導(dǎo)包

#引入技術(shù)指標(biāo)數(shù)據(jù) from __future__ import (absolute_import ,division,print_function,unicode_literals) import datetime #用于datetime對(duì)象操作 import os.path #用于管理路徑 import sys #用于在argvTo[0]中找到腳本名稱 import backtrader as bt #引入backtrader框架 %matplotlib inline

策略

#創(chuàng)建策略 class TestStrategy(bt.Strategy):params = (('maperiod1',5),('maperiod2',13),('maperiod3',21),('maperiod4',34),('maperiod5',55),('printlog',True),('poneplot' , False),#是否打印到同一張圖('pstake' , 100000) #單筆交易股票數(shù)據(jù))def log(self,txt,dt=None,doprint = False):dt = dt or self.datas[0].datetime.date(0)#print('%s,%s' % (dt.isoformat(),txt))"""策略的日志函數(shù)"""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.inds = dict()for i, d in enumerate(self.datas):self.inds[d] = dict()self.inds[d]['ma1'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod1)self.inds[d]['ma2'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod2)self.inds[d]['ma3'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod3)self.inds[d]['ma4'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod4)self.inds[d]['ma5'] = bt.indicators.SimpleMovingAverage( d.close,period = self.params.maperiod5)self.inds[d]['D1'] = bt.ind.CrossOver(self.inds[d]['ma5'],self.inds[d]['ma4']) #交叉信號(hào)self.inds[d]['A1'] = bt.ind.CrossOver(self.inds[d]['ma1'],self.inds[d]['ma2']) #交叉信號(hào) self.inds[d]['C1'] = bt.ind.CrossOver(self.inds[d]['ma2'],self.inds[d]['ma3'])#跳過第一只股票data，第一只股票data作為主圖數(shù)據(jù)if i > 0:if self.p.poneplot:d.plotinfo.plotmaster = self.datas[0]def notify_trade(self,trade):if not trade.isclosed:returnself.log('OPERATION PROFIT,GROSS %.2F,NET %.2F' %(trade.pnl,trade.pnlcomm))#多股回測(cè)時(shí)使用，數(shù)據(jù)讀取。 def prenext(self):self.next()def next(self):# 獲取當(dāng)天日期date = self.datas[0].datetime.date(0)# 獲取當(dāng)天valuevalue = self.broker.getvalue()for i , d in enumerate(self.datas): dt,dn = self.datetime.date(),d._name #獲取時(shí)間及股票代碼 pos = self.getposition(d).size sig1 = ((self.inds[d]['D1'][-1]>0) and (self.inds[d]['A1'][0]>0)) and (self.inds[d]['ma2'][0] >self.inds[d]['ma4'][0])and (self.inds[d]['ma4'][0] >self.inds[d]['ma4'][-1])sig2 = ((self.inds[d]['D1'][-1]>0) or (self.inds[d]['A1'][0]>0 ))and(self.inds[d]['ma2'][0] >self.inds[d]['ma2'][-1])and(d.close[0]/d.open[0]>1.05)and(d.volume[0] /d.volume[-1]>2)sig3 = ((self.inds[d]['D1'][-1]>0) or (self.inds[d]['A1'][0]>0 ))and(self.inds[d]['ma2'][0] >self.inds[d]['ma3'][0] )and(self.inds[d]['ma3'][0] >self.inds[d]['ma4'][0] )and(self.inds[d]['ma4'][0] >self.inds[d]['ma4'][-1] )sig4 = self.inds[d]['C1'][0]<0#print('sig1',sig1)if not pos: # 不在場(chǎng)內(nèi)，則可以買入 vol成交量， ref日前if sig1 or sig2 and sig3: #如果金叉self.buy(data =d,size =self.p.pstake) #買self.log('%s,BUY CREATE, %.2f ,%s' % (dt, d.close[0] ,d._name))#self.order = self.buy()elif sig4: #在場(chǎng)內(nèi)。且死叉self.close(data = d) #賣self.log('%s,SELL CREATE,%.2f,%s' % (dt, d.close[0] ,d._name))#self.order = self.sell()

印花稅

class stampDutyCommissionScheme(bt.CommInfoBase):params = (('stamp_duty',0.005),#印花稅率('percabs',True),)def _gotcommission(self,size,price,pseudoexec):if size >0:#買入，不考慮印花稅return size*price * self.p.commissionelif size<0:#賣出，考慮印花稅return -size*price*(self.p.stamp_duty + self.p.commission)else:return 0

開始回測(cè)

#創(chuàng)建cerebro實(shí)體 cerebro = bt.Cerebro() #添加策略 cerebro.addstrategy(TestStrategy)

添加數(shù)據(jù)

#創(chuàng)建價(jià)格數(shù)據(jù) import akshare as ak import baostock as bs import pandas as pd import datetime#獲取股票池?cái)?shù)據(jù) from os import listdir filename = listdir('D:/stock_data') stk_pools = filenamefor i in stk_pools[:]:try:datapath = 'D:/stock_data/'+idf = pd.read_csv('D:/stock_data/'+i)#將數(shù)據(jù)長(zhǎng)度不足的股票刪去if len(df)<55:passelse:try:data = bt.feeds.GenericCSVData(dataname = datapath,fromdate = datetime.datetime(2010,4,1),todate = datetime.datetime(2021,7,8),nullvalue = 0.0,dtformat = ('%Y-%m-%d'),datetime = 1,open =2,high = 3,low = 4,close = 5,volume = 6,openinterest = -1)cerebro.adddata(data,name = i)except:continue except:continue

?設(shè)置參數(shù)

#設(shè)置啟動(dòng)資金 cerebro.broker.setcash(len(stk_pools[:50])*10000) #設(shè)置交易單位大小 cerebro.addsizer(bt.sizers.FixedSize,stake = 100) #設(shè)置傭金為千分之一 comminfo = stampDutyCommissionScheme(stamp_duty=0.005,commission=0.001) cerebro.broker.addcommissioninfo(comminfo) #不顯示曲線 for d in cerebro.datas:d.plotinfo.plot = False #打印開始信息 print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

回測(cè)數(shù)據(jù)分析

#查看策略效果 cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio') back = cerebro.run(maxcpus=12,exactbars=True,stdstats=False)import warnings warnings.filterwarnings('ignore') strat = back[0] portfolio_stats = strat.analyzers.getbyname('pyfolio') returns, positions, transactions, gross_lev = portfolio_stats.get_pf_items() returns.index = returns.index.tz_convert(None)import quantstats quantstats.reports.html(returns, output='stats.html', title='Stock Sentiment')import webbrowser f = webbrowser.open('stats.html') #打印最后結(jié)果 print('Final Profolio Value : %.2f' %cerebro.broker.getvalue())

總結(jié)

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