='135858', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
password='Zj82334475', # simnow密码
|
||||
server_name='TEST', # 电信1、电信2、移动、TEST、N视界
|
||||
subscribe_list=[b'rb2405'], # 合约列表
|
||||
)
|
||||
|
||||
#实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
# future_account = FutureAccount(
|
||||
# broker_id='', # 期货公司BrokerID
|
||||
# server_dict={'TDServer': "ip:port", 'MDServer': 'ip:port'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
# reserve_server_dict={}, # 备用服务器地址
|
||||
# investor_id='', # 账户
|
||||
# password='', # 密码
|
||||
# app_id='simnow_client_test', # 认证使用AppID
|
||||
# auth_code='0000000000000000', # 认证使用授权码
|
||||
# subscribe_list=[b'rb2405'], # 订阅合约列表
|
||||
# md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
# td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
# )
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
# success = f"行情和交易启动成功！{future_account.subscribe_list}"
|
||||
# send_mail(success)
|
||||
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
@@ -0,0 +1,38 @@
|
||||
'''
|
||||
Author: zhoujie2104231 zhoujie@me.com
|
||||
Date: 2024-02-25 11:17:14
|
||||
LastEditors: zhoujie2104231 zhoujie@me.com
|
||||
LastEditTime: 2024-02-25 21:40:34
|
||||
# 使用说明：使用前需要调整的相关参数如下
|
||||
# 1.确定python到csv文件夹下运行,并修改到对应的csv文件
|
||||
# 2.设置按照index拆分的表名，此处是按照“合约代码”的不同进行拆分
|
||||
# 3.使用gbk或者utf-8编译
|
||||
'''
|
||||
import csv
|
||||
import os
|
||||
import pandas as pd
|
||||
|
||||
def read_large_csv(file_path, chunk_size):
|
||||
reader = pd.read_csv(file_path, iterator=True, encoding="utf-8")
|
||||
chunks = []
|
||||
while True:
|
||||
try:
|
||||
chunk = reader.get_chunk(chunk_size)
|
||||
chunks.append(chunk)
|
||||
except StopIteration:
|
||||
break
|
||||
return pd.concat(chunks, ignore_index=True)
|
||||
|
||||
filepath = './合成tick数据/merged_data_new.csv'
|
||||
chunk_size = 10000
|
||||
data = read_large_csv(filepath, chunk_size)
|
||||
|
||||
groups = data.groupby(data['合约代码'])
|
||||
|
||||
folder_path = "split_csvs"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('split_csvs')
|
||||
|
||||
for group in groups:
|
||||
group[1].to_csv('./split_csvs/{}.csv'.format(str(group[0])), index = False, encoding = 'utf-8')
|
||||
print("%s.csv创建成功！" %(group[0]))
|
||||
@@ -0,0 +1,64 @@
|
||||
'''
|
||||
Author: zhoujie2104231 zhoujie@me.com
|
||||
Date: 2024-02-25 16:19:47
|
||||
LastEditors: zhoujie2104231 zhoujie@me.com
|
||||
LastEditTime: 2024-02-25 16:22:11
|
||||
FilePath: \Gitee_Code\trading_strategy\SS_Code\SF08\split_data_finall.py
|
||||
Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
|
||||
'''
|
||||
import csv
|
||||
import os
|
||||
import pandas as pd
|
||||
|
||||
def read_large_csv(file_path, chunk_size):
|
||||
reader = pd.read_csv(file_path, iterator=True, encoding="utf-8")
|
||||
chunks = []
|
||||
while True:
|
||||
try:
|
||||
chunk = reader.get_chunk(chunk_size)
|
||||
chunks.append(chunk)
|
||||
except StopIteration:
|
||||
break
|
||||
return pd.concat(chunks, ignore_index=True)
|
||||
|
||||
|
||||
# 读取原始CSV文件
|
||||
# with open('merged_data_new.csv', 'r', encoding="utf-8") as f:
|
||||
# reader = csv.reader(f)
|
||||
# data = list(reader)
|
||||
# print("读取文件成功")
|
||||
|
||||
filepath = 'merged_data_new.csv'
|
||||
chunk_size = 10000
|
||||
data = read_large_csv(filepath, chunk_size)
|
||||
|
||||
print("读取文件成功")
|
||||
|
||||
|
||||
# 创建一个字典，key为symbol列的值，value为一个列表，存放与该symbol值相关的数据行
|
||||
symbol_data = {}
|
||||
header_row = data[0]
|
||||
for row in data[1:]: # 跳过第一行标题
|
||||
symbol = row[2]
|
||||
if symbol not in symbol_data:
|
||||
symbol_data[symbol] = []
|
||||
symbol_data[symbol].append(row)
|
||||
|
||||
print("数据字典创建成功")
|
||||
|
||||
# 创建与symbol列值对应的目录
|
||||
folder_path = "split_csvs"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('split_csvs')
|
||||
|
||||
for symbol in symbol_data:
|
||||
os.mkdir(os.path.join('split_csvs', symbol))
|
||||
|
||||
# 将数据写入拆分后的CSV文件中
|
||||
for symbol, rows in symbol_data.items():
|
||||
with open(os.path.join('split_csvs', symbol, f'{symbol}.csv'), 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerows([header_row])
|
||||
writer.writerows(rows)
|
||||
|
||||
print("csv拆分成功")
|
||||
@@ -0,0 +1,132 @@
|
||||
'''
|
||||
Author: zhoujie2104231 zhoujie@me.com
|
||||
# Date: 2024-02-25 17:51:46
|
||||
LastEditors: zhoujie2104231 zhoujie@me.com
|
||||
LastEditTime: 2024-03-07 22:48:22
|
||||
# 使用说明：使用前需要调整的相关参数如下
|
||||
# 1.确定python到csv文件夹下运行,
|
||||
# 2.统一代码的添加:主力连续为888,指数连续可以用999,次主力连续可以使用889,其他的可以不用添加统一代码,注释掉。
|
||||
# 3.文件夹下的文件名按照datetime进行排序修改
|
||||
# 4.data按照时间排序需要根据参数修改['业务日期','最后修改时间','最后修改毫秒']，如果前面文件名按照时间修改好了,不用修改
|
||||
# 5.使用gbk或者utf-8编译
|
||||
'''
|
||||
|
||||
import pandas as pd
|
||||
import os
|
||||
|
||||
# import datetime as dt
|
||||
|
||||
def split_alpha_numeric(string):
|
||||
"""
|
||||
Split a string into alphabetical and numerical characters.
|
||||
|
||||
Args:
|
||||
string: The string to split.
|
||||
|
||||
Returns:
|
||||
A tuple containing two strings, the first containing the alphabetical
|
||||
characters and the second containing the numerical characters.
|
||||
"""
|
||||
|
||||
alpha_chars = ""
|
||||
numeric_chars = ""
|
||||
|
||||
for char in string:
|
||||
if char.isalpha():
|
||||
alpha_chars += char
|
||||
elif char.isdigit():
|
||||
numeric_chars += char
|
||||
|
||||
return alpha_chars, numeric_chars
|
||||
|
||||
#第一中方法：
|
||||
# 获取当前目录下的所有csv文件
|
||||
all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]
|
||||
|
||||
# csv需要筛选的文件名字符
|
||||
sp_char = '_2021'
|
||||
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
|
||||
# 另一种遍历方式
|
||||
# folder_path = "D:/data_transfer/ag888"
|
||||
|
||||
# name_chr = "202309"
|
||||
# csv_files = []
|
||||
|
||||
# for root, dirs, files in os.walk(folder_path):
|
||||
# for file in files:
|
||||
# if file.endswith('.csv'):
|
||||
# # 获取文件名（不包含扩展名）
|
||||
# filename = os.path.splitext(file)[0]
|
||||
# match_file = re.search(r'(?<=^.{7}).{6}(?=.{2})',filename)
|
||||
# try:
|
||||
# if match_file.group() == name_chr:#
|
||||
# full_filename = filename + ".csv"
|
||||
# csv_files.append(full_filename)
|
||||
# else:
|
||||
# #print("文件夹中有csv文件，但没有文件名含%s的csv文件"%(name_chr))
|
||||
# pass
|
||||
# except AttributeError:
|
||||
# continue
|
||||
# else:
|
||||
# #print("文件夹中没有csv文件")
|
||||
# pass
|
||||
|
||||
|
||||
# 将当前的数据按照文件名进行排序，生成list文件
|
||||
#csv_files.sort(key=lambda x: int(x.split('.')[0]))
|
||||
|
||||
# 创建新的DataFrame来存储合并后的数据
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
df = pd.read_csv(file, header=0, encoding='gbk')
|
||||
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset=merged_df.columns.tolist(), inplace=True)
|
||||
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
print("合约代码:", merged_df["合约代码"])
|
||||
|
||||
# 插入新的数据
|
||||
# code_value = csv_files[0].split
|
||||
# merged_df.insert(loc=1,column="统一代码", value="rb888")
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df["合约代码"][0])
|
||||
print("Alphabetical characters:", alpha_chars)
|
||||
# print("Numerical characters:", numeric_chars[1])
|
||||
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
merged_df.insert(loc=1,column="统一代码", value=code_value)
|
||||
|
||||
|
||||
# 将合并后的数据保存到csv文件中
|
||||
folder_path = "合成tick数据2019-2021"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('合成tick数据2019-2021')
|
||||
|
||||
# sorted_merged_df = merged_df.sort_values(by= ['业务日期','最后修改时间','最后修改毫秒'], ascending=[True, True, True])
|
||||
# sorted_merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False)
|
||||
|
||||
merged_df['时间'] = pd.to_datetime(merged_df['时间'])
|
||||
sorted_merged_df = merged_df.sort_values(by = ['时间'], ascending=True)
|
||||
sorted_merged_df.to_csv('./合成tick数据2019-2021/%s%s.csv'%(code_value,sp_char), index=False)
|
||||
del merged_df
|
||||
del sorted_merged_df
|
||||
#merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False) #数据按照时间排序，前面文件夹按照时间修改好了可以直接用这里
|
||||
|
||||
# 打印提示信息
|
||||
print("CSV文件合并成功！")
|
||||
@@ -0,0 +1,134 @@
|
||||
'''
|
||||
Author: zhoujie2104231 zhoujie@me.com
|
||||
# Date: 2024-02-25 17:51:46
|
||||
LastEditors: zhoujie2104231 zhoujie@me.com
|
||||
LastEditTime: 2024-03-17 16:59:35
|
||||
# 使用说明：使用前需要调整的相关参数如下
|
||||
# 1.确定python到csv文件夹下运行,
|
||||
# 2.统一代码的添加:主力连续为888,指数连续可以用999,次主力连续可以使用889,其他的可以不用添加统一代码,注释掉。
|
||||
# 3.文件夹下的文件名按照datetime进行排序修改
|
||||
# 4.data按照时间排序需要根据参数修改['业务日期','最后修改时间','最后修改毫秒']，如果前面文件名按照时间修改好了,不用修改
|
||||
# 5.使用gbk或者utf-8编译
|
||||
'''
|
||||
|
||||
import pandas as pd
|
||||
import os
|
||||
|
||||
# import datetime as dt
|
||||
|
||||
def split_alpha_numeric(string):
|
||||
"""
|
||||
Split a string into alphabetical and numerical characters.
|
||||
|
||||
Args:
|
||||
string: The string to split.
|
||||
|
||||
Returns:
|
||||
A tuple containing two strings, the first containing the alphabetical
|
||||
characters and the second containing the numerical characters.
|
||||
"""
|
||||
|
||||
alpha_chars = ""
|
||||
numeric_chars = ""
|
||||
|
||||
for char in string:
|
||||
if char.isalpha():
|
||||
alpha_chars += char
|
||||
elif char.isdigit():
|
||||
numeric_chars += char
|
||||
|
||||
return alpha_chars, numeric_chars
|
||||
|
||||
#第一中方法：
|
||||
# 获取当前目录下的所有csv文件
|
||||
all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]
|
||||
|
||||
# csv需要筛选的文件名字符
|
||||
sp_char = '_2023'
|
||||
|
||||
# 获取当前目录下的所有文件名包含sp_char的csv文件
|
||||
csv_files = [sp_file for sp_file in all_csv_files if sp_char in sp_file]
|
||||
print("csv_files:", csv_files)
|
||||
|
||||
# 另一种遍历方式
|
||||
# folder_path = "D:/data_transfer/ag888"
|
||||
|
||||
# name_chr = "202309"
|
||||
# csv_files = []
|
||||
|
||||
# for root, dirs, files in os.walk(folder_path):
|
||||
# for file in files:
|
||||
# if file.endswith('.csv'):
|
||||
# # 获取文件名（不包含扩展名）
|
||||
# filename = os.path.splitext(file)[0]
|
||||
# match_file = re.search(r'(?<=^.{7}).{6}(?=.{2})',filename)
|
||||
# try:
|
||||
# if match_file.group() == name_chr:#
|
||||
# full_filename = filename + ".csv"
|
||||
# csv_files.append(full_filename)
|
||||
# else:
|
||||
# #print("文件夹中有csv文件，但没有文件名含%s的csv文件"%(name_chr))
|
||||
# pass
|
||||
# except AttributeError:
|
||||
# continue
|
||||
# else:
|
||||
# #print("文件夹中没有csv文件")
|
||||
# pass
|
||||
|
||||
|
||||
# 将当前的数据按照文件名进行排序，生成list文件
|
||||
#csv_files.sort(key=lambda x: int(x.split('.')[0]))
|
||||
|
||||
# 创建新的DataFrame来存储合并后的数据
|
||||
merged_df = pd.DataFrame()
|
||||
|
||||
# 循环遍历每个csv文件
|
||||
for file in csv_files:
|
||||
# 读取csv文件，并使用第一行为列标题，编译不通过可以改为gbk
|
||||
df = pd.read_csv(file, header=0, encoding='gbk')
|
||||
|
||||
# 删除重复行
|
||||
df.drop_duplicates(inplace=True)
|
||||
|
||||
# 将数据合并到新的DataFrame中
|
||||
merged_df = pd.concat([merged_df, df], ignore_index=True)
|
||||
|
||||
# 删除重复列
|
||||
merged_df.drop_duplicates(subset=merged_df.columns.tolist(), inplace=True)
|
||||
|
||||
# 重置行索引
|
||||
merged_df.reset_index(inplace=True, drop=True)
|
||||
print("合约代码:", merged_df["合约代码"])
|
||||
|
||||
# 插入新的数据
|
||||
# code_value = csv_files[0].split
|
||||
# merged_df.insert(loc=1,column="统一代码", value="rb888")
|
||||
alpha_chars, numeric_chars = split_alpha_numeric(merged_df["合约代码"][0])
|
||||
print("Alphabetical characters:", alpha_chars)
|
||||
# print("Numerical characters:", numeric_chars[1])
|
||||
|
||||
# 添加主力连续的合约代码，主力连续为888，指数连续可以用999，次主力连续可以使用889，表头用“统一代码”
|
||||
code_value = alpha_chars + "888"
|
||||
print("code_value characters:", code_value)
|
||||
merged_df.insert(loc=1,column="统一代码", value=code_value)
|
||||
|
||||
|
||||
# 将合并后的数据保存到csv文件中
|
||||
folder_path = "合成tick数据2022-2023"
|
||||
if not os.path.exists(folder_path):
|
||||
os.mkdir('合成tick数据2022-2023')
|
||||
|
||||
sorted_merged_df = merged_df.sort_values(by= ['业务日期','最后修改时间','最后修改毫秒'], ascending=[True, True, True])
|
||||
sorted_merged_df.to_csv('./合成tick数据2022-2023/%s%s.csv'%(code_value,sp_char), index=False)
|
||||
|
||||
del merged_df
|
||||
del sorted_merged_df
|
||||
|
||||
# merged_df['时间'] = pd.to_datetime(merged_df['时间'])
|
||||
# sorted_merged_df = merged_df.sort_values(by = ['时间'], ascending=True)
|
||||
# sorted_merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False)
|
||||
|
||||
#merged_df.to_csv('./合成tick数据/%s.csv'%(code_value), index=False) #数据按照时间排序，前面文件夹按照时间修改好了可以直接用这里
|
||||
|
||||
# 打印提示信息
|
||||
print("CSV文件合并成功！")
|
||||
@@ -0,0 +1,526 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"root_path = r\"C:/Users/zhouj/Desktop/data\"\n",
|
||||
"output_path = r\"C:/Users/zhouj/Desktop/a88.csv\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"adjust = df['close'].shift() - df['open']\n",
|
||||
"adjust = np.where(df['symbol'] != df['symbol'].shift(), adjust, 0)\n",
|
||||
"df['open_adj'] = df['open'] + adjust.cumsum()\n",
|
||||
"df['close_adj'] = df['close'] + adjust.cumsum()\n",
|
||||
"df['low_adj'] = df['low'] + adjust.cumsum()\n",
|
||||
"df['high_adj'] = df['high'] + adjust.cumsum()\n",
|
||||
"# 等比复权\n",
|
||||
"adjust = df['close'].shift() / df['open']\n",
|
||||
"adjust = np.where(df['symbol'] != df['symbol'].shift(), adjust, 1)\n",
|
||||
"df['open_adj'] = df['open'] * adjust.cumprod()\n",
|
||||
"df['close_adj'] = df['close'] * adjust.cumprod()\n",
|
||||
"df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"df['high_adj'] = df['high'] * adjust.cumprod()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"files = []\n",
|
||||
"\n",
|
||||
"for r, ds, fs in os.walk(root_path):\n",
|
||||
" for f in fs:\n",
|
||||
" # if f[0:4] == '2023':\n",
|
||||
" abs_filepath = os.path.join(r, f)\n",
|
||||
" files.append(abs_filepath)\n",
|
||||
"files = sorted(files)\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame()\n",
|
||||
"for f in files:\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[1, 2, 3, 4, 8, 13, 14, 15, 16],\n",
|
||||
" names=[\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"时间\",\n",
|
||||
" \"最新\",\n",
|
||||
" \"成交量\",\n",
|
||||
" \"买一价\",\n",
|
||||
" \"卖一价\",\n",
|
||||
" \"买一量\",\n",
|
||||
" \"卖一量\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" )\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# files = []\n",
|
||||
"\n",
|
||||
"# for r, ds, fs in os.walk(root_path):\n",
|
||||
"# for f in fs:\n",
|
||||
"# # if f[0:4] == '2023':\n",
|
||||
"# abs_filepath = os.path.join(r, f)\n",
|
||||
"# files.append(abs_filepath)\n",
|
||||
"# files = sorted(files)\n",
|
||||
"\n",
|
||||
"# df = pd.DataFrame()\n",
|
||||
"# for f in files:\n",
|
||||
"# df_temp = pd.read_csv(\n",
|
||||
"# f,\n",
|
||||
"# usecols=[0, 1, 4, 11, 20, 21, 22, 23, 24, 25],\n",
|
||||
"# names=[\n",
|
||||
"# \"交易日\",\n",
|
||||
"# \"合约代码\",\n",
|
||||
"# \"最新价\",\n",
|
||||
"# \"数量\",\n",
|
||||
"# \"最后修改时间\",\n",
|
||||
"# \"最后修改毫秒\",\n",
|
||||
"# \"申买价一\",\n",
|
||||
"# \"申买量一\",\n",
|
||||
"# \"申卖价一\",\n",
|
||||
"# \"申卖量一\",\n",
|
||||
"# ],\n",
|
||||
"# skiprows=1,\n",
|
||||
"# encoding=\"gbk\",\n",
|
||||
"# )\n",
|
||||
"# # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
"# # 'datetime', 'volume'])\n",
|
||||
"# df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.info()\n",
|
||||
"# 21754840"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 统一代码 合约代码 时间 最新 成交量 买一价 卖一价 \\\n",
|
||||
"1305669 a888 a1905 2019-04-22 15:00:00.568 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305670 a888 a1905 2019-04-22 15:00:36.638 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305671 a888 a1909 2019-04-22 20:59:00.014 3412.0 224 3411.0 3412.0 \n",
|
||||
"1305672 a888 a1909 2019-04-22 21:00:00.461 3412.0 108 3412.0 3413.0 \n",
|
||||
"1305673 a888 a1909 2019-04-22 21:00:00.958 3411.0 150 3410.0 3411.0 \n",
|
||||
"\n",
|
||||
" 买一量 卖一量 \n",
|
||||
"1305669 25 10 \n",
|
||||
"1305670 25 10 \n",
|
||||
"1305671 2 8 \n",
|
||||
"1305672 10 19 \n",
|
||||
"1305673 43 3 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(df.loc[1305669:1305673])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等比复权\n",
|
||||
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(1)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
|
||||
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"# df['high_adj'] = df['high'] * adjust.cumprod()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() - df['买一价'], 0)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(0)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] + df['复权因子'].cumsum()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] + df['复权因子'].cumsum()\n",
|
||||
"df['最新_adj'] = df['最新'] + df['复权因子'].cumsum()\n",
|
||||
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
|
||||
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 统一代码 合约代码 时间 最新 成交量 买一价 卖一价 \\\n",
|
||||
"1305669 a888 a1905 2019-04-22 15:00:00.568 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305670 a888 a1905 2019-04-22 15:00:36.638 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305671 a888 a1909 2019-04-22 20:59:00.014 3412.0 224 3411.0 3412.0 \n",
|
||||
"1305672 a888 a1909 2019-04-22 21:00:00.461 3412.0 108 3412.0 3413.0 \n",
|
||||
"1305673 a888 a1909 2019-04-22 21:00:00.958 3411.0 150 3410.0 3411.0 \n",
|
||||
"\n",
|
||||
" 买一量 卖一量 复权因子 买一价_adj 卖一价_adj 最新_adj \n",
|
||||
"1305669 25 10 0.0 3308.0 3311.0 3309.0 \n",
|
||||
"1305670 25 10 0.0 3308.0 3311.0 3309.0 \n",
|
||||
"1305671 2 8 -100.0 3311.0 3312.0 3312.0 \n",
|
||||
"1305672 10 19 0.0 3312.0 3313.0 3312.0 \n",
|
||||
"1305673 43 3 0.0 3310.0 3311.0 3311.0 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(df.loc[1305669:1305673])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['买一价'] = df['买一价_adj']\n",
|
||||
"df['卖一价'] = df['卖一价_adj']\n",
|
||||
"df['最新'] = df['最新_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 统一代码 合约代码 时间 最新 成交量 买一价 卖一价 \\\n",
|
||||
"1305669 a888 a1905 2019-04-22 15:00:00.568 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305670 a888 a1905 2019-04-22 15:00:36.638 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305671 a888 a1909 2019-04-22 20:59:00.014 3312.0 224 3311.0 3312.0 \n",
|
||||
"1305672 a888 a1909 2019-04-22 21:00:00.461 3312.0 108 3312.0 3313.0 \n",
|
||||
"1305673 a888 a1909 2019-04-22 21:00:00.958 3311.0 150 3310.0 3311.0 \n",
|
||||
"\n",
|
||||
" 买一量 卖一量 复权因子 买一价_adj 卖一价_adj 最新_adj \n",
|
||||
"1305669 25 10 0.0 3308.0 3311.0 3309.0 \n",
|
||||
"1305670 25 10 0.0 3308.0 3311.0 3309.0 \n",
|
||||
"1305671 2 8 -100.0 3311.0 3312.0 3312.0 \n",
|
||||
"1305672 10 19 0.0 3312.0 3313.0 3312.0 \n",
|
||||
"1305673 43 3 0.0 3310.0 3311.0 3311.0 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(df.loc[1305669:1305673])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# df.drop('复权因子', axis=1)\n",
|
||||
"# df.drop('买一价_adj', axis=1)\n",
|
||||
"# df.drop('卖一价_adj', axis=1)\n",
|
||||
"del df['复权因子']\n",
|
||||
"del df['买一价_adj']\n",
|
||||
"del df['卖一价_adj']\n",
|
||||
"del df['最新_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 统一代码 合约代码 时间 最新 成交量 买一价 卖一价 \\\n",
|
||||
"1305670 a888 a1905 2019-04-22 15:00:36.638 3309.0 0 3308.0 3311.0 \n",
|
||||
"1305671 a888 a1909 2019-04-22 20:59:00.014 3312.0 224 3311.0 3312.0 \n",
|
||||
"1305672 a888 a1909 2019-04-22 21:00:00.461 3312.0 108 3312.0 3313.0 \n",
|
||||
"1305673 a888 a1909 2019-04-22 21:00:00.958 3311.0 150 3310.0 3311.0 \n",
|
||||
"1305674 a888 a1909 2019-04-22 21:00:01.464 3312.0 86 3311.0 3312.0 \n",
|
||||
"\n",
|
||||
" 买一量 卖一量 \n",
|
||||
"1305670 25 10 \n",
|
||||
"1305671 2 8 \n",
|
||||
"1305672 10 19 \n",
|
||||
"1305673 43 3 \n",
|
||||
"1305674 18 80 \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(df.loc[1305670:1305674])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(output_path, index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"drop_index1 = df.query('最后修改时间>\"15:00:00\" & 最后修改时间<\"21:00:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"# drop_index1 = df.query('最后修改时间>\"15:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index2 = df.query('最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index3 = df.query('最后修改时间>\"23:00:00\" & 最后修改时间<\"23:59:59\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"drop_index4 = df.query('最后修改时间>\"11:30:00\" & 最后修改时间<\"13:30:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.drop(labels=drop_index1, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index2, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index3, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index4, axis=0, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"datetime\"] = pd.to_datetime(\n",
|
||||
" pd.to_datetime(df[\"交易日\"].astype(str)).astype(str)\n",
|
||||
" + \" \"\n",
|
||||
" + df[\"最后修改时间\"].astype(str)\n",
|
||||
" + \".\"\n",
|
||||
" + df[\"最后修改毫秒\"].astype(str)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"最新价\": \"lastprice\",\n",
|
||||
" \"数量\": \"volume\",\n",
|
||||
" \"申买价一\": \"bid_p\",\n",
|
||||
" \"申买量一\": \"bid_v\",\n",
|
||||
" \"申卖价一\": \"ask_p\",\n",
|
||||
" \"申卖量一\": \"ask_v\",\n",
|
||||
" \"合约代码\": \"symbol\",\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"vol_diff\"] = df[\"volume\"].diff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[df[\"vol_diff\"].isnull(), \"vol_diff\"] = df.loc[df[\"vol_diff\"].isnull(), \"volume\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"volume\"] = df[\"vol_diff\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(output_path)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "orderflow",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,273 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 使用说明：\n",
|
||||
" 1.需要修改chdir到当前目录\n",
|
||||
" 2.需要修改最后输出的文件名称\n",
|
||||
" 3.依据情况需要修改保留的列数"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import os"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"文件中所有CSV文件: ['ag888_2019.csv', 'ag888_2020.csv', 'ag888_2021.csv', 'ag888_2022.csv', 'ag888_2022_2023.csv', 'ag888_2023.csv']\n",
|
||||
"需要筛选的文件名关键字: ['_2022']\n",
|
||||
"使用新年份格式采集!!!\n",
|
||||
"筛选结果后的CSV文件: ['ag888_2022.csv', 'ag888_2022_2023.csv']\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"os.chdir('E:/data/ag')\n",
|
||||
"all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]\n",
|
||||
"all_csv_files = sorted(all_csv_files)\n",
|
||||
"print(\"文件中所有CSV文件:\",all_csv_files)\n",
|
||||
"\n",
|
||||
"sp_chars = ['_2022']\n",
|
||||
"sp_chars = sorted(sp_chars)\n",
|
||||
"print(\"需要筛选的文件名关键字:\",sp_chars)\n",
|
||||
"\n",
|
||||
"# 设置后面数据的采集对于的行数# 用 \"old_type\" 或者 \"new_type\" 区分\n",
|
||||
"if all(char in ['_2019','_2020','_2021'] for char in sp_chars):\n",
|
||||
" year_type = 'old_type'\n",
|
||||
" print(\"使用旧年份格式采集!!!\")\n",
|
||||
"elif all(char in ['_2022','_2023'] for char in sp_chars):\n",
|
||||
" year_type = 'new_type' \n",
|
||||
" print(\"使用新年份格式采集!!!\")\n",
|
||||
"else:\n",
|
||||
" print(\"文件夹中CSV没有相关年份的数据或者新旧年份混用!!!\")\n",
|
||||
"\n",
|
||||
"csv_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_chars)]\n",
|
||||
"print(\"筛选结果后的CSV文件:\",csv_files)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame()\n",
|
||||
"for f in csv_files:\n",
|
||||
" if year_type == 'old_type':\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[1, 2, 3, 4, 8, 13, 14, 15, 16],\n",
|
||||
" names=[\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"时间\",\n",
|
||||
" \"最新\",\n",
|
||||
" \"成交量\",\n",
|
||||
" \"买一价\",\n",
|
||||
" \"卖一价\",\n",
|
||||
" \"买一量\",\n",
|
||||
" \"卖一量\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" )\n",
|
||||
" elif year_type == 'new_type':\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
|
||||
" names=[\n",
|
||||
" \"交易日\",\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"最新价\",\n",
|
||||
" \"数量\",\n",
|
||||
" \"最后修改时间\",\n",
|
||||
" \"最后修改毫秒\",\n",
|
||||
" \"申买价一\",\n",
|
||||
" \"申买量一\",\n",
|
||||
" \"申卖价一\",\n",
|
||||
" \"申卖量一\",\n",
|
||||
" \"业务日期\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 查看数据的头部和尾部：head()、tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看dataframe的基本情况\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 等比复权,先不考虑\n",
|
||||
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(1)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
|
||||
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"# df['high_adj'] = df['high'] * adjust.cumprod()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['申卖价一'].shift() - df['申买价一'], 0)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(0)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['申买价一_adj'] = df['申买价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['申卖价一_adj'] = df['申卖价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['最新价_adj'] = df['最新价'] + df['复权因子'].cumsum()\n",
|
||||
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
|
||||
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查找换期需要复权的索引\n",
|
||||
"non_zero_indices = df[df['复权因子'] != 0].index\n",
|
||||
"print(non_zero_indices)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看未调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 将调整后的数值替换原来的值\n",
|
||||
"df['申买价一'] = df['申买价一_adj']\n",
|
||||
"df['申卖价一'] = df['申卖价一_adj']\n",
|
||||
"df['最新价'] = df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 删除多余的值\n",
|
||||
"del df['复权因子']\n",
|
||||
"del df['申买价一_adj']\n",
|
||||
"del df['申卖价一_adj']\n",
|
||||
"del df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv('./ag888_2022_2023.csv', index=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "orderflow",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,428 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"root_path = r\"E:/data/ag\"\n",
|
||||
"output_path = r\"E:/data/ag/ag888.csv\"\n",
|
||||
"# df_tmp = pd.read_csv('E:/data/rb/rb888_2023.csv',encoding=\"utf-8\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"files = []\n",
|
||||
"\n",
|
||||
"for r, ds, fs in os.walk(root_path):\n",
|
||||
" for f in fs:\n",
|
||||
" # if f[0:4] == '2023':\n",
|
||||
" abs_filepath = os.path.join(r, f)\n",
|
||||
" files.append(abs_filepath)\n",
|
||||
"files = sorted(files)\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame()\n",
|
||||
"for f in files:\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
|
||||
" names=[\n",
|
||||
" \"交易日\",\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"最新价\",\n",
|
||||
" \"数量\",\n",
|
||||
" \"最后修改时间\",\n",
|
||||
" \"最后修改毫秒\",\n",
|
||||
" \"申买价一\",\n",
|
||||
" \"申买量一\",\n",
|
||||
" \"申卖价一\",\n",
|
||||
" \"申卖量一\",\n",
|
||||
" \"业务日期\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" )\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df_tmp = pd.read_csv('E:/data/rb/rb888_2023.csv',encoding=\"utf-8\")\n",
|
||||
"#df_tmp.tail()\n",
|
||||
"#df_tmp.tail().to_csv(\"E:/data/rb/rb_tail.csv\",index= False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.info()\n",
|
||||
"# 21754840"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[2493107:2493111]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等比复权,先不考虑\n",
|
||||
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(1)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
|
||||
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"# df['high_adj'] = df['high'] * adjust.cumprod()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['申卖价一'].shift() - df['申买价一'], 0)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(0)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['申买价一_adj'] = df['申买价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['申卖价一_adj'] = df['申卖价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['最新价_adj'] = df['最新价'] + df['复权因子'].cumsum()\n",
|
||||
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
|
||||
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[391880:391890]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['申买价一'] = df['申买价一_adj']\n",
|
||||
"df['申卖价一'] = df['申卖价一_adj']\n",
|
||||
"df['最新价'] = df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[391880:391890]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"non_zero_indices = df[df['复权因子'] != 0].index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(non_zero_indices)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# df.drop('复权因子', axis=1)\n",
|
||||
"# df.drop('买一价_adj', axis=1)\n",
|
||||
"# df.drop('卖一价_adj', axis=1)\n",
|
||||
"del df['复权因子']\n",
|
||||
"del df['申买价一_adj']\n",
|
||||
"del df['申卖价一_adj']\n",
|
||||
"del df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[391880:391890]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(output_path, index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head().to_csv(\"E:/data/rb/rb_ch_temp.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"drop_index1 = df.query('最后修改时间>\"15:00:00\" & 最后修改时间<\"21:00:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"# drop_index1 = df.query('最后修改时间>\"15:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index2 = df.query('最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index3 = df.query('最后修改时间>\"23:00:00\" & 最后修改时间<\"23:59:59\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"drop_index4 = df.query('最后修改时间>\"11:30:00\" & 最后修改时间<\"13:30:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.drop(labels=drop_index1, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index2, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index3, axis=0, inplace=True)\n",
|
||||
"df.drop(drop_index4, axis=0, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"datetime\"] = pd.to_datetime(\n",
|
||||
" pd.to_datetime(df[\"交易日\"].astype(str)).astype(str)\n",
|
||||
" + \" \"\n",
|
||||
" + df[\"最后修改时间\"].astype(str)\n",
|
||||
" + \".\"\n",
|
||||
" + df[\"最后修改毫秒\"].astype(str)\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"最新价\": \"lastprice\",\n",
|
||||
" \"数量\": \"volume\",\n",
|
||||
" \"申买价一\": \"bid_p\",\n",
|
||||
" \"申买量一\": \"bid_v\",\n",
|
||||
" \"申卖价一\": \"ask_p\",\n",
|
||||
" \"申卖量一\": \"ask_v\",\n",
|
||||
" \"合约代码\": \"symbol\",\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"vol_diff\"] = df[\"volume\"].diff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[df[\"vol_diff\"].isnull(), \"vol_diff\"] = df.loc[df[\"vol_diff\"].isnull(), \"volume\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"volume\"] = df[\"vol_diff\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(output_path)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "orderflow",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,222 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import os"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.chdir('E:/data/ag')\n",
|
||||
"all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]\n",
|
||||
"all_csv_files = sorted(all_csv_files)\n",
|
||||
"print(\"文件中所有CSV文件:\",all_csv_files)\n",
|
||||
"\n",
|
||||
"sp_chars = ['_2023','_2022']\n",
|
||||
"sp_chars = sorted(sp_chars)\n",
|
||||
"print(\"需要筛选的文件名关键字:\",sp_chars)\n",
|
||||
"\n",
|
||||
"csv_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_chars)]\n",
|
||||
"print(\"筛选结果后的CSV文件:\",csv_files)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame()\n",
|
||||
"for f in csv_files:\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
|
||||
" names=[\n",
|
||||
" \"交易日\",\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"最新价\",\n",
|
||||
" \"数量\",\n",
|
||||
" \"最后修改时间\",\n",
|
||||
" \"最后修改毫秒\",\n",
|
||||
" \"申买价一\",\n",
|
||||
" \"申买量一\",\n",
|
||||
" \"申卖价一\",\n",
|
||||
" \"申卖量一\",\n",
|
||||
" \"业务日期\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" )\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 查看数据的头部和尾部：head()、tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看dataframe的基本情况\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 等比复权,先不考虑\n",
|
||||
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(1)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
|
||||
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"# df['high_adj'] = df['high'] * adjust.cumprod()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['申卖价一'].shift() - df['申买价一'], 0)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(0)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['申买价一_adj'] = df['申买价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['申卖价一_adj'] = df['申卖价一'] + df['复权因子'].cumsum()\n",
|
||||
"df['最新价_adj'] = df['最新价'] + df['复权因子'].cumsum()\n",
|
||||
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
|
||||
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查找换期需要复权的索引\n",
|
||||
"non_zero_indices = df[df['复权因子'] != 0].index\n",
|
||||
"print(non_zero_indices)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看未调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 将调整后的数值替换原来的值\n",
|
||||
"df['申买价一'] = df['申买价一_adj']\n",
|
||||
"df['申卖价一'] = df['申卖价一_adj']\n",
|
||||
"df['最新价'] = df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 删除多余的值\n",
|
||||
"del df['复权因子']\n",
|
||||
"del df['申买价一_adj']\n",
|
||||
"del df['申卖价一_adj']\n",
|
||||
"del df['最新价_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv('./ag888_2022_2023.csv', index=False)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "orderflow",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,958 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import os"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"root_path = r\".\\tick\\rb\"\n",
|
||||
"output_path = r\".\\data\\rb.csv\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"files = []\n",
|
||||
"\n",
|
||||
"for r, ds, fs in os.walk(root_path):\n",
|
||||
" for f in fs:\n",
|
||||
" # if f[0:4] == '2023':\n",
|
||||
" abs_filepath = os.path.join(r, f)\n",
|
||||
" files.append(abs_filepath)\n",
|
||||
"files = sorted(files)\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame()\n",
|
||||
"for f in files:\n",
|
||||
" df_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[0, 1, 4, 11, 20, 21, 22, 23, 24, 25],\n",
|
||||
" names=[\n",
|
||||
" \"交易日\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"最新价\",\n",
|
||||
" \"数量\",\n",
|
||||
" \"最后修改时间\",\n",
|
||||
" \"最后修改毫秒\",\n",
|
||||
" \"申买价一\",\n",
|
||||
" \"申买量一\",\n",
|
||||
" \"申卖价一\",\n",
|
||||
" \"申卖量一\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"gbk\",\n",
|
||||
" )\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df = pd.concat([df, df_temp])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
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|
||||
" <th></th>\n",
|
||||
" <th>交易日</th>\n",
|
||||
" <th>合约代码</th>\n",
|
||||
" <th>最新价</th>\n",
|
||||
" <th>数量</th>\n",
|
||||
" <th>最后修改时间</th>\n",
|
||||
" <th>最后修改毫秒</th>\n",
|
||||
" <th>申买价一</th>\n",
|
||||
" <th>申买量一</th>\n",
|
||||
" <th>申卖价一</th>\n",
|
||||
" <th>申卖量一</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>41323</th>\n",
|
||||
" <td>20231229</td>\n",
|
||||
" <td>rb2405</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>1201905</td>\n",
|
||||
" <td>14:59:59</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4002.0</td>\n",
|
||||
" <td>247</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>116</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41324</th>\n",
|
||||
" <td>20231229</td>\n",
|
||||
" <td>rb2405</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>1202028</td>\n",
|
||||
" <td>14:59:59</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4002.0</td>\n",
|
||||
" <td>224</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>16</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41325</th>\n",
|
||||
" <td>20231229</td>\n",
|
||||
" <td>rb2405</td>\n",
|
||||
" <td>4002.0</td>\n",
|
||||
" <td>1202060</td>\n",
|
||||
" <td>15:00:00</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>23</td>\n",
|
||||
" <td>4004.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41326</th>\n",
|
||||
" <td>20231229</td>\n",
|
||||
" <td>rb2405</td>\n",
|
||||
" <td>4002.0</td>\n",
|
||||
" <td>1202060</td>\n",
|
||||
" <td>15:00:00</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>23</td>\n",
|
||||
" <td>4004.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>41327</th>\n",
|
||||
" <td>20231229</td>\n",
|
||||
" <td>rb2405</td>\n",
|
||||
" <td>4002.0</td>\n",
|
||||
" <td>1202060</td>\n",
|
||||
" <td>15:17:29</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4003.0</td>\n",
|
||||
" <td>23</td>\n",
|
||||
" <td>4004.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 交易日 合约代码 最新价 数量 最后修改时间 最后修改毫秒 申买价一 申买量一 \\\n",
|
||||
"41323 20231229 rb2405 4003.0 1201905 14:59:59 0 4002.0 247 \n",
|
||||
"41324 20231229 rb2405 4003.0 1202028 14:59:59 500 4002.0 224 \n",
|
||||
"41325 20231229 rb2405 4002.0 1202060 15:00:00 0 4003.0 23 \n",
|
||||
"41326 20231229 rb2405 4002.0 1202060 15:00:00 500 4003.0 23 \n",
|
||||
"41327 20231229 rb2405 4002.0 1202060 15:17:29 500 4003.0 23 \n",
|
||||
"\n",
|
||||
" 申卖价一 申卖量一 \n",
|
||||
"41323 4003.0 116 \n",
|
||||
"41324 4003.0 16 \n",
|
||||
"41325 4004.0 7 \n",
|
||||
"41326 4004.0 7 \n",
|
||||
"41327 4004.0 7 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
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||||
"<div>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>交易日</th>\n",
|
||||
" <th>合约代码</th>\n",
|
||||
" <th>最新价</th>\n",
|
||||
" <th>数量</th>\n",
|
||||
" <th>最后修改时间</th>\n",
|
||||
" <th>最后修改毫秒</th>\n",
|
||||
