310 lines
12 KiB
Python
310 lines
12 KiB
Python
'''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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使用说明：使用前需要调整的相关参数如下
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1.确定python到csv文件夹下运行,修改csv文件为需要运行的csv
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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。
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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，
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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
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'''
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# GetOrderFlow_dj函数需要进一步了解，先不修改
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import time
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from matplotlib.pyplot import table
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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
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import operator
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import os
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# 对于含时区的datetime，可以通过timedelta来修改数据
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#from datetime import datetime, timedelta
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#os.environ['tz'] = 'Asia/ShangHai'
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#time.tzset()
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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):
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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，如果数据转换少8个小时，可以用timedelta处理
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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,具体按照数据的表头进行修改
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tickdata =pd.DataFrame({'datetime':data['datetime'],'symbol':data['合约代码'],'lastprice':data['最新价'],
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'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']})
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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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# 时序重采样 https://zhuanlan.zhihu.com/p/70353374
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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)
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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):
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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(f'./tick生成的OF数据/back_ofdata_dj.csv',index=False)
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#new_df.to_csv(f'{sym}back_ofdata_dj.csv',index=False)
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if __name__ == "__main__":
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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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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']) #
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print(data)
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tick,bar=dataload(data)
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ofdata = orderflow_df_new(tick,bar)
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print(ofdata)
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#保存orderflow数据
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# os.mkdir('./tick生成的OF数据')，或者在to_csv中修改生成的文件名
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folder_path = "tick生成的OF数据"
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if not os.path.exists(folder_path):
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os.mkdir('tick生成的OF数据')
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ofdata.to_csv('./tick生成的OF数据/ofdata_dj.csv')
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#保存回测数据
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back_data(ofdata)
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print('done')
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