增加交易策略、交易指标、量化库代码等文件夹
This commit is contained in:
Win_home
@@ -0,0 +1,319 @@
|
||||
'''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。
|
||||
#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
|
||||
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
|
||||
|
||||
|
||||
|
||||
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
|
||||
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')
|
||||
|
||||
Reference in New Issue
Block a user