" <th>申买价一</th>\n",
|
||||
" <th>申买量一</th>\n",
|
||||
" <th>申卖价一</th>\n",
|
||||
" <th>申卖量一</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4302.0</td>\n",
|
||||
" <td>4643</td>\n",
|
||||
" <td>08:59:00</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4302.0</td>\n",
|
||||
" <td>115</td>\n",
|
||||
" <td>4305.0</td>\n",
|
||||
" <td>96</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4305.0</td>\n",
|
||||
" <td>5750</td>\n",
|
||||
" <td>09:00:00</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4305.0</td>\n",
|
||||
" <td>359</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>36</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4306.0</td>\n",
|
||||
" <td>8039</td>\n",
|
||||
" <td>09:00:01</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4306.0</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>9065</td>\n",
|
||||
" <td>09:00:01</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>43</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>74</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>9682</td>\n",
|
||||
" <td>09:00:02</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4311.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4314.0</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 交易日 合约代码 最新价 数量 最后修改时间 最后修改毫秒 申买价一 申买量一 申卖价一 \\\n",
|
||||
"0 20220104 rb2205 4302.0 4643 08:59:00 500 4302.0 115 4305.0 \n",
|
||||
"1 20220104 rb2205 4305.0 5750 09:00:00 500 4305.0 359 4310.0 \n",
|
||||
"2 20220104 rb2205 4306.0 8039 09:00:01 0 4306.0 18 4308.0 \n",
|
||||
"3 20220104 rb2205 4308.0 9065 09:00:01 500 4308.0 43 4310.0 \n",
|
||||
"4 20220104 rb2205 4310.0 9682 09:00:02 0 4311.0 4 4314.0 \n",
|
||||
"\n",
|
||||
" 申卖量一 \n",
|
||||
"0 96 \n",
|
||||
"1 36 \n",
|
||||
"2 7 \n",
|
||||
"3 74 \n",
|
||||
"4 19 "
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 19813536 entries, 0 to 19813535\n",
|
||||
"Data columns (total 10 columns):\n",
|
||||
" # Column Dtype \n",
|
||||
"--- ------ ----- \n",
|
||||
" 0 交易日 int64 \n",
|
||||
" 1 合约代码 object \n",
|
||||
" 2 最新价 float64\n",
|
||||
" 3 数量 int64 \n",
|
||||
" 4 最后修改时间 object \n",
|
||||
" 5 最后修改毫秒 int64 \n",
|
||||
" 6 申买价一 float64\n",
|
||||
" 7 申买量一 int64 \n",
|
||||
" 8 申卖价一 float64\n",
|
||||
" 9 申卖量一 int64 \n",
|
||||
"dtypes: float64(3), int64(5), object(2)\n",
|
||||
"memory usage: 1.5+ GB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.info()\n",
|
||||
"# 21754840"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"drop_index1 = df.query('最后修改时间>\"15:00:00\" & 最后修改时间<\"21:00:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"# drop_index1 = df.query('最后修改时间>\"15:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index2 = df.query('最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index3 = df.query('最后修改时间>\"23:00:00\" & 最后修改时间<\"23:59:59\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index\n",
|
||||
"drop_index4 = df.query('最后修改时间>\"11:30:00\" & 最后修改时间<\"13:30:00\"')[\n",
|
||||
" \"最后修改时间\"\n",
|
||||
"].index"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
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|
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|
||||
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|
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||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
" 交易日 合约代码 最新价 数量 最后修改时间 最后修改毫秒 申买价一 申买量一 \\\n",
|
||||
"19813530 20231229 rb2405 4003.0 1201836 14:59:58 500 4002.0 288 \n",
|
||||
"19813531 20231229 rb2405 4003.0 1201905 14:59:59 0 4002.0 247 \n",
|
||||
"19813532 20231229 rb2405 4003.0 1202028 14:59:59 500 4002.0 224 \n",
|
||||
"19813533 20231229 rb2405 4002.0 1202060 15:00:00 0 4003.0 23 \n",
|
||||
"19813534 20231229 rb2405 4002.0 1202060 15:00:00 500 4003.0 23 \n",
|
||||
"\n",
|
||||
" 申卖价一 申卖量一 \n",
|
||||
"19813530 4003.0 140 \n",
|
||||
"19813531 4003.0 116 \n",
|
||||
"19813532 4003.0 16 \n",
|
||||
"19813533 4004.0 7 \n",
|
||||
"19813534 4004.0 7 "
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"Int64Index: 19812430 entries, 1 to 19813534\n",
|
||||
"Data columns (total 10 columns):\n",
|
||||
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|
||||
"--- ------ ----- \n",
|
||||
" 0 交易日 int64 \n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"dtypes: float64(3), int64(5), object(2)\n",
|
||||
"memory usage: 1.6+ GB\n"
|
||||
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|
||||
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|
||||
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|
||||
"source": [
|
||||
"df.info()"
|
||||
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|
||||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
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|
||||
"source": [
|
||||
"df[\"datetime\"] = pd.to_datetime(\n",
|
||||
" pd.to_datetime(df[\"交易日\"].astype(str)).astype(str)\n",
|
||||
" + \" \"\n",
|
||||
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|
||||
" + \".\"\n",
|
||||
" + df[\"最后修改毫秒\"].astype(str)\n",
|
||||
")"
|
||||
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|
||||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>116</td>\n",
|
||||
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|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19812427</th>\n",
|
||||
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|
||||
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|
||||
" <td>4003.0</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
" </tr>\n",
|
||||
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|
||||
" <th>19812428</th>\n",
|
||||
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|
||||
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|
||||
" <td>4002.0</td>\n",
|
||||
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|
||||
" <td>15:00:00</td>\n",
|
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>2023-12-29 15:00:00.500</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"text/plain": [
|
||||
" 交易日 合约代码 最新价 数量 最后修改时间 最后修改毫秒 申买价一 申买量一 \\\n",
|
||||
"19812425 20231229 rb2405 4003.0 1201836 14:59:58 500 4002.0 288 \n",
|
||||
"19812426 20231229 rb2405 4003.0 1201905 14:59:59 0 4002.0 247 \n",
|
||||
"19812427 20231229 rb2405 4003.0 1202028 14:59:59 500 4002.0 224 \n",
|
||||
"19812428 20231229 rb2405 4002.0 1202060 15:00:00 0 4003.0 23 \n",
|
||||
"19812429 20231229 rb2405 4002.0 1202060 15:00:00 500 4003.0 23 \n",
|
||||
"\n",
|
||||
" 申卖价一 申卖量一 datetime \n",
|
||||
"19812425 4003.0 140 2023-12-29 14:59:58.500 \n",
|
||||
"19812426 4003.0 116 2023-12-29 14:59:59.000 \n",
|
||||
"19812427 4003.0 16 2023-12-29 14:59:59.500 \n",
|
||||
"19812428 4004.0 7 2023-12-29 15:00:00.000 \n",
|
||||
"19812429 4004.0 7 2023-12-29 15:00:00.500 "
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.rename(\n",
|
||||
" columns={\n",
|
||||
" \"最新价\": \"lastprice\",\n",
|
||||
" \"数量\": \"volume\",\n",
|
||||
" \"申买价一\": \"bid_p\",\n",
|
||||
" \"申买量一\": \"bid_v\",\n",
|
||||
" \"申卖价一\": \"ask_p\",\n",
|
||||
" \"申卖量一\": \"ask_v\",\n",
|
||||
" \"合约代码\": \"symbol\",\n",
|
||||
" },\n",
|
||||
" inplace=True,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"vol_diff\"] = df[\"volume\"].diff()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
" <th></th>\n",
|
||||
" <th>交易日</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>lastprice</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>最后修改时间</th>\n",
|
||||
" <th>最后修改毫秒</th>\n",
|
||||
" <th>bid_p</th>\n",
|
||||
" <th>bid_v</th>\n",
|
||||
" <th>ask_p</th>\n",
|
||||
" <th>ask_v</th>\n",
|
||||
" <th>datetime</th>\n",
|
||||
" <th>vol_diff</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4305.0</td>\n",
|
||||
" <td>5750</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <td>359</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>36</td>\n",
|
||||
" <td>2022-01-04 09:00:00.500</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4306.0</td>\n",
|
||||
" <td>8039</td>\n",
|
||||
" <td>09:00:01</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4306.0</td>\n",
|
||||
" <td>18</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>7</td>\n",
|
||||
" <td>2022-01-04 09:00:01.000</td>\n",
|
||||
" <td>2289.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>9065</td>\n",
|
||||
" <td>09:00:01</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4308.0</td>\n",
|
||||
" <td>43</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>74</td>\n",
|
||||
" <td>2022-01-04 09:00:01.500</td>\n",
|
||||
" <td>1026.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4310.0</td>\n",
|
||||
" <td>9682</td>\n",
|
||||
" <td>09:00:02</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>4311.0</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4314.0</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>2022-01-04 09:00:02.000</td>\n",
|
||||
" <td>617.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>20220104</td>\n",
|
||||
" <td>rb2205</td>\n",
|
||||
" <td>4314.0</td>\n",
|
||||
" <td>10328</td>\n",
|
||||
" <td>09:00:02</td>\n",
|
||||
" <td>500</td>\n",
|
||||
" <td>4314.0</td>\n",
|
||||
" <td>137</td>\n",
|
||||
" <td>4316.0</td>\n",
|
||||
" <td>19</td>\n",
|
||||
" <td>2022-01-04 09:00:02.500</td>\n",
|
||||
" <td>646.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" 交易日 symbol lastprice volume 最后修改时间 最后修改毫秒 bid_p bid_v \\\n",
|
||||
"0 20220104 rb2205 4305.0 5750 09:00:00 500 4305.0 359 \n",
|
||||
"1 20220104 rb2205 4306.0 8039 09:00:01 0 4306.0 18 \n",
|
||||
"2 20220104 rb2205 4308.0 9065 09:00:01 500 4308.0 43 \n",
|
||||
"3 20220104 rb2205 4310.0 9682 09:00:02 0 4311.0 4 \n",
|
||||
"4 20220104 rb2205 4314.0 10328 09:00:02 500 4314.0 137 \n",
|
||||
"\n",
|
||||
" ask_p ask_v datetime vol_diff \n",
|
||||
"0 4310.0 36 2022-01-04 09:00:00.500 NaN \n",
|
||||
"1 4308.0 7 2022-01-04 09:00:01.000 2289.0 \n",
|
||||
"2 4310.0 74 2022-01-04 09:00:01.500 1026.0 \n",
|
||||
"3 4314.0 19 2022-01-04 09:00:02.000 617.0 \n",
|
||||
"4 4316.0 19 2022-01-04 09:00:02.500 646.0 "
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[df[\"vol_diff\"].isnull(), \"vol_diff\"] = df.loc[df[\"vol_diff\"].isnull(), \"volume\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"volume\"] = df[\"vol_diff\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv(output_path)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "orderflow",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.9.17"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,801 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 使用说明：\n",
|
||||
" 1.需要修改chdir到当前目录\n",
|
||||
" 2.需要修改最后输出的文件名称\n",
|
||||
" 3.依据情况需要修改保留的列数\n",
|
||||
" 4.不同品种的交易时间不一样，要修改删除"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"import datetime as datetime"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"os.chdir('E:/data/ru')\n",
|
||||
"all_csv_files = [file for file in os.listdir('.') if file.endswith('.csv')]\n",
|
||||
"all_csv_files = sorted(all_csv_files)\n",
|
||||
"print(\"文件中所有CSV文件:\",all_csv_files)\n",
|
||||
"\n",
|
||||
"sp_old_chars = ['_2019','_2020','_2021']\n",
|
||||
"sp_old_chars = sorted(sp_old_chars)\n",
|
||||
"print(\"旧格式文件名关键字:\",sp_old_chars)\n",
|
||||
"sp_new_chars = ['_2022','_2023']\n",
|
||||
"sp_new_chars = sorted(sp_new_chars)\n",
|
||||
"print(\"新格式文件名关键字:\",sp_new_chars)\n",
|
||||
"\n",
|
||||
"# # 设置后面数据的采集对于的行数# 用 \"old_type\" 或者 \"new_type\" 区分\n",
|
||||
"# if all(char in ['_2019','_2020','_2021'] for char in sp_old_chars):\n",
|
||||
"# year_type = 'old_type'\n",
|
||||
"# print(\"使用旧年份格式采集!!!\")\n",
|
||||
"# elif all(char in ['_2022','_2023'] for char in sp_chars):\n",
|
||||
"# year_type = 'new_type' \n",
|
||||
"# print(\"使用新年份格式采集!!!\")\n",
|
||||
"# else:\n",
|
||||
"# print(\"文件夹中CSV没有相关年份的数据或者新旧年份混用!!!\")\n",
|
||||
"\n",
|
||||
"csv_old_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_old_chars)]\n",
|
||||
"print(\"筛选结果后的CSV文件:\",csv_old_files)\n",
|
||||
"csv_new_files = [file for file in all_csv_files if any(sp_char in file for sp_char in sp_new_chars)]\n",
|
||||
"print(\"筛选结果后的CSV文件:\",csv_new_files)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old = pd.DataFrame()\n",
|
||||
"for f in csv_old_files:\n",
|
||||
" df_old_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[1, 2, 3, 4, 8, 13, 14, 15, 16],\n",
|
||||
" names=[\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"时间\",\n",
|
||||
" \"最新\",\n",
|
||||
" \"成交量\",\n",
|
||||
" \"买一价\",\n",
|
||||
" \"卖一价\",\n",
|
||||
" \"买一量\",\n",
|
||||
" \"卖一量\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" parse_dates=['时间']#注意此处增加的排序，为了后面按时间排序\n",
|
||||
" )\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df_old = pd.concat([df_old, df_old_temp])\n",
|
||||
"del df_old_temp"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old =pd.DataFrame({'main_contract':df_old['统一代码'],'symbol':df_old['合约代码'],'datetime':df_old['时间'],'lastprice':df_old['最新'],'volume':df_old['成交量'],\n",
|
||||
" 'bid_p':df_old['买一价'],'ask_p':df_old['卖一价'],'bid_v':df_old['买一量'],'ask_v':df_old['卖一量']})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old['time'] = df_old['datetime'].dt.strftime('%H:%M:%S')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 不同品种交易时间不一样，需要修改\n",
|
||||
"# 商品期货\n",
|
||||
"drop_index1 = df_old.query('time>\"15:00:00\" & time<\"21:00:00\"')[\"time\"].index\n",
|
||||
"# drop_index1 = df_old.query('time>\"15:00:00\"')[\"time\"].index\n",
|
||||
"# drop_index2 = df_old.query('time>\"01:00:00\" & time<\"09:00:00\"')[\"time\"].index\n",
|
||||
"#drop_index2 = df_old.query('time>\"02:30:00\" & time<\"09:00:00\"')[\"time\"].index\n",
|
||||
"drop_index2 = df_old.query('time<\"09:00:00\"')[\"time\"].index\n",
|
||||
"drop_index3 = df_old.query('time>\"23:00:00\" & time<\"23:59:59\"')[\"time\"].index\n",
|
||||
"# drop_index3 = df_old.query('time>\"11:30:00\" & time<\"13:30:00\"')[\"time\"].index\n",
|
||||
"drop_index4 = df_old.query('time>\"10:15:00\" & time<\"10:30:00\"')[\"time\"].index\n",
|
||||
"\n",
|
||||
"# 清理不在交易时间段的数据\n",
|
||||
"df_old.drop(labels=drop_index1, axis=0, inplace=True)\n",
|
||||
"df_old.drop(drop_index2, axis=0, inplace=True)\n",
|
||||
"df_old.drop(drop_index3, axis=0, inplace=True)\n",
|
||||
"df_old.drop(drop_index4, axis=0, inplace=True)\n",
|
||||
"\n",
|
||||
"df_old.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del df_old['time']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_new = pd.DataFrame()\n",
|
||||
"for f in csv_new_files:\n",
|
||||
" df_new_temp = pd.read_csv(\n",
|
||||
" f,\n",
|
||||
" usecols=[0, 1, 2, 5, 12, 21, 22, 23, 24, 25, 26, 44],\n",
|
||||
" names=[\n",
|
||||
" \"交易日\",\n",
|
||||
" \"统一代码\",\n",
|
||||
" \"合约代码\",\n",
|
||||
" \"最新价\",\n",
|
||||
" \"数量\",\n",
|
||||
" \"最后修改时间\",\n",
|
||||
" \"最后修改毫秒\",\n",
|
||||
" \"申买价一\",\n",
|
||||
" \"申买量一\",\n",
|
||||
" \"申卖价一\",\n",
|
||||
" \"申卖量一\",\n",
|
||||
" \"业务日期\",\n",
|
||||
" ],\n",
|
||||
" skiprows=1,\n",
|
||||
" encoding=\"utf-8\",\n",
|
||||
" parse_dates=['业务日期','最后修改时间','最后修改毫秒']#注意此处增加的排序，为了后面按时间排序\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
" # df_temp = pd.read_csv(f, usecols=[0,5], names=[\n",
|
||||
" # 'datetime', 'volume'])\n",
|
||||
" df_new = pd.concat([df_new, df_new_temp])\n",
|
||||
"del df_new_temp"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 不同品种交易时间不一样，需要修改\n",
|
||||
"# 商品期货\n",
|
||||
"drop_index1 = df_new.query('最后修改时间>\"15:00:00\" & 最后修改时间<\"21:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index1 = df_new.query('最后修改时间>\"15:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df_new.query('最后修改时间>\"01:00:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index2 = df_new.query('最后修改时间>\"02:30:00\" & 最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index2 = df_new.query('最后修改时间<\"09:00:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index3 = df_new.query('最后修改时间>\"23:00:00\" & 最后修改时间<\"23:59:59\"')[\"最后修改时间\"].index\n",
|
||||
"# drop_index3 = df_new.query('最后修改时间>\"11:30:00\" & 最后修改时间<\"13:30:00\"')[\"最后修改时间\"].index\n",
|
||||
"drop_index4 = df_new.query('最后修改时间>\"10:15:00\" & 最后修改时间<\"10:30:00\"')[\"最后修改时间\"].index\n",
|
||||
"\n",
|
||||
"# 清理不在交易时间段的数据\n",
|
||||
"df_new.drop(labels=drop_index1, axis=0, inplace=True)\n",
|
||||
"df_new.drop(drop_index2, axis=0, inplace=True)\n",
|
||||
"df_new.drop(drop_index3, axis=0, inplace=True)\n",
|
||||
"df_new.drop(drop_index4, axis=0, inplace=True)\n",
|
||||
"\n",
|
||||
"df_new.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#日期修正\n",
|
||||
"#df_new['业务日期'] = pd.to_datetime(df_new['业务日期'])\n",
|
||||
"df_new['业务日期'] = df_new['业务日期'].dt.strftime('%Y-%m-%d')\n",
|
||||
"df_new['datetime'] = df_new['业务日期'] + ' '+df_new['最后修改时间'].dt.time.astype(str) + '.' + df_new['最后修改毫秒'].astype(str)\n",
|
||||
"# 将 'datetime' 列的数据类型更改为 datetime 格式，如果数据转换少8个小时，可以用timedelta处理\n",
|
||||
"df_new['datetime'] = pd.to_datetime(df_new['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')\n",
|
||||
"# 如果需要，可以将 datetime 列格式化为字符串\n",
|
||||
"#df_new['formatted_date'] = df_new['datetime'].dt.strftime('%Y-%m-%d %H:%M:%S.%f')\n",
|
||||
"#计算瞬时成交量\n",
|
||||
"df_new['volume'] = df_new['数量'] - df_new['数量'].shift(1)\n",
|
||||
"df_new['volume'] = df_new['volume'].fillna(0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_new =pd.DataFrame({'main_contract':df_new['统一代码'],'symbol':df_new['合约代码'],'datetime':df_new['datetime'],'lastprice':df_new['最新价'],'volume':df_new['volume'],\n",
|
||||
" 'bid_p':df_new['申买价一'],'ask_p':df_new['申卖价一'],'bid_v':df_new['申买量一'],'ask_v':df_new['申卖量一']})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_old.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_new.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df_new.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame()\n",
|
||||
"df = pd.concat([df_old, df_new],axis=0, ignore_index=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del df_old,df_new"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df['main_contract'] = df['main_contract'].astype(str)\n",
|
||||
"df['symbol'] = df['symbol'].astype(str)\n",
|
||||
"df['datetime'] = pd.to_datetime(df['datetime'], errors='coerce', format='%Y-%m-%d %H:%M:%S.%f')\n",
|
||||
"df['lastprice'] = df['lastprice'].astype(float)\n",
|
||||
"df['volume'] = df['volume'].astype(int)\n",
|
||||
"df['bid_p'] = df['bid_p'].astype(float)\n",
|
||||
"df['ask_p'] = df['ask_p'].astype(float)\n",
|
||||
"df['bid_v'] = df['bid_v'].astype(int)\n",
|
||||
"df['ask_v'] = df['ask_v'].astype(int)\n",
|
||||
"#df = df_old.append(df_new, ignore_index=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 删除重复行\n",
|
||||
"df.drop_duplicates(inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 确保日期列按升序排序\n",
|
||||
"df.sort_values(by='datetime', inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 查看数据的头部和尾部：head()、tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.reset_index(drop=True, inplace=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看dataframe的基本情况\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 等比复权,先不考虑\n",
|
||||
"# df['复权因子'] = df['卖一价'].shift() / df['买一价']\n",
|
||||
"df['复权因子'] = np.where(df['合约代码'] != df['合约代码'].shift(), df['卖一价'].shift() / df['买一价'], 1)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(1)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['买一价_adj'] = df['买一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['卖一价_adj'] = df['卖一价'] * df['复权因子'].cumprod()\n",
|
||||
"df['最新_adj'] = df['最新'] * df['复权因子'].cumprod()\n",
|
||||
"# df['low_adj'] = df['low'] * adjust.cumprod()\n",
|
||||
"# df['high_adj'] = df['high'] * adjust.cumprod()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 等差复权\n",
|
||||
"df['复权因子'] = np.where(df['symbol'] != df['symbol'].shift(), df['ask_p'].shift() - df['bid_p'], 0)\n",
|
||||
"df['复权因子'] = df['复权因子'].fillna(0)\n",
|
||||
"# df['复权因子'].loc[0] = 1\n",
|
||||
"df['bid_p_adj'] = df['bid_p'] + df['复权因子'].cumsum()\n",
|
||||
"df['ask_p_adj'] = df['ask_p'] + df['复权因子'].cumsum()\n",
|
||||
"df['lastprice_adj'] = df['lastprice'] + df['复权因子'].cumsum()\n",
|
||||
"# df['low_adj'] = df['low'] + df['复权因子'].cumsum()\n",
|
||||
"# df['high_adj'] = df['high'] + df['复权因子'].cumsum()\n",
|
||||
"# df_new =pd.DataFrame({'main_contract':df_new['统一代码'],'symbol':df_new['合约代码'],'datetime':df_new['datetime'],'lastprice':df_new['最新价'],'volume':df_new['volume'],\n",
|
||||
"# 'bid_p':df_new['申买价一'],'ask_p':df_new['申卖量一'],'bid_v':df_new['申买量一'],'ask_v':df_new['申卖量一']})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(df['复权因子'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"contains_null = df.isnull().values.any()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(contains_null)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查找换期需要复权的索引\n",
|
||||
"non_zero_indices = df[df['复权因子'] != 0].index\n",
|
||||
"print(non_zero_indices)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看未调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 将调整后的数值替换原来的值\n",
|
||||
"df['bid_p'] = df['bid_p_adj']\n",
|
||||
"df['ask_p'] = df['ask_p_adj']\n",
|
||||
"df['lastprice'] = df['lastprice_adj']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 查看调整买价、卖价和最新价的数据\n",
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 删除多余的值\n",
|
||||
"del df['复权因子']\n",
|
||||
"del df['bid_p_adj']\n",
|
||||
"del df['ask_p_adj']\n",
|
||||
"del df['lastprice_adj']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.loc[non_zero_indices[0]-5:non_zero_indices[0]+5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv('./ru888.csv', index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"import datetime as datetime\n",
|
||||
"import pyarrow as pa\n",
|
||||
"import pyarrow.feather as feather"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature 测试\n",
|
||||
"df = pd.read_csv('E:/data/ru/ru888.csv',encoding='UTF-8',parse_dates=['datetime'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"feather.write_feather(df, 'df_feather.feather')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = feather.read_feather('df_feather.feather')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>main_contract</th>\n",
|
||||
" <th>symbol</th>\n",
|
||||
" <th>datetime</th>\n",
|
||||
" <th>lastprice</th>\n",
|
||||
" <th>volume</th>\n",
|
||||
" <th>bid_p</th>\n",
|
||||
" <th>ask_p</th>\n",
|
||||
" <th>bid_v</th>\n",
|
||||
" <th>ask_v</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>44287432</th>\n",
|
||||
" <td>ru888</td>\n",
|
||||
" <td>ru2405</td>\n",
|
||||
" <td>2023-12-29 14:59:58.500</td>\n",
|
||||
" <td>6755.0</td>\n",
|
||||
" <td>27</td>\n",
|
||||
" <td>6750.0</td>\n",
|
||||
" <td>6755.0</td>\n",
|
||||
" <td>128</td>\n",
|
||||
" <td>15</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>44287433</th>\n",
|
||||
" <td>ru888</td>\n",
|
||||
" <td>ru2405</td>\n",
|
||||
" <td>2023-12-29 14:59:59.000</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>27</td>\n",
|
||||
" <td>6755.0</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>14</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>44287434</th>\n",
|
||||
" <td>ru888</td>\n",
|
||||
" <td>ru2405</td>\n",
|
||||
" <td>2023-12-29 14:59:59.500</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>17</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>6765.0</td>\n",
|
||||
" <td>35</td>\n",
|
||||
" <td>33</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>44287435</th>\n",
|
||||
" <td>ru888</td>\n",
|
||||
" <td>ru2405</td>\n",
|
||||
" <td>2023-12-29 15:00:00.000</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>6</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>6765.0</td>\n",
|
||||
" <td>45</td>\n",
|
||||
" <td>42</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>44287436</th>\n",
|
||||
" <td>ru888</td>\n",
|
||||
" <td>ru2405</td>\n",
|
||||
" <td>2023-12-29 15:00:00.500</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>0</td>\n",
|
||||
" <td>6760.0</td>\n",
|
||||
" <td>6765.0</td>\n",
|
||||
" <td>45</td>\n",
|
||||
" <td>42</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" main_contract symbol datetime lastprice volume \\\n",
|
||||
"44287432 ru888 ru2405 2023-12-29 14:59:58.500 6755.0 27 \n",
|
||||
"44287433 ru888 ru2405 2023-12-29 14:59:59.000 6760.0 27 \n",
|
||||
"44287434 ru888 ru2405 2023-12-29 14:59:59.500 6760.0 17 \n",
|
||||
"44287435 ru888 ru2405 2023-12-29 15:00:00.000 6760.0 6 \n",
|
||||
"44287436 ru888 ru2405 2023-12-29 15:00:00.500 6760.0 0 \n",
|
||||
"\n",
|
||||
" bid_p ask_p bid_v ask_v \n",
|
||||
"44287432 6750.0 6755.0 128 15 \n",
|
||||
"44287433 6755.0 6760.0 2 14 \n",
|
||||
"44287434 6760.0 6765.0 35 33 \n",
|
||||
"44287435 6760.0 6765.0 45 42 \n",
|
||||
"44287436 6760.0 6765.0 45 42 "
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@@ -0,0 +1,2 @@
|
||||
@echo off
|
||||
for /f "tokens=2 delims=," %%a in ('tasklist /v /fo csv ^| findstr /i "订单流横截面_日内模型.py"') do taskkill /pid %%~a
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,983 @@
|
||||
'''
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import csv
|
||||
import re
|
||||
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trade_dfs = {} #pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={} #过渡tick
|
||||
|
||||
截面数据_资金流向={}
|
||||
top_two_symbols=[]
|
||||
|
||||
合约信息={
|
||||
"A": {"合约单位": 10, "保证金": 0.12},
|
||||
"AG": {"合约单位": 15, "保证金": 0.12},
|
||||
"AL": {"合约单位": 5, "保证金": 0.12},
|
||||
"AO": {"合约单位": 20, "保证金": 0.09},
|
||||
"AP": {"合约单位": 10, "保证金": 0.1},
|
||||
"AU": {"合约单位": 1000, "保证金": 0.1},
|
||||
"B": {"合约单位": 10, "保证金": 0.09},
|
||||
"BC": {"合约单位": 5, "保证金": 0.12},
|
||||
"BR": {"合约单位": 5, "保证金": 0.12},
|
||||
"BU": {"合约单位": 10, "保证金": 0.15},
|
||||
"C": {"合约单位": 10, "保证金": 0.12},
|
||||
"CF": {"合约单位": 5, "保证金": 0.07},
|
||||
"CJ": {"合约单位": 5, "保证金": 0.12},
|
||||
"CS": {"合约单位": 10, "保证金": 0.09},
|
||||
"CU": {"合约单位": 5, "保证金": 0.12},
|
||||
"CY": {"合约单位": 5, "保证金": 0.07},
|
||||
"EB": {"合约单位": 5, "保证金": 0.12},
|
||||
"EC": {"合约单位": 50, "保证金": 0.12},
|
||||
"EG": {"合约单位": 10, "保证金": 0.12},
|
||||
"FB": {"合约单位": 10, "保证金": 0.1},
|
||||
"FG": {"合约单位": 20, "保证金": 0.09},
|
||||
"FU": {"合约单位": 10, "保证金": 0.15},
|
||||
"HC": {"合约单位": 10, "保证金": 0.13},
|
||||
"I": {"合约单位": 100, "保证金": 0.13},
|
||||
"IC": {"合约单位": 1, "保证金": 0.14},
|
||||
"IF": {"合约单位": 1, "保证金": 0.12},
|
||||
"IH": {"合约单位": 1, "保证金": 0.12},
|
||||
"IM": {"合约单位": 1, "保证金": 0.15},
|
||||
"J": {"合约单位": 100, "保证金": 0.2},
|
||||
"JD": {"合约单位": 10, "保证金": 0.09},
|
||||
"JM": {"合约单位": 60, "保证金": 0.2},
|
||||
"L": {"合约单位": 5, "保证金": 0.11},
|
||||
"LC": {"合约单位": 1, "保证金": 0.09},
|
||||
"LH": {"合约单位": 16, "保证金": 0.15},
|
||||
"LU": {"合约单位": 10, "保证金": 0.15},
|
||||
|
||||
"M": {"合约单位": 10, "保证金": 0.1},
|
||||
"MA": {"合约单位": 10, "保证金": 0.08},
|
||||
"NI": {"合约单位": 1, "保证金": 0.19},
|
||||
"NR": {"合约单位": 10, "保证金": 0.1},
|
||||
"OI": {"合约单位": 10, "保证金": 0.09},
|
||||
"P": {"合约单位": 10, "保证金": 0.12},
|
||||
"PB": {"合约单位": 5, "保证金": 0.14},
|
||||
"PF": {"合约单位": 5, "保证金": 0.08},
|
||||
"PG": {"合约单位": 20, "保证金": 0.13},
|
||||
"PK": {"合约单位": 5, "保证金": 0.08},
|
||||
"PP": {"合约单位": 5, "保证金": 0.11},
|
||||
"PX": {"合约单位": 5, "保证金": 0.12},
|
||||
"RB": {"合约单位": 10, "保证金": 0.13},
|
||||
"RM": {"合约单位": 10, "保证金": 0.09},
|
||||
"RR": {"合约单位": 10, "保证金": 0.06},
|
||||
"RU": {"合约单位": 10, "保证金": 0.1},
|
||||
"SA": {"合约单位": 20, "保证金": 0.09},
|
||||
"SC": {"合约单位": 1000, "保证金": 0.15},
|
||||
"SF": {"合约单位": 5, "保证金": 0.12},
|
||||
"SH": {"合约单位": 30, "保证金": 0.09},
|
||||
"SI": {"合约单位": 5, "保证金": 0.1},
|
||||
"SM": {"合约单位": 5, "保证金": 0.12},
|
||||
"SN": {"合约单位": 1, "保证金": 0.14},
|
||||
"SP": {"合约单位": 10, "保证金": 0.15},
|
||||
"SR": {"合约单位": 10, "保证金": 0.07},
|
||||
"SS": {"合约单位": 5, "保证金": 0.14},
|
||||
"T": {"合约单位": 10000, "保证金": 0.02},
|
||||
"TA": {"合约单位": 5, "保证金": 0.07},
|
||||
"TF": {"合约单位": 10000, "保证金": 0.012},
|
||||
"TL": {"合约单位": 10000, "保证金": 0.035},
|
||||
"TS": {"合约单位": 20000, "保证金": 0.005},
|
||||
"UR": {"合约单位": 20, "保证金": 0.08},
|
||||
"V": {"合约单位": 5, "保证金": 0.11},
|
||||
"Y": {"合约单位": 10, "保证金": 0.09},
|
||||
"ZN": {"合约单位": 5, "保证金": 0.14},
|
||||
|
||||
}
|
||||
|
||||
|
||||
clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
class ParamObj:
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
symbol = None #合约名称
|
||||
Lots = None #下单手数
|
||||
py = None #设置委托价格的偏移，更加容易促成成交
|
||||
trailing_stop_percent = None #跟踪出场参数
|
||||
fixed_stop_loss_percent = None #固定出场参数
|
||||
dj_X = None #开仓的堆积参数
|
||||
delta = None #开仓的delta参数
|
||||
sum_delta = None #开仓的delta累积参数
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
|
||||
# 策略需要用到的变量
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
多头止损价格 = 0
|
||||
空头止损价格 = 0
|
||||
|
||||
多头成本价格 = 0
|
||||
空头成本价格 = 0
|
||||
|
||||
多头开仓历时=0
|
||||
空头开仓历时=0
|
||||
|
||||
平_多时间=4
|
||||
平_空时间=4
|
||||
|
||||
def __init__(self, symbol, Lots, py,dj_X, delta, sum_delta,失衡,堆积,周期,平_多时间,平_空时间):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
self.平_多时间=平_多时间
|
||||
self.平_空时间=平_空时间
|
||||
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
# 将df数据合并到trader_df中
|
||||
# if symbol not in trade_dfs.keys():
|
||||
# trade_df = pd.DataFrame({})
|
||||
# else:
|
||||
# trade_df = trade_dfs[symbol]
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
|
||||
#print(trader_df)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i <= len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
#订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
#保存OF数据
|
||||
def save_trade_data(self,df,instrument_id):
|
||||
# 定义文件路径
|
||||
csv_file_path = f"ofdata/{instrument_id}_ofdata.csv"
|
||||
directory = os.path.dirname(csv_file_path)
|
||||
|
||||
# 检查目录是否存在，如果不存在则创建它
|
||||
if not os.path.exists(directory):
|
||||
os.makedirs(directory)
|
||||
|
||||
# 如果文件夹存在，则追加数据；否则，创建新文件并保存整个 DataFrame
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个 DataFrame
|
||||
df.to_csv(csv_file_path, index=False)
|
||||
|
||||
def w_log(self,symbol,log_message):
|
||||
|
||||
# 创建文件夹（如果不存在）
|
||||
log_dir = 'tradelogs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 写入日志到 CSV 文件
|
||||
now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
log_file = os.path.join(log_dir, f'{symbol}log.csv')
|
||||
# 打印日志到控制台
|
||||
print(f'{now}: {log_message}')
|
||||
with open(log_file, 'a', encoding='utf-8',newline='') as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
# 如果文件为空，写入表头
|
||||
if csvfile.tell() == 0:
|
||||
writer.writerow(['time', 'logs'])
|
||||
writer.writerow([now, log_message])
|
||||
|
||||
# 计算单品种资金流向数据
|
||||
def 资金流向计算(self,df):
|
||||
# 将 'delta' 和 'close' 转换为数值类型，强制转换错误
|
||||
df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
|
||||
df['close'] = pd.to_numeric(df['close'], errors='coerce')
|
||||
|
||||
# 删除 'delta' 或 'close' 中的 NaN 值
|
||||
df = df.dropna(subset=['volume', 'close'])
|
||||
symbol=df['symbol'].iloc[-1]
|
||||
symbol = ''.join(filter(str.isalpha, symbol)).upper()
|
||||
资金净流向 = abs(sum(df['volume']*df['close']))*合约信息[symbol]['合约单位']*合约信息[symbol]['保证金']
|
||||
更新状态=True
|
||||
# 将结果放入 DataFrame
|
||||
result_df = pd.DataFrame({
|
||||
'资金净流向': [资金净流向],
|
||||
'更新状态': [更新状态]
|
||||
})
|
||||
|
||||
return result_df
|
||||
|
||||
# 收盘清仓
|
||||
def 收盘清仓(self,symbol,data):
|
||||
param = self.param_dict[symbol]
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(22, 55)
|
||||
clearing_time2_end = s_time(23, 0)
|
||||
now = datetime.now()
|
||||
# 创建一个标志变量，用于记录是否已经执行过清仓操作
|
||||
#param.clearing_executed = False
|
||||
|
||||
# 检查当前时间是否在第一个清仓操作的时间范围内，并且清仓操作未执行过
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
#trade_dfs.drop(trade_dfs.index,inplace=True)#清除当天的行情数据
|
||||
if param.pos>0:
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}多头清仓操作")
|
||||
pass
|
||||
elif param.pos<0:
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}空头清仓操作")
|
||||
pass
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
|
||||
# 检查当前时间是否在第二个清仓操作的时间范围内，并且清仓操作未执行过
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
#trade_dfs.drop(trade_dfs.index,inplace=True) #清除当天的行情数据
|
||||
if param.pos>0:
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}多头清仓操作")
|
||||
pass
|
||||
elif param.pos<0:
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}空头清仓操作")
|
||||
pass
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
# 如果不在任何清仓操作的时间范围内，可以执行其他操作或不执行任何操作
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
#print("不在清仓操作时间范围内")
|
||||
|
||||
return param.clearing_executed
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
now=datetime.now()
|
||||
data = symbol_queue.get(block=True, timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
#OF计算数据开始
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
#清仓开关
|
||||
run_kg=self.收盘清仓(instrument_id,data)
|
||||
最新价=data['LastPrice']
|
||||
sympc=data['InstrumentID'].decode()
|
||||
# #多头时间出场
|
||||
if param.pos>0 and param.多头开仓历时>param.平_多时间 :
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.多头止损价格=0
|
||||
param.多头开仓历时=0
|
||||
param.多头成本价格=0
|
||||
self.w_log(sympc,f'历时{param.平_多时间}***多头***{sympc}时间出场价格{最新价}')
|
||||
|
||||
#多头价格止损
|
||||
if param.pos>0 and 最新价<param.多头止损价格 :
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.多头止损价格=0
|
||||
param.多头开仓历时=0
|
||||
param.多头成本价格=0
|
||||
self.w_log(sympc,f'多头止损触发: {最新价},多头止损价格：{param.多头止损价格} ***{sympc}多头止损***')
|
||||
|
||||
# #多头跟踪止盈-----------------------暂时用不到------------------------
|
||||
# elif param.pos>0 and len(trade_df)>1 and 最新价<trade_df['10_MA'].iloc[-1] and 最新价>param.多头成本价格 :
|
||||
# #平多
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
# param.pos=0
|
||||
# param.多头止损价格=0
|
||||
# param.多头开仓历时=0
|
||||
# param.多头成本价格=0
|
||||
# self.w_log(sympc,f'多头跟踪止盈触发: {最新价} ***{sympc}多头止盈***')
|
||||
|
||||
# # #空头时间出场
|
||||
if param.pos<0 and param.空头开仓历时>param.平_空时间 :
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.空头止损价格=0
|
||||
param.空头开仓历时=0
|
||||
param.空头成本价格=0
|
||||
self.w_log(sympc,f'历时{param.平_空时间}***空头***{sympc}时间出场价格{最新价}')
|
||||
|
||||
#空头价格止损
|
||||
if param.pos<0 and 最新价>param.空头止损价格 :
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.空头止损价格=0
|
||||
param.空头开仓历时=0
|
||||
param.空头成本价格=0
|
||||
self.w_log(sympc,f'空头止损触发: {最新价},空头止损价格：{param.空头止损价格} ***{sympc}空头止损***')
|
||||
|
||||
# #空头跟踪止盈-----------------------暂时用不到------------------------
|
||||
# elif param.pos<0 and len(trade_df)>1 and 最新价>trade_df['10_MA'].iloc[-1] and 最新价<param.空头成本价格 :
|
||||
# #平空
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
# param.pos=0
|
||||
# param.空头止损价格=0
|
||||
# param.空头开仓历时=0
|
||||
# param.空头成本价格=0
|
||||
# self.w_log(sympc,f'空头跟踪止盈触发: {最新价},***{sympc}空头止盈***')
|
||||
|
||||
#交易程序开始
|
||||
if len(trade_df)>param.cont_df and run_kg==False:
|
||||
#开仓历时更新：
|
||||
if param.pos>0:
|
||||
param.多头开仓历时+=1
|
||||
elif param.pos<0:
|
||||
param.空头开仓历时+=1
|
||||
|
||||
#计算截面数据
|
||||
截面数据_资金流向[instrument_id]=self.资金流向计算(trade_df)
|
||||
|
||||
#日均线
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
|
||||
# 计算5日均线和10日均线，即使数据不足5日或10日
|
||||
trade_df['5_MA'] = trade_df['close'].rolling(window=5, min_periods=1).mean()
|
||||
trade_df['10_MA'] = trade_df['close'].rolling(window=10, min_periods=1).mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
|
||||
#小于日均线
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X and trade_df['5_MA'].iloc[-1] > trade_df['10_MA'].iloc[-1]
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X and trade_df['5_MA'].iloc[-1] < trade_df['10_MA'].iloc[-1]
|
||||
symm=data['InstrumentID'].decode()
|
||||
#开平仓
|
||||
#换仓平多
|
||||
# if param.pos>0 and symm not in top_two_symbols:
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
# param.pos=0
|
||||
# param.多头止损价格=0
|
||||
# param.多头开仓历时=0
|
||||
# param.多头成本价格=0
|
||||
# self.w_log(symm,f'当前截面品种{top_two_symbols},{symm}不在范围，***多平仓***')
|
||||
#多头开仓
|
||||
if param.pos==0 and 开多组合 and symm in top_two_symbols :
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
param.pos=1
|
||||
param.多头止损价格=trade_df['low'].iloc[-2]
|
||||
param.多头成本价格=data['AskPrice1']
|
||||
param.多头开仓历时=1
|
||||
self.w_log(symm,f'品种：{symm},委托价格: {param.多头成本价格}***开多***')
|
||||
|
||||
#换仓平空
|
||||
# if param.pos<0 and symm not in top_two_symbols :
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
# param.pos=0
|
||||
# param.空头止损价格=0
|
||||
# param.空头开仓历时=0
|
||||
# param.空头成本价格=0
|
||||
# self.w_log(symm,f'当前截面品种{top_two_symbols},{symm}不在范围，***空平仓***')
|
||||
#空头开仓
|
||||
if param.pos==0 and 开空条件 and symm in top_two_symbols :
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
param.pos=-1
|
||||
param.空头止损价格=trade_df['high'].iloc[-2]
|
||||
param.空头成本价格=data['BidPrice1']
|
||||
param.空头开仓历时=1
|
||||
self.w_log(symm,f'品种：{symm},委托价格: {param.空头成本价格}***开空***')
|
||||
#print(trade_df)
|
||||
#保存OF数据
|
||||
self.save_trade_data(trade_df,symm)
|
||||
#保存bar计数
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
# print(f"当前合约队列为空，等待新数据插入。")
|
||||
pass
|
||||
|
||||
# 将CTP推送的行情数据分发给对应线程队列去执行
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False) # 往对应合约队列中插入行情
|
||||
# print(f"{instrument_id}合约数据插入。")
|
||||
except queue.Full:
|
||||
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
thread1 = threading.Thread(target=横截面计算)
