import backtrader as bt
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import backtrader.analyzers as btanalyzers#定義指標
class Ketler(bt.Indicator):params = dict(ema=20,atr= 17)lines = ('expo','atr','upper','lower')plotinfo = dict(subplot =False)plotlines = dict(upper = dict(ls = '--'),lower = dict(_samecolor = True))def __init__(self):self.l.expo = bt.talib.EMA(self.datas[0].close,timeperiod=self.params.ema)self.l.atr = bt.talib.ATR(self.data.hight,self.data.low,self.data.close,timeperiod = self.params.atr)self.l.upper = self.l.expo + self.l.atrself.lower = self.l.expo-self.l.atr
#Backtrader的策略主體
class Strategy(bt.Strategy):def log(self,txt,dt=None):"""Logging function for this strategy"""dt = dt or self.datas[0].datetime.date(0)print('%s,%s'%(dt.isoformat(),txt))def __init__(self):self.ketler = Ketler()self.close = self.data.closedef notify_order(self,order):if order.status in [order.Submitted,order.Accepted]:# Buy/Sell order submmitted/accepted to/by broker - Nothing to doreturn#Check if an order has been completed#Attention: broker could reject order if not enough cashif order.status in [order.Completed]:if order.isbuy():self.log('BUY EXECUTED ,Price:{:.2f},Cost:{:.2f},Comm {:.2f}'.format(order.executed.price,order.executed.value,order.executed.comm))self.buyprice = order.executed.priceself.buycomm = order.executed.commelse:self.log('SELL EXECUTED,Price:{:.2f},Cost:{:.2f},Comm(:.2f)'.format(order.executed.price,order.executed.value,order.executed.comm))self.bar_executed = len(self)elif order.status in [order.Canceled,order.Margin,order.Rejected]:self.log('Order Canceled/Margin/Rejected')self.order = Nonedef notify_trade(self,trade):if not trade.isclosed:returnself.log('OPERATION PROFIT,GROSS %.2f,NET %.2f' %(trade.pnl,trade.pnlcomm))def next(self):if not self.position:if self.close[0] > self.ketler.upper[0]:self.order = self.order_target_percent(target= 0.95)else:if self.close[0]<self.ketler.expo[0]:self.order = self.sell()
#策略主體
if __name__ == '__main__':cerebro = bt.Cerebro()data = bt.feeds.YahooFinanceData(dataname = 'AAPL',fromdate = datetime.datetime(2015,1,1),todate = datetime.datetime(2021,1,17),timeframe = bt.TimeFrame.Days)cerebro.adddata(data)cerebro.addstrategy(Strategy)cerebro.broker.setcash(1000000)cerebro.broker.setcommission(commission=0)cerebro.addsizer(bt.sizers.PercentSizer,percents=98)cerebro.addanalyzer(btanalyzers.SharpeRatio, _name = 'shape')cerebro.addanalyzer(btanalyzers.DrawDown,_name = 'drawdown')cerebro.addanalyzer(btanalyzers.Returns,_name='returns')print('Starte Portfolio Value {}'.format(cerebro.broker.getvalue()))back =cerebro.run()print('end portfolio vlaue {}'.format(cerebro.broker.getvalue()))par_list = [[x.analyzers.returns.get_analysis()['rtot'],x.analyzers.returns.get_analysis()['rnorm100'],x.analyzers.drawdown.get_analysis()['max']['drawdown'],x.analyzers.sharpe.get_analysis()['sharperatio']]for x in back]par_df = pd.DataFrame(par_list,columns = ['Total Return','APR','Drawdown','SharpRatio'])print(par_df)cerebro.plot(style='candle')
