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TurtleBacktest.py
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TurtleBacktest.py
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# -*- coding: utf-8 -*-
#!/usr/bin/env python3
from __future__ import (absolute_import, division, print_function,unicode_literals)
import datetime
from datetime import datetime
import boto3
import json
import numpy as np
import pandas as pd
import os.path
import sys
import pytz
import time
from os.path import exists
import backtrader as bt
class TestSizer(bt.Sizer):
params = (('stake', 1),)
def _getsizing(self, comminfo, cash, data, isbuy):
if isbuy:
return self.p.stake
position = self.broker.getposition(data)
if not position.size:
return 0
else:
return position.size
return self.p.stake
class TestStrategy(bt.Strategy):
params = ( ('maperiod', 15), ('printlog', False), )
def log(self, txt, dt=None, doprint=False):
if self.params.printlog or doprint:
dt = dt or self.datas[0].datetime.date(0)
# print('%s, %s' % (dt.isoformat(), txt))
def __init__(self):
self.dataclose = self.datas[0].close
self.datahigh = self.datas[0].high
self.datalow = self.datas[0].low
self.order = None
self.buyprice = 0
self.buycomm = 0
self.newstake = 0
self.buytime = 0
# 参数计算,唐奇安通道上轨、唐奇安通道下轨、ATR
self.DonchianHi = bt.indicators.Highest(self.datahigh(-1), period=20, subplot=False)
self.DonchianLo = bt.indicators.Lowest(self.datalow(-1), period=10, subplot=False)
self.TR = bt.indicators.Max((self.datahigh(0)- self.datalow(0)), abs(self.dataclose(-1) - self.datahigh(0)), abs(self.dataclose(-1) - self.datalow(0) ))
self.ATR = bt.indicators.SimpleMovingAverage(self.TR, period=14, subplot=True)
# 唐奇安通道上轨突破、唐奇安通道下轨突破
self.CrossoverHi = bt.ind.CrossOver(self.dataclose(0), self.DonchianHi)
self.CrossoverLo = bt.ind.CrossOver(self.dataclose(0), self.DonchianLo)
def notify_order(self, order):
if order.status in [order.Submitted, order.Accepted]:
return
if order.status in [order.Completed]:
if order.isbuy():
self.log(
'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm),doprint=True)
self.buyprice = order.executed.price
self.buycomm = order.executed.comm
else:
self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
(order.executed.price,
order.executed.value,
order.executed.comm),doprint=True)
self.bar_executed = len(self)
elif order.status in [order.Canceled, order.Margin, order.Rejected]:
self.log('Order Canceled/Margin/Rejected')
self.order = None
def notify_trade(self, trade):
if not trade.isclosed:
return
self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm))
def next(self):
if self.order:
return
#入场
if self.CrossoverHi > 0 and self.buytime == 0:
self.newstake = self.broker.getvalue() * 0.01 / self.ATR
self.newstake = int(self.newstake / 100) * 100
self.sizer.p.stake = self.newstake
self.buytime = 1
self.order = self.buy()
#加仓
elif self.datas[0].close >self.buyprice+0.5*self.ATR[0] and self.buytime > 0 and self.buytime < 5:
self.newstake = self.broker.getvalue() * 0.01 / self.ATR
self.newstake = int(self.newstake / 100) * 100
self.sizer.p.stake = self.newstake
self.order = self.buy()
self.buytime = self.buytime + 1
#出场
elif self.CrossoverLo < 0 and self.buytime > 0:
self.order = self.sell()
self.buytime = 0
#止损
elif self.datas[0].close < (self.buyprice - 2*self.ATR[0]) and self.buytime > 0:
self.order = self.sell()
self.buytime = 0
def stop(self):
self.log('(MA Period %2d) Ending Value %.2f' % (self.params.maperiod, self.broker.getvalue()), doprint=True)
def downloadFile(bucket_name, object_name, file_name):
s3 = boto3.client('s3',region_name='ap-northeast-1')
s3.download_file(bucket_name, object_name, file_name)
def uploadFile(file_name,bucket_name, key_name):
s3 = boto3.client('s3',region_name='ap-northeast-1')
s3.upload_file(file_name,bucket_name, key_name)
def BackTest(code,source_bucket_name,dest_bucket_name):
cerebro = bt.Cerebro()
# 增加一个策略
cerebro.addstrategy(TestStrategy)
#获取数据
start_date = datetime(2010, 1, 1)
end_date = datetime(2022, 12, 30)
filename = code + '.csv'
dest_filename = 'Result' + filename
objectname = 'daily/' + filename
downloadFile(source_bucket_name, objectname, filename)
#os.chdir('/home/ec2-user/efs/workdir/industry/daily')
stock_hfq_df = pd.read_csv(filename)
stock_hfq_df.index = stock_hfq_df['date'].apply(lambda x: datetime.strptime(x,'%Y-%m-%d'))
stock_hfq_df = stock_hfq_df[['open','close','high','low','volume']]
data = bt.feeds.PandasData(dataname=stock_hfq_df, fromdate=start_date, todate=end_date) # 加载数据
cerebro.adddata(data) # 将数据传入回测系统
cerebro.broker.setcash(1000000.0)
cerebro.broker.setcommission(commission=0.0002)# 设置交易手续费为 0.02%
# 设置买入策略
cerebro.addsizer(TestSizer)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
# 在结束时写入结果到S3存储桶
f = open(dest_filename, "a")
f.write('Final Portfolio Value: %.2f\n' % cerebro.broker.getvalue())
f.write('Return: %.4f' % (float(cerebro.broker.getvalue())/1e6 - 1))
f.close()
targetobjectname = 'result/' + dest_filename
uploadFile(dest_filename, dest_bucket_name, targetobjectname)
if __name__ == '__main__':
bucket_name = 'quantbacktest'
industry = 'bank'
txtname = industry + '.txt'
#stock_list = []
# 开始计时
start_time = time.time()
for line in open(txtname):
code = line[:6]
#stock_list.append(code)
filename = 'daily/' + code + '.csv'
#stock_hfq_df = pd.read_csv(filename)
BackTest(code, bucket_name, bucket_name)
# print(filename + 'Done')
# 结束计时
end_time = time.time()
# 计算执行时间
execution_time = end_time - start_time
print(f"计算执行时间为: {execution_time}秒")
sys.exit(0)