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(10) #为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],)) # 为每个合约单独创建一个线程计算开仓逻辑
|
||||
threads.append(t)
|
||||
t.start()
|
||||
thread1.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
thread1.join()
|
||||
|
||||
# 定义横截面计算函数
|
||||
def 横截面计算():
|
||||
global top_two_symbols
|
||||
while True:
|
||||
if len(截面数据_资金流向)>5:
|
||||
goss=1
|
||||
# 检查是否有任何品种的更新状态为 False，如果有则直接返回
|
||||
for symbol, data in 截面数据_资金流向.items():
|
||||
if not data['更新状态'].iloc[-1]:
|
||||
#print(f"品种 {symbol} 的更新状态为 False，跳过计算。")
|
||||
goss=0
|
||||
break
|
||||
if goss==1:
|
||||
# 继续计算资金净流向
|
||||
sorted_items = sorted(截面数据_资金流向.items(), key=lambda item: item[1]['资金净流向'].iloc[-1], reverse=True)
|
||||
max_symbol = sorted_items[0][0]
|
||||
two_max_symbol = sorted_items[1][0]
|
||||
three_max_symbol = sorted_items[2][0]
|
||||
four_max_symbol = sorted_items[3][0]
|
||||
five_max_symbol = sorted_items[4][0]
|
||||
top_two_symbols = [max_symbol, two_max_symbol,three_max_symbol,four_max_symbol,five_max_symbol]
|
||||
|
||||
print(f'选出的活跃品种: {top_two_symbols}')
|
||||
print(f'截面数据_资金流向: {截面数据_资金流向}')
|
||||
# 将所有品种的更新状态设置为 False
|
||||
for symbol in 截面数据_资金流向:
|
||||
截面数据_资金流向[symbol]['更新状态'] = False
|
||||
time.sleep(1)
|
||||
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
历时平仓=4
|
||||
|
||||
param_dict = {}
|
||||
param_dict['SR409'] = ParamObj(symbol='SR409', Lots=1, py=6,dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['eb2408'] = ParamObj(symbol='eb2408', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['SA409'] = ParamObj(symbol='SA409', Lots=1, py=6,dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['pp409'] = ParamObj(symbol='pp409', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['OI409'] = ParamObj(symbol='OI409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['a2409'] = ParamObj(symbol='a2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['p2409'] = ParamObj(symbol='p2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['RM409'] = ParamObj(symbol='RM409', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['sp2409'] = ParamObj(symbol='sp2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
future_account = get_simulate_account(
|
||||
investor_id='', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
password='', # simnow密码
|
||||
server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
||||
subscribe_list=list(param_dict.keys()), # 合约列表
|
||||
)
|
||||
|
||||
# #实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
# future_account = FutureAccount(
|
||||
# broker_id='', # 期货公司BrokerID
|
||||
# server_dict={'TDServer': "ip:port", 'MDServer': 'ip:port'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
# reserve_server_dict={}, # 备用服务器地址
|
||||
# investor_id='', # 账户
|
||||
# password='', # 密码
|
||||
# app_id='simnow_client_test', # 认证使用AppID
|
||||
# auth_code='0000000000000000', # 认证使用授权码
|
||||
# subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
||||
# md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
# td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
# )
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
@@ -0,0 +1,982 @@
|
||||
'''
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
该代码的主要目的是处理Tick数据并生成交易信号。代码中定义了一个tickcome函数，它接收到Tick数据后会进行一系列的处理，包括构建Tick字典、更新上一个Tick的成交量、保存Tick数据、生成K线数据等。其中涉及到的一些函数有：
|
||||
|
||||
on_tick(tick): 处理单个Tick数据，根据Tick数据生成K线数据。
|
||||
tickdata(df, symbol): 处理Tick数据，生成K线数据。
|
||||
orderflow_df_new(df_tick, df_min, symbol): 处理Tick和K线数据，生成订单流数据。
|
||||
GetOrderFlow_dj(kData): 计算订单流的信号指标。
|
||||
除此之外，代码中还定义了一个MyTrader类，继承自TraderApiBase，用于实现交易相关的功能。
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
'''
|
||||
from concurrent.futures import ThreadPoolExecutor
|
||||
from multiprocessing import Process, Queue
|
||||
import queue
|
||||
import threading
|
||||
from AlgoPlus.CTP.MdApi import run_tick_engine
|
||||
from AlgoPlus.CTP.FutureAccount import get_simulate_account
|
||||
from AlgoPlus.CTP.FutureAccount import FutureAccount
|
||||
from AlgoPlus.CTP.TraderApiBase import TraderApiBase
|
||||
from AlgoPlus.ta.time_bar import tick_to_bar
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from datetime import time as s_time
|
||||
import operator
|
||||
import time
|
||||
import numpy as np
|
||||
import os
|
||||
import csv
|
||||
import re
|
||||
|
||||
|
||||
tickdatadict = {} # 存储Tick数据的字典
|
||||
quotedict = {} # 存储行情数据的字典
|
||||
ofdatadict = {} # 存储K线数据的字典
|
||||
trade_dfs = {} #pd.DataFrame({}) # 存储交易数据的DataFrame对象
|
||||
previous_volume = {} # 上一个Tick的成交量
|
||||
tsymbollist={} #过渡tick
|
||||
|
||||
截面数据_资金流向={}
|
||||
top_two_symbols=[]
|
||||
|
||||
合约信息={
|
||||
"A": {"合约单位": 10, "保证金": 0.12},
|
||||
"AG": {"合约单位": 15, "保证金": 0.12},
|
||||
"AL": {"合约单位": 5, "保证金": 0.12},
|
||||
"AO": {"合约单位": 20, "保证金": 0.09},
|
||||
"AP": {"合约单位": 10, "保证金": 0.1},
|
||||
"AU": {"合约单位": 1000, "保证金": 0.1},
|
||||
"B": {"合约单位": 10, "保证金": 0.09},
|
||||
"BC": {"合约单位": 5, "保证金": 0.12},
|
||||
"BR": {"合约单位": 5, "保证金": 0.12},
|
||||
"BU": {"合约单位": 10, "保证金": 0.15},
|
||||
"C": {"合约单位": 10, "保证金": 0.12},
|
||||
"CF": {"合约单位": 5, "保证金": 0.07},
|
||||
"CJ": {"合约单位": 5, "保证金": 0.12},
|
||||
"CS": {"合约单位": 10, "保证金": 0.09},
|
||||
"CU": {"合约单位": 5, "保证金": 0.12},
|
||||
"CY": {"合约单位": 5, "保证金": 0.07},
|
||||
"EB": {"合约单位": 5, "保证金": 0.12},
|
||||
"EC": {"合约单位": 50, "保证金": 0.12},
|
||||
"EG": {"合约单位": 10, "保证金": 0.12},
|
||||
"FB": {"合约单位": 10, "保证金": 0.1},
|
||||
"FG": {"合约单位": 20, "保证金": 0.09},
|
||||
"FU": {"合约单位": 10, "保证金": 0.15},
|
||||
"HC": {"合约单位": 10, "保证金": 0.13},
|
||||
"I": {"合约单位": 100, "保证金": 0.13},
|
||||
"IC": {"合约单位": 1, "保证金": 0.14},
|
||||
"IF": {"合约单位": 1, "保证金": 0.12},
|
||||
"IH": {"合约单位": 1, "保证金": 0.12},
|
||||
"IM": {"合约单位": 1, "保证金": 0.15},
|
||||
"J": {"合约单位": 100, "保证金": 0.2},
|
||||
"JD": {"合约单位": 10, "保证金": 0.09},
|
||||
"JM": {"合约单位": 60, "保证金": 0.2},
|
||||
"L": {"合约单位": 5, "保证金": 0.11},
|
||||
"LC": {"合约单位": 1, "保证金": 0.09},
|
||||
"LH": {"合约单位": 16, "保证金": 0.15},
|
||||
"LU": {"合约单位": 10, "保证金": 0.15},
|
||||
|
||||
"M": {"合约单位": 10, "保证金": 0.1},
|
||||
"MA": {"合约单位": 10, "保证金": 0.08},
|
||||
"NI": {"合约单位": 1, "保证金": 0.19},
|
||||
"NR": {"合约单位": 10, "保证金": 0.1},
|
||||
"OI": {"合约单位": 10, "保证金": 0.09},
|
||||
"P": {"合约单位": 10, "保证金": 0.12},
|
||||
"PB": {"合约单位": 5, "保证金": 0.14},
|
||||
"PF": {"合约单位": 5, "保证金": 0.08},
|
||||
"PG": {"合约单位": 20, "保证金": 0.13},
|
||||
"PK": {"合约单位": 5, "保证金": 0.08},
|
||||
"PP": {"合约单位": 5, "保证金": 0.11},
|
||||
"PX": {"合约单位": 5, "保证金": 0.12},
|
||||
"RB": {"合约单位": 10, "保证金": 0.13},
|
||||
"RM": {"合约单位": 10, "保证金": 0.09},
|
||||
"RR": {"合约单位": 10, "保证金": 0.06},
|
||||
"RU": {"合约单位": 10, "保证金": 0.1},
|
||||
"SA": {"合约单位": 20, "保证金": 0.09},
|
||||
"SC": {"合约单位": 1000, "保证金": 0.15},
|
||||
"SF": {"合约单位": 5, "保证金": 0.12},
|
||||
"SH": {"合约单位": 30, "保证金": 0.09},
|
||||
"SI": {"合约单位": 5, "保证金": 0.1},
|
||||
"SM": {"合约单位": 5, "保证金": 0.12},
|
||||
"SN": {"合约单位": 1, "保证金": 0.14},
|
||||
"SP": {"合约单位": 10, "保证金": 0.15},
|
||||
"SR": {"合约单位": 10, "保证金": 0.07},
|
||||
"SS": {"合约单位": 5, "保证金": 0.14},
|
||||
"T": {"合约单位": 10000, "保证金": 0.02},
|
||||
"TA": {"合约单位": 5, "保证金": 0.07},
|
||||
"TF": {"合约单位": 10000, "保证金": 0.012},
|
||||
"TL": {"合约单位": 10000, "保证金": 0.035},
|
||||
"TS": {"合约单位": 20000, "保证金": 0.005},
|
||||
"UR": {"合约单位": 20, "保证金": 0.08},
|
||||
"V": {"合约单位": 5, "保证金": 0.11},
|
||||
"Y": {"合约单位": 10, "保证金": 0.09},
|
||||
"ZN": {"合约单位": 5, "保证金": 0.14},
|
||||
|
||||
}
|
||||
|
||||
|
||||
clearing_time_dict = {'sc': s_time(2,30), 'bc': s_time(1,0), 'lu': s_time(23,0), 'nr': s_time(23,0),'au': s_time(2,30), 'ag': s_time(2,30),
|
||||
'ss': s_time(1,0), 'sn': s_time(1,0), 'ni': s_time(1,0), 'pb': s_time(1,0),'zn': s_time(1,0), 'al': s_time(1,0), 'cu': s_time(1,0),
|
||||
'ru': s_time(23,0), 'rb': s_time(23,0), 'hc': s_time(23,0), 'fu': s_time(23,0), 'bu': s_time(23,0), 'sp': s_time(23,0),
|
||||
'PF': s_time(23,0), 'SR': s_time(23,0), 'CF': s_time(23,0), 'CY': s_time(23,0), 'RM': s_time(23,0), 'MA': s_time(23,0),
|
||||
'TA': s_time(23,0), 'ZC': s_time(23,0), 'FG': s_time(23,0), 'OI': s_time(23,0), 'SA': s_time(23,0),
|
||||
'p': s_time(23,0), 'j': s_time(23,0), 'jm': s_time(23,0), 'i': s_time(23,0), 'l': s_time(23,0), 'v': s_time(23,0),
|
||||
'pp': s_time(23,0), 'eg': s_time(23,0), 'c': s_time(23,0), 'cs': s_time(23,0), 'y': s_time(23,0), 'm': s_time(23,0),
|
||||
'a': s_time(23,0), 'b': s_time(23,0), 'rr': s_time(23,0), 'eb': s_time(23,0), 'pg': s_time(23,0)}
|
||||
|
||||
|
||||
#交易程序---------------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||||
class ParamObj:
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
# 策略需要用到的参数，在新建合约对象的时候传入！！
|
||||
symbol = None #合约名称
|
||||
Lots = None #下单手数
|
||||
py = None #设置委托价格的偏移，更加容易促成成交
|
||||
trailing_stop_percent = None #跟踪出场参数
|
||||
fixed_stop_loss_percent = None #固定出场参数
|
||||
dj_X = None #开仓的堆积参数
|
||||
delta = None #开仓的delta参数
|
||||
sum_delta = None #开仓的delta累积参数
|
||||
失衡=None
|
||||
堆积=None
|
||||
周期=None
|
||||
|
||||
# 策略需要用到的变量
|
||||
cont_df = 0
|
||||
pos = 0
|
||||
clearing_executed = False
|
||||
kgdata = True
|
||||
|
||||
多头止损价格 = 0
|
||||
空头止损价格 = 0
|
||||
|
||||
多头成本价格 = 0
|
||||
空头成本价格 = 0
|
||||
|
||||
多头开仓历时=0
|
||||
空头开仓历时=0
|
||||
|
||||
平_多时间=4
|
||||
平_空时间=4
|
||||
|
||||
def __init__(self, symbol, Lots, py,dj_X, delta, sum_delta,失衡,堆积,周期,平_多时间,平_空时间):
|
||||
self.symbol = symbol
|
||||
self.Lots = Lots
|
||||
self.py = py
|
||||
self.dj_X = dj_X
|
||||
self.delta = delta
|
||||
self.sum_delta = sum_delta
|
||||
self.失衡=失衡
|
||||
self.堆积=堆积
|
||||
self.周期=周期
|
||||
self.平_多时间=平_多时间
|
||||
self.平_空时间=平_空时间
|
||||
|
||||
|
||||
class MyTrader(TraderApiBase):
|
||||
def __init__(self, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
self.param_dict = {}
|
||||
self.queue_dict = {}
|
||||
self.品种=' '
|
||||
|
||||
|
||||
def tickcome(self,md_queue):
|
||||
global previous_volume
|
||||
data=md_queue
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
ActionDay = data['ActionDay'].decode() # 交易日日期
|
||||
update_time = data['UpdateTime'].decode() # 更新时间
|
||||
|
||||
update_millisec = str(data['UpdateMillisec']) # 更新毫秒数
|
||||
created_at = ActionDay[:4] + '-' + ActionDay[4:6] + '-' + ActionDay[6:] + ' ' + update_time + '.' + update_millisec #创建时间
|
||||
# 构建tick字典
|
||||
tick = {
|
||||
'symbol': instrument_id, # 品种代码和交易所ID
|
||||
'created_at':datetime.strptime(created_at, "%Y-%m-%d %H:%M:%S.%f"),
|
||||
#'created_at': created_at, # 创建时间
|
||||
'price': float(data['LastPrice']), # 最新价
|
||||
'last_volume': int(data['Volume']) - previous_volume.get(instrument_id, 0) if previous_volume.get(instrument_id, 0) != 0 else 0, # 瞬时成交量
|
||||
'bid_p': float(data['BidPrice1']), # 买价
|
||||
'bid_v': int(data['BidVolume1']), # 买量
|
||||
'ask_p': float(data['AskPrice1']), # 卖价
|
||||
'ask_v': int(data['AskVolume1']), # 卖量
|
||||
'UpperLimitPrice': float(data['UpperLimitPrice']), # 涨停价
|
||||
'LowerLimitPrice': float(data['LowerLimitPrice']), # 跌停价
|
||||
'TradingDay': data['TradingDay'].decode(), # 交易日日期
|
||||
'cum_volume': int(data['Volume']), # 最新总成交量
|
||||
'cum_amount': float(data['Turnover']), # 最新总成交额
|
||||
'cum_position': int(data['OpenInterest']), # 合约持仓量
|
||||
}
|
||||
# print('&&&&&&&&',instrument_id, tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 更新上一个Tick的成交量
|
||||
previous_volume[instrument_id] = int(data['Volume'])
|
||||
if tick['last_volume']>0:
|
||||
#print(tick['created_at'],'vol:',tick['last_volume'])
|
||||
# 处理Tick数据
|
||||
self.on_tick(tick)
|
||||
|
||||
def can_time(self,hour, minute):
|
||||
hour = str(hour)
|
||||
minute = str(minute)
|
||||
if len(minute) == 1:
|
||||
minute = "0" + minute
|
||||
return int(hour + minute)
|
||||
|
||||
def on_tick(self,tick):
|
||||
|
||||
tm=self.can_time(tick['created_at'].hour,tick['created_at'].minute)
|
||||
#print(tick['symbol'])
|
||||
#print(1)
|
||||
#if tm>1500 and tm<2100 :
|
||||
# return
|
||||
if tick['last_volume']==0:
|
||||
return
|
||||
quotes = tick
|
||||
timetick=str(tick['created_at']).replace('+08:00', '')
|
||||
tsymbol=tick['symbol']
|
||||
if tsymbol not in tsymbollist.keys():
|
||||
# 获取tick的买卖价和买卖量
|
||||
|
||||
tsymbollist[tsymbol]=tick
|
||||
bid_p=quotes['bid_p']
|
||||
ask_p=quotes['ask_p']
|
||||
bid_v=quotes['bid_v']
|
||||
ask_v=quotes['ask_v']
|
||||
else:
|
||||
# 获取上一个tick的买卖价和买卖量
|
||||
rquotes =tsymbollist[tsymbol]
|
||||
bid_p=rquotes['bid_p']
|
||||
ask_p=rquotes['ask_p']
|
||||
bid_v=rquotes['bid_v']
|
||||
ask_v=rquotes['ask_v']
|
||||
tsymbollist[tsymbol]=tick
|
||||
tick_dt=pd.DataFrame({'datetime':timetick,'symbol':tick['symbol'],'mainsym':tick['symbol'].rstrip('0123456789').upper(),'lastprice':tick['price'],
|
||||
'vol':tick['last_volume'],
|
||||
'bid_p':bid_p,'ask_p':ask_p,'bid_v':bid_v,'ask_v':ask_v},index=[0])
|
||||
sym = tick_dt['symbol'][0]
|
||||
#print(tick_dt)
|
||||
self.tickdata(tick_dt,sym)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def data_of(self,symbol, df):
|
||||
global trade_dfs
|
||||
# 将df数据合并到trader_df中
|
||||
# if symbol not in trade_dfs.keys():
|
||||
# trade_df = pd.DataFrame({})
|
||||
# else:
|
||||
# trade_df = trade_dfs[symbol]
|
||||
trade_dfs[symbol] = pd.concat([trade_dfs[symbol], df], ignore_index=True)
|
||||
# print('!!!!!!!!!!!trader_df: ', symbol, df['datetime'].iloc[-1])
|
||||
#print(trader_df)
|
||||
|
||||
def process(self,bidDict, askDict, symbol):
|
||||
try:
|
||||
# 尝试从quotedict中获取对应品种的报价数据
|
||||
dic = quotedict[symbol]
|
||||
bidDictResult = dic['bidDictResult']
|
||||
askDictResult = dic['askDictResult']
|
||||
except:
|
||||
# 如果获取失败，则初始化bidDictResult和askDictResult为空字典
|
||||
bidDictResult, askDictResult = {}, {}
|
||||
|
||||
# 将所有买盘字典和卖盘字典的key合并，并按升序排序
|
||||
sList = sorted(set(list(bidDict.keys()) + list(askDict.keys())))
|
||||
|
||||
# 遍历所有的key，将相同key的值进行累加
|
||||
for s in sList:
|
||||
if s in bidDict:
|
||||
if s in bidDictResult:
|
||||
bidDictResult[s] = int(bidDict[s]) + bidDictResult[s]
|
||||
else:
|
||||
bidDictResult[s] = int(bidDict[s])
|
||||
if s not in askDictResult:
|
||||
askDictResult[s] = 0
|
||||
else:
|
||||
if s in askDictResult:
|
||||
askDictResult[s] = int(askDict[s]) + askDictResult[s]
|
||||
else:
|
||||
askDictResult[s] = int(askDict[s])
|
||||
if s not in bidDictResult:
|
||||
bidDictResult[s] = 0
|
||||
|
||||
# 构建包含bidDictResult和askDictResult的字典，并存入quotedict中
|
||||
df = {'bidDictResult': bidDictResult, 'askDictResult': askDictResult}
|
||||
quotedict[symbol] = df
|
||||
|
||||
return bidDictResult, askDictResult
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
|
||||
def tickdata(self,df,symbol):
|
||||
tickdata =pd.DataFrame({'datetime':df['datetime'],'symbol':df['symbol'],'lastprice':df['lastprice'],
|
||||
'volume':df['vol'],'bid_p':df['bid_p'],'bid_v':df['bid_v'],'ask_p':df['ask_p'],'ask_v':df['ask_v']})
|
||||
try:
|
||||
if symbol in tickdatadict.keys():
|
||||
rdf=tickdatadict[symbol]
|
||||
rdftm=pd.to_datetime(rdf['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
now=str(tickdata['datetime'][0])
|
||||
if now>rdftm:
|
||||
try:
|
||||
oo=ofdatadict[symbol]
|
||||
self.data_of(symbol, oo)
|
||||
#print('oo',oo)
|
||||
if symbol in quotedict.keys():
|
||||
quotedict.pop(symbol)
|
||||
if symbol in tickdatadict.keys():
|
||||
tickdatadict.pop(symbol)
|
||||
if symbol in ofdatadict.keys():
|
||||
ofdatadict.pop(symbol)
|
||||
except IOError as e:
|
||||
print('rdftm捕获到异常',e)
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
else:
|
||||
tickdata['bartime'] = rdf['bartime']
|
||||
tickdata['open'] = rdf['open']
|
||||
tickdata['high'] = max(tickdata['lastprice'].values,rdf['high'].values)
|
||||
tickdata['low'] = min(tickdata['lastprice'].values,rdf['low'].values)
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['volume']=df['vol']+rdf['volume'].values
|
||||
tickdata['starttime'] = rdf['starttime']
|
||||
else :
|
||||
print('新bar的第一个tick进入')
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['datetime'])
|
||||
tickdata['open'] = tickdata['lastprice']
|
||||
tickdata['high'] = tickdata['lastprice']
|
||||
tickdata['low'] = tickdata['lastprice']
|
||||
tickdata['close'] = tickdata['lastprice']
|
||||
tickdata['starttime'] = tickdata['datetime']
|
||||
except IOError as e:
|
||||
print('捕获到异常',e)
|
||||
|
||||
|
||||
tickdata['bartime'] = pd.to_datetime(tickdata['bartime'])
|
||||
param = self.param_dict[self.品种]
|
||||
bardata = tickdata.resample(on = 'bartime',rule = param.周期,label = 'right',closed = 'right').agg({'starttime':'first','symbol':'last','open':'first','high':'max','low':'min','close':'last','volume':'sum'}).reset_index(drop = False)
|
||||
bardata =bardata.dropna().reset_index(drop = True)
|
||||
bardata['bartime'] = pd.to_datetime(bardata['bartime'][0]).strftime('%Y-%m-%d %H:%M:%S')
|
||||
tickdatadict[symbol]=bardata
|
||||
tickdata['volume']=df['vol'].values
|
||||
#print(bardata['symbol'].values,bardata['bartime'].values)
|
||||
self.orderflow_df_new(tickdata,bardata,symbol)
|
||||
# time.sleep(0.5)
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def orderflow_df_new(self,df_tick,df_min,symbol):
|
||||
startArray = pd.to_datetime(df_min['starttime']).values
|
||||
voluememin= df_min['volume'].values
|
||||
highs=df_min['high'].values
|
||||
lows=df_min['low'].values
|
||||
opens=df_min['open'].values
|
||||
closes=df_min['close'].values
|
||||
#endArray = pd.to_datetime(df_min['bartime']).values
|
||||
endArray = df_min['bartime'].values
|
||||
#print(endArray)
|
||||
deltaArray = np.zeros((len(endArray),))
|
||||
tTickArray = pd.to_datetime(df_tick['datetime']).values
|
||||
bp1TickArray = df_tick['bid_p'].values
|
||||
ap1TickArray = df_tick['ask_p'].values
|
||||
lastTickArray = df_tick['lastprice'].values
|
||||
volumeTickArray = df_tick['volume'].values
|
||||
symbolarray = df_tick['symbol'].values
|
||||
indexFinal = 0
|
||||
for index,tEnd in enumerate(endArray):
|
||||
dt=endArray[index]
|
||||
start = startArray[index]
|
||||
bidDict = {}
|
||||
askDict = {}
|
||||
bar_vol=voluememin[index]
|
||||
bar_close=closes[index]
|
||||
bar_open=opens[index]
|
||||
bar_low=lows[index]
|
||||
bar_high=highs[index]
|
||||
bar_symbol=symbolarray[index]
|
||||
# for indexTick in range(indexFinal,len(df_tick)):
|
||||
# if tTickArray[indexTick] >= tEnd:
|
||||
# break
|
||||
# elif (tTickArray[indexTick] >= start) & (tTickArray[indexTick] < tEnd):
|
||||
Bp = round(bp1TickArray[0],4)
|
||||
Ap = round(ap1TickArray[0],4)
|
||||
LastPrice = round(lastTickArray[0],4)
|
||||
Volume = volumeTickArray[0]
|
||||
if LastPrice >= Ap:
|
||||
if str(LastPrice) in askDict.keys():
|
||||
askDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
askDict[str(LastPrice)] = Volume
|
||||
if LastPrice <= Bp:
|
||||
if str(LastPrice) in bidDict.keys():
|
||||
bidDict[str(LastPrice)] += Volume
|
||||
else:
|
||||
bidDict[str(LastPrice)] = Volume
|
||||
# indexFinal = indexTick
|
||||
bidDictResult,askDictResult = self.process(bidDict,askDict,symbol)
|
||||
bidDictResult=dict(sorted(bidDictResult.items(),key=operator.itemgetter(0)))
|
||||
askDictResult=dict(sorted(askDictResult.items(),key=operator.itemgetter(0)))
|
||||
prinslist=list(bidDictResult.keys())
|
||||
asklist=list(askDictResult.values())
|
||||
bidlist=list(bidDictResult.values())
|
||||
delta=(sum(askDictResult.values()) - sum(bidDictResult.values()))
|
||||
#print(prinslist,asklist,bidlist)
|
||||
#print(len(prinslist),len(bidDictResult),len(askDictResult))
|
||||
df=pd.DataFrame({'price':pd.Series([prinslist]),'Ask':pd.Series([asklist]),'Bid':pd.Series([bidlist])})
|
||||
#df=pd.DataFrame({'price':pd.Series(bidDictResult.keys()),'Ask':pd.Series(askDictResult.values()),'Bid':pd.Series(bidDictResult.values())})
|
||||
df['symbol']=bar_symbol
|
||||
df['datetime']=dt
|
||||
df['delta']=str(delta)
|
||||
df['close']=bar_close
|
||||
df['open']=bar_open
|
||||
df['high']=bar_high
|
||||
df['low']=bar_low
|
||||
df['volume']=bar_vol
|
||||
#df['ticktime']=tTickArray[0]
|
||||
df['dj'] = self.GetOrderFlow_dj(df)
|
||||
ofdatadict[symbol]=df
|
||||
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
def GetOrderFlow_dj(self,kData):
|
||||
param = self.param_dict[self.品种]
|
||||
Config = {
|
||||
'Value1': param.失衡,
|
||||
'Value2': param.堆积,
|
||||
'Value4': True,
|
||||
}
|
||||
aryData = kData
|
||||
djcout = 0
|
||||
|
||||
# 遍历kData中的每一行，计算djcout指标
|
||||
for index, row in aryData.iterrows():
|
||||
kItem = aryData.iloc[index]
|
||||
high = kItem['high']
|
||||
low = kItem['low']
|
||||
close = kItem['close']
|
||||
open = kItem['open']
|
||||
dtime = kItem['datetime']
|
||||
price_s = kItem['price']
|
||||
Ask_s = kItem['Ask']
|
||||
Bid_s = kItem['Bid']
|
||||
delta = kItem['delta']
|
||||
|
||||
price_s = price_s
|
||||
Ask_s = Ask_s
|
||||
Bid_s = Bid_s
|
||||
|
||||
gj = 0
|
||||
xq = 0
|
||||
gxx = 0
|
||||
xxx = 0
|
||||
|
||||
# 遍历price_s中的每一个元素，计算相关指标
|
||||
for i in np.arange(0, len(price_s), 1):
|
||||
duiji = {
|
||||
'price': 0,
|
||||
'time': 0,
|
||||
'longshort': 0,
|
||||
}
|
||||
|
||||
if i == 0:
|
||||
delta = delta
|
||||
|
||||
order= {
|
||||
"Price":price_s[i],
|
||||
"Bid":{ "Value":Bid_s[i]},
|
||||
"Ask":{ "Value":Ask_s[i]}
|
||||
}
|
||||
#空头堆积
|
||||
if i >= 0 and i < len(price_s) - 1:
|
||||
if (order["Bid"]["Value"] > Ask_s[i + 1] * int(Config['Value1'])):
|
||||
gxx += 1
|
||||
gj += 1
|
||||
if gj >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = -1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += -1
|
||||
else:
|
||||
gj = 0
|
||||
#多头堆积
|
||||
if i >= 1 and i <= len(price_s) - 1:
|
||||
if (order["Ask"]["Value"] > Bid_s[i - 1] * int(Config['Value1'])):
|
||||
xq += 1
|
||||
xxx += 1
|
||||
if xq >= int(Config['Value2']) and Config['Value4'] == True:
|
||||
duiji['price'] = price_s[i]
|
||||
duiji['time'] = dtime
|
||||
duiji['longshort'] = 1
|
||||
if float(duiji['price']) > 0:
|
||||
djcout += 1
|
||||
else:
|
||||
xq = 0
|
||||
|
||||
# 返回计算得到的djcout值
|
||||
return djcout
|
||||
|
||||
def OnRtnTrade(self, pTrade):
|
||||
print("||成交回报||", pTrade)
|
||||
|
||||
def OnRspOrderInsert(self, pInputOrder, pRspInfo, nRequestID, bIsLast):
|
||||
print("||OnRspOrderInsert||", pInputOrder, pRspInfo, nRequestID, bIsLast)
|
||||
|
||||
# 订单状态通知
|
||||
def OnRtnOrder(self, pOrder):
|
||||
print("||订单回报||", pOrder)
|
||||
|
||||
#保存OF数据
|
||||
def save_trade_data(self,df,instrument_id):
|
||||
# 定义文件路径
|
||||
csv_file_path = f"ofdata/{instrument_id}_ofdata.csv"
|
||||
directory = os.path.dirname(csv_file_path)
|
||||
|
||||
# 检查目录是否存在，如果不存在则创建它
|
||||
if not os.path.exists(directory):
|
||||
os.makedirs(directory)
|
||||
|
||||
# 如果文件夹存在，则追加数据；否则，创建新文件并保存整个 DataFrame
|
||||
if os.path.exists(csv_file_path):
|
||||
# 仅保存最后一行数据
|
||||
df.tail(1).to_csv(csv_file_path, mode='a', header=False, index=False)
|
||||
else:
|
||||
# 创建新文件并保存整个 DataFrame
|
||||
df.to_csv(csv_file_path, index=False)
|
||||
|
||||
def w_log(self,symbol,log_message):
|
||||
|
||||
# 创建文件夹（如果不存在）
|
||||
log_dir = 'tradelogs'
|
||||
if not os.path.exists(log_dir):
|
||||
os.makedirs(log_dir)
|
||||
|
||||
# 写入日志到 CSV 文件
|
||||
now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
log_file = os.path.join(log_dir, f'{symbol}log.csv')
|
||||
# 打印日志到控制台
|
||||
print(f'{now}: {log_message}')
|
||||
with open(log_file, 'a', encoding='utf-8',newline='') as csvfile:
|
||||
writer = csv.writer(csvfile)
|
||||
# 如果文件为空，写入表头
|
||||
if csvfile.tell() == 0:
|
||||
writer.writerow(['time', 'logs'])
|
||||
writer.writerow([now, log_message])
|
||||
|
||||
# 计算单品种资金流向数据
|
||||
def 资金流向计算(self,df):
|
||||
# 将 'delta' 和 'close' 转换为数值类型，强制转换错误
|
||||
df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
|
||||
df['close'] = pd.to_numeric(df['close'], errors='coerce')
|
||||
|
||||
# 删除 'delta' 或 'close' 中的 NaN 值
|
||||
df = df.dropna(subset=['volume', 'close'])
|
||||
symbol=df['symbol'].iloc[-1]
|
||||
symbol = ''.join(filter(str.isalpha, symbol)).upper()
|
||||
资金净流向 = abs(sum(df['volume']*df['close']))*合约信息[symbol]['合约单位']*合约信息[symbol]['保证金']
|
||||
更新状态=True
|
||||
# 将结果放入 DataFrame
|
||||
result_df = pd.DataFrame({
|
||||
'资金净流向': [资金净流向],
|
||||
'更新状态': [更新状态]
|
||||
})
|
||||
|
||||
return result_df
|
||||
|
||||
# 收盘清仓
|
||||
def 收盘清仓(self,symbol,data):
|
||||
param = self.param_dict[symbol]
|
||||
# 获取当前时间
|
||||
current_time = datetime.now().time()
|
||||
|
||||
# 设置清仓操作的时间范围1：14:55到15:00
|
||||
clearing_time1_start = s_time(14, 55)
|
||||
clearing_time1_end = s_time(15, 0)
|
||||
|
||||
# 设置清仓操作的时间范围2：00:55到01:00
|
||||
clearing_time2_start = s_time(22, 55)
|
||||
clearing_time2_end = s_time(23, 0)
|
||||
now = datetime.now()
|
||||
# 创建一个标志变量，用于记录是否已经执行过清仓操作
|
||||
#param.clearing_executed = False
|
||||
|
||||
# 检查当前时间是否在第一个清仓操作的时间范围内，并且清仓操作未执行过
|
||||
if clearing_time1_start <= current_time <= clearing_time1_end and not param.clearing_executed :
|
||||
#trade_dfs.drop(trade_dfs.index,inplace=True)#清除当天的行情数据
|
||||
if param.pos>0:
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}多头清仓操作")
|
||||
pass
|
||||
elif param.pos<0:
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}空头清仓操作")
|
||||
pass
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
|
||||
# 检查当前时间是否在第二个清仓操作的时间范围内，并且清仓操作未执行过
|
||||
elif clearing_time2_start <= current_time <= clearing_time2_end and not param.clearing_executed :
|
||||
#trade_dfs.drop(trade_dfs.index,inplace=True) #清除当天的行情数据
|
||||
if param.pos>0:
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}多头清仓操作")
|
||||
pass
|
||||
elif param.pos<0:
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
self.w_log(symbol,f"{symbol}空头清仓操作")
|
||||
pass
|
||||
param.clearing_executed = True # 设置标志变量为已执行
|
||||
# 如果不在任何清仓操作的时间范围内，可以执行其他操作或不执行任何操作
|
||||
else:
|
||||
param.clearing_executed = False
|
||||
pass
|
||||
#print("不在清仓操作时间范围内")
|
||||
|
||||
return param.clearing_executed
|
||||
|
||||
def cal_sig(self, symbol_queue):
|
||||
while True:
|
||||
try:
|
||||
now=datetime.now()
|
||||
data = symbol_queue.get(block=True, timeout=5) # 如果5秒没收到新的tick行情，则抛出异常
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
size = symbol_queue.qsize()
|
||||
if size > 1:
|
||||
print(f'当前{instrument_id}共享队列长度为{size}, 有点阻塞！！！！！')
|
||||
param = self.param_dict[instrument_id]
|
||||
self.品种=instrument_id
|
||||
#OF计算数据开始
|
||||
self.tickcome(data)
|
||||
trade_df = trade_dfs[instrument_id]
|
||||
#清仓开关
|
||||
run_kg=self.收盘清仓(instrument_id,data)
|
||||
最新价=data['LastPrice']
|
||||
sympc=data['InstrumentID'].decode()
|
||||
# #多头时间出场-----------------------暂时用不到------------------------
|
||||
# if param.pos>0 and param.多头开仓历时>param.平_多时间 :
|
||||
# #平多
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
# param.pos=0
|
||||
# param.多头止损价格=0
|
||||
# param.多头开仓历时=0
|
||||
# param.多头成本价格=0
|
||||
# self.w_log(sympc,f'历时{param.平_多时间}***多头***{sympc}时间出场价格{最新价}')
|
||||
|
||||
#多头价格止损
|
||||
if param.pos>0 and 最新价<param.多头止损价格 :
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.多头止损价格=0
|
||||
param.多头开仓历时=0
|
||||
param.多头成本价格=0
|
||||
self.w_log(sympc,f'多头止损触发: {最新价},多头止损价格：{param.多头止损价格} ***{sympc}多头止损***')
|
||||
|
||||
# #多头跟踪止盈
|
||||
elif param.pos>0 and len(trade_df)>1 and 最新价<trade_df['10_MA'].iloc[-1] and 最新价>param.多头成本价格 :
|
||||
#平多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
param.pos=0
|
||||
param.多头止损价格=0
|
||||
param.多头开仓历时=0
|
||||
param.多头成本价格=0
|
||||
self.w_log(sympc,f'多头跟踪止盈触发: {最新价} ***{sympc}多头止盈***')
|
||||
|
||||
# # #空头时间出场-----------------------暂时用不到------------------------
|
||||
# if param.pos<0 and param.空头开仓历时>param.平_空时间 :
|
||||
# #平空
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
# param.pos=0
|
||||
# param.空头止损价格=0
|
||||
# param.空头开仓历时=0
|
||||
# param.空头成本价格=0
|
||||
# self.w_log(sympc,f'历时{param.平_空时间}***空头***{sympc}时间出场价格{最新价}')
|
||||
|
||||
#空头价格止损
|
||||
if param.pos<0 and 最新价>param.空头止损价格 :
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.空头止损价格=0
|
||||
param.空头开仓历时=0
|
||||
param.空头成本价格=0
|
||||
self.w_log(sympc,f'空头止损触发: {最新价},空头止损价格：{param.空头止损价格} ***{sympc}空头止损***')
|
||||
|
||||
# #空头跟踪止盈
|
||||
elif param.pos<0 and len(trade_df)>1 and 最新价>trade_df['10_MA'].iloc[-1] and 最新价<param.空头成本价格 :
|
||||
#平空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
param.pos=0
|
||||
param.空头止损价格=0
|
||||
param.空头开仓历时=0
|
||||
param.空头成本价格=0
|
||||
self.w_log(sympc,f'空头跟踪止盈触发: {最新价},***{sympc}空头止盈***')
|
||||
|
||||
#交易程序开始
|
||||
if len(trade_df)>param.cont_df and run_kg==False:
|
||||
#开仓历时更新：
|
||||
if param.pos>0:
|
||||
param.多头开仓历时+=1
|
||||
elif param.pos<0:
|
||||
param.空头开仓历时+=1
|
||||
|
||||
#计算截面数据
|
||||
截面数据_资金流向[instrument_id]=self.资金流向计算(trade_df)
|
||||
|
||||
#日均线
|
||||
trade_df['dayma']=trade_df['close'].mean()
|
||||
|
||||
# 计算5日均线和10日均线，即使数据不足5日或10日
|
||||
trade_df['5_MA'] = trade_df['close'].rolling(window=5, min_periods=1).mean()
|
||||
trade_df['10_MA'] = trade_df['close'].rolling(window=10, min_periods=1).mean()
|
||||
|
||||
# 计算累积的delta值
|
||||
trade_df['delta'] = trade_df['delta'].astype(float)
|
||||
trade_df['delta累计'] = trade_df['delta'].cumsum()
|
||||
|
||||
#大于日均线
|
||||
开多1=trade_df['dayma'].iloc[-1] > 0 and trade_df['close'].iloc[-1] > trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量大于X
|
||||
开多4=trade_df['delta累计'].iloc[-1] > param.sum_delta and trade_df['delta'].iloc[-1] > param.delta
|
||||
|
||||
#小于日均线
|
||||
开空1=trade_df['dayma'].iloc[-1]>0 and trade_df['close'].iloc[-1] < trade_df['dayma'].iloc[-1]
|
||||
|
||||
#累计多空净量小于X
|
||||
开空4=trade_df['delta累计'].iloc[-1] < -param.sum_delta and trade_df['delta'].iloc[-1] < -param.delta
|
||||
|
||||
开多组合= 开多1 and 开多4 and trade_df['dj'].iloc[-1]>param.dj_X and trade_df['5_MA'].iloc[-1] > trade_df['10_MA'].iloc[-1]
|
||||
开空条件= 开空1 and 开空4 and trade_df['dj'].iloc[-1]<-param.dj_X and trade_df['5_MA'].iloc[-1] < trade_df['10_MA'].iloc[-1]
|
||||
symm=data['InstrumentID'].decode()
|
||||
#开平仓
|
||||
#换仓平多
|
||||
# if param.pos>0 and symm not in top_two_symbols:
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'3')
|
||||
# param.pos=0
|
||||
# param.多头止损价格=0
|
||||
# param.多头开仓历时=0
|
||||
# param.多头成本价格=0
|
||||
# self.w_log(symm,f'当前截面品种{top_two_symbols},{symm}不在范围，***多平仓***')
|
||||
#多头开仓
|
||||
if param.pos==0 and 开多组合 and symm in top_two_symbols :
|
||||
#开多
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'0')
|
||||
param.pos=1
|
||||
param.多头止损价格=trade_df['low'].iloc[-2]
|
||||
param.多头成本价格=data['AskPrice1']
|
||||
param.多头开仓历时=1
|
||||
self.w_log(symm,f'品种：{symm},委托价格: {param.多头成本价格}***开多***')
|
||||
|
||||
#换仓平空
|
||||
# if param.pos<0 and symm not in top_two_symbols :
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'1')
|
||||
# self.insert_order(data['ExchangeID'], data['InstrumentID'], data['AskPrice1']+param.py,param.Lots,b'0',b'3')
|
||||
# param.pos=0
|
||||
# param.空头止损价格=0
|
||||
# param.空头开仓历时=0
|
||||
# param.空头成本价格=0
|
||||
# self.w_log(symm,f'当前截面品种{top_two_symbols},{symm}不在范围，***空平仓***')
|
||||
#空头开仓
|
||||
if param.pos==0 and 开空条件 and symm in top_two_symbols :
|
||||
#开空
|
||||
self.insert_order(data['ExchangeID'], data['InstrumentID'], data['BidPrice1']-param.py,param.Lots,b'1',b'0')
|
||||
param.pos=-1
|
||||
param.空头止损价格=trade_df['high'].iloc[-2]
|
||||
param.空头成本价格=data['BidPrice1']
|
||||
param.空头开仓历时=1
|
||||
self.w_log(symm,f'品种：{symm},委托价格: {param.空头成本价格}***开空***')
|
||||
#print(trade_df)
|
||||
#保存OF数据
|
||||
self.save_trade_data(trade_df,symm)
|
||||
#保存bar计数
|
||||
param.cont_df=len(trade_df)
|
||||
except queue.Empty:
|
||||
# print(f"当前合约队列为空，等待新数据插入。")
|
||||
pass
|
||||
|
||||
# 将CTP推送的行情数据分发给对应线程队列去执行
|
||||
def distribute_tick(self):
|
||||
while True:
|
||||
if self.status == 0:
|
||||
data = None
|
||||
while not self.md_queue.empty():
|
||||
data = self.md_queue.get(block=False)
|
||||
instrument_id = data['InstrumentID'].decode() # 品种代码
|
||||
try:
|
||||
self.queue_dict[instrument_id].put(data, block=False) # 往对应合约队列中插入行情
|
||||
# print(f"{instrument_id}合约数据插入。")
|
||||
except queue.Full:
|
||||
# 当某个线程阻塞导致对应队列容量超限时抛出异常，不会影响其他合约的信号计算
|
||||
print(f"{instrument_id}合约信号计算阻塞导致对应队列已满，请检查对应代码逻辑后重启。")
|
||||
else:
|
||||
time.sleep(1)
|
||||
|
||||
def start(self, param_dict):
|
||||
threads = []
|
||||
self.param_dict = param_dict
|
||||
thread1 = threading.Thread(target=横截面计算)
|
||||
for symbol in param_dict.keys():
|
||||
trade_dfs[symbol] = pd.DataFrame({})
|
||||
self.queue_dict[symbol] = queue.Queue(10) #为每个合约创建一个限制数为10的队列，当计算发生阻塞导致队列达到限制数时会抛出异常
|
||||
t = threading.Thread(target=self.cal_sig, args=(self.queue_dict[symbol],)) # 为每个合约单独创建一个线程计算开仓逻辑
|
||||
threads.append(t)
|
||||
t.start()
|
||||
thread1.start()
|
||||
self.distribute_tick()
|
||||
for t in threads:
|
||||
t.join()
|
||||
thread1.join()
|
||||
|
||||
# 定义横截面计算函数
|
||||
def 横截面计算():
|
||||
global top_two_symbols
|
||||
while True:
|
||||
if len(截面数据_资金流向)>5:
|
||||
goss=1
|
||||
# 检查是否有任何品种的更新状态为 False，如果有则直接返回
|
||||
for symbol, data in 截面数据_资金流向.items():
|
||||
if not data['更新状态'].iloc[-1]:
|
||||
#print(f"品种 {symbol} 的更新状态为 False，跳过计算。")
|
||||
goss=0
|
||||
break
|
||||
if goss==1:
|
||||
# 继续计算资金净流向
|
||||
sorted_items = sorted(截面数据_资金流向.items(), key=lambda item: item[1]['资金净流向'].iloc[-1], reverse=True)
|
||||
max_symbol = sorted_items[0][0]
|
||||
two_max_symbol = sorted_items[1][0]
|
||||
three_max_symbol = sorted_items[2][0]
|
||||
four_max_symbol = sorted_items[3][0]
|
||||
five_max_symbol = sorted_items[4][0]
|
||||
top_two_symbols = [max_symbol, two_max_symbol,three_max_symbol,four_max_symbol,five_max_symbol]
|
||||
|
||||
print(f'选出的活跃品种: {top_two_symbols}')
|
||||
print(f'截面数据_资金流向: {截面数据_资金流向}')
|
||||
# 将所有品种的更新状态设置为 False
|
||||
for symbol in 截面数据_资金流向:
|
||||
截面数据_资金流向[symbol]['更新状态'] = False
|
||||
time.sleep(1)
|
||||
|
||||
def run_trader(param_dict, broker_id, td_server, investor_id, password, app_id, auth_code, md_queue=None, page_dir='', private_resume_type=2, public_resume_type=2):
|
||||
my_trader = MyTrader(broker_id, td_server, investor_id, password, app_id, auth_code, md_queue, page_dir, private_resume_type, public_resume_type)
|
||||
my_trader.start(param_dict)
|
||||
|
||||
if __name__ == '__main__':
|
||||
#global symbol
|
||||
#公众号：松鼠Quant
|
||||
#主页：www.quant789.com
|
||||
#本策略仅作学习交流使用，实盘交易盈亏投资者个人负责！！！
|
||||
#版权归松鼠Quant所有，禁止转发、转卖源码违者必究。
|
||||
|
||||
#注意：运行前请先安装好algoplus,
|
||||
# pip install AlgoPlus
|
||||
#http://www.algo.plus/ctp/python/0103001.html
|
||||
|
||||
|
||||
# 实盘参数字典，需要实盘交易的合约，新建对应的参数对象即可，以下参数仅供测试使用，不作为实盘参考！！！！
|
||||
历时平仓=4
|
||||
|
||||
param_dict = {}
|
||||
param_dict['SR409'] = ParamObj(symbol='SR409', Lots=1, py=6,dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['eb2408'] = ParamObj(symbol='eb2408', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['SA409'] = ParamObj(symbol='SA409', Lots=1, py=6,dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['pp409'] = ParamObj(symbol='pp409', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['OI409'] = ParamObj(symbol='OI409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['a2409'] = ParamObj(symbol='a2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['p2409'] = ParamObj(symbol='p2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['RM409'] = ParamObj(symbol='RM409', Lots=2, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
param_dict['sp2409'] = ParamObj(symbol='sp2409', Lots=1, py=6, dj_X=0,delta=0,sum_delta=0,失衡=3,堆积=3,周期='1T',平_多时间=历时平仓,平_空时间=历时平仓)
|
||||
|
||||
|
||||
#用simnow模拟，不要忘记屏蔽下方实盘的future_account字典
|
||||
future_account = get_simulate_account(
|
||||
investor_id='', # simnow账户，注意是登录账户的ID，SIMNOW个人首页查看
|
||||
password='', # simnow密码
|
||||
server_name='电信1', # 电信1、电信2、移动、TEST、N视界
|
||||
subscribe_list=list(param_dict.keys()), # 合约列表
|
||||
)
|
||||
|
||||
# #实盘用这个，不要忘记屏蔽上方simnow的future_account字典
|
||||
# future_account = FutureAccount(
|
||||
# broker_id='', # 期货公司BrokerID
|
||||
# server_dict={'TDServer': "ip:port", 'MDServer': 'ip:port'}, # TDServer为交易服务器，MDServer为行情服务器。服务器地址格式为"ip:port。"
|
||||
# reserve_server_dict={}, # 备用服务器地址
|
||||
# investor_id='', # 账户
|
||||
# password='', # 密码
|
||||
# app_id='simnow_client_test', # 认证使用AppID
|
||||
# auth_code='0000000000000000', # 认证使用授权码
|
||||
# subscribe_list=list(param_dict.keys()), # 订阅合约列表
|
||||
# md_flow_path='./log', # MdApi流文件存储地址，默认MD_LOCATION
|
||||
# td_flow_path='./log', # TraderApi流文件存储地址，默认TD_LOCATION
|
||||
# )
|
||||
|
||||
|
||||
print('开始',len(future_account.subscribe_list))
|
||||
# 共享队列
|
||||
share_queue = Queue(maxsize=200)
|
||||
|
||||
# 行情进程
|
||||
md_process = Process(target=run_tick_engine, args=(future_account, [share_queue]))
|
||||
|
||||
|
||||
# 交易进程
|
||||
trader_process = Process(target=run_trader, args=(
|
||||
param_dict,
|
||||
future_account.broker_id,
|
||||
future_account.server_dict['TDServer'],
|
||||
future_account.investor_id,
|
||||
future_account.password,
|
||||
future_account.app_id,
|
||||
future_account.auth_code,
|
||||
share_queue, # 队列
|
||||
future_account.td_flow_path
|
||||
))
|
||||
|
||||
md_process.start()
|
||||
trader_process.start()
|
||||
|
||||
md_process.join()
|
||||
trader_process.join()
|
||||
|
||||
|
||||
@@ -0,0 +1,74 @@
|
||||
,主连代码_今日,品种代码,主连代码_昨日,主连代码_相等
|
||||
0,a2409,a,a2409,True
|
||||
1,ag2408,ag,ag2408,True
|
||||
2,al2408,al,al2408,True
|
||||
3,ao2408,ao,ao2408,True
|
||||
4,AP410,AP,AP410,True
|
||||
5,au2410,au,au2410,True
|
||||
6,b2409,b,b2409,True
|
||||
7,bb2501,bb,bb2501,True
|
||||
8,bc2408,bc,bc2408,True
|
||||
9,br2408,br,br2408,True
|
||||
10,bu2409,bu,bu2409,True
|
||||
11,c2409,c,c2409,True
|
||||
12,CF409,CF,CF409,True
|
||||
13,CJ409,CJ,CJ409,True
|
||||
14,cs2409,cs,cs2409,True
|
||||
15,cu2408,cu,cu2408,True
|
||||
16,CY409,CY,CY409,True
|
||||
17,eb2408,eb,eb2408,True
|
||||
18,ec2410,ec,ec2410,True
|
||||
19,eg2409,eg,eg2409,True
|
||||
20,fb2409,fb,fb2409,True
|
||||
21,FG409,FG,FG409,True
|
||||
22,fu2409,fu,fu2409,True
|
||||
23,hc2410,hc,hc2410,True
|
||||
24,i2409,i,i2409,True
|
||||
25,IC2407,IC,IC2407,True
|
||||
26,IF2407,IF,IF2407,True
|
||||
27,IH2407,IH,IH2407,True
|
||||
28,IM2407,IM,IM2407,True
|
||||
29,j2409,j,j2409,True
|
||||
30,jd2409,jd,jd2409,True
|
||||
31,jm2409,jm,jm2409,True
|
||||
32,l2409,l,l2409,True
|
||||
33,lc2411,lc,lc2411,True
|
||||
34,lh2409,lh,lh2409,True
|
||||
35,lu2409,lu,lu2409,True
|
||||
36,m2409,m,m2409,True
|
||||
37,MA409,MA,MA409,True
|
||||
38,ni2409,ni,ni2409,True
|
||||
39,nr2409,nr,nr2409,True
|
||||
40,OI409,OI,OI409,True
|
||||
41,p2409,p,p2409,True
|
||||
42,pb2408,pb,pb2408,True
|
||||
43,PF409,PF,PF409,True
|
||||
44,pg2409,pg,pg2409,True
|
||||
45,PK410,PK,PK410,True
|
||||
46,pp2409,pp,pp2409,True
|
||||
47,PX409,PX,PX409,True
|
||||
48,rb2410,rb,rb2410,True
|
||||
49,RM409,RM,RM409,True
|
||||
50,rr2409,rr,rr2409,True
|
||||
51,RS411,RS,RS411,True
|
||||
52,ru2409,ru,ru2409,True
|
||||
53,SA409,SA,SA409,True
|
||||
54,sc2408,sc,sc2408,True
|
||||
55,SF409,SF,SF409,True
|
||||
56,SH409,SH,SH409,True
|
||||
57,si2409,si,si2409,True
|
||||
58,SM501,SM,SM501,True
|
||||
59,sn2408,sn,sn2408,True
|
||||
60,sp2409,sp,sp2409,True
|
||||
61,SR409,SR,SR409,True
|
||||
62,ss2409,ss,ss2409,True
|
||||
63,T2409,T,T2409,True
|
||||
64,TA409,TA,TA409,True
|
||||
65,TF2409,TF,TF2409,True
|
||||
66,TL2409,TL,TL2409,True
|
||||
67,TS2409,TS,TS2409,True
|
||||
68,UR409,UR,UR409,True
|
||||
69,v2409,v,v2409,True
|
||||
70,wr2409,wr,wr2409,True
|
||||
71,y2409,y,y2409,True
|
||||
72,zn2408,zn,zn2408,True
|
||||
|
@@ -0,0 +1,770 @@
|
||||
交易所,合约代码,合约名称,品种代码,品种名称,合约乘数,最小跳动,开仓费率（按金额）,开仓费用（按手）,平仓费率（按金额）,平仓费用（按手）,平今费率（按金额）,平今费用（按手）,做多保证金率（按金额）,做多保证金（按手）,做空保证金率（按金额）,做空保证金（按手）,上日结算价,上日收盘价,最新价,成交量,持仓量,1手开仓费用,1手平仓费用,1手平今费用,做多1手保证金,做空1手保证金,1手市值,1Tick平仓盈亏,1Tick平仓净利,2Tick平仓净利,1Tick平仓收益率%,2Tick平仓收益率%,1Tick平今净利,2Tick平今净利,1Tick平今收益率%,2Tick平今收益率%,交割月份
|
||||
CZCE,AP410,苹果10月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6896.0,6926.0,6926.0,0,167339,5.01,5.01,20.01,10389.0,10389.0,69260.0,10.0,-0.03,9.97,-0.0,0.096,-15.03,-5.03,-0.145,-0.048,410
|
||||
CZCE,AP411,苹果11月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6610.0,6632.0,6632.0,0,16009,5.01,5.01,20.01,9948.0,9948.0,66320.0,10.0,-0.03,9.97,-0.0,0.1,-15.03,-5.03,-0.151,-0.051,411
|
||||
CZCE,AP412,苹果12月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6738.0,6749.0,6749.0,0,878,5.01,5.01,20.01,10123.5,10123.5,67490.0,10.0,-0.03,9.97,-0.0,0.099,-15.03,-5.03,-0.148,-0.05,412
|
||||
CZCE,AP501,苹果1月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6828.0,6850.0,6850.0,0,37560,5.01,5.01,20.01,10275.0,10275.0,68500.0,10.0,-0.03,9.97,-0.0,0.097,-15.03,-5.03,-0.146,-0.049,501
|
||||
CZCE,AP503,苹果3月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6861.0,6878.0,6878.0,0,751,5.01,5.01,20.01,10317.0,10317.0,68780.0,10.0,-0.03,9.97,-0.0,0.097,-15.03,-5.03,-0.146,-0.049,503
|
||||
CZCE,AP504,苹果4月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6936.0,6940.0,6940.0,0,192,5.01,5.01,20.01,10410.0,10410.0,69400.0,10.0,-0.03,9.97,-0.0,0.096,-15.03,-5.03,-0.144,-0.048,504
|
||||
CZCE,AP505,苹果5月,AP,苹果,10,1.0,0.0,5.01,0.0,5.01,0.0,20.01,0.15,0,0.15,0,6976.0,7000.0,7000.0,0,1271,5.01,5.01,20.01,10500.0,10500.0,70000.0,10.0,-0.03,9.97,-0.0,0.095,-15.03,-5.03,-0.143,-0.048,505
|
||||
CZCE,CF407,棉花7月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.25,0,0.25,0,14185.0,14090.0,14260.0,692,7156,4.31,4.31,0.0,17825.0,17825.0,71300.0,25.0,16.37,41.37,0.092,0.232,20.68,45.68,0.116,0.256,407
|
||||
CZCE,CF409,棉花9月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.12,0,0.12,0,14470.0,14405.0,14540.0,112968,518388,4.31,4.31,0.0,8724.0,8724.0,72700.0,25.0,16.37,41.37,0.188,0.474,20.68,45.68,0.237,0.524,409
|
||||
CZCE,CF411,棉花11月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.12,0,0.12,0,14540.0,14500.0,14610.0,9642,9218,4.31,4.31,0.0,8766.0,8766.0,73050.0,25.0,16.37,41.37,0.187,0.472,20.68,45.68,0.236,0.521,411
|
||||
CZCE,CF501,棉花1月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.12,0,0.12,0,14515.0,14475.0,14570.0,23952,161744,4.31,4.31,0.0,8742.0,8742.0,72850.0,25.0,16.37,41.37,0.187,0.473,20.68,45.68,0.237,0.523,501
|
||||
CZCE,CF503,棉花3月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.12,0,0.12,0,14530.0,14515.0,14605.0,71,4740,4.31,4.31,0.0,8763.0,8763.0,73025.0,25.0,16.37,41.37,0.187,0.472,20.68,45.68,0.236,0.521,503
|
||||
CZCE,CF505,棉花5月,CF,一号棉花,5,5.0,0.0,4.31,0.0,4.31,0.0,0.0,0.12,0,0.12,0,14550.0,14530.0,14615.0,436,5352,4.31,4.31,0.0,8769.0,8769.0,73075.0,25.0,16.37,41.37,0.187,0.472,20.68,45.68,0.236,0.521,505
|
||||
CZCE,CJ407,红枣7月,CJ,红枣,5,5.0,0.0,10.01,0.0,10.01,0.0,10.01,0.25,0,0.25,0,10270.0,10300.0,10300.0,0,10,10.01,10.01,10.01,12875.0,12875.0,51500.0,25.0,4.97,29.97,0.039,0.233,4.97,29.97,0.039,0.233,407
|
||||
CZCE,CJ409,红枣9月,CJ,红枣,5,5.0,0.0,10.01,0.0,10.01,0.0,10.01,0.2,0,0.2,0,10325.0,10420.0,10420.0,0,59460,10.01,10.01,10.01,10420.0,10420.0,52100.0,25.0,4.97,29.97,0.048,0.288,4.97,29.97,0.048,0.288,409
|
||||
CZCE,CJ412,红枣12月,CJ,红枣,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,10885.0,10970.0,10970.0,0,1220,3.01,3.01,3.01,9324.5,9324.5,54850.0,25.0,18.97,43.97,0.203,0.472,18.97,43.97,0.203,0.472,412
|
||||
CZCE,CJ501,红枣1月,CJ,红枣,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,10920.0,11015.0,11015.0,0,44096,3.01,3.01,3.01,9362.75,9362.75,55075.0,25.0,18.97,43.97,0.203,0.47,18.97,43.97,0.203,0.47,501
|
||||
CZCE,CJ503,红枣3月,CJ,红枣,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,10930.0,11005.0,11005.0,0,1476,3.01,3.01,3.01,9354.25,9354.25,55025.0,25.0,18.97,43.97,0.203,0.47,18.97,43.97,0.203,0.47,503
|
||||
CZCE,CJ505,红枣5月,CJ,红枣,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,11005.0,11105.0,11105.0,0,3477,3.01,3.01,3.01,9439.25,9439.25,55525.0,25.0,18.97,43.97,0.201,0.466,18.97,43.97,0.201,0.466,505
|
||||
CZCE,CY407,棉纱7月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.3,0,0.3,0,19515.0,19515.0,19515.0,0,4,4.02,4.02,0.01,29272.5,29272.5,97575.0,25.0,16.97,41.97,0.058,0.143,20.98,45.98,0.072,0.157,407
|
||||
CZCE,CY408,棉纱8月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20300.0,20300.0,20300.0,0,0,4.02,4.02,0.01,17255.0,17255.0,101500.0,25.0,16.97,41.97,0.098,0.243,20.98,45.98,0.122,0.266,408
|
||||
CZCE,CY409,棉纱9月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20125.0,20020.0,20140.0,669,4999,4.02,4.02,0.01,17119.0,17119.0,100700.0,25.0,16.97,41.97,0.099,0.245,20.98,45.98,0.123,0.269,409
|
||||
CZCE,CY410,棉纱10月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20100.0,19970.0,19970.0,0,0,4.02,4.02,0.01,16974.5,16974.5,99850.0,25.0,16.97,41.97,0.1,0.247,20.98,45.98,0.124,0.271,410
|
||||
CZCE,CY411,棉纱11月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20495.0,20495.0,20535.0,1,1,4.02,4.02,0.01,17454.75,17454.75,102675.0,25.0,16.97,41.97,0.097,0.24,20.98,45.98,0.12,0.263,411
|
||||
CZCE,CY412,棉纱12月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20775.0,20775.0,20775.0,0,1,4.02,4.02,0.01,17658.75,17658.75,103875.0,25.0,16.97,41.97,0.096,0.238,20.98,45.98,0.119,0.26,412
|
||||
CZCE,CY501,棉纱1月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20430.0,20290.0,20435.0,32,523,4.02,4.02,0.01,17369.75,17369.75,102175.0,25.0,16.97,41.97,0.098,0.242,20.98,45.98,0.121,0.265,501
|
||||
CZCE,CY502,棉纱2月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,20725.0,20725.0,20725.0,0,1,4.02,4.02,0.01,17616.25,17616.25,103625.0,25.0,16.97,41.97,0.096,0.238,20.98,45.98,0.119,0.261,502
|
||||
CZCE,CY503,棉纱3月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,21245.0,21245.0,21245.0,0,4,4.02,4.02,0.01,18058.25,18058.25,106225.0,25.0,16.97,41.97,0.094,0.232,20.98,45.98,0.116,0.255,503
|
||||
CZCE,CY504,棉纱4月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,21015.0,21015.0,21015.0,0,1,4.02,4.02,0.01,17862.75,17862.75,105075.0,25.0,16.97,41.97,0.095,0.235,20.98,45.98,0.117,0.257,504
|
||||
CZCE,CY505,棉纱5月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,21050.0,21025.0,21070.0,5,76,4.02,4.02,0.01,17909.5,17909.5,105350.0,25.0,16.97,41.97,0.095,0.234,20.98,45.98,0.117,0.257,505
|
||||
CZCE,CY506,棉纱6月,CY,棉纱,5,5.0,0.0,4.01,0.0,4.01,0.0,0.0,0.17,0,0.17,0,21185.0,21185.0,21185.0,0,0,4.02,4.02,0.01,18007.25,18007.25,105925.0,25.0,16.97,41.97,0.094,0.233,20.98,45.98,0.116,0.255,506
|
||||
CZCE,FG407,玻璃7月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.25,0,0.25,0,1353.0,1351.0,1351.0,0,981,6.01,6.01,6.01,6755.0,6755.0,27020.0,20.0,7.98,27.98,0.118,0.414,7.98,27.98,0.118,0.414,407
|
||||
CZCE,FG408,玻璃8月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1463.0,1454.0,1467.0,70,5250,6.01,6.01,6.01,4987.8,4987.8,29340.0,20.0,7.98,27.98,0.16,0.561,7.98,27.98,0.16,0.561,408
|
||||
CZCE,FG409,玻璃9月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1477.0,1464.0,1474.0,149466,697642,6.01,6.01,6.01,5011.6,5011.6,29480.0,20.0,7.98,27.98,0.159,0.558,7.98,27.98,0.159,0.558,409
|
||||
CZCE,FG410,玻璃10月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1508.0,1507.0,1515.0,427,6538,6.01,6.01,6.01,5151.0,5151.0,30300.0,20.0,7.98,27.98,0.155,0.543,7.98,27.98,0.155,0.543,410
|
||||
CZCE,FG411,玻璃11月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1505.0,1502.0,1515.0,111,4510,6.01,6.01,6.01,5151.0,5151.0,30300.0,20.0,7.98,27.98,0.155,0.543,7.98,27.98,0.155,0.543,411
|
||||
CZCE,FG412,玻璃12月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1516.0,1510.0,1521.0,68,8375,6.01,6.01,6.01,5171.4,5171.4,30420.0,20.0,7.98,27.98,0.154,0.541,7.98,27.98,0.154,0.541,412
|
||||
CZCE,FG501,玻璃1月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1505.0,1490.0,1497.0,9884,229236,6.01,6.01,6.01,5089.8,5089.8,29940.0,20.0,7.98,27.98,0.157,0.55,7.98,27.98,0.157,0.55,501
|
||||
CZCE,FG502,玻璃2月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1534.0,1516.0,1526.0,72,10162,6.01,6.01,6.01,5188.4,5188.4,30520.0,20.0,7.98,27.98,0.154,0.539,7.98,27.98,0.154,0.539,502
|
||||
CZCE,FG503,玻璃3月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1530.0,1515.0,1526.0,32,10178,6.01,6.01,6.01,5188.4,5188.4,30520.0,20.0,7.98,27.98,0.154,0.539,7.98,27.98,0.154,0.539,503
|
||||
CZCE,FG504,玻璃4月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1558.0,1540.0,1550.0,17,5469,6.01,6.01,6.01,5270.0,5270.0,31000.0,20.0,7.98,27.98,0.151,0.531,7.98,27.98,0.151,0.531,504
|
||||
CZCE,FG505,玻璃5月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1546.0,1535.0,1543.0,158,12183,6.01,6.01,6.01,5246.2,5246.2,30860.0,20.0,7.98,27.98,0.152,0.533,7.98,27.98,0.152,0.533,505
|
||||
CZCE,FG506,玻璃6月,FG,玻璃,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.17,0,0.17,0,1571.0,1562.0,1567.0,9,108,6.01,6.01,6.01,5327.8,5327.8,31340.0,20.0,7.98,27.98,0.15,0.525,7.98,27.98,0.15,0.525,506
|
||||
CFFEX,IC2407,中证500期货2407,IC,中证500指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4838.2,4831.4,0.0,0,73064,22.29,22.29,222.31,144942.0,144942.0,966280.0,40.0,-4.58,35.42,-0.003,0.024,-204.6,-164.6,-0.141,-0.114,2407
|
||||
CFFEX,IC2408,中证500期货2408,IC,中证500指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4812.8,4806.4,0.0,0,9915,22.18,22.18,221.16,144192.0,144192.0,961280.0,40.0,-4.35,35.65,-0.003,0.025,-203.34,-163.34,-0.141,-0.113,2408
|
||||
CFFEX,IC2409,中证500期货2409,IC,中证500指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4791.2,4785.0,0.0,0,97149,22.08,22.08,220.18,143550.0,143550.0,957000.0,40.0,-4.16,35.84,-0.003,0.025,-202.26,-162.26,-0.141,-0.113,2409
|
||||
CFFEX,IC2412,中证500期货2412,IC,中证500指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4746.2,4740.8,0.0,0,65446,21.87,21.87,218.14,142224.0,142224.0,948160.0,40.0,-3.75,36.25,-0.003,0.025,-200.02,-160.02,-0.141,-0.113,2412
|
||||
CFFEX,IF2407,股指2407,IF,沪深300指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,3413.8,3412.4,0.0,0,75866,23.62,23.62,235.53,153558.0,153558.0,1023720.0,60.0,12.77,72.77,0.008,0.047,-199.14,-139.14,-0.13,-0.091,2407
|
||||
CFFEX,IF2408,股指2408,IF,沪深300指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,3402.0,3401.4,0.0,0,7424,23.54,23.54,234.77,153063.0,153063.0,1020420.0,60.0,12.92,72.92,0.008,0.048,-198.31,-138.31,-0.13,-0.09,2408
|
||||
CFFEX,IF2409,股指2409,IF,沪深300指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,3397.8,3396.2,0.0,0,106387,23.5,23.5,234.41,152829.0,152829.0,1018860.0,60.0,12.99,72.99,0.008,0.048,-197.91,-137.91,-0.13,-0.09,2409
|
||||
CFFEX,IF2412,股指2412,IF,沪深300指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,3395.4,3394.4,0.0,0,42931,23.49,23.49,234.28,152748.0,152748.0,1018320.0,60.0,13.02,73.02,0.009,0.048,-197.78,-137.78,-0.129,-0.09,2412
|
||||
CFFEX,IH2407,上证50期货2407,IH,上证50指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,2364.4,2364.4,0.0,0,36666,16.37,16.37,163.2,106398.0,106398.0,709320.0,60.0,27.27,87.27,0.026,0.082,-119.56,-59.56,-0.112,-0.056,2407
|
||||
CFFEX,IH2408,上证50期货2408,IH,上证50指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,2358.4,2357.6,0.0,0,5019,16.32,16.32,162.73,106092.0,106092.0,707280.0,60.0,27.36,87.36,0.026,0.082,-119.05,-59.05,-0.112,-0.056,2408
|
||||
CFFEX,IH2409,上证50期货2409,IH,上证50指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,2358.2,2357.0,0.0,0,48213,16.32,16.32,162.69,106065.0,106065.0,707100.0,60.0,27.37,87.37,0.026,0.082,-119.0,-59.0,-0.112,-0.056,2409
|
||||
CFFEX,IH2412,上证50期货2412,IH,上证50指数,300,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,2362.2,2361.6,0.0,0,13680,16.35,16.35,163.0,106272.0,106272.0,708480.0,60.0,27.3,87.3,0.026,0.082,-119.35,-59.35,-0.112,-0.056,2412
|
||||
CFFEX,IM2407,中证1000股指期货2407,IM,中证1000指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4764.8,4758.0,0.0,0,90954,21.95,21.95,218.94,142740.0,142740.0,951600.0,40.0,-3.91,36.09,-0.003,0.025,-200.89,-160.89,-0.141,-0.113,2407
|
||||
CFFEX,IM2408,中证1000股指期货2408,IM,中证1000指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4722.0,4718.2,0.0,0,12870,21.77,21.77,217.1,141546.0,141546.0,943640.0,40.0,-3.54,36.46,-0.003,0.026,-198.87,-158.87,-0.141,-0.112,2408
|
||||
CFFEX,IM2409,中证1000股指期货2409,IM,中证1000指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4682.2,4675.0,0.0,0,103557,21.57,21.57,215.12,140250.0,140250.0,935000.0,40.0,-3.14,36.86,-0.002,0.026,-196.69,-156.69,-0.14,-0.112,2409
|
||||
CFFEX,IM2412,中证1000股指期货2412,IM,中证1000指数,200,0.2,2.3e-05,0.01,2.3e-05,0.01,0.00023,0.01,0.15,0,0.15,0,4593.2,4585.0,0.0,0,63896,21.16,21.16,210.98,137550.0,137550.0,917000.0,40.0,-2.31,37.69,-0.002,0.027,-192.13,-152.13,-0.14,-0.111,2412
|
||||
CZCE,JR407,粳稻7月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.3,0,0.3,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,15972.0,15972.0,53240.0,20.0,13.97,33.97,0.087,0.213,13.97,33.97,0.087,0.213,407
|
||||
CZCE,JR409,粳稻9月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,13310.0,13310.0,53240.0,20.0,13.97,33.97,0.105,0.255,13.97,33.97,0.105,0.255,409
|
||||
CZCE,JR411,粳稻11月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,13310.0,13310.0,53240.0,20.0,13.97,33.97,0.105,0.255,13.97,33.97,0.105,0.255,411
|
||||
CZCE,JR501,粳稻1月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,13310.0,13310.0,53240.0,20.0,13.97,33.97,0.105,0.255,13.97,33.97,0.105,0.255,501
|
||||
CZCE,JR503,粳稻3月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,13310.0,13310.0,53240.0,20.0,13.97,33.97,0.105,0.255,13.97,33.97,0.105,0.255,503
|
||||
CZCE,JR505,粳稻5月,JR,粳稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2662.0,2662.0,2662.0,0,0,3.01,3.01,3.01,13310.0,13310.0,53240.0,20.0,13.97,33.97,0.105,0.255,13.97,33.97,0.105,0.255,505
|
||||
CZCE,LR407,晚籼7月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.3,0,0.3,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,14160.0,14160.0,47200.0,20.0,13.97,33.97,0.099,0.24,13.97,33.97,0.099,0.24,407
|
||||
CZCE,LR409,晚籼9月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,11800.0,11800.0,47200.0,20.0,13.97,33.97,0.118,0.288,13.97,33.97,0.118,0.288,409
|
||||
CZCE,LR411,晚籼11月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,11800.0,11800.0,47200.0,20.0,13.97,33.97,0.118,0.288,13.97,33.97,0.118,0.288,411
|
||||
CZCE,LR501,晚籼1月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,11800.0,11800.0,47200.0,20.0,13.97,33.97,0.118,0.288,13.97,33.97,0.118,0.288,501
|
||||
CZCE,LR503,晚籼3月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,11800.0,11800.0,47200.0,20.0,13.97,33.97,0.118,0.288,13.97,33.97,0.118,0.288,503
|
||||
CZCE,LR505,晚籼5月,LR,晚籼稻,20,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,2360.0,2360.0,2360.0,0,0,3.01,3.01,3.01,11800.0,11800.0,47200.0,20.0,13.97,33.97,0.118,0.288,13.97,33.97,0.118,0.288,505
|
||||
CZCE,MA407,甲醇7月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.26,0,0.26,0,2497.0,2497.0,2515.0,2,1654,2.53,2.53,2.53,6539.0,6539.0,25150.0,10.0,4.95,14.95,0.076,0.229,4.95,14.95,0.076,0.229,407
|
||||
CZCE,MA408,甲醇8月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2507.0,2503.0,2539.0,1410,7341,2.55,2.55,2.55,3554.6,3554.6,25390.0,10.0,4.9,14.9,0.138,0.419,4.9,14.9,0.138,0.419,408
|
||||
CZCE,MA409,甲醇9月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2538.0,2537.0,2567.0,361891,752071,2.58,2.58,2.58,3593.8,3593.8,25670.0,10.0,4.84,14.84,0.135,0.413,4.84,14.84,0.135,0.413,409
|
||||
CZCE,MA410,甲醇10月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2568.0,2569.0,2597.0,3922,7204,2.61,2.61,2.61,3635.8,3635.8,25970.0,10.0,4.78,14.78,0.132,0.407,4.78,14.78,0.132,0.407,410
|
||||
CZCE,MA411,甲醇11月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2580.0,2581.0,2609.0,161,6437,2.62,2.62,2.62,3652.6,3652.6,26090.0,10.0,4.76,14.76,0.13,0.404,4.76,14.76,0.13,0.404,411
|
||||
CZCE,MA412,甲醇12月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2608.0,2608.0,2635.0,84,2808,2.65,2.65,2.65,3689.0,3689.0,26350.0,10.0,4.71,14.71,0.128,0.399,4.71,14.71,0.128,0.399,412
|
||||
CZCE,MA501,甲醇1月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2619.0,2615.0,2644.0,33498,209746,2.66,2.66,2.66,3701.6,3701.6,26440.0,10.0,4.69,14.69,0.127,0.397,4.69,14.69,0.127,0.397,501
|
||||
CZCE,MA502,甲醇2月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2589.0,2586.0,2613.0,208,6134,2.62,2.62,2.62,3658.2,3658.2,26130.0,10.0,4.75,14.75,0.13,0.403,4.75,14.75,0.13,0.403,502
|
||||
CZCE,MA503,甲醇3月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2569.0,2565.0,2591.0,9,2138,2.6,2.6,2.6,3627.4,3627.4,25910.0,10.0,4.79,14.79,0.132,0.408,4.79,14.79,0.132,0.408,503
|
||||
CZCE,MA504,甲醇4月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2550.0,2548.0,2574.0,5,726,2.59,2.59,2.59,3603.6,3603.6,25740.0,10.0,4.83,14.83,0.134,0.412,4.83,14.83,0.134,0.412,504
|
||||
CZCE,MA505,甲醇5月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2511.0,2510.0,2536.0,523,6134,2.55,2.55,2.55,3550.4,3550.4,25360.0,10.0,4.9,14.9,0.138,0.42,4.9,14.9,0.138,0.42,505
|
||||
CZCE,MA506,甲醇6月,MA,甲醇N,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,2522.0,2510.0,2541.0,6,38,2.55,2.55,2.55,3557.4,3557.4,25410.0,10.0,4.89,14.89,0.138,0.419,4.89,14.89,0.138,0.419,506
|
||||
CZCE,OI407,菜油7月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.26,0,0.26,0,8275.0,8275.0,8275.0,0,2859,2.01,2.01,2.01,21515.0,21515.0,82750.0,10.0,5.97,15.97,0.028,0.074,5.97,15.97,0.028,0.074,407
|
||||
CZCE,OI409,菜油9月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.13,0,0.13,0,8377.0,8305.0,8316.0,187254,287680,2.01,2.01,2.01,10810.8,10810.8,83160.0,10.0,5.97,15.97,0.055,0.148,5.97,15.97,0.055,0.148,409
|
||||
CZCE,OI411,菜油11月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.13,0,0.13,0,8445.0,8382.0,8392.0,5108,29406,2.02,2.02,2.02,10909.6,10909.6,83920.0,10.0,5.97,15.97,0.055,0.146,5.97,15.97,0.055,0.146,411
|
||||
CZCE,OI501,菜油1月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.13,0,0.13,0,8493.0,8437.0,8451.0,28089,120009,2.02,2.02,2.02,10986.3,10986.3,84510.0,10.0,5.97,15.97,0.054,0.145,5.97,15.97,0.054,0.145,501
|
||||
CZCE,OI503,菜油3月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.13,0,0.13,0,8537.0,8475.0,8486.0,143,4141,2.02,2.02,2.02,11031.8,11031.8,84860.0,10.0,5.97,15.97,0.054,0.145,5.97,15.97,0.054,0.145,503
|
||||
CZCE,OI505,菜油5月,OI,菜籽油,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.13,0,0.13,0,8566.0,8511.0,8524.0,1822,18830,2.02,2.02,2.02,11081.2,11081.2,85240.0,10.0,5.97,15.97,0.054,0.144,5.97,15.97,0.054,0.144,505
|
||||
CZCE,PF407,短纤7月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.26,0,0.26,0,7544.0,7536.0,7536.0,0,2504,3.01,3.01,3.01,9796.8,9796.8,37680.0,10.0,3.98,13.98,0.041,0.143,3.98,13.98,0.041,0.143,407
|
||||
CZCE,PF408,短纤8月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7576.0,7564.0,7594.0,3491,36888,3.01,3.01,3.01,5315.8,5315.8,37970.0,10.0,3.98,13.98,0.075,0.263,3.98,13.98,0.075,0.263,408
|
||||
CZCE,PF409,短纤9月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7572.0,7560.0,7584.0,26457,140577,3.01,3.01,3.01,5308.8,5308.8,37920.0,10.0,3.98,13.98,0.075,0.263,3.98,13.98,0.075,0.263,409
|
||||
CZCE,PF410,短纤10月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7616.0,7604.0,7624.0,18345,122162,3.01,3.01,3.01,5336.8,5336.8,38120.0,10.0,3.98,13.98,0.074,0.262,3.98,13.98,0.074,0.262,410
|
||||
CZCE,PF411,短纤11月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7586.0,7578.0,7592.0,12772,33969,3.01,3.01,3.01,5314.4,5314.4,37960.0,10.0,3.98,13.98,0.075,0.263,3.98,13.98,0.075,0.263,411
|
||||
CZCE,PF412,短纤12月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7566.0,7556.0,7556.0,0,46,3.01,3.01,3.01,5289.2,5289.2,37780.0,10.0,3.98,13.98,0.075,0.264,3.98,13.98,0.075,0.264,412
|
||||
CZCE,PF501,短纤1月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7550.0,7536.0,7566.0,4,392,3.01,3.01,3.01,5296.2,5296.2,37830.0,10.0,3.98,13.98,0.075,0.264,3.98,13.98,0.075,0.264,501
|
||||
CZCE,PF502,短纤2月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7594.0,7588.0,7594.0,2,11,3.01,3.01,3.01,5315.8,5315.8,37970.0,10.0,3.98,13.98,0.075,0.263,3.98,13.98,0.075,0.263,502
|
||||
CZCE,PF503,短纤3月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7574.0,7574.0,7586.0,2,14,3.01,3.01,3.01,5310.2,5310.2,37930.0,10.0,3.98,13.98,0.075,0.263,3.98,13.98,0.075,0.263,503
|
||||
CZCE,PF504,短纤4月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7568.0,7568.0,7576.0,2,8,3.01,3.01,3.01,5303.2,5303.2,37880.0,10.0,3.98,13.98,0.075,0.264,3.98,13.98,0.075,0.264,504
|
||||
CZCE,PF505,短纤5月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7552.0,7544.0,7574.0,2,24,3.01,3.01,3.01,5301.8,5301.8,37870.0,10.0,3.98,13.98,0.075,0.264,3.98,13.98,0.075,0.264,505
|
||||
CZCE,PF506,短纤6月,PF,短纤,5,2.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,7628.0,7628.0,7628.0,0,9,3.01,3.01,3.01,5339.6,5339.6,38140.0,10.0,3.98,13.98,0.074,0.262,3.98,13.98,0.074,0.262,506
|
||||
CZCE,PK410,花生10月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8676.0,8670.0,8670.0,0,133417,4.01,4.01,4.01,6069.0,6069.0,43350.0,10.0,1.97,11.97,0.033,0.197,1.97,11.97,0.033,0.197,410
|
||||
CZCE,PK411,花生11月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8534.0,8538.0,8538.0,0,62683,4.01,4.01,4.01,5976.6,5976.6,42690.0,10.0,1.97,11.97,0.033,0.2,1.97,11.97,0.033,0.2,411
|
||||
CZCE,PK412,花生12月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8554.0,8570.0,8570.0,0,653,4.01,4.01,4.01,5999.0,5999.0,42850.0,10.0,1.97,11.97,0.033,0.2,1.97,11.97,0.033,0.2,412
|
||||
CZCE,PK501,花生1月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8570.0,8564.0,8564.0,0,10324,4.01,4.01,4.01,5994.8,5994.8,42820.0,10.0,1.97,11.97,0.033,0.2,1.97,11.97,0.033,0.2,501
|
||||
CZCE,PK503,花生3月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8662.0,8660.0,8660.0,0,717,4.01,4.01,4.01,6062.0,6062.0,43300.0,10.0,1.97,11.97,0.033,0.198,1.97,11.97,0.033,0.198,503
|
||||
CZCE,PK504,花生4月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8668.0,8666.0,8666.0,0,853,4.01,4.01,4.01,6066.2,6066.2,43330.0,10.0,1.97,11.97,0.033,0.197,1.97,11.97,0.033,0.197,504
|
||||
CZCE,PK505,花生5月,PK,花生仁,5,2.0,0.0,4.01,0.0,4.01,0.0,4.01,0.14,0,0.14,0,8680.0,8666.0,8666.0,0,330,4.01,4.01,4.01,6066.2,6066.2,43330.0,10.0,1.97,11.97,0.033,0.197,1.97,11.97,0.033,0.197,505
|
||||
CZCE,PM407,普麦7月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.45,0,0.45,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,70245.0,70245.0,156100.0,50.0,-10.04,39.96,-0.014,0.057,-10.04,39.96,-0.014,0.057,407
|
||||
CZCE,PM409,普麦9月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,62440.0,62440.0,156100.0,50.0,-10.04,39.96,-0.016,0.064,-10.04,39.96,-0.016,0.064,409
|
||||
CZCE,PM411,普麦11月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,62440.0,62440.0,156100.0,50.0,-10.04,39.96,-0.016,0.064,-10.04,39.96,-0.016,0.064,411
|
||||
CZCE,PM501,普麦1月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,62440.0,62440.0,156100.0,50.0,-10.04,39.96,-0.016,0.064,-10.04,39.96,-0.016,0.064,501
|
||||
CZCE,PM503,普麦3月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,62440.0,62440.0,156100.0,50.0,-10.04,39.96,-0.016,0.064,-10.04,39.96,-0.016,0.064,503
|
||||
CZCE,PM505,普麦5月,PM,普通小麦,50,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3122.0,3122.0,3122.0,0,0,30.02,30.02,30.02,62440.0,62440.0,156100.0,50.0,-10.04,39.96,-0.016,0.064,-10.04,39.96,-0.016,0.064,505
|
||||
CZCE,PX407,对二甲苯7月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.26,0,0.26,0,7674.0,7674.0,7674.0,0,0,3.85,3.85,0.0,9976.2,9976.2,38370.0,10.0,2.3,12.3,0.023,0.123,6.15,16.15,0.062,0.162,407
|
||||
CZCE,PX408,对二甲苯8月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,8546.0,8546.0,8546.0,0,21,4.29,4.29,0.0,6836.8,6836.8,42730.0,10.0,1.43,11.43,0.021,0.167,5.71,15.71,0.084,0.23,408
|
||||
CZCE,PX409,对二甲苯9月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8500.0,8482.0,8510.0,9610,73567,4.27,4.27,0.0,5957.0,5957.0,42550.0,10.0,1.46,11.46,0.025,0.192,5.73,15.73,0.096,0.264,409
|
||||
CZCE,PX410,对二甲苯10月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8520.0,8506.0,8530.0,1044,31273,4.28,4.28,0.0,5971.0,5971.0,42650.0,10.0,1.44,11.44,0.024,0.192,5.72,15.72,0.096,0.263,410
|
||||
CZCE,PX411,对二甲苯11月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8532.0,8532.0,8532.0,0,1,4.28,4.28,0.0,5972.4,5972.4,42660.0,10.0,1.44,11.44,0.024,0.192,5.72,15.72,0.096,0.263,411
|
||||
CZCE,PX412,对二甲苯12月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8540.0,8540.0,8540.0,0,6,4.28,4.28,0.0,5978.0,5978.0,42700.0,10.0,1.43,11.43,0.024,0.191,5.71,15.71,0.096,0.263,412
|
||||
CZCE,PX501,对二甲苯1月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8508.0,8488.0,8516.0,351,27433,4.27,4.27,0.0,5961.2,5961.2,42580.0,10.0,1.46,11.46,0.024,0.192,5.73,15.73,0.096,0.264,501
|
||||
CZCE,PX502,对二甲苯2月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8540.0,8540.0,8540.0,0,4,4.28,4.28,0.0,5978.0,5978.0,42700.0,10.0,1.43,11.43,0.024,0.191,5.71,15.71,0.096,0.263,502
|
||||
CZCE,PX503,对二甲苯3月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8582.0,8582.0,8582.0,0,3,4.3,4.3,0.0,6007.4,6007.4,42910.0,10.0,1.39,11.39,0.023,0.19,5.69,15.69,0.095,0.261,503
|
||||
CZCE,PX504,对二甲苯4月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8548.0,8548.0,8548.0,0,2,4.29,4.29,0.0,5983.6,5983.6,42740.0,10.0,1.43,11.43,0.024,0.191,5.71,15.71,0.095,0.263,504
|
||||
CZCE,PX505,对二甲苯5月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8518.0,8510.0,8530.0,1,47,4.28,4.28,0.0,5971.0,5971.0,42650.0,10.0,1.44,11.44,0.024,0.192,5.72,15.72,0.096,0.263,505
|
||||
CZCE,PX506,对二甲苯6月,PX,对二甲苯,5,2.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,8540.0,8540.0,8540.0,0,3,4.28,4.28,0.0,5978.0,5978.0,42700.0,10.0,1.43,11.43,0.024,0.191,5.71,15.71,0.096,0.263,506
|
||||
CZCE,RI407,早籼7月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.3,0,0.3,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,14874.0,14874.0,49580.0,20.0,14.97,34.97,0.101,0.235,14.97,34.97,0.101,0.235,407
|
||||
CZCE,RI409,早籼9月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,12395.0,12395.0,49580.0,20.0,14.97,34.97,0.121,0.282,14.97,34.97,0.121,0.282,409
|
||||
CZCE,RI411,早籼11月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,12395.0,12395.0,49580.0,20.0,14.97,34.97,0.121,0.282,14.97,34.97,0.121,0.282,411
|
||||
CZCE,RI501,早籼1月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,12395.0,12395.0,49580.0,20.0,14.97,34.97,0.121,0.282,14.97,34.97,0.121,0.282,501
|
||||
CZCE,RI503,早籼3月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,12395.0,12395.0,49580.0,20.0,14.97,34.97,0.121,0.282,14.97,34.97,0.121,0.282,503
|
||||
CZCE,RI505,早籼5月,RI,早籼稻,20,1.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,2479.0,2479.0,2479.0,0,0,2.51,2.51,2.51,12395.0,12395.0,49580.0,20.0,14.97,34.97,0.121,0.282,14.97,34.97,0.121,0.282,505
|
||||
CZCE,RM407,菜粕7月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.26,0,0.26,0,2420.0,2401.0,2401.0,0,940,1.51,1.51,1.51,6242.6,6242.6,24010.0,10.0,6.98,16.98,0.112,0.272,6.98,16.98,0.112,0.272,407
|
||||
CZCE,RM408,菜粕8月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2511.0,2497.0,2495.0,958,16376,1.51,1.51,1.51,3243.5,3243.5,24950.0,10.0,6.98,16.98,0.215,0.523,6.98,16.98,0.215,0.523,408
|
||||
CZCE,RM409,菜粕9月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2520.0,2514.0,2512.0,240835,1012764,1.51,1.51,1.51,3265.6,3265.6,25120.0,10.0,6.98,16.98,0.214,0.52,6.98,16.98,0.214,0.52,409
|
||||
CZCE,RM411,菜粕11月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2453.0,2444.0,2449.0,18761,137511,1.51,1.51,1.51,3183.7,3183.7,24490.0,10.0,6.98,16.98,0.219,0.533,6.98,16.98,0.219,0.533,411
|
||||
CZCE,RM501,菜粕1月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2505.0,2500.0,2499.0,58240,450609,1.51,1.51,1.51,3248.7,3248.7,24990.0,10.0,6.98,16.98,0.215,0.523,6.98,16.98,0.215,0.523,501
|
||||
CZCE,RM503,菜粕3月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2492.0,2492.0,2500.0,2778,11743,1.51,1.51,1.51,3250.0,3250.0,25000.0,10.0,6.98,16.98,0.215,0.522,6.98,16.98,0.215,0.522,503
|
||||
CZCE,RM505,菜粕5月,RM,菜籽粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,2550.0,2537.0,2547.0,1695,36855,1.51,1.51,1.51,3311.1,3311.1,25470.0,10.0,6.98,16.98,0.211,0.513,6.98,16.98,0.211,0.513,505
|
||||
CZCE,RS407,菜籽7月,RS,油菜籽,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.4,0,0.4,0,4886.0,4886.0,4886.0,0,5,2.01,2.01,2.01,19544.0,19544.0,48860.0,10.0,5.97,15.97,0.031,0.082,5.97,15.97,0.031,0.082,407
|
||||
CZCE,RS408,菜籽8月,RS,油菜籽,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.4,0,0.4,0,5015.0,5015.0,5015.0,0,7,2.01,2.01,2.01,20060.0,20060.0,50150.0,10.0,5.97,15.97,0.03,0.08,5.97,15.97,0.03,0.08,408
|
||||
CZCE,RS409,菜籽9月,RS,油菜籽,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.4,0,0.4,0,5086.0,5051.0,5051.0,0,19,2.01,2.01,2.01,20204.0,20204.0,50510.0,10.0,5.97,15.97,0.03,0.079,5.97,15.97,0.03,0.079,409
|
||||
CZCE,RS411,菜籽11月,RS,油菜籽,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.4,0,0.4,0,5216.0,5146.0,5146.0,0,155,2.01,2.01,2.01,20584.0,20584.0,51460.0,10.0,5.97,15.97,0.029,0.078,5.97,15.97,0.029,0.078,411
|
||||
CZCE,SA407,纯碱7月,SA,纯碱,20,1.0,0.0004,0.01,0.0004,0.01,0.0004,0.01,0.25,0,0.25,0,1933.0,1901.0,1870.0,55,2698,14.97,14.97,14.97,9350.0,9350.0,37400.0,20.0,-9.94,10.06,-0.106,0.108,-9.94,10.06,-0.106,0.108,407
|
||||
CZCE,SA408,纯碱8月,SA,纯碱,20,1.0,0.0004,0.01,0.0004,0.01,0.0004,0.01,0.17,0,0.17,0,1980.0,1938.0,1941.0,1861,23066,15.54,15.54,15.54,6599.4,6599.4,38820.0,20.0,-11.08,8.92,-0.168,0.135,-11.08,8.92,-0.168,0.135,408
|
||||
CZCE,SA409,纯碱9月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,2003.0,1952.0,1954.0,285964,819230,7.83,7.83,7.83,6643.6,6643.6,39080.0,20.0,4.34,24.34,0.065,0.366,4.34,24.34,0.065,0.366,409
|
||||
CZCE,SA410,纯碱10月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,2010.0,1967.0,1970.0,672,10089,7.89,7.89,7.89,6698.0,6698.0,39400.0,20.0,4.22,24.22,0.063,0.362,4.22,24.22,0.063,0.362,410
|
||||
CZCE,SA411,纯碱11月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1987.0,1942.0,1955.0,367,9577,7.83,7.83,7.83,6647.0,6647.0,39100.0,20.0,4.34,24.34,0.065,0.366,4.34,24.34,0.065,0.366,411
|
||||
CZCE,SA412,纯碱12月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1979.0,1941.0,1948.0,194,8634,7.8,7.8,7.8,6623.2,6623.2,38960.0,20.0,4.39,24.39,0.066,0.368,4.39,24.39,0.066,0.368,412
|
||||
CZCE,SA501,纯碱1月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1960.0,1920.0,1931.0,20526,306756,7.74,7.74,7.74,6565.4,6565.4,38620.0,20.0,4.53,24.53,0.069,0.374,4.53,24.53,0.069,0.374,501
|
||||
CZCE,SA502,纯碱2月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1968.0,1940.0,1952.0,31,8115,7.82,7.82,7.82,6636.8,6636.8,39040.0,20.0,4.36,24.36,0.066,0.367,4.36,24.36,0.066,0.367,502
|
||||
CZCE,SA503,纯碱3月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1954.0,1928.0,1939.0,72,9221,7.77,7.77,7.77,6592.6,6592.6,38780.0,20.0,4.46,24.46,0.068,0.371,4.46,24.46,0.068,0.371,503
|
||||
CZCE,SA504,纯碱4月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1980.0,1961.0,1961.0,38,5061,7.86,7.86,7.86,6667.4,6667.4,39220.0,20.0,4.29,24.29,0.064,0.364,4.29,24.29,0.064,0.364,504
|
||||
CZCE,SA505,纯碱5月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1962.0,1927.0,1938.0,1281,19899,7.76,7.76,7.76,6589.2,6589.2,38760.0,20.0,4.47,24.47,0.068,0.371,4.47,24.47,0.068,0.371,505
|
||||
CZCE,SA506,纯碱6月,SA,纯碱,20,1.0,0.0002,0.01,0.0002,0.01,0.0002,0.01,0.17,0,0.17,0,1973.0,1962.0,1961.0,23,560,7.86,7.86,7.86,6667.4,6667.4,39220.0,20.0,4.29,24.29,0.064,0.364,4.29,24.29,0.064,0.364,506
|
||||
CZCE,SF407,硅铁7月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.27,0,0.27,0,6700.0,6686.0,6686.0,0,7775,12.01,12.01,12.01,9026.1,9026.1,33430.0,10.0,-14.02,-4.02,-0.155,-0.045,-14.02,-4.02,-0.155,-0.045,407
|
||||
CZCE,SF408,硅铁8月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6672.0,6654.0,6654.0,0,5411,12.01,12.01,12.01,5323.2,5323.2,33270.0,10.0,-14.02,-4.02,-0.263,-0.076,-14.02,-4.02,-0.263,-0.076,408
|
||||
CZCE,SF409,硅铁9月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6696.0,6672.0,6672.0,0,163656,12.01,12.01,12.01,5337.6,5337.6,33360.0,10.0,-14.02,-4.02,-0.263,-0.075,-14.02,-4.02,-0.263,-0.075,409
|
||||
CZCE,SF410,硅铁10月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6644.0,6632.0,6632.0,0,77803,12.01,12.01,12.01,5305.6,5305.6,33160.0,10.0,-14.02,-4.02,-0.264,-0.076,-14.02,-4.02,-0.264,-0.076,410
|
||||
CZCE,SF411,硅铁11月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6802.0,6790.0,6790.0,0,21139,12.01,12.01,12.01,5432.0,5432.0,33950.0,10.0,-14.02,-4.02,-0.258,-0.074,-14.02,-4.02,-0.258,-0.074,411
|
||||
CZCE,SF412,硅铁12月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6826.0,6804.0,6804.0,0,1189,12.01,12.01,12.01,5443.2,5443.2,34020.0,10.0,-14.02,-4.02,-0.258,-0.074,-14.02,-4.02,-0.258,-0.074,412
|
||||
CZCE,SF501,硅铁1月,SF,硅铁,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.16,0,0.16,0,6770.0,6758.0,6758.0,0,29304,12.01,12.01,12.01,5406.4,5406.4,33790.0,10.0,-14.02,-4.02,-0.259,-0.074,-14.02,-4.02,-0.259,-0.074,501
|
||||
CZCE,SF502,硅铁2月,SF,硅铁,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,6746.0,6716.0,6716.0,0,981,3.01,3.01,0.0,5372.8,5372.8,33580.0,10.0,3.98,13.98,0.074,0.26,6.99,16.99,0.13,0.316,502
|
||||
CZCE,SF503,硅铁3月,SF,硅铁,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,6866.0,6840.0,6840.0,0,1673,3.01,3.01,0.0,5472.0,5472.0,34200.0,10.0,3.98,13.98,0.073,0.255,6.99,16.99,0.128,0.31,503
|
||||
CZCE,SF504,硅铁4月,SF,硅铁,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,6836.0,6824.0,6824.0,0,266,3.01,3.01,0.0,5459.2,5459.2,34120.0,10.0,3.98,13.98,0.073,0.256,6.99,16.99,0.128,0.311,504
|
||||
CZCE,SF505,硅铁5月,SF,硅铁,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,6828.0,6802.0,6802.0,0,1632,3.01,3.01,0.0,5441.6,5441.6,34010.0,10.0,3.98,13.98,0.073,0.257,6.99,16.99,0.128,0.312,505
|
||||
CZCE,SF506,硅铁6月,SF,硅铁,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,6788.0,6782.0,6782.0,0,77,3.01,3.01,0.0,5425.6,5425.6,33910.0,10.0,3.98,13.98,0.073,0.258,6.99,16.99,0.129,0.313,506
|
||||
CZCE,SH407,烧碱7月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.26,0,0.26,0,2243.0,2243.0,2243.0,0,44,6.74,6.74,0.0,17495.4,17495.4,67290.0,30.0,16.51,46.51,0.094,0.266,23.25,53.25,0.133,0.304,407
|
||||
CZCE,SH408,烧碱8月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2499.0,2504.0,2504.0,0,386,7.53,7.53,0.0,10516.8,10516.8,75120.0,30.0,14.95,44.95,0.142,0.427,22.47,52.47,0.214,0.499,408
|
||||
CZCE,SH409,烧碱9月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2582.0,2574.0,2572.0,13871,56210,7.73,7.73,0.0,10802.4,10802.4,77160.0,30.0,14.54,44.54,0.135,0.412,22.26,52.26,0.206,0.484,409
|
||||
CZCE,SH410,烧碱10月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2658.0,2655.0,2648.0,348,17371,7.96,7.96,0.0,11121.6,11121.6,79440.0,30.0,14.08,44.08,0.127,0.396,22.04,52.04,0.198,0.468,410
|
||||
CZCE,SH411,烧碱11月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2669.0,2654.0,2657.0,4,94,7.99,7.99,0.0,11159.4,11159.4,79710.0,30.0,14.03,44.03,0.126,0.395,22.01,52.01,0.197,0.466,411
|
||||
CZCE,SH412,烧碱12月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2701.0,2704.0,2704.0,0,59,8.13,8.13,0.0,11356.8,11356.8,81120.0,30.0,13.75,43.75,0.121,0.385,21.87,51.87,0.193,0.457,412
|
||||
CZCE,SH501,烧碱1月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2700.0,2698.0,2680.0,1172,13570,8.05,8.05,0.0,11256.0,11256.0,80400.0,30.0,13.89,43.89,0.123,0.39,21.94,51.94,0.195,0.461,501
|
||||
CZCE,SH502,烧碱2月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2736.0,2720.0,2720.0,0,87,8.17,8.17,0.0,11424.0,11424.0,81600.0,30.0,13.65,43.65,0.119,0.382,21.82,51.82,0.191,0.454,502
|
||||
CZCE,SH503,烧碱3月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2756.0,2744.0,2744.0,0,3,8.25,8.25,0.0,11524.8,11524.8,82320.0,30.0,13.51,43.51,0.117,0.378,21.75,51.75,0.189,0.449,503
|
||||
CZCE,SH504,烧碱4月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2765.0,2761.0,2761.0,0,36,8.3,8.3,0.0,11596.2,11596.2,82830.0,30.0,13.4,43.4,0.116,0.374,21.7,51.7,0.187,0.446,504
|
||||
CZCE,SH505,烧碱5月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2782.0,2774.0,2761.0,12,841,8.3,8.3,0.0,11596.2,11596.2,82830.0,30.0,13.4,43.4,0.116,0.374,21.7,51.7,0.187,0.446,505
|
||||
CZCE,SH506,烧碱6月,SH,烧碱,30,1.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.14,0,0.14,0,2804.0,2804.0,2804.0,0,4,8.43,8.43,0.01,11776.8,11776.8,84120.0,30.0,13.15,43.15,0.112,0.366,21.57,51.57,0.183,0.438,506
|
||||
CZCE,SM407,锰硅7月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.27,0,0.27,0,7116.0,7008.0,7008.0,0,27684,12.01,12.01,12.01,9460.8,9460.8,35040.0,10.0,-14.02,-4.02,-0.148,-0.043,-14.02,-4.02,-0.148,-0.043,407
|
||||
CZCE,SM408,锰硅8月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7116.0,7056.0,7056.0,0,30052,12.01,12.01,12.01,6703.2,6703.2,35280.0,10.0,-14.02,-4.02,-0.209,-0.06,-14.02,-4.02,-0.209,-0.06,408
|
||||
CZCE,SM409,锰硅9月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7192.0,7088.0,7088.0,0,263265,12.01,12.01,12.01,6733.6,6733.6,35440.0,10.0,-14.02,-4.02,-0.208,-0.06,-14.02,-4.02,-0.208,-0.06,409
|
||||
CZCE,SM410,锰硅10月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7194.0,7106.0,7106.0,0,8624,12.01,12.01,12.01,6750.7,6750.7,35530.0,10.0,-14.02,-4.02,-0.208,-0.06,-14.02,-4.02,-0.208,-0.06,410
|
||||
CZCE,SM411,锰硅11月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7478.0,7322.0,7322.0,0,21941,12.01,12.01,12.01,6955.9,6955.9,36610.0,10.0,-14.02,-4.02,-0.202,-0.058,-14.02,-4.02,-0.202,-0.058,411
|
||||
CZCE,SM412,锰硅12月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7408.0,7302.0,7302.0,0,1503,12.01,12.01,12.01,6936.9,6936.9,36510.0,10.0,-14.02,-4.02,-0.202,-0.058,-14.02,-4.02,-0.202,-0.058,412
|
||||
CZCE,SM501,锰硅1月,SM,锰硅,5,2.0,0.0,12.01,0.0,12.01,0.0,12.01,0.19,0,0.19,0,7412.0,7324.0,7324.0,0,279664,12.01,12.01,12.01,6957.8,6957.8,36620.0,10.0,-14.02,-4.02,-0.202,-0.058,-14.02,-4.02,-0.202,-0.058,501
|
||||
CZCE,SM502,锰硅2月,SM,锰硅,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,7318.0,7242.0,7242.0,0,3241,3.01,3.01,0.0,5793.6,5793.6,36210.0,10.0,3.98,13.98,0.069,0.241,6.99,16.99,0.121,0.293,502
|
||||
CZCE,SM503,锰硅3月,SM,锰硅,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,7260.0,7192.0,7192.0,0,5320,3.01,3.01,0.0,5753.6,5753.6,35960.0,10.0,3.98,13.98,0.069,0.243,6.99,16.99,0.121,0.295,503
|
||||
CZCE,SM504,锰硅4月,SM,锰硅,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,7252.0,7158.0,7158.0,0,2875,3.01,3.01,0.0,5726.4,5726.4,35790.0,10.0,3.98,13.98,0.069,0.244,6.99,16.99,0.122,0.297,504
|
||||
CZCE,SM505,锰硅5月,SM,锰硅,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,7238.0,7156.0,7156.0,0,27285,3.01,3.01,0.0,5724.8,5724.8,35780.0,10.0,3.98,13.98,0.069,0.244,6.99,16.99,0.122,0.297,505
|
||||
CZCE,SM506,锰硅6月,SM,锰硅,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.16,0,0.16,0,7220.0,7138.0,7138.0,0,201,3.01,3.01,0.0,5710.4,5710.4,35690.0,10.0,3.98,13.98,0.07,0.245,6.99,16.99,0.122,0.297,506
|
||||
CZCE,SR407,白糖7月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.25,0,0.25,0,6260.0,6260.0,6260.0,0,3257,3.01,3.01,0.0,15650.0,15650.0,62600.0,10.0,3.97,13.97,0.025,0.089,6.98,16.98,0.045,0.109,407
|
||||
CZCE,SR409,白糖9月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.12,0,0.12,0,6174.0,6150.0,6197.0,127012,312949,3.01,3.01,0.0,7436.4,7436.4,61970.0,10.0,3.97,13.97,0.053,0.188,6.98,16.98,0.094,0.228,409
|
||||
CZCE,SR411,白糖11月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.12,0,0.12,0,5996.0,5977.0,6014.0,10993,78694,3.01,3.01,0.0,7216.8,7216.8,60140.0,10.0,3.97,13.97,0.055,0.194,6.98,16.98,0.097,0.235,411
|
||||
CZCE,SR501,白糖1月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.12,0,0.12,0,5924.0,5908.0,5944.0,15996,135768,3.01,3.01,0.0,7132.8,7132.8,59440.0,10.0,3.97,13.97,0.056,0.196,6.98,16.98,0.098,0.238,501
|
||||
CZCE,SR503,白糖3月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.12,0,0.12,0,5885.0,5869.0,5908.0,101,4103,3.01,3.01,0.0,7089.6,7089.6,59080.0,10.0,3.97,13.97,0.056,0.197,6.98,16.98,0.098,0.24,503
|
||||
CZCE,SR505,白糖5月,SR,白砂糖,10,1.0,0.0,3.01,0.0,3.01,0.0,0.0,0.12,0,0.12,0,5859.0,5837.0,5869.0,389,8834,3.01,3.01,0.0,7042.8,7042.8,58690.0,10.0,3.97,13.97,0.056,0.198,6.98,16.98,0.099,0.241,505
|
||||
CFFEX,T2409,长期国债2409,T,10年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.03,0,0.03,0,105.115,105.11,0.0,0,188590,3.07,3.07,0.06,31533.0,31533.0,1051100.0,50.0,43.85,93.85,0.139,0.298,46.86,96.86,0.149,0.307,2409
|
||||
CFFEX,T2412,长期国债2412,T,10年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.03,0,0.03,0,104.97,104.98,0.0,0,14515,3.07,3.07,0.06,31494.0,31494.0,1049800.0,50.0,43.85,93.85,0.139,0.298,46.86,96.86,0.149,0.308,2412
|
||||
CFFEX,T2503,长期国债2503,T,10年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.03,0,0.03,0,104.81,104.835,0.0,0,1079,3.07,3.07,0.06,31450.5,31450.5,1048350.0,50.0,43.85,93.85,0.139,0.298,46.86,96.86,0.149,0.308,2503
|
||||
CZCE,TA407,PTA7月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.27,0,0.27,0,5886.0,5894.0,5894.0,0,7979,3.01,3.01,0.0,7956.9,7956.9,29470.0,10.0,3.98,13.98,0.05,0.176,6.99,16.99,0.088,0.213,407
|
||||
CZCE,TA408,PTA8月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5896.0,5886.0,5904.0,5099,18820,3.01,3.01,0.0,4132.8,4132.8,29520.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,408
|
||||
CZCE,TA409,PTA9月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5902.0,5894.0,5908.0,115173,889263,3.01,3.01,0.0,4135.6,4135.6,29540.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,409
|
||||
CZCE,TA410,PTA10月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5918.0,5908.0,5920.0,3229,8494,3.01,3.01,0.0,4144.0,4144.0,29600.0,10.0,3.98,13.98,0.096,0.337,6.99,16.99,0.169,0.41,410
|
||||
CZCE,TA411,PTA11月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5908.0,5898.0,5916.0,3,2345,3.01,3.01,0.0,4141.2,4141.2,29580.0,10.0,3.98,13.98,0.096,0.337,6.99,16.99,0.169,0.41,411
|
||||
CZCE,TA412,PTA12月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5898.0,5896.0,5908.0,19,2232,3.01,3.01,0.0,4135.6,4135.6,29540.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,412
|
||||
CZCE,TA501,PTA1月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5906.0,5900.0,5908.0,16948,294585,3.01,3.01,0.0,4135.6,4135.6,29540.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,501
|
||||
CZCE,TA502,PTA2月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5944.0,5920.0,5912.0,6,4363,3.01,3.01,0.0,4138.4,4138.4,29560.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.41,502
|
||||
CZCE,TA503,PTA3月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5922.0,5892.0,5908.0,8,4135,3.01,3.01,0.0,4135.6,4135.6,29540.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,503
|
||||
CZCE,TA504,PTA4月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5910.0,5910.0,5902.0,3,274,3.01,3.01,0.0,4131.4,4131.4,29510.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,504
|
||||
CZCE,TA505,PTA5月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5902.0,5900.0,5908.0,59,2143,3.01,3.01,0.0,4135.6,4135.6,29540.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,505
|
||||
CZCE,TA506,PTA6月,TA,精对苯二甲酸,5,2.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,5900.0,5900.0,5900.0,0,11,3.01,3.01,0.0,4130.0,4130.0,29500.0,10.0,3.98,13.98,0.096,0.338,6.99,16.99,0.169,0.411,506
|
||||
CFFEX,TF2409,国债2409,TF,5年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.022,0,0.022,0,103.87,103.865,0.0,0,116126,3.07,3.07,0.06,22850.3,22850.3,1038650.0,50.0,43.86,93.86,0.192,0.411,46.87,96.87,0.205,0.424,2409
|
||||
CFFEX,TF2412,国债2412,TF,5年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.022,0,0.022,0,103.765,103.775,0.0,0,6521,3.07,3.07,0.06,22830.5,22830.5,1037750.0,50.0,43.86,93.86,0.192,0.411,46.87,96.87,0.205,0.424,2412
|
||||
CFFEX,TF2503,国债2503,TF,5年期国债期货,10000,0.005,0.0,3.01,0.0,3.01,0.0,0.0,0.022,0,0.022,0,103.74,103.745,0.0,0,825,3.07,3.07,0.06,22823.9,22823.9,1037450.0,50.0,43.86,93.86,0.192,0.411,46.87,96.87,0.205,0.424,2503
|
||||
CFFEX,TL2409,30年期国债期货2409,TL,30年期国债期货,10000,0.01,0.0,3.01,0.0,3.01,0.0,0.0,0.05,0,0.05,0,108.45,108.46,0.0,0,75624,3.08,3.08,0.07,54230.0,54230.0,1084600.0,100.0,93.85,193.85,0.173,0.357,96.86,196.86,0.179,0.363,2409
|
||||
CFFEX,TL2412,30年期国债期货2412,TL,30年期国债期货,10000,0.01,0.0,3.01,0.0,3.01,0.0,0.0,0.05,0,0.05,0,108.16,108.18,0.0,0,17096,3.07,3.07,0.06,54090.0,54090.0,1081800.0,100.0,93.85,193.85,0.174,0.358,96.86,196.86,0.179,0.364,2412
|
||||
CFFEX,TL2503,30年期国债期货2503,TL,30年期国债期货,10000,0.01,0.0,3.01,0.0,3.01,0.0,0.0,0.05,0,0.05,0,107.98,108.02,0.0,0,1222,3.07,3.07,0.06,54010.0,54010.0,1080200.0,100.0,93.85,193.85,0.174,0.359,96.86,196.86,0.179,0.364,2503
|
||||
CFFEX,TS2409,2年期国债期货2409,TS,2年期国债期货,20000,0.002,0.0,3.01,0.0,3.01,0.0,0.0,0.01,0,0.01,0,101.936,101.938,0.0,0,54860,3.13,3.13,0.12,20387.6,20387.6,2038760.0,40.0,33.74,73.74,0.165,0.362,36.75,76.75,0.18,0.376,2409
|
||||
CFFEX,TS2412,2年期国债期货2412,TS,2年期国债期货,20000,0.002,0.0,3.01,0.0,3.01,0.0,0.0,0.01,0,0.01,0,101.916,101.92,0.0,0,4104,3.13,3.13,0.12,20384.0,20384.0,2038400.0,40.0,33.74,73.74,0.165,0.362,36.75,76.75,0.18,0.376,2412
|
||||
CFFEX,TS2503,2年期国债期货2503,TS,2年期国债期货,20000,0.002,0.0,3.01,0.0,3.01,0.0,0.0,0.01,0,0.01,0,101.926,101.928,0.0,0,580,3.13,3.13,0.12,20385.6,20385.6,2038560.0,40.0,33.74,73.74,0.165,0.362,36.75,76.75,0.18,0.376,2503
|
||||
CZCE,UR407,尿素7月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,2153.0,2150.0,2150.0,0,752,4.31,4.31,4.31,10750.0,10750.0,43000.0,20.0,11.37,31.37,0.106,0.292,11.37,31.37,0.106,0.292,407
|
||||
CZCE,UR408,尿素8月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.15,0,0.15,0,2135.0,2136.0,2136.0,0,635,4.28,4.28,4.28,6408.0,6408.0,42720.0,20.0,11.43,31.43,0.178,0.49,11.43,31.43,0.178,0.49,408
|
||||
CZCE,UR409,尿素9月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.15,0,0.15,0,2061.0,2055.0,2055.0,0,185561,4.12,4.12,4.12,6165.0,6165.0,41100.0,20.0,11.76,31.76,0.191,0.515,11.76,31.76,0.191,0.515,409
|
||||
CZCE,UR410,尿素10月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,2033.0,2025.0,2025.0,0,1539,4.06,4.06,4.06,5265.0,5265.0,40500.0,20.0,11.88,31.88,0.226,0.605,11.88,31.88,0.226,0.605,410
|
||||
CZCE,UR411,尿素11月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,2056.0,2050.0,2050.0,0,1028,4.11,4.11,4.11,5330.0,5330.0,41000.0,20.0,11.78,31.78,0.221,0.596,11.78,31.78,0.221,0.596,411
|
||||
CZCE,UR412,尿素12月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,2029.0,2021.0,2021.0,0,364,4.05,4.05,4.05,5254.6,5254.6,40420.0,20.0,11.89,31.89,0.226,0.607,11.89,31.89,0.226,0.607,412
|
||||
CZCE,UR501,尿素1月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,2001.0,1995.0,1995.0,0,43565,4.0,4.0,4.0,5187.0,5187.0,39900.0,20.0,12.0,32.0,0.231,0.617,12.0,32.0,0.231,0.617,501
|
||||
CZCE,UR502,尿素2月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,1973.0,1972.0,1972.0,0,1214,3.96,3.96,3.96,5127.2,5127.2,39440.0,20.0,12.09,32.09,0.236,0.626,12.09,32.09,0.236,0.626,502
|
||||
CZCE,UR503,尿素3月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,2006.0,1999.0,1999.0,0,448,4.01,4.01,4.01,5197.4,5197.4,39980.0,20.0,11.98,31.98,0.23,0.615,11.98,31.98,0.23,0.615,503
|
||||
CZCE,UR504,尿素4月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,1981.0,1979.0,1979.0,0,416,3.97,3.97,3.97,5145.4,5145.4,39580.0,20.0,12.06,32.06,0.234,0.623,12.06,32.06,0.234,0.623,504
|
||||
CZCE,UR505,尿素5月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,1955.0,1950.0,1950.0,0,9381,3.91,3.91,3.91,5070.0,5070.0,39000.0,20.0,12.18,32.18,0.24,0.635,12.18,32.18,0.24,0.635,505
|
||||
CZCE,UR506,尿素6月,UR,尿素,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,1940.0,1935.0,1935.0,0,983,3.88,3.88,3.88,5031.0,5031.0,38700.0,20.0,12.24,32.24,0.243,0.641,12.24,32.24,0.243,0.641,506
|
||||
CZCE,WH407,强麦7月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.45,0,0.45,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,28782.0,28782.0,63960.0,20.0,-40.03,-20.03,-0.139,-0.07,-40.03,-20.03,-0.139,-0.07,407
|
||||
CZCE,WH409,强麦9月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,25584.0,25584.0,63960.0,20.0,-40.03,-20.03,-0.156,-0.078,-40.03,-20.03,-0.156,-0.078,409
|
||||
CZCE,WH411,强麦11月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,25584.0,25584.0,63960.0,20.0,-40.03,-20.03,-0.156,-0.078,-40.03,-20.03,-0.156,-0.078,411
|
||||
CZCE,WH501,强麦1月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,25584.0,25584.0,63960.0,20.0,-40.03,-20.03,-0.156,-0.078,-40.03,-20.03,-0.156,-0.078,501
|
||||
CZCE,WH503,强麦3月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,25584.0,25584.0,63960.0,20.0,-40.03,-20.03,-0.156,-0.078,-40.03,-20.03,-0.156,-0.078,503
|
||||
CZCE,WH505,强麦5月,WH,优质强筋小麦,20,1.0,0.0,30.01,0.0,30.01,0.0,30.01,0.4,0,0.4,0,3198.0,3198.0,3198.0,0,0,30.01,30.01,30.01,25584.0,25584.0,63960.0,20.0,-40.03,-20.03,-0.156,-0.078,-40.03,-20.03,-0.156,-0.078,505
|
||||
CZCE,ZC408,煤8月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,408
|
||||
CZCE,ZC409,煤9月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,409
|
||||
CZCE,ZC410,煤10月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,410
|
||||
CZCE,ZC411,煤11月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,411
|
||||
CZCE,ZC412,煤12月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,412
|
||||
CZCE,ZC501,煤1月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,501
|
||||
CZCE,ZC502,煤2月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,502
|
||||
CZCE,ZC503,煤3月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,503
|
||||
CZCE,ZC504,煤4月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,504
|
||||
CZCE,ZC505,煤5月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,505
|
||||
CZCE,ZC506,煤6月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,506
|
||||
CZCE,ZC507,煤7月,ZC,动力煤N,100,0.2,0.0,150.01,0.0,150.01,0.0,150.01,0.55,0,0.55,0,801.4,801.4,801.4,0,0,150.01,150.01,150.01,44077.0,44077.0,80140.0,20.0,-280.03,-260.03,-0.635,-0.59,-280.03,-260.03,-0.635,-0.59,507
|
||||
DCE,a2407,豆一2407,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.25,0,0.25,0,4536.0,4639.0,4639.0,0,4,2.01,2.01,2.01,11597.5,11597.5,46390.0,10.0,5.97,15.97,0.052,0.138,5.97,15.97,0.052,0.138,2407
|
||||
DCE,a2409,豆一2409,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.12,0,0.12,0,4632.0,4633.0,4620.0,22084,130961,2.01,2.01,2.01,5544.0,5544.0,46200.0,10.0,5.97,15.97,0.108,0.288,5.97,15.97,0.108,0.288,2409
|
||||
DCE,a2411,豆一2411,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.12,0,0.12,0,4522.0,4523.0,4526.0,739,18012,2.01,2.01,2.01,5431.2,5431.2,45260.0,10.0,5.97,15.97,0.11,0.294,5.97,15.97,0.11,0.294,2411
|
||||
DCE,a2501,豆一2501,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.12,0,0.12,0,4487.0,4487.0,4493.0,2338,36864,2.01,2.01,2.01,5391.6,5391.6,44930.0,10.0,5.97,15.97,0.111,0.296,5.97,15.97,0.111,0.296,2501
|
||||
DCE,a2503,豆一2503,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.12,0,0.12,0,4457.0,4460.0,4465.0,39,2865,2.01,2.01,2.01,5358.0,5358.0,44650.0,10.0,5.97,15.97,0.112,0.298,5.97,15.97,0.112,0.298,2503
|
||||
DCE,a2505,豆一2505,a,黄大豆1号,10,1.0,0.0,2.01,0.0,2.01,0.0,2.01,0.12,0,0.12,0,4478.0,4483.0,4488.0,105,2824,2.01,2.01,2.01,5385.6,5385.6,44880.0,10.0,5.97,15.97,0.111,0.297,5.97,15.97,0.111,0.297,2505
|
||||
SHFE,ag2407,ag2407,ag,白银,15,1.0,5e-05,0.01,5e-05,0.01,5e-05,0.01,0.25,0,0.25,0,8120.0,8120.0,8150.0,10,6690,6.13,6.13,6.13,30562.5,30562.5,122250.0,15.0,2.74,17.74,0.009,0.058,2.74,17.74,0.009,0.058,2407
|
||||
SHFE,ag2408,ag2408,ag,白银,15,1.0,5e-05,0.01,5e-05,0.01,5e-05,0.01,0.17,0,0.17,0,8112.0,8128.0,8136.0,304225,273408,6.12,6.12,6.12,20746.8,20746.8,122040.0,15.0,2.76,17.76,0.013,0.086,2.76,17.76,0.013,0.086,2408
|
||||
SHFE,ag2409,ag2409,ag,白银,15,1.0,5e-05,0.01,5e-05,0.01,5e-05,0.01,0.17,0,0.17,0,8133.0,8146.0,8152.0,12136,25264,6.13,6.13,6.13,20787.6,20787.6,122280.0,15.0,2.74,17.74,0.013,0.085,2.74,17.74,0.013,0.085,2409
|
||||
SHFE,ag2410,ag2410,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8144.0,8156.0,8163.0,84162,206760,1.24,1.24,1.24,20815.65,20815.65,122445.0,15.0,12.52,27.52,0.06,0.132,12.52,27.52,0.06,0.132,2410
|
||||
SHFE,ag2411,ag2411,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8163.0,8177.0,8179.0,5272,17971,1.24,1.24,1.24,20856.45,20856.45,122685.0,15.0,12.51,27.51,0.06,0.132,12.51,27.51,0.06,0.132,2411
|
||||
SHFE,ag2412,ag2412,ag,白银,15,1.0,5e-05,0.01,5e-05,0.01,5e-05,0.01,0.17,0,0.17,0,8173.0,8185.0,8194.0,87383,387376,6.16,6.16,6.16,20894.7,20894.7,122910.0,15.0,2.67,17.67,0.013,0.085,2.67,17.67,0.013,0.085,2412
|
||||
SHFE,ag2501,ag2501,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8196.0,8207.0,8211.0,3230,12992,1.25,1.25,1.25,20938.05,20938.05,123165.0,15.0,12.5,27.5,0.06,0.131,12.5,27.5,0.06,0.131,2501
|
||||
SHFE,ag2502,ag2502,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8211.0,8219.0,8225.0,6588,46635,1.25,1.25,1.25,20973.75,20973.75,123375.0,15.0,12.5,27.5,0.06,0.131,12.5,27.5,0.06,0.131,2502
|
||||
SHFE,ag2503,ag2503,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8232.0,8234.0,8244.0,1552,10692,1.25,1.25,1.25,21022.2,21022.2,123660.0,15.0,12.49,27.49,0.059,0.131,12.49,27.49,0.059,0.131,2503
|
||||
SHFE,ag2504,ag2504,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8246.0,8248.0,8256.0,2163,23377,1.26,1.26,1.26,21052.8,21052.8,123840.0,15.0,12.49,27.49,0.059,0.131,12.49,27.49,0.059,0.131,2504
|
||||
SHFE,ag2505,ag2505,ag,白银,15,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.17,0,0.17,0,8261.0,8270.0,8277.0,1765,6631,1.26,1.26,1.26,21106.35,21106.35,124155.0,15.0,12.48,27.48,0.059,0.13,12.48,27.48,0.059,0.13,2505
|
||||
SHFE,ag2506,ag2506,ag,白银,15,1.0,5e-05,0.01,5e-05,0.01,5e-05,0.01,0.17,0,0.17,0,8268.0,8279.0,8288.0,574,3106,6.23,6.23,6.23,21134.4,21134.4,124320.0,15.0,2.53,17.53,0.012,0.083,2.53,17.53,0.012,0.083,2506
|
||||
SHFE,al2407,al2407,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.25,0,0.25,0,20235.0,20090.0,20130.0,615,14105,3.02,3.02,3.02,25162.5,25162.5,100650.0,25.0,18.97,43.97,0.075,0.175,18.97,43.97,0.075,0.175,2407
|
||||
SHFE,al2408,al2408,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.15,0,0.15,0,20250.0,20100.0,20175.0,48483,156096,3.02,3.02,3.02,15131.25,15131.25,100875.0,25.0,18.97,43.97,0.125,0.291,18.97,43.97,0.125,0.291,2408
|
||||
SHFE,al2409,al2409,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20290.0,20140.0,20215.0,33184,180607,3.02,3.02,3.02,14150.5,14150.5,101075.0,25.0,18.97,43.97,0.134,0.311,18.97,43.97,0.134,0.311,2409
|
||||
SHFE,al2410,al2410,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20305.0,20175.0,20250.0,6046,61575,3.02,3.02,3.02,14175.0,14175.0,101250.0,25.0,18.97,43.97,0.134,0.31,18.97,43.97,0.134,0.31,2410
|
||||
SHFE,al2411,al2411,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20330.0,20205.0,20265.0,1536,27711,3.02,3.02,3.02,14185.5,14185.5,101325.0,25.0,18.97,43.97,0.134,0.31,18.97,43.97,0.134,0.31,2411
|
||||
SHFE,al2412,al2412,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20315.0,20230.0,20270.0,1551,23514,3.02,3.02,3.02,14189.0,14189.0,101350.0,25.0,18.97,43.97,0.134,0.31,18.97,43.97,0.134,0.31,2412
|
||||
SHFE,al2501,al2501,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20360.0,20250.0,20305.0,310,11686,3.02,3.02,3.02,14213.5,14213.5,101525.0,25.0,18.97,43.97,0.133,0.309,18.97,43.97,0.133,0.309,2501
|
||||
SHFE,al2502,al2502,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20340.0,20285.0,20340.0,64,4258,3.02,3.02,3.02,14238.0,14238.0,101700.0,25.0,18.97,43.97,0.133,0.309,18.97,43.97,0.133,0.309,2502
|
||||
SHFE,al2503,al2503,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20440.0,20330.0,20405.0,3,1515,3.02,3.02,3.02,14283.5,14283.5,102025.0,25.0,18.97,43.97,0.133,0.308,18.97,43.97,0.133,0.308,2503
|
||||
SHFE,al2504,al2504,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20470.0,20350.0,20400.0,6,765,3.02,3.02,3.02,14280.0,14280.0,102000.0,25.0,18.97,43.97,0.133,0.308,18.97,43.97,0.133,0.308,2504
|
||||
SHFE,al2505,al2505,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20480.0,20375.0,20410.0,5,470,3.02,3.02,3.02,14287.0,14287.0,102050.0,25.0,18.97,43.97,0.133,0.308,18.97,43.97,0.133,0.308,2505
|
||||
SHFE,al2506,al2506,al,铝,5,5.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,20450.0,20365.0,20410.0,10,113,3.02,3.02,3.02,14287.0,14287.0,102050.0,25.0,18.97,43.97,0.133,0.308,18.97,43.97,0.133,0.308,2506
|
||||
SHFE,ao2407,ao2407,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,3771.0,3766.0,3766.0,0,3525,7.55,7.55,7.55,18830.0,18830.0,75320.0,20.0,4.91,24.91,0.026,0.132,4.91,24.91,0.026,0.132,2407
|
||||
SHFE,ao2408,ao2408,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.15,0,0.15,0,3793.0,3731.0,3662.0,90468,47576,7.34,7.34,7.34,10986.0,10986.0,73240.0,20.0,5.32,25.32,0.048,0.231,5.32,25.32,0.048,0.231,2408
|
||||
SHFE,ao2409,ao2409,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3801.0,3740.0,3641.0,69659,79416,7.3,7.3,7.3,10194.8,10194.8,72820.0,20.0,5.41,25.41,0.053,0.249,5.41,25.41,0.053,0.249,2409
|
||||
SHFE,ao2410,ao2410,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3809.0,3750.0,3642.0,11780,16762,7.3,7.3,7.3,10197.6,10197.6,72840.0,20.0,5.4,25.4,0.053,0.249,5.4,25.4,0.053,0.249,2410
|
||||
SHFE,ao2411,ao2411,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3812.0,3750.0,3651.0,3744,9493,7.32,7.32,7.32,10222.8,10222.8,73020.0,20.0,5.37,25.37,0.053,0.248,5.37,25.37,0.053,0.248,2411
|
||||
SHFE,ao2412,ao2412,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3800.0,3755.0,3661.0,352,1948,7.34,7.34,7.34,10250.8,10250.8,73220.0,20.0,5.33,25.33,0.052,0.247,5.33,25.33,0.052,0.247,2412
|
||||
SHFE,ao2501,ao2501,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3808.0,3751.0,3659.0,748,1262,7.33,7.33,7.33,10245.2,10245.2,73180.0,20.0,5.34,25.34,0.052,0.247,5.34,25.34,0.052,0.247,2501
|
||||
SHFE,ao2502,ao2502,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3778.0,3749.0,3660.0,39,142,7.33,7.33,7.33,10248.0,10248.0,73200.0,20.0,5.33,25.33,0.052,0.247,5.33,25.33,0.052,0.247,2502
|
||||
SHFE,ao2503,ao2503,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3772.0,3740.0,3657.0,26,107,7.33,7.33,7.33,10239.6,10239.6,73140.0,20.0,5.34,25.34,0.052,0.248,5.34,25.34,0.052,0.248,2503
|
||||
SHFE,ao2504,ao2504,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3777.0,3736.0,3647.0,44,126,7.31,7.31,7.31,10211.6,10211.6,72940.0,20.0,5.38,25.38,0.053,0.249,5.38,25.38,0.053,0.249,2504
|
||||
SHFE,ao2505,ao2505,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3793.0,3739.0,3653.0,46,99,7.32,7.32,7.32,10228.4,10228.4,73060.0,20.0,5.36,25.36,0.052,0.248,5.36,25.36,0.052,0.248,2505
|
||||
SHFE,ao2506,ao2506,ao,氧化铝,20,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.14,0,0.14,0,3737.0,3711.0,3635.0,24,34,7.28,7.28,7.28,10178.0,10178.0,72700.0,20.0,5.43,25.43,0.053,0.25,5.43,25.43,0.053,0.25,2506
|
||||
SHFE,au2407,au2407,au,黄金,1000,0.02,0.0,10.01,0.0,10.01,0.0,0.0,0.25,0,0.25,0,557.46,557.46,557.46,0,96,10.04,10.04,0.03,139365.0,139365.0,557460.0,20.0,-0.09,19.91,-0.0,0.014,9.92,29.92,0.007,0.021,2407
|
||||
SHFE,au2408,au2408,au,黄金,1000,0.02,0.0,10.01,0.0,10.01,0.0,6.01,0.17,0,0.17,0,558.78,561.1,562.22,19810,87433,10.04,10.04,6.04,95577.4,95577.4,562220.0,20.0,-0.09,19.91,-0.0,0.021,3.91,23.91,0.004,0.025,2408
|
||||
SHFE,au2409,au2409,au,黄金,1000,0.02,0.0,10.01,0.0,10.01,0.0,0.0,0.17,0,0.17,0,561.46,562.78,564.06,118,324,10.04,10.04,0.03,95890.2,95890.2,564060.0,20.0,-0.09,19.91,-0.0,0.021,9.92,29.92,0.01,0.031,2409
|
||||
SHFE,au2410,au2410,au,黄金,1000,0.02,0.0,2.01,0.0,2.01,0.0,0.0,0.17,0,0.17,0,561.22,563.4,564.72,34715,140549,2.04,2.04,0.03,96002.4,96002.4,564720.0,20.0,15.91,35.91,0.017,0.037,17.92,37.92,0.019,0.04,2410
|
||||
SHFE,au2412,au2412,au,黄金,1000,0.02,0.0,10.01,0.0,10.01,0.0,0.0,0.17,0,0.17,0,563.44,565.58,566.96,8542,120256,10.04,10.04,0.03,96383.2,96383.2,566960.0,20.0,-0.09,19.91,-0.0,0.021,9.92,29.92,0.01,0.031,2412
|
||||
SHFE,au2502,au2502,au,黄金,1000,0.02,0.0,2.01,0.0,2.01,0.0,0.0,0.17,0,0.17,0,565.56,567.8,569.1,1152,15972,2.04,2.04,0.03,96747.0,96747.0,569100.0,20.0,15.91,35.91,0.016,0.037,17.92,37.92,0.019,0.039,2502
|
||||
SHFE,au2504,au2504,au,黄金,1000,0.02,0.0,2.01,0.0,2.01,0.0,0.0,0.17,0,0.17,0,567.78,570.04,571.3,1136,14497,2.04,2.04,0.03,97121.0,97121.0,571300.0,20.0,15.91,35.91,0.016,0.037,17.92,37.92,0.018,0.039,2504
|
||||
SHFE,au2506,au2506,au,黄金,1000,0.02,0.0,10.01,0.0,10.01,0.0,0.0,0.17,0,0.17,0,570.86,572.78,573.6,288,7299,10.04,10.04,0.03,97512.0,97512.0,573600.0,20.0,-0.09,19.91,-0.0,0.02,9.92,29.92,0.01,0.031,2506
|
||||
DCE,b2407,豆二2407,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.26,0,0.26,0,3601.0,3601.0,3601.0,0,5,1.01,1.01,1.01,9362.6,9362.6,36010.0,10.0,7.98,17.98,0.085,0.192,7.98,17.98,0.085,0.192,2407
|
||||
DCE,b2408,豆二2408,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3671.0,3657.0,3666.0,1193,8918,1.01,1.01,1.01,4765.8,4765.8,36660.0,10.0,7.98,17.98,0.167,0.377,7.98,17.98,0.167,0.377,2408
|
||||
DCE,b2409,豆二2409,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3752.0,3733.0,3737.0,39878,117584,1.01,1.01,1.01,4858.1,4858.1,37370.0,10.0,7.98,17.98,0.164,0.37,7.98,17.98,0.164,0.37,2409
|
||||
DCE,b2410,豆二2410,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3751.0,3744.0,3750.0,2237,10645,1.01,1.01,1.01,4875.0,4875.0,37500.0,10.0,7.98,17.98,0.164,0.369,7.98,17.98,0.164,0.369,2410
|
||||
DCE,b2411,豆二2411,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3788.0,3785.0,3795.0,801,4977,1.01,1.01,1.01,4933.5,4933.5,37950.0,10.0,7.98,17.98,0.162,0.364,7.98,17.98,0.162,0.364,2411
|
||||
DCE,b2412,豆二2412,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3832.0,3822.0,3837.0,182,2960,1.01,1.01,1.01,4988.1,4988.1,38370.0,10.0,7.98,17.98,0.16,0.36,7.98,17.98,0.16,0.36,2412
|
||||
DCE,b2501,豆二2501,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3842.0,3822.0,3837.0,206,3502,1.01,1.01,1.01,4988.1,4988.1,38370.0,10.0,7.98,17.98,0.16,0.36,7.98,17.98,0.16,0.36,2501
|
||||
DCE,b2502,豆二2502,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3819.0,3772.0,3794.0,5,195,1.01,1.01,1.01,4932.2,4932.2,37940.0,10.0,7.98,17.98,0.162,0.364,7.98,17.98,0.162,0.364,2502
|
||||
DCE,b2503,豆二2503,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3742.0,3716.0,3727.0,8,445,1.01,1.01,1.01,4845.1,4845.1,37270.0,10.0,7.98,17.98,0.165,0.371,7.98,17.98,0.165,0.371,2503
|
||||
DCE,b2504,豆二2504,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3715.0,3672.0,3687.0,5,183,1.01,1.01,1.01,4793.1,4793.1,36870.0,10.0,7.98,17.98,0.166,0.375,7.98,17.98,0.166,0.375,2504
|
||||
DCE,b2505,豆二2505,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3674.0,3650.0,3668.0,92,5500,1.01,1.01,1.01,4768.4,4768.4,36680.0,10.0,7.98,17.98,0.167,0.377,7.98,17.98,0.167,0.377,2505
|
||||
DCE,b2506,豆二2506,b,黄大豆2号,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,3671.0,3663.0,3681.0,1,41,1.01,1.01,1.01,4785.3,4785.3,36810.0,10.0,7.98,17.98,0.167,0.376,7.98,17.98,0.167,0.376,2506
|
||||
DCE,bb2407,胶合板2407,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2407
|
||||
DCE,bb2408,胶合板2408,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2408
|
||||
DCE,bb2409,胶合板2409,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2409
|
||||
DCE,bb2410,胶合板2410,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2410
|
||||
DCE,bb2411,胶合板2411,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2411
|
||||
DCE,bb2412,胶合板2412,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,400.0,400.0,400.0,0,0,20.02,20.02,20.02,90000.0,90000.0,200000.0,25.0,-15.04,9.96,-0.017,0.011,-15.04,9.96,-0.017,0.011,2412
|
||||
DCE,bb2501,胶合板2501,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,193.05,191.0,191.0,0,60,9.57,9.57,9.57,42975.0,42975.0,95500.0,25.0,5.87,30.87,0.014,0.072,5.87,30.87,0.014,0.072,2501
|
||||
DCE,bb2502,胶合板2502,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,218.4,218.4,218.4,0,0,10.94,10.94,10.94,49140.0,49140.0,109200.0,25.0,3.13,28.13,0.006,0.057,3.13,28.13,0.006,0.057,2502
|
||||
DCE,bb2503,胶合板2503,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,194.85,194.85,194.85,0,0,9.76,9.76,9.76,43841.25,43841.25,97425.0,25.0,5.48,30.48,0.013,0.07,5.48,30.48,0.013,0.07,2503
|
||||
DCE,bb2504,胶合板2504,bb,胶合板,500,0.05,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.45,0,0.45,0,167.3,167.3,167.3,0,18,8.38,8.38,8.38,37642.5,37642.5,83650.0,25.0,8.24,33.24,0.022,0.088,8.24,33.24,0.022,0.088,2504
|
||||
INE,bc2407,bc2407,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.25,0,0.25,0,70900.0,70900.0,70900.0,0,3250,3.58,3.58,0.02,88625.0,88625.0,354500.0,50.0,42.85,92.85,0.048,0.105,46.4,96.4,0.052,0.109,2407
|
||||
INE,bc2408,bc2408,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,71230.0,70890.0,71350.0,2695,6944,3.6,3.6,0.02,53512.5,53512.5,356750.0,50.0,42.8,92.8,0.08,0.173,46.38,96.38,0.087,0.18,2408
|
||||
INE,bc2409,bc2409,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71420.0,71070.0,71540.0,976,5986,3.61,3.61,0.02,50078.0,50078.0,357700.0,50.0,42.78,92.78,0.085,0.185,46.37,96.37,0.093,0.192,2409
|
||||
INE,bc2410,bc2410,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71540.0,71340.0,71640.0,58,216,3.61,3.61,0.02,50148.0,50148.0,358200.0,50.0,42.77,92.77,0.085,0.185,46.37,96.37,0.092,0.192,2410
|
||||
INE,bc2411,bc2411,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71460.0,71300.0,71820.0,1,31,3.62,3.62,0.02,50274.0,50274.0,359100.0,50.0,42.75,92.75,0.085,0.184,46.36,96.36,0.092,0.192,2411
|
||||
INE,bc2412,bc2412,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71350.0,71350.0,71350.0,0,2,3.6,3.6,0.02,49945.0,49945.0,356750.0,50.0,42.8,92.8,0.086,0.186,46.38,96.38,0.093,0.193,2412
|
||||
INE,bc2501,bc2501,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71050.0,71050.0,71050.0,0,2,3.58,3.58,0.02,49735.0,49735.0,355250.0,50.0,42.83,92.83,0.086,0.187,46.39,96.39,0.093,0.194,2501
|
||||
INE,bc2502,bc2502,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71370.0,71370.0,71370.0,0,4,3.6,3.6,0.02,49959.0,49959.0,356850.0,50.0,42.8,92.8,0.086,0.186,46.38,96.38,0.093,0.193,2502
|
||||
INE,bc2503,bc2503,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,71110.0,71110.0,71110.0,0,2,3.59,3.59,0.02,49777.0,49777.0,355550.0,50.0,42.83,92.83,0.086,0.186,46.39,96.39,0.093,0.194,2503
|
||||
INE,bc2504,bc2504,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,70480.0,70480.0,70480.0,0,1,3.56,3.56,0.02,49336.0,49336.0,352400.0,50.0,42.89,92.89,0.087,0.188,46.42,96.42,0.094,0.195,2504
|
||||
INE,bc2505,bc2505,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,72330.0,72330.0,72330.0,0,0,3.65,3.65,0.02,50631.0,50631.0,361650.0,50.0,42.7,92.7,0.084,0.183,46.33,96.33,0.092,0.19,2505
|
||||
INE,bc2506,bc2506,bc,国际铜,5,10.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.14,0,0.14,0,72680.0,72680.0,72680.0,0,0,3.67,3.67,0.02,50876.0,50876.0,363400.0,50.0,42.67,92.67,0.084,0.182,46.31,96.31,0.091,0.189,2506
|
||||
SHFE,br2407,br2407,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,0.0001,0.01,0.25,0,0.25,0,14820.0,14715.0,14715.0,0,1336,1.49,1.49,7.37,18393.75,18393.75,73575.0,25.0,22.03,47.03,0.12,0.256,16.14,41.14,0.088,0.224,2407
|
||||
SHFE,br2408,br2408,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,0.0001,0.01,0.17,0,0.17,0,14765.0,14640.0,14745.0,22235,23676,1.49,1.49,7.39,12533.25,12533.25,73725.0,25.0,22.02,47.02,0.176,0.375,16.12,41.12,0.129,0.328,2408
|
||||
SHFE,br2409,br2409,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14670.0,14545.0,14655.0,16423,39727,1.48,1.48,1.48,12456.75,12456.75,73275.0,25.0,22.04,47.04,0.177,0.378,22.04,47.04,0.177,0.378,2409
|
||||
SHFE,br2410,br2410,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14600.0,14490.0,14575.0,447,596,1.47,1.47,1.47,12388.75,12388.75,72875.0,25.0,22.06,47.06,0.178,0.38,22.06,47.06,0.178,0.38,2410
|
||||
SHFE,br2411,br2411,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14605.0,14385.0,14530.0,12,144,1.47,1.47,1.47,12350.5,12350.5,72650.0,25.0,22.07,47.07,0.179,0.381,22.07,47.07,0.179,0.381,2411
|
||||
SHFE,br2412,br2412,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14400.0,14400.0,14550.0,1,9,1.47,1.47,1.47,12367.5,12367.5,72750.0,25.0,22.06,47.06,0.178,0.381,22.06,47.06,0.178,0.381,2412
|
||||
SHFE,br2501,br2501,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14515.0,14320.0,14500.0,52,419,1.46,1.46,1.46,12325.0,12325.0,72500.0,25.0,22.07,47.07,0.179,0.382,22.07,47.07,0.179,0.382,2501
|
||||
SHFE,br2502,br2502,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14525.0,14460.0,14460.0,0,23,1.46,1.46,1.46,12291.0,12291.0,72300.0,25.0,22.08,47.08,0.18,0.383,22.08,47.08,0.18,0.383,2502
|
||||
SHFE,br2503,br2503,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14625.0,14625.0,14625.0,0,19,1.48,1.48,1.48,12431.25,12431.25,73125.0,25.0,22.05,47.05,0.177,0.378,22.05,47.05,0.177,0.378,2503
|
||||
SHFE,br2504,br2504,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14405.0,14290.0,14290.0,0,21,1.44,1.44,1.44,12146.5,12146.5,71450.0,25.0,22.11,47.11,0.182,0.388,22.11,47.11,0.182,0.388,2504
|
||||
SHFE,br2505,br2505,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14395.0,14290.0,14340.0,3,47,1.45,1.45,1.45,12189.0,12189.0,71700.0,25.0,22.1,47.1,0.181,0.386,22.1,47.1,0.181,0.386,2505
|
||||
SHFE,br2506,br2506,br,丁二烯橡胶,5,5.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.17,0,0.17,0,14390.0,14015.0,14285.0,1,4,1.44,1.44,1.44,12142.25,12142.25,71425.0,25.0,22.11,47.11,0.182,0.388,22.11,47.11,0.182,0.388,2506
|
||||
SHFE,bu2407,bu2407,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.25,0,0.25,0,3560.0,3560.0,3560.0,0,780,1.79,1.79,0.0,8900.0,8900.0,35600.0,10.0,6.42,16.42,0.072,0.184,8.21,18.21,0.092,0.205,2407
|
||||
SHFE,bu2408,bu2408,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3622.0,3604.0,3607.0,7,895,1.82,1.82,0.0,5410.5,5410.5,36070.0,10.0,6.37,16.37,0.118,0.303,8.18,18.18,0.151,0.336,2408
|
||||
SHFE,bu2409,bu2409,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3650.0,3626.0,3635.0,52379,208195,1.83,1.83,0.0,5452.5,5452.5,36350.0,10.0,6.34,16.34,0.116,0.3,8.17,18.17,0.15,0.333,2409
|
||||
SHFE,bu2410,bu2410,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3644.0,3617.0,3629.0,12569,92167,1.83,1.83,0.0,5443.5,5443.5,36290.0,10.0,6.35,16.35,0.117,0.3,8.17,18.17,0.15,0.334,2410
|
||||
SHFE,bu2411,bu2411,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3625.0,3597.0,3608.0,3085,66689,1.82,1.82,0.0,5412.0,5412.0,36080.0,10.0,6.37,16.37,0.118,0.302,8.18,18.18,0.151,0.336,2411
|
||||
SHFE,bu2412,bu2412,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3588.0,3572.0,3578.0,1690,19428,1.8,1.8,0.0,5367.0,5367.0,35780.0,10.0,6.4,16.4,0.119,0.306,8.2,18.2,0.153,0.339,2412
|
||||
SHFE,bu2501,bu2501,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3572.0,3560.0,3565.0,4,158,1.79,1.79,0.0,5347.5,5347.5,35650.0,10.0,6.41,16.41,0.12,0.307,8.2,18.2,0.153,0.34,2501
|
||||
SHFE,bu2502,bu2502,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3598.0,3598.0,3598.0,0,5,1.81,1.81,0.0,5397.0,5397.0,35980.0,10.0,6.38,16.38,0.118,0.303,8.19,18.19,0.152,0.337,2502
|
||||
SHFE,bu2503,bu2503,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3568.0,3562.0,3562.0,0,2190,1.79,1.79,0.0,5343.0,5343.0,35620.0,10.0,6.41,16.41,0.12,0.307,8.2,18.2,0.154,0.341,2503
|
||||
SHFE,bu2504,bu2504,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3599.0,3599.0,3599.0,0,2,1.81,1.81,0.0,5398.5,5398.5,35990.0,10.0,6.38,16.38,0.118,0.303,8.19,18.19,0.152,0.337,2504
|
||||
SHFE,bu2505,bu2505,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3589.0,3589.0,3589.0,0,6,1.81,1.81,0.0,5383.5,5383.5,35890.0,10.0,6.39,16.39,0.119,0.304,8.19,18.19,0.152,0.338,2505
|
||||
SHFE,bu2506,bu2506,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3588.0,3570.0,3577.0,3,2234,1.8,1.8,0.0,5365.5,5365.5,35770.0,10.0,6.4,16.4,0.119,0.306,8.2,18.2,0.153,0.339,2506
|
||||
SHFE,bu2509,bu2509,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3581.0,3581.0,3581.0,0,33,1.8,1.8,0.0,5371.5,5371.5,35810.0,10.0,6.39,16.39,0.119,0.305,8.2,18.2,0.153,0.339,2509
|
||||
SHFE,bu2512,bu2512,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3560.0,3550.0,3550.0,0,30,1.79,1.79,0.0,5325.0,5325.0,35500.0,10.0,6.43,16.43,0.121,0.308,8.21,18.21,0.154,0.342,2512
|
||||
SHFE,bu2603,bu2603,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3553.0,3550.0,3530.0,2,15,1.78,1.78,0.0,5295.0,5295.0,35300.0,10.0,6.45,16.45,0.122,0.311,8.22,18.22,0.155,0.344,2603
|
||||
SHFE,bu2606,bu2606,bu,石油沥青,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3566.0,3566.0,3566.0,0,5,1.8,1.8,0.0,5349.0,5349.0,35660.0,10.0,6.41,16.41,0.12,0.307,8.2,18.2,0.153,0.34,2606
|
||||
DCE,c2407,玉米2407,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.25,0,0.25,0,2445.0,2445.0,2445.0,0,0,1.21,1.21,1.21,6112.5,6112.5,24450.0,10.0,7.58,17.58,0.124,0.288,7.58,17.58,0.124,0.288,2407
|
||||
DCE,c2409,玉米2409,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.12,0,0.12,0,2424.0,2420.0,2420.0,104842,772613,1.21,1.21,1.21,2904.0,2904.0,24200.0,10.0,7.58,17.58,0.261,0.605,7.58,17.58,0.261,0.605,2409
|
||||
DCE,c2411,玉米2411,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.12,0,0.12,0,2343.0,2337.0,2342.0,15448,285224,1.21,1.21,1.21,2810.4,2810.4,23420.0,10.0,7.58,17.58,0.27,0.625,7.58,17.58,0.27,0.625,2411
|
||||
DCE,c2501,玉米2501,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.12,0,0.12,0,2335.0,2328.0,2333.0,9921,234309,1.21,1.21,1.21,2799.6,2799.6,23330.0,10.0,7.58,17.58,0.271,0.628,7.58,17.58,0.271,0.628,2501
|
||||
DCE,c2503,玉米2503,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.12,0,0.12,0,2341.0,2334.0,2337.0,1190,55022,1.21,1.21,1.21,2804.4,2804.4,23370.0,10.0,7.58,17.58,0.27,0.627,7.58,17.58,0.27,0.627,2503
|
||||
DCE,c2505,玉米2505,c,黄玉米,10,1.0,0.0,1.21,0.0,1.21,0.0,1.21,0.12,0,0.12,0,2388.0,2377.0,2381.0,212,6487,1.21,1.21,1.21,2857.2,2857.2,23810.0,10.0,7.58,17.58,0.265,0.615,7.58,17.58,0.265,0.615,2505
|
||||
DCE,cs2407,玉米淀粉2407,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.25,0,0.25,0,2800.0,2800.0,2800.0,0,0,1.51,1.51,1.51,7000.0,7000.0,28000.0,10.0,6.98,16.98,0.1,0.243,6.98,16.98,0.1,0.243,2407
|
||||
DCE,cs2409,玉米淀粉2409,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.11,0,0.11,0,2893.0,2881.0,2873.0,39962,156258,1.51,1.51,1.51,3160.3,3160.3,28730.0,10.0,6.98,16.98,0.221,0.537,6.98,16.98,0.221,0.537,2409
|
||||
DCE,cs2411,玉米淀粉2411,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.11,0,0.11,0,2796.0,2788.0,2789.0,4025,47456,1.51,1.51,1.51,3067.9,3067.9,27890.0,10.0,6.98,16.98,0.227,0.553,6.98,16.98,0.227,0.553,2411
|
||||
DCE,cs2501,玉米淀粉2501,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.11,0,0.11,0,2789.0,2781.0,2780.0,919,17191,1.51,1.51,1.51,3058.0,3058.0,27800.0,10.0,6.98,16.98,0.228,0.555,6.98,16.98,0.228,0.555,2501
|
||||
DCE,cs2503,玉米淀粉2503,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.11,0,0.11,0,2765.0,2756.0,2759.0,645,8797,1.51,1.51,1.51,3034.9,3034.9,27590.0,10.0,6.98,16.98,0.23,0.559,6.98,16.98,0.23,0.559,2503
|
||||
DCE,cs2505,玉米淀粉2505,cs,玉米淀粉,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.11,0,0.11,0,2805.0,2800.0,2803.0,6,170,1.51,1.51,1.51,3083.3,3083.3,28030.0,10.0,6.98,16.98,0.226,0.551,6.98,16.98,0.226,0.551,2505
|
||||
SHFE,cu2407,cu2407,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.25,0,0.25,0,79580.0,79400.0,79800.0,750,26145,19.98,19.98,39.93,99750.0,99750.0,399000.0,50.0,10.03,60.03,0.01,0.06,-9.92,40.08,-0.01,0.04,2407
|
||||
SHFE,cu2408,cu2408,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.15,0,0.15,0,79870.0,79580.0,80020.0,23542,195740,20.04,20.04,40.04,60015.0,60015.0,400100.0,50.0,9.92,59.92,0.017,0.1,-10.08,39.92,-0.017,0.067,2408
|
||||
SHFE,cu2409,cu2409,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80120.0,79820.0,80250.0,19183,153288,20.1,20.1,40.16,56175.0,56175.0,401250.0,50.0,9.81,59.81,0.017,0.106,-10.26,39.74,-0.018,0.071,2409
|
||||
SHFE,cu2410,cu2410,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80300.0,80020.0,80440.0,3905,61520,20.14,20.14,40.25,56308.0,56308.0,402200.0,50.0,9.71,59.71,0.017,0.106,-10.4,39.6,-0.018,0.07,2410
|
||||
SHFE,cu2411,cu2411,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80560.0,80190.0,80660.0,1415,30605,20.2,20.2,40.36,56462.0,56462.0,403300.0,50.0,9.6,59.6,0.017,0.106,-10.56,39.44,-0.019,0.07,2411
|
||||
SHFE,cu2412,cu2412,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80670.0,80350.0,80740.0,1298,29676,20.22,20.22,40.4,56518.0,56518.0,403700.0,50.0,9.56,59.56,0.017,0.105,-10.62,39.38,-0.019,0.07,2412
|
||||
SHFE,cu2501,cu2501,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80850.0,80520.0,80880.0,272,15427,20.25,20.25,40.47,56616.0,56616.0,404400.0,50.0,9.49,59.49,0.017,0.105,-10.73,39.27,-0.019,0.069,2501
|
||||
SHFE,cu2502,cu2502,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80870.0,80610.0,80980.0,109,7311,20.28,20.28,40.52,56686.0,56686.0,404900.0,50.0,9.44,59.44,0.017,0.105,-10.8,39.2,-0.019,0.069,2502
|
||||
SHFE,cu2503,cu2503,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80830.0,80730.0,81040.0,52,5786,20.29,20.29,40.55,56728.0,56728.0,405200.0,50.0,9.41,59.41,0.017,0.105,-10.85,39.15,-0.019,0.069,2503
|
||||
SHFE,cu2504,cu2504,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80860.0,80780.0,81090.0,34,2798,20.31,20.31,40.58,56763.0,56763.0,405450.0,50.0,9.39,59.39,0.017,0.105,-10.89,39.11,-0.019,0.069,2504
|
||||
SHFE,cu2505,cu2505,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80830.0,80760.0,81150.0,123,2036,20.32,20.32,40.61,56805.0,56805.0,405750.0,50.0,9.36,59.36,0.016,0.104,-10.93,39.07,-0.019,0.069,2505
|
||||
SHFE,cu2506,cu2506,cu,铜,5,10.0,5e-05,0.01,5e-05,0.01,0.0001,0.01,0.14,0,0.14,0,80830.0,80660.0,81130.0,14,486,20.32,20.32,40.6,56791.0,56791.0,405650.0,50.0,9.37,59.37,0.016,0.105,-10.92,39.08,-0.019,0.069,2506
|
||||
DCE,eb2407,苯乙烯2407,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.26,0,0.26,0,9488.0,9511.0,9511.0,406,9807,3.01,3.01,3.01,12364.3,12364.3,47555.0,5.0,-1.03,3.97,-0.008,0.032,-1.03,3.97,-0.008,0.032,2407
|
||||
DCE,eb2408,苯乙烯2408,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,9302.0,9282.0,9368.0,134479,260744,3.01,3.01,3.01,6557.6,6557.6,46840.0,5.0,-1.03,3.97,-0.016,0.061,-1.03,3.97,-0.016,0.061,2408
|
||||
DCE,eb2409,苯乙烯2409,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,9177.0,9146.0,9227.0,40374,133545,3.01,3.01,3.01,6458.9,6458.9,46135.0,5.0,-1.03,3.97,-0.016,0.062,-1.03,3.97,-0.016,0.062,2409
|
||||
DCE,eb2410,苯乙烯2410,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,9028.0,8996.0,9075.0,9220,43152,3.01,3.01,3.01,6352.5,6352.5,45375.0,5.0,-1.03,3.97,-0.016,0.063,-1.03,3.97,-0.016,0.063,2410
|
||||
DCE,eb2411,苯乙烯2411,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8882.0,8847.0,8943.0,5323,13712,3.01,3.01,3.01,6260.1,6260.1,44715.0,5.0,-1.03,3.97,-0.016,0.063,-1.03,3.97,-0.016,0.063,2411
|
||||
DCE,eb2412,苯乙烯2412,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8763.0,8732.0,8826.0,3296,3404,3.01,3.01,3.01,6178.2,6178.2,44130.0,5.0,-1.03,3.97,-0.017,0.064,-1.03,3.97,-0.017,0.064,2412
|
||||
DCE,eb2501,苯乙烯2501,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8702.0,8638.0,8742.0,436,1181,3.01,3.01,3.01,6119.4,6119.4,43710.0,5.0,-1.03,3.97,-0.017,0.065,-1.03,3.97,-0.017,0.065,2501
|
||||
DCE,eb2502,苯乙烯2502,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8651.0,8582.0,8700.0,20,289,3.01,3.01,3.01,6090.0,6090.0,43500.0,5.0,-1.03,3.97,-0.017,0.065,-1.03,3.97,-0.017,0.065,2502
|
||||
DCE,eb2503,苯乙烯2503,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8638.0,8519.0,8519.0,0,232,3.01,3.01,3.01,5963.3,5963.3,42595.0,5.0,-1.03,3.97,-0.017,0.067,-1.03,3.97,-0.017,0.067,2503
|
||||
DCE,eb2504,苯乙烯2504,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8610.0,8495.0,8563.0,2,81,3.01,3.01,3.01,5994.1,5994.1,42815.0,5.0,-1.03,3.97,-0.017,0.066,-1.03,3.97,-0.017,0.066,2504
|
||||
DCE,eb2505,苯乙烯2505,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8549.0,8484.0,8600.0,8,190,3.01,3.01,3.01,6020.0,6020.0,43000.0,5.0,-1.03,3.97,-0.017,0.066,-1.03,3.97,-0.017,0.066,2505
|
||||
DCE,eb2506,苯乙烯2506,eb,苯乙烯,5,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,8477.0,8470.0,8470.0,0,12,3.01,3.01,3.01,5929.0,5929.0,42350.0,5.0,-1.03,3.97,-0.017,0.067,-1.03,3.97,-0.017,0.067,2506
|
||||
INE,ec2408,ec2408,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,5742.6,5745.3,5745.3,0,33777,172.39,172.39,344.75,83306.85,83306.85,287265.0,5.0,-339.77,-334.77,-0.408,-0.402,-512.13,-507.13,-0.615,-0.609,2408
|
||||
INE,ec2410,ec2410,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,4482.0,4435.0,4435.0,0,28743,133.07,133.07,266.12,64307.5,64307.5,221750.0,5.0,-261.15,-256.15,-0.406,-0.398,-394.2,-389.2,-0.613,-0.605,2410
|
||||
INE,ec2412,ec2412,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,4011.2,3927.0,3927.0,0,15311,117.83,117.83,235.64,56941.5,56941.5,196350.0,5.0,-230.66,-225.66,-0.405,-0.396,-348.47,-343.47,-0.612,-0.603,2412
|
||||
INE,ec2502,ec2502,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,3262.0,3139.9,3139.9,0,9162,94.22,94.22,188.41,45528.55,45528.55,156995.0,5.0,-183.43,-178.43,-0.403,-0.392,-277.63,-272.63,-0.61,-0.599,2502
|
||||
INE,ec2504,ec2504,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,2630.5,2514.9,2514.9,0,14765,75.46,75.46,150.91,36466.05,36466.05,125745.0,5.0,-145.93,-140.93,-0.4,-0.386,-221.38,-216.38,-0.607,-0.593,2504
|
||||
INE,ec2506,ec2506,ec,SCFIS欧线,50,0.1,0.0006,0.01,0.0006,0.01,0.0012,0.01,0.29,0,0.29,0,2685.6,2539.6,2539.6,0,7037,76.21,76.21,152.39,36824.2,36824.2,126980.0,5.0,-147.41,-142.41,-0.4,-0.387,-223.6,-218.6,-0.607,-0.594,2506
|
||||
DCE,eg2407,乙二醇2407,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.26,0,0.26,0,4627.0,4627.0,4627.0,0,0,3.01,3.01,3.01,12030.2,12030.2,46270.0,10.0,3.97,13.97,0.033,0.116,3.97,13.97,0.033,0.116,2407
|
||||
DCE,eg2408,乙二醇2408,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4712.0,4720.0,4747.0,140,1665,3.01,3.01,3.01,6645.8,6645.8,47470.0,10.0,3.97,13.97,0.06,0.21,3.97,13.97,0.06,0.21,2408
|
||||
DCE,eg2409,乙二醇2409,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4714.0,4723.0,4752.0,219937,454929,3.01,3.01,3.01,6652.8,6652.8,47520.0,10.0,3.97,13.97,0.06,0.21,3.97,13.97,0.06,0.21,2409
|
||||
DCE,eg2410,乙二醇2410,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4697.0,4705.0,4724.0,520,1736,3.01,3.01,3.01,6613.6,6613.6,47240.0,10.0,3.97,13.97,0.06,0.211,3.97,13.97,0.06,0.211,2410
|
||||
DCE,eg2411,乙二醇2411,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4691.0,4688.0,4718.0,25,407,3.01,3.01,3.01,6605.2,6605.2,47180.0,10.0,3.97,13.97,0.06,0.212,3.97,13.97,0.06,0.212,2411
|
||||
DCE,eg2412,乙二醇2412,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4674.0,4682.0,4704.0,17,101,3.01,3.01,3.01,6585.6,6585.6,47040.0,10.0,3.97,13.97,0.06,0.212,3.97,13.97,0.06,0.212,2412
|
||||
DCE,eg2501,乙二醇2501,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4659.0,4670.0,4687.0,12994,64922,3.01,3.01,3.01,6561.8,6561.8,46870.0,10.0,3.97,13.97,0.061,0.213,3.97,13.97,0.061,0.213,2501
|
||||
DCE,eg2502,乙二醇2502,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4666.0,4648.0,4699.0,10,252,3.01,3.01,3.01,6578.6,6578.6,46990.0,10.0,3.97,13.97,0.06,0.212,3.97,13.97,0.06,0.212,2502
|
||||
DCE,eg2503,乙二醇2503,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4683.0,4650.0,4719.0,2,661,3.01,3.01,3.01,6606.6,6606.6,47190.0,10.0,3.97,13.97,0.06,0.212,3.97,13.97,0.06,0.212,2503
|
||||
DCE,eg2504,乙二醇2504,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4677.0,4685.0,4685.0,0,8,3.01,3.01,3.01,6559.0,6559.0,46850.0,10.0,3.97,13.97,0.061,0.213,3.97,13.97,0.061,0.213,2504
|
||||
DCE,eg2505,乙二醇2505,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4656.0,4665.0,4683.0,4,36,3.01,3.01,3.01,6556.2,6556.2,46830.0,10.0,3.97,13.97,0.061,0.213,3.97,13.97,0.061,0.213,2505
|
||||
DCE,eg2506,乙二醇2506,eg,乙二醇,10,1.0,0.0,3.01,0.0,3.01,0.0,3.01,0.14,0,0.14,0,4688.0,4688.0,4690.0,2,1,3.01,3.01,3.01,6566.0,6566.0,46900.0,10.0,3.97,13.97,0.061,0.213,3.97,13.97,0.061,0.213,2506
|
||||
DCE,fb2407,纤维板2407,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.35,0,0.35,0,1262.5,1262.5,1262.5,0,0,1.27,1.27,1.27,4418.75,4418.75,12625.0,5.0,2.45,7.45,0.056,0.169,2.45,7.45,0.056,0.169,2407
|
||||
DCE,fb2408,纤维板2408,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1272.5,1272.5,1272.5,0,3,1.28,1.28,1.28,3181.25,3181.25,12725.0,5.0,2.43,7.43,0.076,0.234,2.43,7.43,0.076,0.234,2408
|
||||
DCE,fb2409,纤维板2409,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1288.5,1286.5,1286.5,0,1472,1.3,1.3,1.3,3216.25,3216.25,12865.0,5.0,2.41,7.41,0.075,0.23,2.41,7.41,0.075,0.23,2409
|
||||
DCE,fb2410,纤维板2410,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1295.0,1295.0,1295.0,0,6,1.31,1.31,1.31,3237.5,3237.5,12950.0,5.0,2.39,7.39,0.074,0.228,2.39,7.39,0.074,0.228,2410
|
||||
DCE,fb2411,纤维板2411,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1278.5,1278.5,1278.5,0,4,1.29,1.29,1.29,3196.25,3196.25,12785.0,5.0,2.42,7.42,0.076,0.232,2.42,7.42,0.076,0.232,2411
|
||||
DCE,fb2412,纤维板2412,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1280.0,1278.5,1278.5,0,13,1.29,1.29,1.29,3196.25,3196.25,12785.0,5.0,2.42,7.42,0.076,0.232,2.42,7.42,0.076,0.232,2412
|
||||
DCE,fb2501,纤维板2501,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1283.0,1283.0,1283.0,0,17,1.29,1.29,1.29,3207.5,3207.5,12830.0,5.0,2.41,7.41,0.075,0.231,2.41,7.41,0.075,0.231,2501
|
||||
DCE,fb2502,纤维板2502,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1298.5,1298.5,1298.5,0,2,1.31,1.31,1.31,3246.25,3246.25,12985.0,5.0,2.38,7.38,0.073,0.227,2.38,7.38,0.073,0.227,2502
|
||||
DCE,fb2503,纤维板2503,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1294.0,1294.0,1294.0,0,1,1.3,1.3,1.3,3235.0,3235.0,12940.0,5.0,2.39,7.39,0.074,0.228,2.39,7.39,0.074,0.228,2503
|
||||
DCE,fb2504,纤维板2504,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1290.0,1290.0,1290.0,0,1,1.3,1.3,1.3,3225.0,3225.0,12900.0,5.0,2.4,7.4,0.074,0.229,2.4,7.4,0.074,0.229,2504
|
||||
DCE,fb2505,纤维板2505,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1301.0,1300.0,1300.0,0,7,1.31,1.31,1.31,3250.0,3250.0,13000.0,5.0,2.38,7.38,0.073,0.227,2.38,7.38,0.073,0.227,2505
|
||||
DCE,fb2506,纤维板2506,fb,纤维板,10,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.25,0,0.25,0,1275.0,1275.0,1275.0,0,1,1.29,1.29,1.29,3187.5,3187.5,12750.0,5.0,2.43,7.43,0.076,0.233,2.43,7.43,0.076,0.233,2506
|
||||
SHFE,fu2408,fu2408,fu,燃料油,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.2,0,0.2,0,3751.0,3755.0,3757.0,89,890,1.89,1.89,0.0,7514.0,7514.0,37570.0,10.0,6.22,16.22,0.083,0.216,8.11,18.11,0.108,0.241,2408
|
||||
SHFE,fu2409,fu2409,fu,燃料油,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3528.0,3490.0,3510.0,150840,234353,1.77,1.77,0.0,5265.0,5265.0,35100.0,10.0,6.47,16.47,0.123,0.313,8.23,18.23,0.156,0.346,2409
|
||||
SHFE,fu2410,fu2410,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3461.0,3425.0,3447.0,2689,9759,0.36,0.36,0.0,5170.5,5170.5,34470.0,10.0,9.29,19.29,0.18,0.373,9.64,19.64,0.186,0.38,2410
|
||||
SHFE,fu2411,fu2411,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3402.0,3367.0,3384.0,24879,153916,0.35,0.35,0.0,5076.0,5076.0,33840.0,10.0,9.3,19.3,0.183,0.38,9.65,19.65,0.19,0.387,2411
|
||||
SHFE,fu2412,fu2412,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3373.0,3340.0,3352.0,1139,35837,0.35,0.35,0.0,5028.0,5028.0,33520.0,10.0,9.31,19.31,0.185,0.384,9.65,19.65,0.192,0.391,2412
|
||||
SHFE,fu2501,fu2501,fu,燃料油,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3354.0,3308.0,3324.0,11273,76964,1.67,1.67,0.0,4986.0,4986.0,33240.0,10.0,6.65,16.65,0.133,0.334,8.32,18.32,0.167,0.368,2501
|
||||
SHFE,fu2502,fu2502,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3309.0,3288.0,3303.0,111,18835,0.34,0.34,0.0,4954.5,4954.5,33030.0,10.0,9.32,19.32,0.188,0.39,9.66,19.66,0.195,0.397,2502
|
||||
SHFE,fu2503,fu2503,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3293.0,3271.0,3284.0,60,6528,0.34,0.34,0.0,4926.0,4926.0,32840.0,10.0,9.32,19.32,0.189,0.392,9.66,19.66,0.196,0.399,2503
|
||||
SHFE,fu2504,fu2504,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3287.0,3255.0,3271.0,46,349,0.34,0.34,0.0,4906.5,4906.5,32710.0,10.0,9.32,19.32,0.19,0.394,9.66,19.66,0.197,0.401,2504
|
||||
SHFE,fu2505,fu2505,fu,燃料油,10,1.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,3283.0,3242.0,3254.0,179,6345,1.64,1.64,0.0,4881.0,4881.0,32540.0,10.0,6.72,16.72,0.138,0.343,8.36,18.36,0.171,0.376,2505
|
||||
SHFE,fu2506,fu2506,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3260.0,3231.0,3243.0,11,343,0.34,0.34,0.0,4864.5,4864.5,32430.0,10.0,9.33,19.33,0.192,0.397,9.66,19.66,0.199,0.404,2506
|
||||
SHFE,fu2507,fu2507,fu,燃料油,10,1.0,1e-05,0.01,1e-05,0.01,0.0,0.0,0.15,0,0.15,0,3254.0,3218.0,3230.0,2,141,0.33,0.33,0.0,4845.0,4845.0,32300.0,10.0,9.33,19.33,0.193,0.399,9.66,19.66,0.199,0.406,2507
|
||||
SHFE,hc2407,hc2407,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.26,0,0.26,0,3700.0,3700.0,3700.0,0,1800,3.71,3.71,3.71,9620.0,9620.0,37000.0,10.0,2.58,12.58,0.027,0.131,2.58,12.58,0.027,0.131,2407
|
||||
SHFE,hc2408,hc2408,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.16,0,0.16,0,3700.0,3692.0,3692.0,0,533,3.7,3.7,3.7,5907.2,5907.2,36920.0,10.0,2.59,12.59,0.044,0.213,2.59,12.59,0.044,0.213,2408
|
||||
SHFE,hc2409,hc2409,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3681.0,3673.0,3682.0,108,6403,3.69,3.69,3.69,4786.6,4786.6,36820.0,10.0,2.61,12.61,0.055,0.263,2.61,12.61,0.055,0.263,2409
|
||||
SHFE,hc2410,hc2410,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3694.0,3681.0,3693.0,82480,1066901,3.71,3.71,3.71,4800.9,4800.9,36930.0,10.0,2.59,12.59,0.054,0.262,2.59,12.59,0.054,0.262,2410
|
||||
SHFE,hc2411,hc2411,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3700.0,3685.0,3698.0,8285,184930,0.75,0.75,0.75,4807.4,4807.4,36980.0,10.0,8.5,18.5,0.177,0.385,8.5,18.5,0.177,0.385,2411
|
||||
SHFE,hc2412,hc2412,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3707.0,3703.0,3709.0,205,544,0.75,0.75,0.75,4821.7,4821.7,37090.0,10.0,8.49,18.49,0.176,0.384,8.49,18.49,0.176,0.384,2412
|
||||
SHFE,hc2501,hc2501,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3718.0,3705.0,3717.0,6681,222195,3.73,3.73,3.73,4832.1,4832.1,37170.0,10.0,2.54,12.54,0.053,0.26,2.54,12.54,0.053,0.26,2501
|
||||
SHFE,hc2502,hc2502,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3727.0,3721.0,3727.0,3,391,0.76,0.76,0.76,4845.1,4845.1,37270.0,10.0,8.48,18.48,0.175,0.382,8.48,18.48,0.175,0.382,2502
|
||||
SHFE,hc2503,hc2503,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3732.0,3723.0,3726.0,33,1429,0.76,0.76,0.76,4843.8,4843.8,37260.0,10.0,8.49,18.49,0.175,0.382,8.49,18.49,0.175,0.382,2503
|
||||
SHFE,hc2504,hc2504,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3733.0,3727.0,3727.0,0,169,0.76,0.76,0.76,4845.1,4845.1,37270.0,10.0,8.48,18.48,0.175,0.382,8.48,18.48,0.175,0.382,2504
|
||||
SHFE,hc2505,hc2505,hc,热轧卷板,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3746.0,3729.0,3743.0,146,10920,3.76,3.76,3.76,4865.9,4865.9,37430.0,10.0,2.49,12.49,0.051,0.257,2.49,12.49,0.051,0.257,2505
|
||||
SHFE,hc2506,hc2506,hc,热轧卷板,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3736.0,3731.0,3734.0,5,31,0.76,0.76,0.76,4854.2,4854.2,37340.0,10.0,8.48,18.48,0.175,0.381,8.48,18.48,0.175,0.381,2506
|
||||
DCE,i2407,铁矿石2407,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.26,0,0.26,0,874.0,874.0,874.0,0,200,8.76,8.76,8.76,22724.0,22724.0,87400.0,50.0,32.49,82.49,0.143,0.363,32.49,82.49,0.143,0.363,2407
|
||||
DCE,i2408,铁矿石2408,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,828.0,821.0,826.0,2103,14327,8.27,8.27,8.27,17346.0,17346.0,82600.0,50.0,33.45,83.45,0.193,0.481,33.45,83.45,0.193,0.481,2408
|
||||
DCE,i2409,铁矿石2409,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,821.5,813.0,818.5,73026,342520,8.2,8.2,8.2,17188.5,17188.5,81850.0,50.0,33.6,83.6,0.195,0.486,33.6,83.6,0.195,0.486,2409
|
||||
DCE,i2410,铁矿石2410,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,817.0,807.5,814.0,2576,60230,8.15,8.15,8.15,17094.0,17094.0,81400.0,50.0,33.69,83.69,0.197,0.49,33.69,83.69,0.197,0.49,2410
|
||||
DCE,i2411,铁矿石2411,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,812.0,804.5,810.0,1324,28941,8.11,8.11,8.11,17010.0,17010.0,81000.0,50.0,33.77,83.77,0.199,0.492,33.77,83.77,0.199,0.492,2411
|
||||
DCE,i2412,铁矿石2412,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,810.0,800.5,807.0,615,14012,8.08,8.08,8.08,16947.0,16947.0,80700.0,50.0,33.83,83.83,0.2,0.495,33.83,83.83,0.2,0.495,2412
|
||||
DCE,i2501,铁矿石2501,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,804.0,795.5,801.5,23094,226078,8.03,8.03,8.03,16831.5,16831.5,80150.0,50.0,33.94,83.94,0.202,0.499,33.94,83.94,0.202,0.499,2501
|
||||
DCE,i2502,铁矿石2502,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,801.5,794.5,800.0,110,10864,8.01,8.01,8.01,16800.0,16800.0,80000.0,50.0,33.97,83.97,0.202,0.5,33.97,83.97,0.202,0.5,2502
|
||||
DCE,i2503,铁矿石2503,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,799.5,793.5,799.0,237,5674,8.0,8.0,8.0,16779.0,16779.0,79900.0,50.0,33.99,83.99,0.203,0.501,33.99,83.99,0.203,0.501,2503
|
||||
DCE,i2504,铁矿石2504,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,799.0,792.0,797.5,43,908,7.99,7.99,7.99,16747.5,16747.5,79750.0,50.0,34.02,84.02,0.203,0.502,34.02,84.02,0.203,0.502,2504
|
||||
DCE,i2505,铁矿石2505,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,794.5,787.0,792.0,1734,41648,7.93,7.93,7.93,16632.0,16632.0,79200.0,50.0,34.13,84.13,0.205,0.506,34.13,84.13,0.205,0.506,2505
|
||||
DCE,i2506,铁矿石2506,i,铁矿石,100,0.5,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.21,0,0.21,0,791.5,784.0,789.5,17,326,7.91,7.91,7.91,16579.5,16579.5,78950.0,50.0,34.18,84.18,0.206,0.508,34.18,84.18,0.206,0.508,2506
|
||||
DCE,j2407,焦炭2407,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2007.0,2007.0,2007.0,0,0,28.12,20.09,28.12,50175.0,50175.0,200700.0,50.0,1.79,51.79,0.004,0.103,-6.24,43.76,-0.012,0.087,2407
|
||||
DCE,j2408,焦炭2408,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2204.5,2198.0,2198.0,0,26,30.8,22.0,30.8,54950.0,54950.0,219800.0,50.0,-2.8,47.2,-0.005,0.086,-11.59,38.41,-0.021,0.07,2408
|
||||
DCE,j2409,焦炭2409,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2221.5,2215.0,2221.5,3896,26666,31.12,22.24,31.12,55537.5,55537.5,222150.0,50.0,-3.36,46.64,-0.006,0.084,-12.25,37.75,-0.022,0.068,2409
|
||||
DCE,j2410,焦炭2410,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2266.5,2266.5,2252.0,2,21,31.55,22.54,31.55,56300.0,56300.0,225200.0,50.0,-4.1,45.9,-0.007,0.082,-13.1,36.9,-0.023,0.066,2410
|
||||
DCE,j2411,焦炭2411,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2269.5,2269.5,2269.5,0,1,31.8,22.72,31.8,56737.5,56737.5,226950.0,50.0,-4.52,45.48,-0.008,0.08,-13.59,36.41,-0.024,0.064,2411
|
||||
DCE,j2412,焦炭2412,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2326.0,2326.0,2326.0,0,3,32.59,23.28,32.59,58150.0,58150.0,232600.0,50.0,-5.87,44.13,-0.01,0.076,-15.18,34.82,-0.026,0.06,2412
|
||||
DCE,j2501,焦炭2501,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2307.5,2306.0,2313.0,215,4304,32.41,23.15,32.41,57825.0,57825.0,231300.0,50.0,-5.56,44.44,-0.01,0.077,-14.81,35.19,-0.026,0.061,2501
|
||||
DCE,j2502,焦炭2502,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2297.0,2297.0,2297.0,0,3,32.18,22.99,32.18,57425.0,57425.0,229700.0,50.0,-5.18,44.82,-0.009,0.078,-14.36,35.64,-0.025,0.062,2502
|
||||
DCE,j2503,焦炭2503,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2324.0,2324.0,2324.0,0,1,32.56,23.26,32.56,58100.0,58100.0,232400.0,50.0,-5.82,44.18,-0.01,0.076,-15.12,34.88,-0.026,0.06,2503
|
||||
DCE,j2504,焦炭2504,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2310.5,2310.5,2310.5,0,0,32.37,23.13,32.37,57762.5,57762.5,231050.0,50.0,-5.5,44.5,-0.01,0.077,-14.74,35.26,-0.026,0.061,2504
|
||||
DCE,j2505,焦炭2505,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2315.5,2318.0,2318.0,0,60,32.48,23.2,32.48,57950.0,57950.0,231800.0,50.0,-5.68,44.32,-0.01,0.076,-14.95,35.05,-0.026,0.06,2505
|
||||
DCE,j2506,焦炭2506,j,冶金焦炭,100,0.5,0.00014,0.01,0.0001,0.01,0.00014,0.01,0.25,0,0.25,0,2285.0,2285.0,2285.0,0,1,32.01,22.87,32.01,57125.0,57125.0,228500.0,50.0,-4.89,45.11,-0.009,0.079,-14.03,35.97,-0.025,0.063,2506
|
||||
DCE,jd2407,鸡蛋2407,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.26,0,0.26,0,4179.0,4150.0,4150.0,0,12,6.24,6.24,6.24,10790.0,10790.0,41500.0,10.0,-2.47,7.53,-0.023,0.07,-2.47,7.53,-0.023,0.07,2407
|
||||
DCE,jd2408,鸡蛋2408,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,4448.0,4447.0,4447.0,0,23090,6.68,6.68,6.68,5781.1,5781.1,44470.0,10.0,-3.37,6.63,-0.058,0.115,-3.37,6.63,-0.058,0.115,2408
|
||||
DCE,jd2409,鸡蛋2409,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,4056.0,4062.0,4062.0,0,178656,6.11,6.11,6.11,5280.6,5280.6,40620.0,10.0,-2.21,7.79,-0.042,0.148,-2.21,7.79,-0.042,0.148,2409
|
||||
DCE,jd2410,鸡蛋2410,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3865.0,3853.0,3853.0,0,43282,5.79,5.79,5.79,5008.9,5008.9,38530.0,10.0,-1.58,8.42,-0.032,0.168,-1.58,8.42,-0.032,0.168,2410
|
||||
DCE,jd2411,鸡蛋2411,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3983.0,3977.0,3977.0,0,25590,5.98,5.98,5.98,5170.1,5170.1,39770.0,10.0,-1.96,8.04,-0.038,0.156,-1.96,8.04,-0.038,0.156,2411
|
||||
DCE,jd2412,鸡蛋2412,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,4119.0,4113.0,4113.0,0,15118,6.18,6.18,6.18,5346.9,5346.9,41130.0,10.0,-2.36,7.64,-0.044,0.143,-2.36,7.64,-0.044,0.143,2412
|
||||
DCE,jd2501,鸡蛋2501,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3763.0,3746.0,3746.0,0,34713,5.63,5.63,5.63,4869.8,4869.8,37460.0,10.0,-1.26,8.74,-0.026,0.179,-1.26,8.74,-0.026,0.179,2501
|
||||
DCE,jd2502,鸡蛋2502,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3484.0,3468.0,3468.0,0,8619,5.21,5.21,5.21,4508.4,4508.4,34680.0,10.0,-0.43,9.57,-0.009,0.212,-0.43,9.57,-0.009,0.212,2502
|
||||
DCE,jd2503,鸡蛋2503,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3504.0,3493.0,3493.0,0,1856,5.25,5.25,5.25,4540.9,4540.9,34930.0,10.0,-0.5,9.5,-0.011,0.209,-0.5,9.5,-0.011,0.209,2503
|
||||
DCE,jd2504,鸡蛋2504,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3635.0,3626.0,3626.0,0,1031,5.45,5.45,5.45,4713.8,4713.8,36260.0,10.0,-0.9,9.1,-0.019,0.193,-0.9,9.1,-0.019,0.193,2504
|
||||
DCE,jd2505,鸡蛋2505,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3653.0,3648.0,3648.0,0,2180,5.48,5.48,5.48,4742.4,4742.4,36480.0,10.0,-0.97,9.03,-0.02,0.19,-0.97,9.03,-0.02,0.19,2505
|
||||
DCE,jd2506,鸡蛋2506,jd,鸡蛋,10,1.0,0.00015,0.01,0.00015,0.01,0.00015,0.01,0.13,0,0.13,0,3477.0,3460.0,3460.0,0,1042,5.2,5.2,5.2,4498.0,4498.0,34600.0,10.0,-0.4,9.6,-0.009,0.213,-0.4,9.6,-0.009,0.213,2506
|
||||
DCE,jm2407,焦煤2407,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1506.0,1506.0,1506.0,0,0,27.12,9.05,27.12,22590.0,22590.0,90360.0,30.0,-6.17,23.83,-0.027,0.105,-24.25,5.75,-0.107,0.025,2407
|
||||
DCE,jm2408,焦煤2408,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1552.5,1550.5,1554.5,31,66,28.0,9.34,28.0,23317.5,23317.5,93270.0,30.0,-7.34,22.66,-0.031,0.097,-25.99,4.01,-0.111,0.017,2408
|
||||
DCE,jm2409,焦煤2409,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1543.5,1531.0,1542.5,25008,160242,27.78,9.27,27.78,23137.5,23137.5,92550.0,30.0,-7.05,22.95,-0.03,0.099,-25.56,4.44,-0.11,0.019,2409
|
||||
DCE,jm2410,焦煤2410,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1583.5,1582.0,1596.0,82,59,28.74,9.59,28.74,23940.0,23940.0,95760.0,30.0,-8.34,21.66,-0.035,0.09,-27.49,2.51,-0.115,0.01,2410
|
||||
DCE,jm2411,焦煤2411,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1678.0,1668.0,1668.0,0,19,30.04,10.02,30.04,25020.0,25020.0,100080.0,30.0,-10.06,19.94,-0.04,0.08,-30.08,-0.08,-0.12,-0.0,2411
|
||||
DCE,jm2412,焦煤2412,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1664.0,1659.0,1676.0,1,93,30.18,10.07,30.18,25140.0,25140.0,100560.0,30.0,-10.26,19.74,-0.041,0.079,-30.37,-0.37,-0.121,-0.001,2412
|
||||
DCE,jm2501,焦煤2501,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1691.5,1683.0,1691.5,1351,25783,30.46,10.17,30.46,25372.5,25372.5,101490.0,30.0,-10.63,19.37,-0.042,0.076,-30.93,-0.93,-0.122,-0.004,2501
|
||||
DCE,jm2502,焦煤2502,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1737.5,1720.5,1720.5,0,4,30.99,10.34,30.99,25807.5,25807.5,103230.0,30.0,-11.32,18.68,-0.044,0.072,-31.97,-1.97,-0.124,-0.008,2502
|
||||
DCE,jm2503,焦煤2503,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1717.0,1717.0,1717.0,0,21,30.92,10.32,30.92,25755.0,25755.0,103020.0,30.0,-11.24,18.76,-0.044,0.073,-31.84,-1.84,-0.124,-0.007,2503
|
||||
DCE,jm2504,焦煤2504,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1749.0,1749.0,1749.0,0,8,31.5,10.51,31.5,26235.0,26235.0,104940.0,30.0,-12.01,17.99,-0.046,0.069,-33.0,-3.0,-0.126,-0.011,2504
|
||||
DCE,jm2505,焦煤2505,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1723.0,1697.0,1710.0,2,740,30.8,10.28,30.8,25650.0,25650.0,102600.0,30.0,-11.07,18.93,-0.043,0.074,-31.59,-1.59,-0.123,-0.006,2505
|
||||
DCE,jm2506,焦煤2506,jm,焦煤,60,0.5,0.0003,0.01,0.0001,0.01,0.0003,0.01,0.25,0,0.25,0,1726.5,1726.5,1726.5,0,1,31.09,10.38,31.09,25897.5,25897.5,103590.0,30.0,-11.47,18.53,-0.044,0.072,-32.19,-2.19,-0.124,-0.008,2506
|
||||
DCE,l2407,聚乙烯2407,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.26,0,0.26,0,8369.0,8369.0,8369.0,0,257,1.01,1.01,1.01,10879.7,10879.7,41845.0,5.0,2.97,7.97,0.027,0.073,2.97,7.97,0.027,0.073,2407
|
||||
DCE,l2408,聚乙烯2408,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8382.0,8353.0,8371.0,86,1354,1.01,1.01,1.01,5441.15,5441.15,41855.0,5.0,2.97,7.97,0.055,0.147,2.97,7.97,0.055,0.147,2408
|
||||
DCE,l2409,聚乙烯2409,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8413.0,8385.0,8402.0,75480,403404,1.01,1.01,1.01,5461.3,5461.3,42010.0,5.0,2.97,7.97,0.054,0.146,2.97,7.97,0.054,0.146,2409
|
||||
DCE,l2410,聚乙烯2410,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8397.0,8378.0,8398.0,49,1239,1.01,1.01,1.01,5458.7,5458.7,41990.0,5.0,2.97,7.97,0.054,0.146,2.97,7.97,0.054,0.146,2410
|
||||
DCE,l2411,聚乙烯2411,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8434.0,8405.0,8426.0,2,60,1.01,1.01,1.01,5476.9,5476.9,42130.0,5.0,2.97,7.97,0.054,0.146,2.97,7.97,0.054,0.146,2411
|
||||
DCE,l2412,聚乙烯2412,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8448.0,8410.0,8433.0,2,56,1.01,1.01,1.01,5481.45,5481.45,42165.0,5.0,2.97,7.97,0.054,0.145,2.97,7.97,0.054,0.145,2412
|
||||
DCE,l2501,聚乙烯2501,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8420.0,8398.0,8417.0,6787,66796,1.01,1.01,1.01,5471.05,5471.05,42085.0,5.0,2.97,7.97,0.054,0.146,2.97,7.97,0.054,0.146,2501
|
||||
DCE,l2502,聚乙烯2502,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8413.0,8395.0,8404.0,3,71,1.01,1.01,1.01,5462.6,5462.6,42020.0,5.0,2.97,7.97,0.054,0.146,2.97,7.97,0.054,0.146,2502
|
||||
DCE,l2503,聚乙烯2503,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8424.0,8400.0,8395.0,4,100,1.01,1.01,1.01,5456.75,5456.75,41975.0,5.0,2.97,7.97,0.055,0.146,2.97,7.97,0.055,0.146,2503
|
||||
DCE,l2504,聚乙烯2504,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8464.0,8460.0,8460.0,0,36,1.01,1.01,1.01,5499.0,5499.0,42300.0,5.0,2.97,7.97,0.054,0.145,2.97,7.97,0.054,0.145,2504
|
||||
DCE,l2505,聚乙烯2505,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8399.0,8381.0,8382.0,103,575,1.01,1.01,1.01,5448.3,5448.3,41910.0,5.0,2.97,7.97,0.055,0.146,2.97,7.97,0.055,0.146,2505
|
||||
DCE,l2506,聚乙烯2506,l,线型低密度聚乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,8454.0,8454.0,8454.0,0,4,1.01,1.01,1.01,5495.1,5495.1,42270.0,5.0,2.97,7.97,0.054,0.145,2.97,7.97,0.054,0.145,2506
|
||||
GFEX,lc2407,碳酸锂2407,lc,碳酸锂,1,50.0,0.00016,0.01,0.00016,0.01,0.00016,0.01,0.28,0,0.28,0,87750.0,86800.0,0.0,0,342,13.9,13.9,13.9,24304.0,24304.0,86800.0,50.0,22.19,72.19,0.091,0.297,22.19,72.19,0.091,0.297,2407
|
||||
GFEX,lc2408,碳酸锂2408,lc,碳酸锂,1,50.0,0.00016,0.01,0.00016,0.01,0.00016,0.01,0.2,0,0.2,0,91250.0,89900.0,0.0,0,56355,14.4,14.4,14.4,17980.0,17980.0,89900.0,50.0,21.2,71.2,0.118,0.396,21.2,71.2,0.118,0.396,2408
|
||||
GFEX,lc2409,碳酸锂2409,lc,碳酸锂,1,50.0,0.00016,0.01,0.00016,0.01,0.00016,0.01,0.2,0,0.2,0,91850.0,90600.0,0.0,0,27745,14.51,14.51,14.51,18120.0,18120.0,90600.0,50.0,20.98,70.98,0.116,0.392,20.98,70.98,0.116,0.392,2409
|
||||
GFEX,lc2410,碳酸锂2410,lc,碳酸锂,1,50.0,0.00016,0.01,0.00016,0.01,0.00016,0.01,0.2,0,0.2,0,92350.0,91200.0,0.0,0,10064,14.61,14.61,14.61,18240.0,18240.0,91200.0,50.0,20.79,70.79,0.114,0.388,20.79,70.79,0.114,0.388,2410
|
||||
GFEX,lc2411,碳酸锂2411,lc,碳酸锂,1,50.0,0.00016,0.01,0.00016,0.01,0.00016,0.01,0.2,0,0.2,0,93100.0,91800.0,0.0,0,176216,14.7,14.7,14.7,18360.0,18360.0,91800.0,50.0,20.59,70.59,0.112,0.384,20.59,70.59,0.112,0.384,2411
|
||||
GFEX,lc2412,碳酸锂2412,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,95600.0,94650.0,0.0,0,15109,7.59,7.59,0.01,18930.0,18930.0,94650.0,50.0,34.82,84.82,0.184,0.448,42.41,92.41,0.224,0.488,2412
|
||||
GFEX,lc2501,碳酸锂2501,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,96400.0,95350.0,0.0,0,21099,7.64,7.64,0.01,19070.0,19070.0,95350.0,50.0,34.71,84.71,0.182,0.444,42.35,92.35,0.222,0.484,2501
|
||||
GFEX,lc2502,碳酸锂2502,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,96900.0,95600.0,0.0,0,2263,7.66,7.66,0.01,19120.0,19120.0,95600.0,50.0,34.67,84.67,0.181,0.443,42.33,92.33,0.221,0.483,2502
|
||||
GFEX,lc2503,碳酸锂2503,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,96600.0,96000.0,0.0,0,1451,7.7,7.7,0.01,19200.0,19200.0,96000.0,50.0,34.61,84.61,0.18,0.441,42.3,92.3,0.22,0.481,2503
|
||||
GFEX,lc2504,碳酸锂2504,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,98100.0,97550.0,0.0,0,1286,7.82,7.82,0.01,19510.0,19510.0,97550.0,50.0,34.36,84.36,0.176,0.432,42.17,92.17,0.216,0.472,2504
|
||||
GFEX,lc2505,碳酸锂2505,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,98800.0,98000.0,0.0,0,2321,7.86,7.86,0.01,19600.0,19600.0,98000.0,50.0,34.29,84.29,0.175,0.43,42.14,92.14,0.215,0.47,2505
|
||||
GFEX,lc2506,碳酸锂2506,lc,碳酸锂,1,50.0,8e-05,0.01,8e-05,0.01,0.0,0.0,0.2,0,0.2,0,99700.0,98300.0,0.0,0,881,7.88,7.88,0.01,19660.0,19660.0,98300.0,50.0,34.24,84.24,0.174,0.428,42.11,92.11,0.214,0.469,2506
|
||||
DCE,lh2407,生猪2407,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.26,0,0.26,0,17670.0,17530.0,17530.0,0,74,56.12,28.07,56.12,72924.8,72924.8,280480.0,80.0,-4.2,75.8,-0.006,0.104,-32.25,47.75,-0.044,0.065,2407
|
||||
DCE,lh2409,生猪2409,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.14,0,0.14,0,17760.0,17510.0,17510.0,0,63950,56.06,28.04,56.06,39222.4,39222.4,280160.0,80.0,-4.1,75.9,-0.01,0.194,-32.12,47.88,-0.082,0.122,2409
|
||||
DCE,lh2411,生猪2411,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.14,0,0.14,0,17755.0,17670.0,17670.0,0,56259,56.57,28.3,56.57,39580.8,39580.8,282720.0,80.0,-4.87,75.13,-0.012,0.19,-33.14,46.86,-0.084,0.118,2411
|
||||
DCE,lh2501,生猪2501,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.14,0,0.14,0,16755.0,16710.0,16710.0,0,29204,53.5,26.76,53.5,37430.4,37430.4,267360.0,80.0,-0.26,79.74,-0.001,0.213,-27.0,53.0,-0.072,0.142,2501
|
||||
DCE,lh2503,生猪2503,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.14,0,0.14,0,15380.0,15290.0,15290.0,0,16024,48.95,24.49,48.95,34249.6,34249.6,244640.0,80.0,6.56,86.56,0.019,0.253,-17.91,62.09,-0.052,0.181,2503
|
||||
DCE,lh2505,生猪2505,lh,生猪,16,5.0,0.0002,0.01,0.0001,0.01,0.0002,0.01,0.14,0,0.14,0,15580.0,15550.0,15550.0,0,2270,49.78,24.9,49.78,34832.0,34832.0,248800.0,80.0,5.31,85.31,0.015,0.245,-19.57,60.43,-0.056,0.173,2505
|
||||
INE,lu2408,lu2408,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4318.0,4292.0,4320.0,462,4851,0.44,0.44,0.44,6480.0,6480.0,43200.0,10.0,9.11,19.11,0.141,0.295,9.11,19.11,0.141,0.295,2408
|
||||
INE,lu2409,lu2409,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4329.0,4294.0,4319.0,24117,63427,0.44,0.44,0.44,6478.5,6478.5,43190.0,10.0,9.11,19.11,0.141,0.295,9.11,19.11,0.141,0.295,2409
|
||||
INE,lu2410,lu2410,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4320.0,4285.0,4309.0,8933,65899,0.44,0.44,0.44,6463.5,6463.5,43090.0,10.0,9.11,19.11,0.141,0.296,9.11,19.11,0.141,0.296,2410
|
||||
INE,lu2411,lu2411,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4304.0,4271.0,4288.0,36,2834,0.44,0.44,0.44,6432.0,6432.0,42880.0,10.0,9.12,19.12,0.142,0.297,9.12,19.12,0.142,0.297,2411
|
||||
INE,lu2412,lu2412,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4272.0,4249.0,4272.0,1,1605,0.44,0.44,0.44,6408.0,6408.0,42720.0,10.0,9.12,19.12,0.142,0.298,9.12,19.12,0.142,0.298,2412
|
||||
INE,lu2501,lu2501,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4251.0,4232.0,4245.0,5,337,0.44,0.44,0.44,6367.5,6367.5,42450.0,10.0,9.13,19.13,0.143,0.3,9.13,19.13,0.143,0.3,2501
|
||||
INE,lu2502,lu2502,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4221.0,4208.0,4230.0,3,1764,0.44,0.44,0.44,6345.0,6345.0,42300.0,10.0,9.13,19.13,0.144,0.301,9.13,19.13,0.144,0.301,2502
|
||||
INE,lu2503,lu2503,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4206.0,4186.0,4186.0,0,188,0.43,0.43,0.43,6279.0,6279.0,41860.0,10.0,9.14,19.14,0.146,0.305,9.14,19.14,0.146,0.305,2503
|
||||
INE,lu2504,lu2504,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4226.0,4226.0,4226.0,0,29,0.44,0.44,0.44,6339.0,6339.0,42260.0,10.0,9.13,19.13,0.144,0.302,9.13,19.13,0.144,0.302,2504
|
||||
INE,lu2505,lu2505,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4157.0,4156.0,4156.0,0,410,0.43,0.43,0.43,6234.0,6234.0,41560.0,10.0,9.14,19.14,0.147,0.307,9.14,19.14,0.147,0.307,2505
|
||||
INE,lu2506,lu2506,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4182.0,4182.0,4182.0,0,18,0.43,0.43,0.43,6273.0,6273.0,41820.0,10.0,9.14,19.14,0.146,0.305,9.14,19.14,0.146,0.305,2506
|
||||
INE,lu2507,lu2507,lu,低硫燃料油,10,1.0,1e-05,0.01,1e-05,0.01,1e-05,0.01,0.15,0,0.15,0,4251.0,4251.0,4251.0,0,15,0.44,0.44,0.44,6376.5,6376.5,42510.0,10.0,9.12,19.12,0.143,0.3,9.12,19.12,0.143,0.3,2507
|
||||
DCE,m2407,豆粕2407,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.26,0,0.26,0,3082.0,3024.0,3024.0,0,4250,1.51,1.51,1.51,7862.4,7862.4,30240.0,10.0,6.98,16.98,0.089,0.216,6.98,16.98,0.089,0.216,2407
|
||||
DCE,m2408,豆粕2408,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3203.0,3201.0,3210.0,5327,56153,1.51,1.51,1.51,4173.0,4173.0,32100.0,10.0,6.98,16.98,0.167,0.407,6.98,16.98,0.167,0.407,2408
|
||||
DCE,m2409,豆粕2409,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3230.0,3225.0,3231.0,253652,1582210,1.51,1.51,1.51,4200.3,4200.3,32310.0,10.0,6.98,16.98,0.166,0.404,6.98,16.98,0.166,0.404,2409
|
||||
DCE,m2411,豆粕2411,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3253.0,3250.0,3256.0,16633,427507,1.51,1.51,1.51,4232.8,4232.8,32560.0,10.0,6.98,16.98,0.165,0.401,6.98,16.98,0.165,0.401,2411
|
||||
DCE,m2412,豆粕2412,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3307.0,3296.0,3299.0,2336,47907,1.51,1.51,1.51,4288.7,4288.7,32990.0,10.0,6.98,16.98,0.163,0.396,6.98,16.98,0.163,0.396,2412
|
||||
DCE,m2501,豆粕2501,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3284.0,3282.0,3287.0,68136,724952,1.51,1.51,1.51,4273.1,4273.1,32870.0,10.0,6.98,16.98,0.163,0.397,6.98,16.98,0.163,0.397,2501
|
||||
DCE,m2503,豆粕2503,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3114.0,3108.0,3116.0,2252,51599,1.51,1.51,1.51,4050.8,4050.8,31160.0,10.0,6.98,16.98,0.172,0.419,6.98,16.98,0.172,0.419,2503
|
||||
DCE,m2505,豆粕2505,m,豆粕,10,1.0,0.0,1.51,0.0,1.51,0.0,1.51,0.13,0,0.13,0,3038.0,3023.0,3034.0,21708,480828,1.51,1.51,1.51,3944.2,3944.2,30340.0,10.0,6.98,16.98,0.177,0.43,6.98,16.98,0.177,0.43,2505
|
||||
SHFE,ni2407,ni2407,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,9.01,0.25,0,0.25,0,135610.0,133000.0,133500.0,480,5010,3.02,3.02,9.02,33375.0,33375.0,133500.0,10.0,3.96,13.96,0.012,0.042,-2.04,7.96,-0.006,0.024,2407
|
||||
SHFE,ni2408,ni2408,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,9.01,0.17,0,0.17,0,134830.0,133430.0,133930.0,4011,26904,3.02,3.02,9.02,22768.1,22768.1,133930.0,10.0,3.96,13.96,0.017,0.061,-2.04,7.96,-0.009,0.035,2408
|
||||
SHFE,ni2409,ni2409,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,9.01,0.17,0,0.17,0,135270.0,133740.0,134260.0,39889,120494,3.02,3.02,9.02,22824.2,22824.2,134260.0,10.0,3.96,13.96,0.017,0.061,-2.04,7.96,-0.009,0.035,2409
|
||||
SHFE,ni2410,ni2410,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,135580.0,134190.0,134690.0,2583,18834,3.02,3.02,3.02,22897.3,22897.3,134690.0,10.0,3.96,13.96,0.017,0.061,3.96,13.96,0.017,0.061,2410
|
||||
SHFE,ni2411,ni2411,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,135930.0,134440.0,135150.0,256,4866,3.02,3.02,3.02,22975.5,22975.5,135150.0,10.0,3.96,13.96,0.017,0.061,3.96,13.96,0.017,0.061,2411
|
||||
SHFE,ni2412,ni2412,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,136370.0,134950.0,135690.0,105,2408,3.02,3.02,3.02,23067.3,23067.3,135690.0,10.0,3.96,13.96,0.017,0.061,3.96,13.96,0.017,0.061,2412
|
||||
SHFE,ni2501,ni2501,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,136800.0,135490.0,136080.0,167,3376,3.02,3.02,3.02,23133.6,23133.6,136080.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2501
|
||||
SHFE,ni2502,ni2502,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,136900.0,136140.0,136140.0,0,298,3.02,3.02,3.02,23143.8,23143.8,136140.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2502
|
||||
SHFE,ni2503,ni2503,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,138020.0,137100.0,137000.0,1,187,3.02,3.02,3.02,23290.0,23290.0,137000.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2503
|
||||
SHFE,ni2504,ni2504,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,138450.0,137290.0,137300.0,2,64,3.02,3.02,3.02,23341.0,23341.0,137300.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2504
|
||||
SHFE,ni2505,ni2505,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,138700.0,138000.0,137800.0,4,169,3.02,3.02,3.02,23426.0,23426.0,137800.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2505
|
||||
SHFE,ni2506,ni2506,ni,镍,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,139250.0,137870.0,137870.0,0,35,3.02,3.02,3.02,23437.9,23437.9,137870.0,10.0,3.96,13.96,0.017,0.06,3.96,13.96,0.017,0.06,2506
|
||||
INE,nr2407,nr2407,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.25,0,0.25,0,12170.0,12170.0,12170.0,10,2070,2.45,2.45,0.01,30425.0,30425.0,121700.0,50.0,45.1,95.1,0.148,0.313,47.54,97.54,0.156,0.321,2407
|
||||
INE,nr2408,nr2408,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.15,0,0.15,0,12205.0,12205.0,12265.0,2801,17779,2.47,2.47,0.01,18397.5,18397.5,122650.0,50.0,45.06,95.06,0.245,0.517,47.52,97.52,0.258,0.53,2408
|
||||
INE,nr2409,nr2409,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12270.0,12280.0,12340.0,20759,67736,2.49,2.49,0.01,16042.0,16042.0,123400.0,50.0,45.03,95.03,0.281,0.592,47.51,97.51,0.296,0.608,2409
|
||||
INE,nr2410,nr2410,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12370.0,12360.0,12420.0,1308,9378,2.5,2.5,0.01,16146.0,16146.0,124200.0,50.0,45.0,95.0,0.279,0.588,47.49,97.49,0.294,0.604,2410
|
||||
INE,nr2411,nr2411,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12445.0,12465.0,12515.0,138,1504,2.52,2.52,0.01,16269.5,16269.5,125150.0,50.0,44.96,94.96,0.276,0.584,47.47,97.47,0.292,0.599,2411
|
||||
INE,nr2412,nr2412,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12525.0,12650.0,12650.0,0,235,2.55,2.55,0.01,16445.0,16445.0,126500.0,50.0,44.9,94.9,0.273,0.577,47.44,97.44,0.289,0.593,2412
|
||||
INE,nr2501,nr2501,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12650.0,12615.0,12700.0,31,457,2.56,2.56,0.01,16510.0,16510.0,127000.0,50.0,44.88,94.88,0.272,0.575,47.43,97.43,0.287,0.59,2501
|
||||
INE,nr2502,nr2502,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12670.0,12670.0,12730.0,1,29,2.56,2.56,0.01,16549.0,16549.0,127300.0,50.0,44.87,94.87,0.271,0.573,47.43,97.43,0.287,0.589,2502
|
||||
INE,nr2503,nr2503,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12800.0,12800.0,12800.0,0,48,2.58,2.58,0.01,16640.0,16640.0,128000.0,50.0,44.84,94.84,0.269,0.57,47.41,97.41,0.285,0.585,2503
|
||||
INE,nr2504,nr2504,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12850.0,12850.0,12850.0,0,25,2.59,2.59,0.01,16705.0,16705.0,128500.0,50.0,44.82,94.82,0.268,0.568,47.4,97.4,0.284,0.583,2504
|
||||
INE,nr2505,nr2505,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,12935.0,12910.0,12910.0,0,34,2.6,2.6,0.01,16783.0,16783.0,129100.0,50.0,44.8,94.8,0.267,0.565,47.39,97.39,0.282,0.58,2505
|
||||
INE,nr2506,nr2506,nr,20号胶,10,5.0,2e-05,0.01,2e-05,0.01,0.0,0.0,0.13,0,0.13,0,13045.0,13045.0,13045.0,0,8,2.63,2.63,0.01,16958.5,16958.5,130450.0,50.0,44.75,94.75,0.264,0.559,47.37,97.37,0.279,0.574,2506
|
||||
DCE,p2407,棕榈油2407,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.25,0,0.25,0,7834.0,7834.0,7834.0,0,0,2.51,2.51,2.51,19585.0,19585.0,78340.0,20.0,14.97,34.97,0.076,0.179,14.97,34.97,0.076,0.179,2407
|
||||
DCE,p2408,棕榈油2408,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7692.0,7634.0,7690.0,29,1722,2.51,2.51,2.51,9997.0,9997.0,76900.0,20.0,14.97,34.97,0.15,0.35,14.97,34.97,0.15,0.35,2408
|
||||
DCE,p2409,棕榈油2409,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7648.0,7612.0,7664.0,206793,432072,2.51,2.51,2.51,9963.2,9963.2,76640.0,20.0,14.97,34.97,0.15,0.351,14.97,34.97,0.15,0.351,2409
|
||||
DCE,p2410,棕榈油2410,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7622.0,7592.0,7642.0,244,2341,2.51,2.51,2.51,9934.6,9934.6,76420.0,20.0,14.97,34.97,0.151,0.352,14.97,34.97,0.151,0.352,2410
|
||||
DCE,p2411,棕榈油2411,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7618.0,7584.0,7622.0,83,463,2.51,2.51,2.51,9908.6,9908.6,76220.0,20.0,14.97,34.97,0.151,0.353,14.97,34.97,0.151,0.353,2411
|
||||
DCE,p2412,棕榈油2412,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7606.0,7566.0,7600.0,34,325,2.51,2.51,2.51,9880.0,9880.0,76000.0,20.0,14.97,34.97,0.152,0.354,14.97,34.97,0.152,0.354,2412
|
||||
DCE,p2501,棕榈油2501,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7558.0,7542.0,7594.0,28513,229001,2.51,2.51,2.51,9872.2,9872.2,75940.0,20.0,14.97,34.97,0.152,0.354,14.97,34.97,0.152,0.354,2501
|
||||
DCE,p2502,棕榈油2502,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7562.0,7540.0,7540.0,0,309,2.51,2.51,2.51,9802.0,9802.0,75400.0,20.0,14.97,34.97,0.153,0.357,14.97,34.97,0.153,0.357,2502
|
||||
DCE,p2503,棕榈油2503,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7538.0,7550.0,7590.0,19,2278,2.51,2.51,2.51,9867.0,9867.0,75900.0,20.0,14.97,34.97,0.152,0.354,14.97,34.97,0.152,0.354,2503
|
||||
DCE,p2504,棕榈油2504,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7570.0,7544.0,7586.0,6,223,2.51,2.51,2.51,9861.8,9861.8,75860.0,20.0,14.97,34.97,0.152,0.355,14.97,34.97,0.152,0.355,2504
|
||||
DCE,p2505,棕榈油2505,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7560.0,7552.0,7600.0,1582,33844,2.51,2.51,2.51,9880.0,9880.0,76000.0,20.0,14.97,34.97,0.152,0.354,14.97,34.97,0.152,0.354,2505
|
||||
DCE,p2506,棕榈油2506,p,棕榈油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7554.0,7542.0,7576.0,7,175,2.51,2.51,2.51,9848.8,9848.8,75760.0,20.0,14.97,34.97,0.152,0.355,14.97,34.97,0.152,0.355,2506
|
||||
SHFE,pb2407,pb2407,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.25,0,0.25,0,19545.0,19490.0,19480.0,285,7070,3.91,3.91,0.01,24350.0,24350.0,97400.0,25.0,17.18,42.18,0.071,0.173,21.08,46.08,0.087,0.189,2407
|
||||
SHFE,pb2408,pb2408,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.15,0,0.15,0,19480.0,19430.0,19440.0,34120,87149,3.9,3.9,0.01,14580.0,14580.0,97200.0,25.0,17.19,42.19,0.118,0.289,21.09,46.09,0.145,0.316,2408
|
||||
SHFE,pb2409,pb2409,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,19235.0,19175.0,19200.0,17336,62901,3.86,3.86,0.01,13440.0,13440.0,96000.0,25.0,17.29,42.29,0.129,0.315,21.14,46.14,0.157,0.343,2409
|
||||
SHFE,pb2410,pb2410,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,19025.0,18960.0,18975.0,1543,11593,3.81,3.81,0.01,13282.5,13282.5,94875.0,25.0,17.38,42.38,0.131,0.319,21.18,46.18,0.159,0.348,2410
|
||||
SHFE,pb2411,pb2411,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18885.0,18815.0,18825.0,148,3047,3.78,3.78,0.01,13177.5,13177.5,94125.0,25.0,17.44,42.44,0.132,0.322,21.21,46.21,0.161,0.351,2411
|
||||
SHFE,pb2412,pb2412,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18840.0,18765.0,18815.0,15,811,3.78,3.78,0.01,13170.5,13170.5,94075.0,25.0,17.44,42.44,0.132,0.322,21.22,46.22,0.161,0.351,2412
|
||||
SHFE,pb2501,pb2501,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18760.0,18690.0,18685.0,14,667,3.75,3.75,0.01,13079.5,13079.5,93425.0,25.0,17.49,42.49,0.134,0.325,21.24,46.24,0.162,0.354,2501
|
||||
SHFE,pb2502,pb2502,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18730.0,18665.0,18650.0,3,199,3.75,3.75,0.01,13055.0,13055.0,93250.0,25.0,17.51,42.51,0.134,0.326,21.25,46.25,0.163,0.354,2502
|
||||
SHFE,pb2503,pb2503,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18690.0,18630.0,18630.0,0,157,3.74,3.74,0.01,13041.0,13041.0,93150.0,25.0,17.52,42.52,0.134,0.326,21.25,46.25,0.163,0.355,2503
|
||||
SHFE,pb2504,pb2504,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18620.0,18600.0,18575.0,6,108,3.73,3.73,0.01,13002.5,13002.5,92875.0,25.0,17.54,42.54,0.135,0.327,21.26,46.26,0.164,0.356,2504
|
||||
SHFE,pb2505,pb2505,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18555.0,18545.0,18545.0,14,208,3.72,3.72,0.01,12981.5,12981.5,92725.0,25.0,17.55,42.55,0.135,0.328,21.27,46.27,0.164,0.356,2505
|
||||
SHFE,pb2506,pb2506,pb,铅,5,5.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,18530.0,18530.0,18545.0,2,131,3.72,3.72,0.01,12981.5,12981.5,92725.0,25.0,17.55,42.55,0.135,0.328,21.27,46.27,0.164,0.356,2506
|
||||
DCE,pg2407,液化石油气2407,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.25,0,0.25,0,5001.0,5001.0,5001.0,0,1195,6.02,6.02,6.02,25005.0,25005.0,100020.0,20.0,7.97,27.97,0.032,0.112,7.97,27.97,0.032,0.112,2407
|
||||
DCE,pg2408,液化石油气2408,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4628.0,4595.0,4624.0,385,2452,6.02,6.02,6.02,12022.4,12022.4,92480.0,20.0,7.97,27.97,0.066,0.233,7.97,27.97,0.066,0.233,2408
|
||||
DCE,pg2409,液化石油气2409,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4548.0,4520.0,4553.0,24272,77938,6.02,6.02,6.02,11837.8,11837.8,91060.0,20.0,7.97,27.97,0.067,0.236,7.97,27.97,0.067,0.236,2409
|
||||
DCE,pg2410,液化石油气2410,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,5174.0,5148.0,5183.0,2463,37866,6.02,6.02,6.02,13475.8,13475.8,103660.0,20.0,7.97,27.97,0.059,0.208,7.97,27.97,0.059,0.208,2410
|
||||
DCE,pg2411,液化石油气2411,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,5091.0,5073.0,5103.0,186,5008,6.02,6.02,6.02,13267.8,13267.8,102060.0,20.0,7.97,27.97,0.06,0.211,7.97,27.97,0.06,0.211,2411
|
||||
DCE,pg2412,液化石油气2412,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,5006.0,4981.0,5013.0,51,388,6.02,6.02,6.02,13033.8,13033.8,100260.0,20.0,7.97,27.97,0.061,0.215,7.97,27.97,0.061,0.215,2412
|
||||
DCE,pg2501,液化石油气2501,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4906.0,4882.0,4901.0,36,1775,6.02,6.02,6.02,12742.6,12742.6,98020.0,20.0,7.97,27.97,0.063,0.219,7.97,27.97,0.063,0.219,2501
|
||||
DCE,pg2502,液化石油气2502,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4799.0,4755.0,4755.0,0,144,6.02,6.02,6.02,12363.0,12363.0,95100.0,20.0,7.97,27.97,0.064,0.226,7.97,27.97,0.064,0.226,2502
|
||||
DCE,pg2503,液化石油气2503,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4611.0,4593.0,4617.0,14,919,6.02,6.02,6.02,12004.2,12004.2,92340.0,20.0,7.97,27.97,0.066,0.233,7.97,27.97,0.066,0.233,2503
|
||||
DCE,pg2504,液化石油气2504,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4964.0,4940.0,4969.0,3,755,6.02,6.02,6.02,12919.4,12919.4,99380.0,20.0,7.97,27.97,0.062,0.216,7.97,27.97,0.062,0.216,2504
|
||||
DCE,pg2505,液化石油气2505,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4811.0,4810.0,4826.0,2,327,6.02,6.02,6.02,12547.6,12547.6,96520.0,20.0,7.97,27.97,0.064,0.223,7.97,27.97,0.064,0.223,2505
|
||||
DCE,pg2506,液化石油气2506,pg,液化石油气,20,1.0,0.0,6.01,0.0,6.01,0.0,6.01,0.13,0,0.13,0,4825.0,4825.0,4825.0,0,7,6.02,6.02,6.02,12545.0,12545.0,96500.0,20.0,7.97,27.97,0.064,0.223,7.97,27.97,0.064,0.223,2506
|
||||
DCE,pp2407,聚丙烯2407,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.26,0,0.26,0,7650.0,7627.0,7627.0,0,1142,1.01,1.01,1.01,9915.1,9915.1,38135.0,5.0,2.98,7.98,0.03,0.08,2.98,7.98,0.03,0.08,2407
|
||||
DCE,pp2408,聚丙烯2408,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7662.0,7641.0,7657.0,358,5492,1.01,1.01,1.01,4977.05,4977.05,38285.0,5.0,2.98,7.98,0.06,0.16,2.98,7.98,0.06,0.16,2408
|
||||
DCE,pp2409,聚丙烯2409,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7689.0,7669.0,7693.0,85480,401207,1.01,1.01,1.01,5000.45,5000.45,38465.0,5.0,2.98,7.98,0.06,0.159,2.98,7.98,0.06,0.159,2409
|
||||
DCE,pp2410,聚丙烯2410,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7685.0,7681.0,7703.0,336,2991,1.01,1.01,1.01,5006.95,5006.95,38515.0,5.0,2.98,7.98,0.059,0.159,2.98,7.98,0.059,0.159,2410
|
||||
DCE,pp2411,聚丙烯2411,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7703.0,7692.0,7732.0,6,171,1.01,1.01,1.01,5025.8,5025.8,38660.0,5.0,2.98,7.98,0.059,0.159,2.98,7.98,0.059,0.159,2411
|
||||
DCE,pp2412,聚丙烯2412,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7732.0,7696.0,7743.0,12,118,1.01,1.01,1.01,5032.95,5032.95,38715.0,5.0,2.98,7.98,0.059,0.158,2.98,7.98,0.059,0.158,2412
|
||||
DCE,pp2501,聚丙烯2501,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7716.0,7697.0,7722.0,14070,70676,1.01,1.01,1.01,5019.3,5019.3,38610.0,5.0,2.98,7.98,0.059,0.159,2.98,7.98,0.059,0.159,2501
|
||||
DCE,pp2502,聚丙烯2502,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7693.0,7679.0,7712.0,1,102,1.01,1.01,1.01,5012.8,5012.8,38560.0,5.0,2.98,7.98,0.059,0.159,2.98,7.98,0.059,0.159,2502
|
||||
DCE,pp2503,聚丙烯2503,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7692.0,7687.0,7687.0,0,31,1.01,1.01,1.01,4996.55,4996.55,38435.0,5.0,2.98,7.98,0.06,0.16,2.98,7.98,0.06,0.16,2503
|
||||
DCE,pp2504,聚丙烯2504,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7770.0,7770.0,7770.0,0,22,1.01,1.01,1.01,5050.5,5050.5,38850.0,5.0,2.98,7.98,0.059,0.158,2.98,7.98,0.059,0.158,2504
|
||||
DCE,pp2505,聚丙烯2505,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7692.0,7666.0,7691.0,722,6984,1.01,1.01,1.01,4999.15,4999.15,38455.0,5.0,2.98,7.98,0.06,0.16,2.98,7.98,0.06,0.16,2505
|
||||
DCE,pp2506,聚丙烯2506,pp,聚丙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,7709.0,7709.0,7709.0,0,12,1.01,1.01,1.01,5010.85,5010.85,38545.0,5.0,2.98,7.98,0.059,0.159,2.98,7.98,0.059,0.159,2506
|
||||
SHFE,rb2407,rb2407,rb,螺纹钢,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.26,0,0.26,0,3311.0,3311.0,3298.0,90,2010,3.31,3.31,3.31,8574.8,8574.8,32980.0,10.0,3.38,13.38,0.039,0.156,3.38,13.38,0.039,0.156,2407
|
||||
SHFE,rb2408,rb2408,rb,螺纹钢,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.16,0,0.16,0,3319.0,3301.0,3318.0,107,5267,3.33,3.33,3.33,5308.8,5308.8,33180.0,10.0,3.34,13.34,0.063,0.251,3.34,13.34,0.063,0.251,2408
|
||||
SHFE,rb2409,rb2409,rb,螺纹钢,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3396.0,3380.0,3400.0,2916,39489,3.41,3.41,3.41,4420.0,4420.0,34000.0,10.0,3.18,13.18,0.072,0.298,3.18,13.18,0.072,0.298,2409
|
||||
SHFE,rb2410,rb2410,rb,螺纹钢,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3492.0,3478.0,3493.0,306361,2243085,3.51,3.51,3.51,4540.9,4540.9,34930.0,10.0,2.99,12.99,0.066,0.286,2.99,12.99,0.066,0.286,2410
|
||||
SHFE,rb2411,rb2411,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3520.0,3501.0,3518.0,15855,303589,0.72,0.72,0.72,4573.4,4573.4,35180.0,10.0,8.57,18.57,0.187,0.406,8.57,18.57,0.187,0.406,2411
|
||||
SHFE,rb2412,rb2412,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3556.0,3532.0,3549.0,61,67840,0.72,0.72,0.72,4613.7,4613.7,35490.0,10.0,8.56,18.56,0.185,0.402,8.56,18.56,0.185,0.402,2412
|
||||
SHFE,rb2501,rb2501,rb,螺纹钢,10,1.0,0.0003,0.01,0.0003,0.01,0.0003,0.01,0.13,0,0.13,0,3581.0,3562.0,3578.0,15677,333691,10.75,10.75,10.75,4651.4,4651.4,35780.0,10.0,-11.49,-1.49,-0.247,-0.032,-11.49,-1.49,-0.247,-0.032,2501
|
||||
SHFE,rb2502,rb2502,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3593.0,3576.0,3586.0,7,3217,0.73,0.73,0.73,4661.8,4661.8,35860.0,10.0,8.54,18.54,0.183,0.398,8.54,18.54,0.183,0.398,2502
|
||||
SHFE,rb2503,rb2503,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3608.0,3588.0,3609.0,167,4086,0.73,0.73,0.73,4691.7,4691.7,36090.0,10.0,8.53,18.53,0.182,0.395,8.53,18.53,0.182,0.395,2503
|
||||
SHFE,rb2504,rb2504,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3624.0,3604.0,3618.0,23,342,0.74,0.74,0.74,4703.4,4703.4,36180.0,10.0,8.53,18.53,0.181,0.394,8.53,18.53,0.181,0.394,2504
|
||||
SHFE,rb2505,rb2505,rb,螺纹钢,10,1.0,0.0001,0.01,0.0001,0.01,0.0001,0.01,0.13,0,0.13,0,3638.0,3617.0,3639.0,1614,33344,3.65,3.65,3.65,4730.7,4730.7,36390.0,10.0,2.7,12.7,0.057,0.268,2.7,12.7,0.057,0.268,2505
|
||||
SHFE,rb2506,rb2506,rb,螺纹钢,10,1.0,2e-05,0.01,2e-05,0.01,2e-05,0.01,0.13,0,0.13,0,3653.0,3640.0,3651.0,14,158,0.74,0.74,0.74,4746.3,4746.3,36510.0,10.0,8.52,18.52,0.179,0.39,8.52,18.52,0.179,0.39,2506
|
||||
DCE,rr2407,粳米2407,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.25,0,0.25,0,3425.0,3425.0,3425.0,0,0,1.01,1.01,1.01,8562.5,8562.5,34250.0,10.0,7.98,17.98,0.093,0.21,7.98,17.98,0.093,0.21,2407
|
||||
DCE,rr2408,粳米2408,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3485.0,3488.0,3488.0,762,1954,1.01,1.01,1.01,3836.8,3836.8,34880.0,10.0,7.98,17.98,0.208,0.469,7.98,17.98,0.208,0.469,2408
|
||||
DCE,rr2409,粳米2409,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3485.0,3487.0,3485.0,602,5229,1.01,1.01,1.01,3833.5,3833.5,34850.0,10.0,7.98,17.98,0.208,0.469,7.98,17.98,0.208,0.469,2409
|
||||
DCE,rr2410,粳米2410,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3504.0,3503.0,3504.0,398,1271,1.01,1.01,1.01,3854.4,3854.4,35040.0,10.0,7.98,17.98,0.207,0.466,7.98,17.98,0.207,0.466,2410
|
||||
DCE,rr2411,粳米2411,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3528.0,3531.0,3526.0,6,159,1.01,1.01,1.01,3878.6,3878.6,35260.0,10.0,7.98,17.98,0.206,0.463,7.98,17.98,0.206,0.463,2411
|
||||
DCE,rr2412,粳米2412,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3534.0,3535.0,3530.0,3,90,1.01,1.01,1.01,3883.0,3883.0,35300.0,10.0,7.98,17.98,0.205,0.463,7.98,17.98,0.205,0.463,2412
|
||||
DCE,rr2501,粳米2501,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3534.0,3533.0,3534.0,3,119,1.01,1.01,1.01,3887.4,3887.4,35340.0,10.0,7.98,17.98,0.205,0.462,7.98,17.98,0.205,0.462,2501
|
||||
DCE,rr2502,粳米2502,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3561.0,3561.0,3544.0,1,129,1.01,1.01,1.01,3898.4,3898.4,35440.0,10.0,7.98,17.98,0.205,0.461,7.98,17.98,0.205,0.461,2502
|
||||
DCE,rr2503,粳米2503,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3556.0,3556.0,3540.0,1,42,1.01,1.01,1.01,3894.0,3894.0,35400.0,10.0,7.98,17.98,0.205,0.462,7.98,17.98,0.205,0.462,2503
|
||||
DCE,rr2504,粳米2504,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3560.0,3569.0,3569.0,0,10,1.01,1.01,1.01,3925.9,3925.9,35690.0,10.0,7.98,17.98,0.203,0.458,7.98,17.98,0.203,0.458,2504
|
||||
DCE,rr2505,粳米2505,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3590.0,3591.0,3590.0,6,1981,1.01,1.01,1.01,3949.0,3949.0,35900.0,10.0,7.98,17.98,0.202,0.455,7.98,17.98,0.202,0.455,2505
|
||||
DCE,rr2506,粳米2506,rr,粳米,10,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.11,0,0.11,0,3585.0,3585.0,3585.0,0,19,1.01,1.01,1.01,3943.5,3943.5,35850.0,10.0,7.98,17.98,0.202,0.456,7.98,17.98,0.202,0.456,2506
|
||||
SHFE,ru2407,ru2407,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.25,0,0.25,0,14430.0,14430.0,14430.0,0,620,3.02,3.02,0.01,36075.0,36075.0,144300.0,50.0,43.96,93.96,0.122,0.26,46.97,96.97,0.13,0.269,2407
|
||||
SHFE,ru2408,ru2408,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.15,0,0.15,0,14560.0,14555.0,14620.0,65,1356,3.02,3.02,0.01,21930.0,21930.0,146200.0,50.0,43.96,93.96,0.2,0.428,46.97,96.97,0.214,0.442,2408
|
||||
SHFE,ru2409,ru2409,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,12.01,0.13,0,0.13,0,14645.0,14625.0,14690.0,73828,213058,3.02,3.02,12.02,19097.0,19097.0,146900.0,50.0,43.96,93.96,0.23,0.492,34.96,84.96,0.183,0.445,2409
|
||||
SHFE,ru2410,ru2410,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,14705.0,14680.0,14755.0,14,320,3.02,3.02,0.01,19181.5,19181.5,147550.0,50.0,43.96,93.96,0.229,0.49,46.97,96.97,0.245,0.506,2410
|
||||
SHFE,ru2411,ru2411,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,14715.0,14695.0,14760.0,377,4200,3.02,3.02,0.01,19188.0,19188.0,147600.0,50.0,43.96,93.96,0.229,0.49,46.97,96.97,0.245,0.505,2411
|
||||
SHFE,ru2501,ru2501,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,15815.0,15805.0,15865.0,14440,57191,3.02,3.02,0.01,20624.5,20624.5,158650.0,50.0,43.96,93.96,0.213,0.456,46.97,96.97,0.228,0.47,2501
|
||||
SHFE,ru2503,ru2503,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,15840.0,15835.0,15900.0,1,231,3.02,3.02,0.01,20670.0,20670.0,159000.0,50.0,43.96,93.96,0.213,0.455,46.97,96.97,0.227,0.469,2503
|
||||
SHFE,ru2504,ru2504,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,15930.0,15930.0,15930.0,0,20,3.02,3.02,0.01,20709.0,20709.0,159300.0,50.0,43.96,93.96,0.212,0.454,46.97,96.97,0.227,0.468,2504
|
||||
SHFE,ru2505,ru2505,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,15945.0,15940.0,15990.0,302,5246,3.02,3.02,0.01,20787.0,20787.0,159900.0,50.0,43.96,93.96,0.211,0.452,46.97,96.97,0.226,0.466,2505
|
||||
SHFE,ru2506,ru2506,ru,天然橡胶,10,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.13,0,0.13,0,15960.0,15960.0,16000.0,1,21,3.02,3.02,0.01,20800.0,20800.0,160000.0,50.0,43.96,93.96,0.211,0.452,46.97,96.97,0.226,0.466,2506
|
||||
INE,sc2408,sc2408,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,621.3,614.5,617.9,20934,25742,20.05,20.05,0.04,92685.0,92685.0,617900.0,100.0,59.91,159.91,0.065,0.173,79.92,179.92,0.086,0.194,2408
|
||||
INE,sc2409,sc2409,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,617.0,612.0,615.7,9806,20024,20.05,20.05,0.04,92355.0,92355.0,615700.0,100.0,59.91,159.91,0.065,0.173,79.92,179.92,0.087,0.195,2409
|
||||
INE,sc2410,sc2410,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,613.1,609.3,612.2,1699,6335,20.05,20.05,0.04,91830.0,91830.0,612200.0,100.0,59.91,159.91,0.065,0.174,79.92,179.92,0.087,0.196,2410
|
||||
INE,sc2411,sc2411,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,608.5,605.4,608.3,97,633,20.05,20.05,0.04,91245.0,91245.0,608300.0,100.0,59.91,159.91,0.066,0.175,79.92,179.92,0.088,0.197,2411
|
||||
INE,sc2412,sc2412,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,604.9,601.1,603.0,8,377,20.05,20.05,0.04,90450.0,90450.0,603000.0,100.0,59.91,159.91,0.066,0.177,79.92,179.92,0.088,0.199,2412
|
||||
INE,sc2501,sc2501,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,600.0,596.6,600.1,16,134,20.05,20.05,0.04,90015.0,90015.0,600100.0,100.0,59.91,159.91,0.067,0.178,79.92,179.92,0.089,0.2,2501
|
||||
INE,sc2502,sc2502,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,597.7,599.7,595.0,3,37,20.05,20.05,0.04,89250.0,89250.0,595000.0,100.0,59.91,159.91,0.067,0.179,79.92,179.92,0.09,0.202,2502
|
||||
INE,sc2503,sc2503,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,592.9,592.1,591.9,1,47,20.05,20.05,0.04,88785.0,88785.0,591900.0,100.0,59.91,159.91,0.067,0.18,79.92,179.92,0.09,0.203,2503
|
||||
INE,sc2504,sc2504,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,592.7,592.7,592.7,0,22,20.05,20.05,0.04,88905.0,88905.0,592700.0,100.0,59.91,159.91,0.067,0.18,79.92,179.92,0.09,0.202,2504
|
||||
INE,sc2505,sc2505,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,586.7,585.0,585.0,0,27,20.05,20.05,0.04,87750.0,87750.0,585000.0,100.0,59.91,159.91,0.068,0.182,79.92,179.92,0.091,0.205,2505
|
||||
INE,sc2506,sc2506,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,586.8,586.8,586.8,0,56,20.05,20.05,0.04,88020.0,88020.0,586800.0,100.0,59.91,159.91,0.068,0.182,79.92,179.92,0.091,0.204,2506
|
||||
INE,sc2507,sc2507,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,592.8,592.8,592.8,0,5,20.05,20.05,0.04,88920.0,88920.0,592800.0,100.0,59.91,159.91,0.067,0.18,79.92,179.92,0.09,0.202,2507
|
||||
INE,sc2509,sc2509,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,584.6,584.6,584.6,0,13,20.05,20.05,0.04,87690.0,87690.0,584600.0,100.0,59.91,159.91,0.068,0.182,79.92,179.92,0.091,0.205,2509
|
||||
INE,sc2512,sc2512,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,578.7,578.7,578.7,0,10,20.04,20.04,0.03,86805.0,86805.0,578700.0,100.0,59.91,159.91,0.069,0.184,79.92,179.92,0.092,0.207,2512
|
||||
INE,sc2603,sc2603,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,579.7,579.7,579.7,0,12,20.04,20.04,0.03,86955.0,86955.0,579700.0,100.0,59.91,159.91,0.069,0.184,79.92,179.92,0.092,0.207,2603
|
||||
INE,sc2606,sc2606,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,577.0,575.3,575.3,0,73,20.04,20.04,0.03,86295.0,86295.0,575300.0,100.0,59.91,159.91,0.069,0.185,79.92,179.92,0.093,0.208,2606
|
||||
INE,sc2609,sc2609,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,573.9,573.9,573.9,0,20,20.04,20.04,0.03,86085.0,86085.0,573900.0,100.0,59.91,159.91,0.07,0.186,79.92,179.92,0.093,0.209,2609
|
||||
INE,sc2612,sc2612,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,567.0,567.0,567.0,0,12,20.04,20.04,0.03,85050.0,85050.0,567000.0,100.0,59.91,159.91,0.07,0.188,79.92,179.92,0.094,0.212,2612
|
||||
INE,sc2703,sc2703,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,570.0,570.0,570.0,0,43,20.04,20.04,0.03,85500.0,85500.0,570000.0,100.0,59.91,159.91,0.07,0.187,79.92,179.92,0.093,0.21,2703
|
||||
INE,sc2706,sc2706,sc,原油,1000,0.1,0.0,20.01,0.0,20.01,0.0,0.0,0.15,0,0.15,0,568.0,568.0,568.0,0,24,20.04,20.04,0.03,85200.0,85200.0,568000.0,100.0,59.91,159.91,0.07,0.188,79.92,179.92,0.094,0.211,2706
|
||||
GFEX,si2407,工业硅2407,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.27,0,0.27,0,11030.0,11000.0,0.0,0,407,5.51,5.51,0.0,14850.0,14850.0,55000.0,25.0,13.97,38.97,0.094,0.262,19.48,44.48,0.131,0.3,2407
|
||||
GFEX,si2408,工业硅2408,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,11125.0,11020.0,0.0,0,23016,5.52,5.52,0.0,8816.0,8816.0,55100.0,25.0,13.95,38.95,0.158,0.442,19.47,44.47,0.221,0.504,2408
|
||||
GFEX,si2409,工业硅2409,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,11170.0,11110.0,0.0,0,236506,5.57,5.57,0.0,8888.0,8888.0,55550.0,25.0,13.86,38.86,0.156,0.437,19.43,44.43,0.219,0.5,2409
|
||||
GFEX,si2410,工业硅2410,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,11260.0,11190.0,0.0,0,38111,5.61,5.61,0.0,8952.0,8952.0,55950.0,25.0,13.78,38.78,0.154,0.433,19.39,44.39,0.217,0.496,2410
|
||||
GFEX,si2411,工业硅2411,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,11355.0,11265.0,0.0,0,36834,5.65,5.65,0.0,9012.0,9012.0,56325.0,25.0,13.71,38.71,0.152,0.43,19.35,44.35,0.215,0.492,2411
|
||||
GFEX,si2412,工业硅2412,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14190.0,14145.0,0.0,0,29263,7.09,7.09,0.0,11316.0,11316.0,70725.0,25.0,10.83,35.83,0.096,0.317,17.91,42.91,0.158,0.379,2412
|
||||
GFEX,si2501,工业硅2501,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14250.0,14230.0,0.0,0,1147,7.13,7.13,0.0,11384.0,11384.0,71150.0,25.0,10.74,35.74,0.094,0.314,17.87,42.87,0.157,0.377,2501
|
||||
GFEX,si2502,工业硅2502,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14300.0,14300.0,0.0,0,43,7.16,7.16,0.0,11440.0,11440.0,71500.0,25.0,10.67,35.67,0.093,0.312,17.83,42.83,0.156,0.374,2502
|
||||
GFEX,si2503,工业硅2503,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14440.0,14440.0,0.0,0,19,7.23,7.23,0.0,11552.0,11552.0,72200.0,25.0,10.53,35.53,0.091,0.308,17.76,42.76,0.154,0.37,2503
|
||||
GFEX,si2504,工业硅2504,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14420.0,14425.0,0.0,0,12,7.23,7.23,0.0,11540.0,11540.0,72125.0,25.0,10.55,35.55,0.091,0.308,17.77,42.77,0.154,0.371,2504
|
||||
GFEX,si2505,工业硅2505,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14620.0,14530.0,0.0,0,152,7.28,7.28,0.0,11624.0,11624.0,72650.0,25.0,10.44,35.44,0.09,0.305,17.72,42.72,0.152,0.367,2505
|
||||
GFEX,si2506,工业硅2506,si,工业硅,5,5.0,0.0001,0.01,0.0001,0.01,0.0,0.0,0.16,0,0.16,0,14620.0,14580.0,0.0,0,50,7.3,7.3,0.0,11664.0,11664.0,72900.0,25.0,10.39,35.39,0.089,0.303,17.69,42.69,0.152,0.366,2506
|
||||
SHFE,sn2407,sn2407,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,36.01,0.25,0,0.25,0,278430.0,276700.0,281910.0,10,2836,3.03,3.03,36.03,70477.5,70477.5,281910.0,10.0,3.95,13.95,0.006,0.02,-29.05,-19.05,-0.041,-0.027,2407
|
||||
SHFE,sn2408,sn2408,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278190.0,277240.0,281820.0,63023,37314,3.03,3.03,3.03,47909.4,47909.4,281820.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2408
|
||||
SHFE,sn2409,sn2409,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278920.0,277970.0,282360.0,18516,24096,3.03,3.03,3.03,48001.2,48001.2,282360.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2409
|
||||
SHFE,sn2410,sn2410,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,279210.0,278210.0,282570.0,2649,6022,3.03,3.03,3.03,48036.9,48036.9,282570.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2410
|
||||
SHFE,sn2411,sn2411,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278900.0,278060.0,282120.0,125,1845,3.03,3.03,3.03,47960.4,47960.4,282120.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2411
|
||||
SHFE,sn2412,sn2412,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278760.0,277900.0,282240.0,87,650,3.03,3.03,3.03,47980.8,47980.8,282240.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2412
|
||||
SHFE,sn2501,sn2501,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278920.0,277480.0,281930.0,64,687,3.03,3.03,3.03,47928.1,47928.1,281930.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2501
|
||||
SHFE,sn2502,sn2502,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,277720.0,277570.0,281030.0,3,118,3.03,3.03,3.03,47775.1,47775.1,281030.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2502
|
||||
SHFE,sn2503,sn2503,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,278360.0,278800.0,281480.0,2,81,3.03,3.03,3.03,47851.6,47851.6,281480.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2503
|
||||
SHFE,sn2504,sn2504,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,277810.0,277860.0,279190.0,1,134,3.03,3.03,3.03,47462.3,47462.3,279190.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2504
|
||||
SHFE,sn2505,sn2505,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,277590.0,277110.0,280980.0,3,41,3.03,3.03,3.03,47766.6,47766.6,280980.0,10.0,3.95,13.95,0.008,0.029,3.95,13.95,0.008,0.029,2505
|
||||
SHFE,sn2506,sn2506,sn,锡,1,10.0,0.0,3.01,0.0,3.01,0.0,3.01,0.17,0,0.17,0,276740.0,276740.0,276740.0,0,11,3.03,3.03,3.03,47045.8,47045.8,276740.0,10.0,3.95,13.95,0.008,0.03,3.95,13.95,0.008,0.03,2506
|
||||
SHFE,sp2407,sp2407,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.25,0,0.25,0,5670.0,5670.0,5670.0,0,4182,2.85,2.85,0.0,14175.0,14175.0,56700.0,20.0,14.3,34.3,0.101,0.242,17.15,37.15,0.121,0.262,2407
|
||||
SHFE,sp2408,sp2408,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.15,0,0.15,0,5662.0,5656.0,5644.0,140,5218,2.84,2.84,0.0,8466.0,8466.0,56440.0,20.0,14.33,34.33,0.169,0.405,17.16,37.16,0.203,0.439,2408
|
||||
SHFE,sp2409,sp2409,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5694.0,5678.0,5666.0,60856,264545,2.85,2.85,0.0,7365.8,7365.8,56660.0,20.0,14.31,34.31,0.194,0.466,17.15,37.15,0.233,0.504,2409
|
||||
SHFE,sp2410,sp2410,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5708.0,5692.0,5692.0,14,1678,2.86,2.86,0.0,7399.6,7399.6,56920.0,20.0,14.28,34.28,0.193,0.463,17.14,37.14,0.232,0.502,2410
|
||||
SHFE,sp2411,sp2411,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5726.0,5710.0,5704.0,1769,62761,2.87,2.87,0.0,7415.2,7415.2,57040.0,20.0,14.27,34.27,0.192,0.462,17.13,37.13,0.231,0.501,2411
|
||||
SHFE,sp2412,sp2412,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5730.0,5722.0,5706.0,18,2262,2.87,2.87,0.0,7417.8,7417.8,57060.0,20.0,14.27,34.27,0.192,0.462,17.13,37.13,0.231,0.501,2412
|
||||
SHFE,sp2501,sp2501,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5878.0,5856.0,5862.0,2510,39787,2.94,2.94,0.0,7620.6,7620.6,58620.0,20.0,14.11,34.11,0.185,0.448,17.05,37.05,0.224,0.486,2501
|
||||
SHFE,sp2502,sp2502,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5888.0,5850.0,5850.0,0,34,2.94,2.94,0.0,7605.0,7605.0,58500.0,20.0,14.12,34.12,0.186,0.449,17.06,37.06,0.224,0.487,2502
|
||||
SHFE,sp2503,sp2503,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5918.0,5872.0,5880.0,7,1223,2.95,2.95,0.0,7644.0,7644.0,58800.0,20.0,14.09,34.09,0.184,0.446,17.04,37.04,0.223,0.485,2503
|
||||
SHFE,sp2504,sp2504,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5880.0,5874.0,5888.0,10,1091,2.96,2.96,0.0,7654.4,7654.4,58880.0,20.0,14.08,34.08,0.184,0.445,17.04,37.04,0.223,0.484,2504
|
||||
SHFE,sp2505,sp2505,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5912.0,5892.0,5910.0,70,341,2.97,2.97,0.0,7683.0,7683.0,59100.0,20.0,14.06,34.06,0.183,0.443,17.03,37.03,0.222,0.482,2505
|
||||
SHFE,sp2506,sp2506,sp,漂针浆,10,2.0,5e-05,0.01,5e-05,0.01,0.0,0.0,0.13,0,0.13,0,5928.0,5902.0,5910.0,2,51,2.97,2.97,0.0,7683.0,7683.0,59100.0,20.0,14.06,34.06,0.183,0.443,17.03,37.03,0.222,0.482,2506
|
||||
SHFE,ss2407,ss2407,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.26,0,0.26,0,13735.0,13720.0,13720.0,0,1728,2.01,2.01,0.0,17836.0,17836.0,68600.0,25.0,20.97,45.97,0.118,0.258,22.98,47.98,0.129,0.269,2407
|
||||
SHFE,ss2408,ss2408,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.16,0,0.16,0,13795.0,13785.0,13765.0,136,6030,2.01,2.01,0.0,11012.0,11012.0,68825.0,25.0,20.97,45.97,0.19,0.417,22.98,47.98,0.209,0.436,2408
|
||||
SHFE,ss2409,ss2409,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,13980.0,13930.0,13935.0,37326,142183,2.01,2.01,0.0,9057.75,9057.75,69675.0,25.0,20.97,45.97,0.232,0.508,22.98,47.98,0.254,0.53,2409
|
||||
SHFE,ss2410,ss2410,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14010.0,13975.0,13985.0,2944,52542,2.01,2.01,0.0,9090.25,9090.25,69925.0,25.0,20.97,45.97,0.231,0.506,22.98,47.98,0.253,0.528,2410
|
||||
SHFE,ss2411,ss2411,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14030.0,13995.0,14005.0,1968,41927,2.01,2.01,0.0,9103.25,9103.25,70025.0,25.0,20.97,45.97,0.23,0.505,22.98,47.98,0.252,0.527,2411
|
||||
SHFE,ss2412,ss2412,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14070.0,14030.0,14030.0,2,1621,2.01,2.01,0.0,9119.5,9119.5,70150.0,25.0,20.97,45.97,0.23,0.504,22.98,47.98,0.252,0.526,2412
|
||||
SHFE,ss2501,ss2501,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14110.0,14080.0,14100.0,685,16604,2.01,2.01,0.0,9165.0,9165.0,70500.0,25.0,20.97,45.97,0.229,0.502,22.98,47.98,0.251,0.524,2501
|
||||
SHFE,ss2502,ss2502,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14220.0,14180.0,14170.0,6,883,2.01,2.01,0.0,9210.5,9210.5,70850.0,25.0,20.97,45.97,0.228,0.499,22.98,47.98,0.25,0.521,2502
|
||||
SHFE,ss2503,ss2503,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14300.0,14275.0,14275.0,0,144,2.01,2.01,0.0,9278.75,9278.75,71375.0,25.0,20.97,45.97,0.226,0.495,22.98,47.98,0.248,0.517,2503
|
||||
SHFE,ss2504,ss2504,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14365.0,14380.0,14365.0,3,45,2.01,2.01,0.0,9337.25,9337.25,71825.0,25.0,20.97,45.97,0.225,0.492,22.98,47.98,0.246,0.514,2504
|
||||
SHFE,ss2505,ss2505,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14425.0,14385.0,14405.0,9,169,2.01,2.01,0.0,9363.25,9363.25,72025.0,25.0,20.97,45.97,0.224,0.491,22.98,47.98,0.245,0.512,2505
|
||||
SHFE,ss2506,ss2506,ss,不锈钢,5,5.0,0.0,2.01,0.0,2.01,0.0,0.0,0.13,0,0.13,0,14465.0,14465.0,14455.0,2,28,2.01,2.01,0.0,9395.75,9395.75,72275.0,25.0,20.97,45.97,0.223,0.489,22.98,47.98,0.245,0.511,2506
|
||||
DCE,v2407,聚氯乙烯2407,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.26,0,0.26,0,5836.0,5790.0,5790.0,202,22420,1.01,1.01,1.01,7527.0,7527.0,28950.0,5.0,2.98,7.98,0.04,0.106,2.98,7.98,0.04,0.106,2407
|
||||
DCE,v2408,聚氯乙烯2408,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,5861.0,5839.0,5843.0,3761,18972,1.01,1.01,1.01,3797.95,3797.95,29215.0,5.0,2.98,7.98,0.078,0.21,2.98,7.98,0.078,0.21,2408
|
||||
DCE,v2409,聚氯乙烯2409,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,5924.0,5897.0,5895.0,205311,956761,1.01,1.01,1.01,3831.75,3831.75,29475.0,5.0,2.98,7.98,0.078,0.208,2.98,7.98,0.078,0.208,2409
|
||||
DCE,v2410,聚氯乙烯2410,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,5966.0,5942.0,5943.0,470,7385,1.01,1.01,1.01,3862.95,3862.95,29715.0,5.0,2.98,7.98,0.077,0.206,2.98,7.98,0.077,0.206,2410
|
||||
DCE,v2411,聚氯乙烯2411,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6013.0,5986.0,5983.0,25,1366,1.01,1.01,1.01,3888.95,3888.95,29915.0,5.0,2.98,7.98,0.077,0.205,2.98,7.98,0.077,0.205,2411
|
||||
DCE,v2412,聚氯乙烯2412,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6049.0,6038.0,6033.0,56,888,1.01,1.01,1.01,3921.45,3921.45,30165.0,5.0,2.98,7.98,0.076,0.203,2.98,7.98,0.076,0.203,2412
|
||||
DCE,v2501,聚氯乙烯2501,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6094.0,6072.0,6072.0,11239,143822,1.01,1.01,1.01,3946.8,3946.8,30360.0,5.0,2.98,7.98,0.075,0.202,2.98,7.98,0.075,0.202,2501
|
||||
DCE,v2502,聚氯乙烯2502,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6115.0,6084.0,6089.0,15,796,1.01,1.01,1.01,3957.85,3957.85,30445.0,5.0,2.98,7.98,0.075,0.202,2.98,7.98,0.075,0.202,2502
|
||||
DCE,v2503,聚氯乙烯2503,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6118.0,6106.0,6094.0,16,1646,1.01,1.01,1.01,3961.1,3961.1,30470.0,5.0,2.98,7.98,0.075,0.201,2.98,7.98,0.075,0.201,2503
|
||||
DCE,v2504,聚氯乙烯2504,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6348.0,6339.0,6337.0,20,503,1.01,1.01,1.01,4119.05,4119.05,31685.0,5.0,2.98,7.98,0.072,0.194,2.98,7.98,0.072,0.194,2504
|
||||
DCE,v2505,聚氯乙烯2505,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6387.0,6365.0,6365.0,323,16891,1.01,1.01,1.01,4137.25,4137.25,31825.0,5.0,2.98,7.98,0.072,0.193,2.98,7.98,0.072,0.193,2505
|
||||
DCE,v2506,聚氯乙烯2506,v,聚氯乙烯,5,1.0,0.0,1.01,0.0,1.01,0.0,1.01,0.13,0,0.13,0,6415.0,6404.0,6403.0,50,84,1.01,1.01,1.01,4161.95,4161.95,32015.0,5.0,2.98,7.98,0.072,0.192,2.98,7.98,0.072,0.192,2506
|
||||
SHFE,wr2407,wr2407,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.25,0,0.25,0,3701.0,3701.0,3701.0,0,0,1.49,1.49,0.0,9252.5,9252.5,37010.0,10.0,7.01,17.01,0.076,0.184,8.51,18.51,0.092,0.2,2407
|
||||
SHFE,wr2408,wr2408,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.15,0,0.15,0,3741.0,3741.0,3741.0,0,4,1.51,1.51,0.0,5611.5,5611.5,37410.0,10.0,6.98,16.98,0.124,0.303,8.49,18.49,0.151,0.329,2408
|
||||
SHFE,wr2409,wr2409,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3661.0,3660.0,3660.0,0,689,1.48,1.48,0.0,5124.0,5124.0,36600.0,10.0,7.05,17.05,0.138,0.333,8.52,18.52,0.166,0.361,2409
|
||||
SHFE,wr2410,wr2410,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3742.0,3742.0,3742.0,0,10,1.51,1.51,0.0,5238.8,5238.8,37420.0,10.0,6.98,16.98,0.133,0.324,8.49,18.49,0.162,0.353,2410
|
||||
SHFE,wr2411,wr2411,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3767.0,3767.0,3767.0,0,2,1.52,1.52,0.0,5273.8,5273.8,37670.0,10.0,6.96,16.96,0.132,0.322,8.48,18.48,0.161,0.35,2411
|
||||
SHFE,wr2412,wr2412,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3455.0,3455.0,3455.0,0,0,1.39,1.39,0.0,4837.0,4837.0,34550.0,10.0,7.21,17.21,0.149,0.356,8.6,18.6,0.178,0.385,2412
|
||||
SHFE,wr2501,wr2501,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3734.0,3750.0,3750.0,0,31,1.51,1.51,0.0,5250.0,5250.0,37500.0,10.0,6.98,16.98,0.133,0.323,8.49,18.49,0.162,0.352,2501
|
||||
SHFE,wr2502,wr2502,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3785.0,3785.0,3785.0,0,0,1.53,1.53,0.0,5299.0,5299.0,37850.0,10.0,6.95,16.95,0.131,0.32,8.47,18.47,0.16,0.349,2502
|
||||
SHFE,wr2503,wr2503,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3840.0,3840.0,3840.0,0,0,1.55,1.55,0.0,5376.0,5376.0,38400.0,10.0,6.9,16.9,0.128,0.314,8.45,18.45,0.157,0.343,2503
|
||||
SHFE,wr2504,wr2504,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3838.0,3838.0,3838.0,0,0,1.55,1.55,0.0,5373.2,5373.2,38380.0,10.0,6.9,16.9,0.129,0.315,8.45,18.45,0.157,0.343,2504
|
||||
SHFE,wr2505,wr2505,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3726.0,3726.0,3726.0,0,0,1.5,1.5,0.0,5216.4,5216.4,37260.0,10.0,6.99,16.99,0.134,0.326,8.5,18.5,0.163,0.355,2505
|
||||
SHFE,wr2506,wr2506,wr,线材,10,1.0,4e-05,0.01,4e-05,0.01,0.0,0.0,0.14,0,0.14,0,3733.0,3733.0,3733.0,0,0,1.51,1.51,0.0,5226.2,5226.2,37330.0,10.0,6.99,16.99,0.134,0.325,8.49,18.49,0.162,0.354,2506
|
||||
DCE,y2407,豆油2407,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.26,0,0.26,0,7580.0,7580.0,7580.0,0,365,2.51,2.51,2.51,19708.0,19708.0,75800.0,20.0,14.97,34.97,0.076,0.177,14.97,34.97,0.076,0.177,2407
|
||||
DCE,y2408,豆油2408,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7612.0,7524.0,7568.0,3274,17346,2.51,2.51,2.51,9838.4,9838.4,75680.0,20.0,14.97,34.97,0.152,0.355,14.97,34.97,0.152,0.355,2408
|
||||
DCE,y2409,豆油2409,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7638.0,7572.0,7616.0,139127,641834,2.51,2.51,2.51,9900.8,9900.8,76160.0,20.0,14.97,34.97,0.151,0.353,14.97,34.97,0.151,0.353,2409
|
||||
DCE,y2411,豆油2411,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7624.0,7582.0,7614.0,1893,12471,2.51,2.51,2.51,9898.2,9898.2,76140.0,20.0,14.97,34.97,0.151,0.353,14.97,34.97,0.151,0.353,2411
|
||||
DCE,y2412,豆油2412,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7710.0,7674.0,7710.0,32,1674,2.51,2.51,2.51,10023.0,10023.0,77100.0,20.0,14.97,34.97,0.149,0.349,14.97,34.97,0.149,0.349,2412
|
||||
DCE,y2501,豆油2501,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7722.0,7670.0,7706.0,30867,275698,2.51,2.51,2.51,10017.8,10017.8,77060.0,20.0,14.97,34.97,0.149,0.349,14.97,34.97,0.149,0.349,2501
|
||||
DCE,y2503,豆油2503,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7588.0,7566.0,7600.0,174,3322,2.51,2.51,2.51,9880.0,9880.0,76000.0,20.0,14.97,34.97,0.152,0.354,14.97,34.97,0.152,0.354,2503
|
||||
DCE,y2505,豆油2505,y,豆油,10,2.0,0.0,2.51,0.0,2.51,0.0,2.51,0.13,0,0.13,0,7524.0,7498.0,7538.0,2480,71088,2.51,2.51,2.51,9799.4,9799.4,75380.0,20.0,14.97,34.97,0.153,0.357,14.97,34.97,0.153,0.357,2505
|
||||
SHFE,zn2407,zn2407,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.25,0,0.25,0,24130.0,24055.0,24275.0,30,4225,3.02,3.02,0.01,30343.75,30343.75,121375.0,25.0,18.97,43.97,0.063,0.145,21.98,46.98,0.072,0.155,2407
|
||||
SHFE,zn2408,zn2408,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.15,0,0.15,0,24240.0,24120.0,24355.0,45884,75529,3.02,3.02,0.01,18266.25,18266.25,121775.0,25.0,18.97,43.97,0.104,0.241,21.98,46.98,0.12,0.257,2408
|
||||
SHFE,zn2409,zn2409,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24280.0,24165.0,24405.0,23716,74609,3.02,3.02,0.01,17083.5,17083.5,122025.0,25.0,18.97,43.97,0.111,0.257,21.98,46.98,0.129,0.275,2409
|
||||
SHFE,zn2410,zn2410,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24285.0,24165.0,24405.0,4707,24192,3.02,3.02,0.01,17083.5,17083.5,122025.0,25.0,18.97,43.97,0.111,0.257,21.98,46.98,0.129,0.275,2410
|
||||
SHFE,zn2411,zn2411,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24240.0,24115.0,24360.0,603,7686,3.02,3.02,0.01,17052.0,17052.0,121800.0,25.0,18.97,43.97,0.111,0.258,21.98,46.98,0.129,0.275,2411
|
||||
SHFE,zn2412,zn2412,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24210.0,24080.0,24300.0,232,3399,3.02,3.02,0.01,17010.0,17010.0,121500.0,25.0,18.97,43.97,0.111,0.258,21.98,46.98,0.129,0.276,2412
|
||||
SHFE,zn2501,zn2501,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24155.0,24055.0,24290.0,59,1096,3.02,3.02,0.01,17003.0,17003.0,121450.0,25.0,18.97,43.97,0.112,0.259,21.98,46.98,0.129,0.276,2501
|
||||
SHFE,zn2502,zn2502,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24135.0,24085.0,24205.0,45,378,3.02,3.02,0.01,16943.5,16943.5,121025.0,25.0,18.97,43.97,0.112,0.259,21.98,46.98,0.13,0.277,2502
|
||||
SHFE,zn2503,zn2503,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24075.0,24000.0,24235.0,1,383,3.02,3.02,0.01,16964.5,16964.5,121175.0,25.0,18.97,43.97,0.112,0.259,21.98,46.98,0.13,0.277,2503
|
||||
SHFE,zn2504,zn2504,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24080.0,24010.0,24165.0,1,68,3.02,3.02,0.01,16915.5,16915.5,120825.0,25.0,18.97,43.97,0.112,0.26,21.98,46.98,0.13,0.278,2504
|
||||
SHFE,zn2505,zn2505,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24055.0,23975.0,24180.0,2,92,3.02,3.02,0.01,16926.0,16926.0,120900.0,25.0,18.97,43.97,0.112,0.26,21.98,46.98,0.13,0.278,2505
|
||||
SHFE,zn2506,zn2506,zn,锌,5,5.0,0.0,3.01,0.0,3.01,0.0,0.0,0.14,0,0.14,0,24035.0,23950.0,24145.0,12,30,3.02,3.02,0.01,16901.5,16901.5,120725.0,25.0,18.97,43.97,0.112,0.26,21.98,46.98,0.13,0.278,2506
|
||||
|
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|
||||
,合约,最新价,涨跌,涨跌幅,开盘价,最高价,最低价,昨收价,涨停价,跌停价,最新持仓,成交量,更新时间,说明,交易品种,主连代码,品种代码,交割月份
|
||||
0,豆一2409(a2409),4623.0,-12.0,-0.3%,4635.0,4641.0,4620.0,4632.0,4909.0,4355.0,131113,17837,2024-07-10 21:58:41,主力,豆一2409,a2409,a,2409
|
||||
1,白银2408(ag2408),8197.0,37.0,0.5%,8160.0,8207.0,8138.0,8112.0,8923.0,7300.0,275003,186519,2024-07-10 21:58:43,主力,白银2408,ag2408,ag,2408
|
||||
2,沪铝2408(al2408),20195.0,55.0,0.3%,20140.0,20265.0,20140.0,20250.0,21665.0,18830.0,156460,42560,2024-07-10 21:58:42,主力,沪铝2408,al2408,al,2408
|
||||
3,氧化铝2408(ao2408),3688.0,-62.0,-1.7%,3750.0,3757.0,3667.0,3793.0,4058.0,3527.0,45211,70246,2024-07-10 21:58:42,主力,氧化铝2408,ao2408,ao,2408
|
||||
4,苹果410(AP410),6926.0,36.0,0.5%,6890.0,6932.0,6842.0,6896.0,7517.0,6275.0,167339,107790,2024-07-10 20:53:26,主力,苹果410,AP410,AP,410
|
||||
5,黄金2410(au2410),566.08,1.3,0.2%,564.8,566.48,564.16,561.22,617.34,505.08,140030,23597,2024-07-10 21:58:42,主力,黄金2410,au2410,au,2410
|
||||
6,豆二2409(b2409),3739.0,-7.0,-0.2%,3746.0,3753.0,3730.0,3752.0,3977.0,3527.0,116847,31830,2024-07-10 21:58:38,主力,豆二2409,b2409,b,2409
|
||||
7,胶板2501(bb2501),191.0,-5.4,-2.7%,196.4,196.4,191.0,193.05,202.7,183.4,60,10,2024-07-10 20:53:13,主力,胶板2501,bb2501,bb,2501
|
||||
8,国际铜2408(bc2408),71530.0,310.0,0.4%,71220.0,71640.0,71140.0,71230.0,76210.0,66240.0,6864,2014,2024-07-10 21:58:43,主力,国际铜2408,bc2408,bc,2408
|
||||
9,合成橡胶2408(br2408),14760.0,80.0,0.5%,14680.0,14805.0,14680.0,14765.0,16240.0,13285.0,23749,18369,2024-07-10 21:58:42,主力,合成橡胶2408,br2408,br,2408
|
||||
10,沥青2409(bu2409),3634.0,1.0,0%,3633.0,3652.0,3629.0,3650.0,3942.0,3358.0,208645,46335,2024-07-10 21:58:42,主力,沥青2409,bu2409,bu,2409
|
||||
11,玉米2409(c2409),2423.0,3.0,0.1%,2420.0,2427.0,2420.0,2424.0,2569.0,2279.0,772305,94934,2024-07-10 21:58:42,主力,玉米2409,c2409,c,2409
|
||||
12,棉花409(CF409),14530.0,105.0,0.7%,14425.0,14560.0,14425.0,14470.0,15340.0,13600.0,518618,103523,2024-07-10 21:58:41,主力,棉花409,CF409,CF,409
|
||||
13,红枣409(CJ409),10420.0,5.0,0%,10415.0,10480.0,10130.0,10325.0,11565.0,9085.0,59460,53341,2024-07-10 20:53:24,主力,红枣409,CJ409,CJ,409
|
||||
14,玉米淀粉2409(cs2409),2876.0,2.0,0.1%,2874.0,2884.0,2872.0,2893.0,3037.0,2749.0,155619,34899,2024-07-10 21:58:42,主力,玉米淀粉2409,cs2409,cs,2409
|
||||
15,沪铜2408(cu2408),80190.0,220.0,0.3%,79970.0,80300.0,79870.0,79870.0,85460.0,74270.0,196248,17954,2024-07-10 21:58:42,主力,沪铜2408,cu2408,cu,2408
|
||||
16,棉纱409(CY409),20135.0,60.0,0.3%,20075.0,20165.0,20030.0,20125.0,21335.0,18915.0,4977,609,2024-07-10 21:58:33,主力,棉纱409,CY409,CY,409
|
||||
17,苯乙烯2408(eb2408),9352.0,43.0,0.5%,9309.0,9376.0,9300.0,9302.0,9953.0,8651.0,260194,112788,2024-07-10 21:58:43,主力,苯乙烯2408,eb2408,eb,2408
|
||||
18,集运指数2410(ec2410),4435.0,35.0,0.8%,4400.0,4618.8,4355.0,4482.0,5199.1,3764.8,28743,45054,2024-07-10 20:53:13,主力,集运指数2410,ec2410,ec,2410
|
||||
19,乙二醇2409(eg2409),4774.0,51.0,1.1%,4723.0,4788.0,4722.0,4714.0,5043.0,4385.0,462368,177134,2024-07-10 21:58:41,主力,乙二醇2409,eg2409,eg,2409
|
||||
20,纤维板2409(fb2409),1286.5,-1.5,-0.1%,1288.0,1300.0,1283.0,1288.5,1352.5,1224.5,1472,3225,2024-07-10 20:53:12,主力,纤维板2409,fb2409,fb,2409
|
||||
21,玻璃409(FG409),1473.0,3.0,0.2%,1470.0,1474.0,1462.0,1477.0,1625.0,1329.0,701892,117098,2024-07-10 21:58:42,主力,玻璃409,FG409,FG,409
|
||||
22,燃油2409(fu2409),3514.0,7.0,0.2%,3507.0,3517.0,3496.0,3528.0,3810.0,3245.0,233905,120845,2024-07-10 21:58:41,主力,燃油2409,fu2409,fu,2409
|
||||
23,热卷2410(hc2410),3691.0,9.0,0.2%,3682.0,3694.0,3681.0,3694.0,3878.0,3509.0,1069877,64407,2024-07-10 21:58:44,主力,热卷2410,hc2410,hc,2410
|
||||
24,铁矿石2409(i2409),820.0,6.5,0.8%,813.5,821.0,813.5,821.5,911.5,731.5,341446,60834,2024-07-10 21:58:43,主力,铁矿石2409,i2409,i,2409
|
||||
25,中证500指数2407(IC2407),4831.4,-22.4,-0.5%,4853.8,4881.8,4827.0,4862.8,5349.0,4376.6,73064,40434,2024-07-10 15:24:13,主力,中证500指数2407,IC2407,IC,2407
|
||||
26,沪深300指数2407(IF2407),3412.4,-3.6,-0.1%,3416.0,3432.0,3409.0,3422.0,3764.2,3079.8,75866,39700,2024-07-10 15:24:13,主力,沪深300指数2407,IF2407,IF,2407
|
||||
27,上证50指数2407(IH2407),2364.4,-0.2,-0%,2364.6,2375.0,2361.8,2370.0,2607.0,2133.0,36666,20718,2024-07-10 15:24:13,主力,上证50指数2407,IH2407,IH,2407
|
||||
28,中证股指期货2407(IM2407),4758.0,2.2,0%,4755.8,4813.6,4747.8,4776.0,5253.6,4298.4,90954,83873,2024-07-10 15:24:13,主力,中证股指期货2407,IM2407,IM,2407
|
||||
29,焦炭2409(j2409),2226.5,10.0,0.5%,2216.5,2228.0,2211.5,2221.5,2399.0,2044.0,26714,3402,2024-07-10 21:58:41,主力,焦炭2409,j2409,j,2409
|
||||
30,鸡蛋2409(jd2409),4062.0,-28.0,-0.7%,4090.0,4092.0,4021.0,4056.0,4299.0,3813.0,178656,241161,2024-07-10 20:53:19,主力,鸡蛋2409,jd2409,jd,2409
|
||||
31,焦煤2409(jm2409),1546.0,8.0,0.5%,1538.0,1548.0,1534.0,1543.5,1666.5,1420.5,160798,21409,2024-07-10 21:58:41,主力,焦煤2409,jm2409,jm,2409
|
||||
32,塑料2409(l2409),8412.0,17.0,0.2%,8395.0,8418.0,8384.0,8413.0,8917.0,7909.0,403983,62527,2024-07-10 21:58:42,主力,塑料2409,l2409,l,2409
|
||||
33,碳酸锂2411(lc2411),91800.0,-2100.0,-2.2%,93900.0,94400.0,91600.0,93900.0,103250.0,84550.0,176216,149171,2024-07-10 15:00:27,主力,碳酸锂2411,lc2411,lc,2411
|
||||
34,生猪2409(lh2409),17510.0,-500.0,-2.8%,18010.0,18030.0,17505.0,17760.0,18825.0,16695.0,63950,60992,2024-07-10 20:53:20,主力,生猪2409,lh2409,lh,2409
|
||||
35,低硫燃料油2409(lu2409),4319.0,14.0,0.3%,4305.0,4323.0,4295.0,4329.0,4675.0,3982.0,63705,20408,2024-07-10 21:58:44,主力,低硫燃料油2409,lu2409,lu,2409
|
||||
36,豆粕2409(m2409),3235.0,-6.0,-0.2%,3241.0,3248.0,3230.0,3230.0,3423.0,3037.0,1581523,202682,2024-07-10 21:58:43,主力,豆粕2409,m2409,m,2409
|
||||
37,甲醇409(MA409),2569.0,25.0,1%,2544.0,2574.0,2542.0,2538.0,2716.0,2360.0,746529,328085,2024-07-10 21:58:44,主力,甲醇409,MA409,MA,409
|
||||
38,沪镍2409(ni2409),134370.0,340.0,0.3%,134030.0,134790.0,134010.0,135270.0,148790.0,121740.0,120389,31972,2024-07-10 21:58:42,主力,沪镍2409,ni2409,ni,2409
|
||||
39,20号胶2409(nr2409),12315.0,35.0,0.3%,12280.0,12370.0,12275.0,12270.0,13005.0,11530.0,67524,16767,2024-07-10 21:58:44,主力,20号胶2409,nr2409,nr,2409
|
||||
40,菜籽油409(OI409),8309.0,-21.0,-0.3%,8330.0,8344.0,8283.0,8377.0,8880.0,7874.0,288033,146832,2024-07-10 21:58:43,主力,菜籽油409,OI409,OI,409
|
||||
41,棕榈油2409(p2409),7662.0,12.0,0.2%,7650.0,7692.0,7638.0,7648.0,8182.0,7114.0,432148,173591,2024-07-10 21:58:42,主力,棕榈油2409,p2409,p,2409
|
||||
42,沪铅2408(pb2408),19460.0,5.0,0%,19455.0,19520.0,19360.0,19480.0,20840.0,18115.0,87358,29903,2024-07-10 21:58:41,主力,沪铅2408,pb2408,pb,2408
|
||||
43,短纤409(PF409),7588.0,28.0,0.4%,7560.0,7594.0,7548.0,7572.0,8104.0,7040.0,140607,24507,2024-07-10 21:58:40,主力,短纤409,PF409,PF,409
|
||||
44,液化石油气2409(pg2409),4557.0,31.0,0.7%,4526.0,4562.0,4522.0,4548.0,4866.0,4230.0,78155,21277,2024-07-10 21:58:43,主力,液化石油气2409,pg2409,pg,2409
|
||||
45,花生410(PK410),8670.0,-54.0,-0.6%,8724.0,8740.0,8612.0,8676.0,9284.0,8068.0,133417,82137,2024-07-10 20:53:11,主力,花生410,PK410,PK,410
|
||||
46,聚丙烯2409(pp2409),7704.0,34.0,0.4%,7670.0,7708.0,7670.0,7689.0,8150.0,7228.0,401883,73091,2024-07-10 21:58:43,主力,聚丙烯2409,pp2409,pp,2409
|
||||
47,对二甲苯 409(PX409),8518.0,28.0,0.3%,8490.0,8522.0,8488.0,8500.0,9096.0,7904.0,73855,8131,2024-07-10 21:58:42,主力,对二甲苯 409,PX409,PX,409
|
||||
48,螺纹钢2410(rb2410),3495.0,9.0,0.3%,3486.0,3497.0,3480.0,3492.0,3666.0,3317.0,2246168,244244,2024-07-10 21:58:42,主力,螺纹钢2410,rb2410,rb,2410
|
||||
49,菜籽粕409(RM409),2515.0,-13.0,-0.5%,2528.0,2530.0,2509.0,2520.0,2672.0,2368.0,1011648,176386,2024-07-10 21:58:43,主力,菜籽粕409,RM409,RM,409
|
||||
50,粳米2409(rr2409),3486.0,1.0,0%,3485.0,3486.0,3483.0,3485.0,3659.0,3311.0,5211,419,2024-07-10 21:55:03,主力,粳米2409,rr2409,rr,2409
|
||||
51,油菜籽411(RS411),5146.0,-166.0,-3.1%,5312.0,5335.0,5140.0,5216.0,5738.0,4694.0,155,85,2024-07-10 20:53:12,主力,油菜籽411,RS411,RS,411
|
||||
52,橡胶2409(ru2409),14690.0,30.0,0.2%,14660.0,14715.0,14645.0,14645.0,15520.0,13765.0,212409,52408,2024-07-10 21:58:42,主力,橡胶2409,ru2409,ru,2409
|
||||
53,纯碱409(SA409),1956.0,-2.0,-0.1%,1958.0,1961.0,1945.0,2003.0,2204.0,1802.0,820754,237739,2024-07-10 21:58:41,主力,纯碱409,SA409,SA,409
|
||||
54,原油2408(sc2408),617.1,0.9,0.1%,616.2,617.7,614.5,621.3,671.0,571.5,25621,15352,2024-07-10 21:58:43,主力,原油2408,sc2408,sc,2408
|
||||
55,硅铁409(SF409),6672.0,-34.0,-0.5%,6706.0,6756.0,6652.0,6696.0,7232.0,6160.0,163656,76007,2024-07-10 20:53:22,主力,硅铁409,SF409,SF,409
|
||||
56,烧碱 409(SH409),2574.0,-6.0,-0.2%,2580.0,2588.0,2564.0,2582.0,2763.0,2401.0,56255,11898,2024-07-10 21:58:41,主力,烧碱 409,SH409,SH,409
|
||||
57,工业硅2409(si2409),11110.0,-180.0,-1.6%,11290.0,11295.0,11095.0,11200.0,11980.0,10420.0,236506,153139,2024-07-10 15:00:27,主力,工业硅2409,si2409,si,2409
|
||||
58,锰硅501(SM501),7324.0,-214.0,-2.8%,7538.0,7598.0,7280.0,7412.0,8154.0,6670.0,279664,291395,2024-07-10 20:53:20,主力,锰硅501,SM501,SM,501
|
||||
59,沪锡2408(sn2408),282070.0,2530.0,0.9%,279540.0,282880.0,279500.0,278190.0,306000.0,250370.0,37361,54822,2024-07-10 21:58:44,主力,沪锡2408,sn2408,sn,2408
|
||||
60,纸浆2409(sp2409),5672.0,14.0,0.2%,5658.0,5684.0,5656.0,5694.0,6034.0,5352.0,264768,50733,2024-07-10 21:58:44,主力,纸浆2409,sp2409,sp,2409
|
||||
61,白糖409(SR409),6187.0,17.0,0.3%,6170.0,6191.0,6163.0,6174.0,6545.0,5803.0,313149,89315,2024-07-10 21:58:44,主力,白糖409,SR409,SR,409
|
||||
62,不锈钢2409(ss2409),13930.0,0.0,0%,13930.0,13960.0,13905.0,13980.0,14675.0,13280.0,142308,29073,2024-07-10 21:58:43,主力,不锈钢2409,ss2409,ss,2409
|
||||
63,10年国债2409(T2409),105.11,0.0,0%,105.085,105.145,105.035,105.065,107.165,102.965,188590,29670,2024-07-10 15:24:13,主力,10年国债2409,T2409,T,2409
|
||||
64,PTA409(TA409),5910.0,18.0,0.3%,5892.0,5912.0,5892.0,5902.0,6258.0,5546.0,892109,96388,2024-07-10 21:58:42,主力,PTA409,TA409,TA,409
|
||||
65,5年期国债2409(TF2409),103.865,0.1,0.1%,103.81,103.89,103.8,103.84,105.085,102.595,116126,25935,2024-07-10 15:24:13,主力,5年期国债2409,TF2409,TF,2409
|
||||
66,30年期国债期货2409(TL2409),108.46,0.1,0.1%,108.35,108.5,108.27,108.34,112.13,104.55,75624,29336,2024-07-10 15:24:13,主力,30年期国债期货2409,TL2409,TL,2409
|
||||
67,2年期国债2409(TS2409),101.938,0.0,0%,101.918,101.946,101.91,101.922,102.43,101.414,54860,20514,2024-07-10 15:24:13,主力,2年期国债2409,TS2409,TS,2409
|
||||
68,尿素409(UR409),2055.0,-22.0,-1.1%,2077.0,2080.0,2050.0,2061.0,2247.0,1875.0,185561,178245,2024-07-10 20:53:21,主力,尿素409,UR409,UR,409
|
||||
69,PVC2409(v2409),5904.0,2.0,0%,5902.0,5907.0,5875.0,5924.0,6279.0,5569.0,951642,162407,2024-07-10 21:58:43,主力,PVC2409,v2409,v,2409
|
||||
70,线材2409(wr2409),3660.0,-25.0,-0.7%,3685.0,3685.0,3655.0,3661.0,3917.0,3404.0,689,190,2024-07-10 21:38:55,主力,线材2409,wr2409,wr,2409
|
||||
71,豆油2409(y2409),7606.0,0.0,0%,7606.0,7622.0,7580.0,7638.0,8096.0,7180.0,644683,106957,2024-07-10 21:58:43,主力,豆油2409,y2409,y,2409
|
||||
72,沪锌2408(zn2408),24370.0,160.0,0.7%,24210.0,24395.0,24205.0,24240.0,25935.0,22540.0,75969,37672,2024-07-10 21:58:41,主力,沪锌2408,zn2408,zn,2408
|
||||
|
@@ -0,0 +1,74 @@
|
||||
,合约,最新价,涨跌,涨跌幅,开盘价,最高价,最低价,昨收价,涨停价,跌停价,最新持仓,成交量,更新时间,说明,交易品种,主连代码,品种代码,交割月份
|
||||
0,豆一2409(a2409),4623.0,-12.0,-0.3%,4635.0,4641.0,4620.0,4632.0,4909.0,4355.0,131113,17837,2024-07-10 21:58:41,主力,豆一2409,a2409,a,2409
|
||||
1,白银2408(ag2408),8197.0,37.0,0.5%,8160.0,8207.0,8138.0,8112.0,8923.0,7300.0,275003,186519,2024-07-10 21:58:43,主力,白银2408,ag2408,ag,2408
|
||||
2,沪铝2408(al2408),20195.0,55.0,0.3%,20140.0,20265.0,20140.0,20250.0,21665.0,18830.0,156460,42560,2024-07-10 21:58:42,主力,沪铝2408,al2408,al,2408
|
||||
3,氧化铝2408(ao2408),3688.0,-62.0,-1.7%,3750.0,3757.0,3667.0,3793.0,4058.0,3527.0,45211,70246,2024-07-10 21:58:42,主力,氧化铝2408,ao2408,ao,2408
|
||||
4,苹果410(AP410),6926.0,36.0,0.5%,6890.0,6932.0,6842.0,6896.0,7517.0,6275.0,167339,107790,2024-07-10 20:53:26,主力,苹果410,AP410,AP,410
|
||||
5,黄金2410(au2410),566.08,1.3,0.2%,564.8,566.48,564.16,561.22,617.34,505.08,140030,23597,2024-07-10 21:58:42,主力,黄金2410,au2410,au,2410
|
||||
6,豆二2409(b2409),3739.0,-7.0,-0.2%,3746.0,3753.0,3730.0,3752.0,3977.0,3527.0,116847,31830,2024-07-10 21:58:38,主力,豆二2409,b2409,b,2409
|
||||
7,胶板2501(bb2501),191.0,-5.4,-2.7%,196.4,196.4,191.0,193.05,202.7,183.4,60,10,2024-07-10 20:53:13,主力,胶板2501,bb2501,bb,2501
|
||||
8,国际铜2408(bc2408),71530.0,310.0,0.4%,71220.0,71640.0,71140.0,71230.0,76210.0,66240.0,6864,2014,2024-07-10 21:58:43,主力,国际铜2408,bc2408,bc,2408
|
||||
9,合成橡胶2408(br2408),14760.0,80.0,0.5%,14680.0,14805.0,14680.0,14765.0,16240.0,13285.0,23749,18369,2024-07-10 21:58:42,主力,合成橡胶2408,br2408,br,2408
|
||||
10,沥青2409(bu2409),3634.0,1.0,0%,3633.0,3652.0,3629.0,3650.0,3942.0,3358.0,208645,46335,2024-07-10 21:58:42,主力,沥青2409,bu2409,bu,2409
|
||||
11,玉米2409(c2409),2423.0,3.0,0.1%,2420.0,2427.0,2420.0,2424.0,2569.0,2279.0,772305,94934,2024-07-10 21:58:42,主力,玉米2409,c2409,c,2409
|
||||
12,棉花409(CF409),14530.0,105.0,0.7%,14425.0,14560.0,14425.0,14470.0,15340.0,13600.0,518618,103523,2024-07-10 21:58:41,主力,棉花409,CF409,CF,409
|
||||
13,红枣409(CJ409),10420.0,5.0,0%,10415.0,10480.0,10130.0,10325.0,11565.0,9085.0,59460,53341,2024-07-10 20:53:24,主力,红枣409,CJ409,CJ,409
|
||||
14,玉米淀粉2409(cs2409),2876.0,2.0,0.1%,2874.0,2884.0,2872.0,2893.0,3037.0,2749.0,155619,34899,2024-07-10 21:58:42,主力,玉米淀粉2409,cs2409,cs,2409
|
||||
15,沪铜2408(cu2408),80190.0,220.0,0.3%,79970.0,80300.0,79870.0,79870.0,85460.0,74270.0,196248,17954,2024-07-10 21:58:42,主力,沪铜2408,cu2408,cu,2408
|
||||
16,棉纱409(CY409),20135.0,60.0,0.3%,20075.0,20165.0,20030.0,20125.0,21335.0,18915.0,4977,609,2024-07-10 21:58:33,主力,棉纱409,CY409,CY,409
|
||||
17,苯乙烯2408(eb2408),9352.0,43.0,0.5%,9309.0,9376.0,9300.0,9302.0,9953.0,8651.0,260194,112788,2024-07-10 21:58:43,主力,苯乙烯2408,eb2408,eb,2408
|
||||
18,集运指数2410(ec2410),4435.0,35.0,0.8%,4400.0,4618.8,4355.0,4482.0,5199.1,3764.8,28743,45054,2024-07-10 20:53:13,主力,集运指数2410,ec2410,ec,2410
|
||||
19,乙二醇2409(eg2409),4774.0,51.0,1.1%,4723.0,4788.0,4722.0,4714.0,5043.0,4385.0,462368,177134,2024-07-10 21:58:41,主力,乙二醇2409,eg2409,eg,2409
|
||||
20,纤维板2409(fb2409),1286.5,-1.5,-0.1%,1288.0,1300.0,1283.0,1288.5,1352.5,1224.5,1472,3225,2024-07-10 20:53:12,主力,纤维板2409,fb2409,fb,2409
|
||||
21,玻璃409(FG409),1473.0,3.0,0.2%,1470.0,1474.0,1462.0,1477.0,1625.0,1329.0,701892,117098,2024-07-10 21:58:42,主力,玻璃409,FG409,FG,409
|
||||
22,燃油2409(fu2409),3514.0,7.0,0.2%,3507.0,3517.0,3496.0,3528.0,3810.0,3245.0,233905,120845,2024-07-10 21:58:41,主力,燃油2409,fu2409,fu,2409
|
||||
23,热卷2410(hc2410),3691.0,9.0,0.2%,3682.0,3694.0,3681.0,3694.0,3878.0,3509.0,1069877,64407,2024-07-10 21:58:44,主力,热卷2410,hc2410,hc,2410
|
||||
24,铁矿石2409(i2409),820.0,6.5,0.8%,813.5,821.0,813.5,821.5,911.5,731.5,341446,60834,2024-07-10 21:58:43,主力,铁矿石2409,i2409,i,2409
|
||||
25,中证500指数2407(IC2407),4831.4,-22.4,-0.5%,4853.8,4881.8,4827.0,4862.8,5349.0,4376.6,73064,40434,2024-07-10 15:24:13,主力,中证500指数2407,IC2407,IC,2407
|
||||
26,沪深300指数2407(IF2407),3412.4,-3.6,-0.1%,3416.0,3432.0,3409.0,3422.0,3764.2,3079.8,75866,39700,2024-07-10 15:24:13,主力,沪深300指数2407,IF2407,IF,2407
|
||||
27,上证50指数2407(IH2407),2364.4,-0.2,-0%,2364.6,2375.0,2361.8,2370.0,2607.0,2133.0,36666,20718,2024-07-10 15:24:13,主力,上证50指数2407,IH2407,IH,2407
|
||||
28,中证股指期货2407(IM2407),4758.0,2.2,0%,4755.8,4813.6,4747.8,4776.0,5253.6,4298.4,90954,83873,2024-07-10 15:24:13,主力,中证股指期货2407,IM2407,IM,2407
|
||||
29,焦炭2409(j2409),2226.5,10.0,0.5%,2216.5,2228.0,2211.5,2221.5,2399.0,2044.0,26714,3402,2024-07-10 21:58:41,主力,焦炭2409,j2409,j,2409
|
||||
30,鸡蛋2409(jd2409),4062.0,-28.0,-0.7%,4090.0,4092.0,4021.0,4056.0,4299.0,3813.0,178656,241161,2024-07-10 20:53:19,主力,鸡蛋2409,jd2409,jd,2409
|
||||
31,焦煤2409(jm2409),1546.0,8.0,0.5%,1538.0,1548.0,1534.0,1543.5,1666.5,1420.5,160798,21409,2024-07-10 21:58:41,主力,焦煤2409,jm2409,jm,2409
|
||||
32,塑料2409(l2409),8412.0,17.0,0.2%,8395.0,8418.0,8384.0,8413.0,8917.0,7909.0,403983,62527,2024-07-10 21:58:42,主力,塑料2409,l2409,l,2409
|
||||
33,碳酸锂2411(lc2411),91800.0,-2100.0,-2.2%,93900.0,94400.0,91600.0,93900.0,103250.0,84550.0,176216,149171,2024-07-10 15:00:27,主力,碳酸锂2411,lc2411,lc,2411
|
||||
34,生猪2409(lh2409),17510.0,-500.0,-2.8%,18010.0,18030.0,17505.0,17760.0,18825.0,16695.0,63950,60992,2024-07-10 20:53:20,主力,生猪2409,lh2409,lh,2409
|
||||
35,低硫燃料油2409(lu2409),4319.0,14.0,0.3%,4305.0,4323.0,4295.0,4329.0,4675.0,3982.0,63705,20408,2024-07-10 21:58:44,主力,低硫燃料油2409,lu2409,lu,2409
|
||||
36,豆粕2409(m2409),3235.0,-6.0,-0.2%,3241.0,3248.0,3230.0,3230.0,3423.0,3037.0,1581523,202682,2024-07-10 21:58:43,主力,豆粕2409,m2409,m,2409
|
||||
37,甲醇409(MA409),2569.0,25.0,1%,2544.0,2574.0,2542.0,2538.0,2716.0,2360.0,746529,328085,2024-07-10 21:58:44,主力,甲醇409,MA409,MA,409
|
||||
38,沪镍2409(ni2409),134370.0,340.0,0.3%,134030.0,134790.0,134010.0,135270.0,148790.0,121740.0,120389,31972,2024-07-10 21:58:42,主力,沪镍2409,ni2409,ni,2409
|
||||
39,20号胶2409(nr2409),12315.0,35.0,0.3%,12280.0,12370.0,12275.0,12270.0,13005.0,11530.0,67524,16767,2024-07-10 21:58:44,主力,20号胶2409,nr2409,nr,2409
|
||||
40,菜籽油409(OI409),8309.0,-21.0,-0.3%,8330.0,8344.0,8283.0,8377.0,8880.0,7874.0,288033,146832,2024-07-10 21:58:43,主力,菜籽油409,OI409,OI,409
|
||||
41,棕榈油2409(p2409),7662.0,12.0,0.2%,7650.0,7692.0,7638.0,7648.0,8182.0,7114.0,432148,173591,2024-07-10 21:58:42,主力,棕榈油2409,p2409,p,2409
|
||||
42,沪铅2408(pb2408),19460.0,5.0,0%,19455.0,19520.0,19360.0,19480.0,20840.0,18115.0,87358,29903,2024-07-10 21:58:41,主力,沪铅2408,pb2408,pb,2408
|
||||
43,短纤409(PF409),7588.0,28.0,0.4%,7560.0,7594.0,7548.0,7572.0,8104.0,7040.0,140607,24507,2024-07-10 21:58:40,主力,短纤409,PF409,PF,409
|
||||
44,液化石油气2409(pg2409),4557.0,31.0,0.7%,4526.0,4562.0,4522.0,4548.0,4866.0,4230.0,78155,21277,2024-07-10 21:58:43,主力,液化石油气2409,pg2409,pg,2409
|
||||
45,花生410(PK410),8670.0,-54.0,-0.6%,8724.0,8740.0,8612.0,8676.0,9284.0,8068.0,133417,82137,2024-07-10 20:53:11,主力,花生410,PK410,PK,410
|
||||
46,聚丙烯2409(pp2409),7704.0,34.0,0.4%,7670.0,7708.0,7670.0,7689.0,8150.0,7228.0,401883,73091,2024-07-10 21:58:43,主力,聚丙烯2409,pp2409,pp,2409
|
||||
47,对二甲苯 409(PX409),8518.0,28.0,0.3%,8490.0,8522.0,8488.0,8500.0,9096.0,7904.0,73855,8131,2024-07-10 21:58:42,主力,对二甲苯 409,PX409,PX,409
|
||||
48,螺纹钢2410(rb2410),3495.0,9.0,0.3%,3486.0,3497.0,3480.0,3492.0,3666.0,3317.0,2246168,244244,2024-07-10 21:58:42,主力,螺纹钢2410,rb2410,rb,2410
|
||||
49,菜籽粕409(RM409),2515.0,-13.0,-0.5%,2528.0,2530.0,2509.0,2520.0,2672.0,2368.0,1011648,176386,2024-07-10 21:58:43,主力,菜籽粕409,RM409,RM,409
|
||||
50,粳米2409(rr2409),3486.0,1.0,0%,3485.0,3486.0,3483.0,3485.0,3659.0,3311.0,5211,419,2024-07-10 21:55:03,主力,粳米2409,rr2409,rr,2409
|
||||
51,油菜籽411(RS411),5146.0,-166.0,-3.1%,5312.0,5335.0,5140.0,5216.0,5738.0,4694.0,155,85,2024-07-10 20:53:12,主力,油菜籽411,RS411,RS,411
|
||||
52,橡胶2409(ru2409),14690.0,30.0,0.2%,14660.0,14715.0,14645.0,14645.0,15520.0,13765.0,212409,52408,2024-07-10 21:58:42,主力,橡胶2409,ru2409,ru,2409
|
||||
53,纯碱409(SA409),1956.0,-2.0,-0.1%,1958.0,1961.0,1945.0,2003.0,2204.0,1802.0,820754,237739,2024-07-10 21:58:41,主力,纯碱409,SA409,SA,409
|
||||
54,原油2408(sc2408),617.1,0.9,0.1%,616.2,617.7,614.5,621.3,671.0,571.5,25621,15352,2024-07-10 21:58:43,主力,原油2408,sc2408,sc,2408
|
||||
55,硅铁409(SF409),6672.0,-34.0,-0.5%,6706.0,6756.0,6652.0,6696.0,7232.0,6160.0,163656,76007,2024-07-10 20:53:22,主力,硅铁409,SF409,SF,409
|
||||
56,烧碱 409(SH409),2574.0,-6.0,-0.2%,2580.0,2588.0,2564.0,2582.0,2763.0,2401.0,56255,11898,2024-07-10 21:58:41,主力,烧碱 409,SH409,SH,409
|
||||
57,工业硅2409(si2409),11110.0,-180.0,-1.6%,11290.0,11295.0,11095.0,11200.0,11980.0,10420.0,236506,153139,2024-07-10 15:00:27,主力,工业硅2409,si2409,si,2409
|
||||
58,锰硅501(SM501),7324.0,-214.0,-2.8%,7538.0,7598.0,7280.0,7412.0,8154.0,6670.0,279664,291395,2024-07-10 20:53:20,主力,锰硅501,SM501,SM,501
|
||||
59,沪锡2408(sn2408),282070.0,2530.0,0.9%,279540.0,282880.0,279500.0,278190.0,306000.0,250370.0,37361,54822,2024-07-10 21:58:44,主力,沪锡2408,sn2408,sn,2408
|
||||
60,纸浆2409(sp2409),5672.0,14.0,0.2%,5658.0,5684.0,5656.0,5694.0,6034.0,5352.0,264768,50733,2024-07-10 21:58:44,主力,纸浆2409,sp2409,sp,2409
|
||||
61,白糖409(SR409),6187.0,17.0,0.3%,6170.0,6191.0,6163.0,6174.0,6545.0,5803.0,313149,89315,2024-07-10 21:58:44,主力,白糖409,SR409,SR,409
|
||||
62,不锈钢2409(ss2409),13930.0,0.0,0%,13930.0,13960.0,13905.0,13980.0,14675.0,13280.0,142308,29073,2024-07-10 21:58:43,主力,不锈钢2409,ss2409,ss,2409
|
||||
63,10年国债2409(T2409),105.11,0.0,0%,105.085,105.145,105.035,105.065,107.165,102.965,188590,29670,2024-07-10 15:24:13,主力,10年国债2409,T2409,T,2409
|
||||
64,PTA409(TA409),5910.0,18.0,0.3%,5892.0,5912.0,5892.0,5902.0,6258.0,5546.0,892109,96388,2024-07-10 21:58:42,主力,PTA409,TA409,TA,409
|
||||
65,5年期国债2409(TF2409),103.865,0.1,0.1%,103.81,103.89,103.8,103.84,105.085,102.595,116126,25935,2024-07-10 15:24:13,主力,5年期国债2409,TF2409,TF,2409
|
||||
66,30年期国债期货2409(TL2409),108.46,0.1,0.1%,108.35,108.5,108.27,108.34,112.13,104.55,75624,29336,2024-07-10 15:24:13,主力,30年期国债期货2409,TL2409,TL,2409
|
||||
67,2年期国债2409(TS2409),101.938,0.0,0%,101.918,101.946,101.91,101.922,102.43,101.414,54860,20514,2024-07-10 15:24:13,主力,2年期国债2409,TS2409,TS,2409
|
||||
68,尿素409(UR409),2055.0,-22.0,-1.1%,2077.0,2080.0,2050.0,2061.0,2247.0,1875.0,185561,178245,2024-07-10 20:53:21,主力,尿素409,UR409,UR,409
|
||||
69,PVC2409(v2409),5904.0,2.0,0%,5902.0,5907.0,5875.0,5924.0,6279.0,5569.0,951642,162407,2024-07-10 21:58:43,主力,PVC2409,v2409,v,2409
|
||||
70,线材2409(wr2409),3660.0,-25.0,-0.7%,3685.0,3685.0,3655.0,3661.0,3917.0,3404.0,689,190,2024-07-10 21:38:55,主力,线材2409,wr2409,wr,2409
|
||||
71,豆油2409(y2409),7606.0,0.0,0%,7606.0,7622.0,7580.0,7638.0,8096.0,7180.0,644683,106957,2024-07-10 21:58:43,主力,豆油2409,y2409,y,2409
|
||||
72,沪锌2408(zn2408),24370.0,160.0,0.7%,24210.0,24395.0,24205.0,24240.0,25935.0,22540.0,75969,37672,2024-07-10 21:58:41,主力,沪锌2408,zn2408,zn,2408
|
||||
|
@@ -0,0 +1,179 @@
|
||||
import subprocess
|
||||
import schedule
|
||||
import time
|
||||
from datetime import datetime
|
||||
|
||||
# jerome：增加akshare库
|
||||
import akshare as ak
|
||||
|
||||
# jerome:增加下列库用于爬虫获取主力连续代码
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import csv
|
||||
import re
|
||||
import os
|
||||
# jerome:增加文件名修改工作
|
||||
import shutil
|
||||
|
||||
# Jerome:需要设置的参数
|
||||
new_directory = r"C:/simnow_trader" #设置运行目录为程序所在目录new_directory
|
||||
# 定义要启动的文件
|
||||
files_to_run = ['dingdanliu_nb.py']
|
||||
# 定义生成的保证金、手续费csv文件
|
||||
fees_filepath = r'./futures_fees_info.csv'
|
||||
contracts_filepath = r'./main_contacts.csv'
|
||||
contracts_yd_filepath = r'./main_contacts_yd.csv'
|
||||
exchange_filepath = r'./exchange_for_months.csv'
|
||||
|
||||
|
||||
|
||||
# jerome:修改运行目录
|
||||
# 获取当前工作目录
|
||||
current_directory = os.getcwd()
|
||||
print("当前工作目录：", current_directory)
|
||||
# 设置新的工作目录
|
||||
os.chdir(new_directory)
|
||||
# 验证新的工作目录
|
||||
updated_directory = os.getcwd()
|
||||
print("已更改为新的工作目录：", updated_directory)
|
||||
|
||||
def run_scripts():
|
||||
print("启动程序...")
|
||||
for file in files_to_run:
|
||||
time.sleep(1)
|
||||
# 使用subprocess模块运行命令
|
||||
subprocess.Popen(['start', 'cmd', '/k', 'python', file], shell=True)
|
||||
print(file)
|
||||
print(datetime.now(),'程序重新启动完成，等待明天关闭重启')
|
||||
|
||||
def close_scripts():
|
||||
print("关闭程序...")
|
||||
# 通过创建一个包含关闭指定窗口命令的批处理文件来关闭CMD窗口
|
||||
def close_specific_cmd_window(cmd_window_title):
|
||||
with open("close_cmd_window.bat", "w") as batch_file:
|
||||
batch_file.write(f'@echo off\nfor /f "tokens=2 delims=," %%a in (\'tasklist /v /fo csv ^| findstr /i "{cmd_window_title}"\') do taskkill /pid %%~a')
|
||||
|
||||
# 运行批处理文件
|
||||
subprocess.run("close_cmd_window.bat", shell=True)
|
||||
|
||||
|
||||
# 循环关闭所有脚本对应的CMD窗口
|
||||
for title in files_to_run:
|
||||
close_specific_cmd_window(title)
|
||||
print(datetime.now(),'已关闭程序，等待重新运行程序')
|
||||
|
||||
# jerome:增加使用akshare获取期货的手续费等数据，并保存到对应目录下
|
||||
def get_futures_fees_info():
|
||||
futures_fees_info_df = ak.futures_fees_info()
|
||||
futures_fees_info_df[['品种代码', '交割月份']] = futures_fees_info_df['合约代码'].apply(lambda x: pd.Series(split_alpha_numeric(x)))
|
||||
futures_fees_info_df.to_csv(fees_filepath, index=False)
|
||||
print("期货保证金、手续费csv文件已经保存!")
|
||||
|
||||
def get_main_contacts():
|
||||
url = "https://www.9qihuo.com/hangqing"
|
||||
|
||||
# 发送GET请求，禁用SSL验证
|
||||
response = requests.get(url, verify=False)
|
||||
response.encoding = 'utf-8' # 确保编码正确
|
||||
|
||||
# 解析网页内容
|
||||
soup = BeautifulSoup(response.text, 'lxml')
|
||||
|
||||
# 找到目标表格
|
||||
table = soup.find('table', {'id': 'tblhangqinglist'})
|
||||
|
||||
# 初始化CSV文件
|
||||
with open(r'./tmp_main_contacts.csv', mode='w', newline='', encoding='utf-8') as file:
|
||||
writer = csv.writer(file)
|
||||
|
||||
# 遍历表格的所有行
|
||||
for row in table.find_all('tr'):
|
||||
# 获取每一行的所有单元格
|
||||
cols = row.find_all(['th', 'td'])
|
||||
# 提取文本内容并写入CSV文件
|
||||
writer.writerow([col.text.strip() for col in cols])
|
||||
|
||||
df = pd.read_csv(r'./tmp_main_contacts.csv',encoding='utf-8')
|
||||
df['交易品种'] = df['合约'].str.split(r'[()]', n=1, expand=True)[0]
|
||||
df['主连代码'] = df['合约'].str.split(r'[()]', n=2, expand=True)[1]
|
||||
|
||||
df[['品种代码', '交割月份']] = df['主连代码'].apply(lambda x: pd.Series(split_alpha_numeric(x)))
|
||||
if os.path.exists(contracts_filepath):
|
||||
os.remove(contracts_filepath)
|
||||
print("原有今日期货主连csv文件已经删除!")
|
||||
|
||||
df.to_csv(r'./main_contacts.csv')
|
||||
os.remove(r'./tmp_main_contacts.csv')
|
||||
print("今日期货主连csv文件已经保存!")
|
||||
|
||||
# 拆分字母和数字的函数
|
||||
def split_alpha_numeric(s):
|
||||
match = re.match(r"([a-zA-Z]+)([0-9]+)", s)
|
||||
if match:
|
||||
return match.groups()
|
||||
else:
|
||||
return (s, None) # 如果没有匹配，返回原始字符串和None
|
||||
|
||||
def rename_file():
|
||||
# source_file = r'./main_contacts.csv'
|
||||
# target_file = r'./main_contacts_yd.csv'
|
||||
|
||||
# 检查是否存在 main_contacts_yd.csv，如果存在则删除
|
||||
if os.path.exists(contracts_yd_filepath):
|
||||
os.remove(contracts_yd_filepath)
|
||||
print("原有昨日期货主连csv文件已经删除!")
|
||||
|
||||
shutil.copy(contracts_filepath, contracts_yd_filepath)
|
||||
# print(f"{contracts_filepath} has been copied to {contracts_yd_filepath}")
|
||||
print("今日期货主连csv文件已经修改为昨日主连csv文件!")
|
||||
|
||||
# # 重命名文件
|
||||
# if os.path.exists(source_file):
|
||||
# os.rename(source_file, target_file)
|
||||
# print(f'Renamed {source_file} to {target_file}')
|
||||
# else:
|
||||
# print(f'{source_file} does not exist')
|
||||
|
||||
def exchange_for_months():
|
||||
td_df = pd.read_csv(contracts_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
|
||||
if not os.path.exists(contracts_yd_filepath):
|
||||
shutil.copy(contracts_filepath, contracts_yd_filepath)
|
||||
print("昨日主连csv文件不存在,已经使用今日主连csv替代!")
|
||||
yd_df = pd.read_csv(contracts_yd_filepath, header = 0, usecols= [16, 17],names=['主连代码', '品种代码'])
|
||||
|
||||
# 合并两个 DataFrame
|
||||
merged_df = pd.merge(td_df, yd_df, on='品种代码', suffixes=('_今日', '_昨日'))
|
||||
# 检查 '主连代码' 是否相等
|
||||
merged_df['主连代码_相等'] = merged_df['主连代码_今日'] == merged_df['主连代码_昨日']
|
||||
|
||||
if os.path.exists(exchange_filepath):
|
||||
os.remove(exchange_filepath)
|
||||
print("原有换月对比csv文件已经删除!")
|
||||
merged_df.to_csv(exchange_filepath)
|
||||
print("换月对比csv文件已经生成!")
|
||||
|
||||
# 程序时间建议(以每晚9点交易为最开始时间）：1生成费率表和今日主连文件;2生成换月对比文件；3、启动晚间交易程序并登录账户；
|
||||
# 4关闭晚间交易程序；5启动白天交易程序并登录账户；6生成昨日主连文件；7关闭白天交易程序。
|
||||
# 设置定时任务，关闭程序
|
||||
schedule.every().day.at("15:10").do(close_scripts)
|
||||
schedule.every().day.at("03:00").do(close_scripts)
|
||||
|
||||
# 设置定时任务，启动程序
|
||||
schedule.every().day.at("08:55").do(run_scripts)
|
||||
schedule.every().day.at("20:55").do(run_scripts)
|
||||
|
||||
# 设置定时任务，生成费率表文件和今日主连文件
|
||||
schedule.every().day.at("20:53").do(get_futures_fees_info)
|
||||
schedule.every().day.at("20:53").do(get_main_contacts)
|
||||
|
||||
# 设置定时任务，生成换月对比文件
|
||||
schedule.every().day.at("20:55").do(exchange_for_months)
|
||||
|
||||
# 设置定时任务，安排任务每天15:30执行, 生成昨日主连文件
|
||||
schedule.every().day.at("15:05").do(rename_file)
|
||||
|
||||
# 保持脚本运行，等待定时任务触发
|
||||
while True:
|
||||
schedule.run_pending()
|
||||
time.sleep(1)
|
||||
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Reference in New Issue
Block a user