import datetimeimport pandas as pdimport backtrader as bt
from datetime import datetime
import matplotlib
import akshare as ak
%matplotlib inlineclass SmaCross(bt.Strategy):# 全局设定交易策略的参数params = (('pfast', 5), ('pslow', 20),)def __init__(self):sma1 = bt.ind.SMA(period=self.p.pfast)  # fast moving averagesma2 = bt.ind.SMA(period=self.p.pslow)  # slow moving averageself.crossover = bt.ind.CrossOver(sma1, sma2)  # crossover signaldef next(self):if self.crossover > 0:  # if fast crosses slow to the upsideself.close()print(self.position)self.buy(size=1500) # enter longprint("Buy {} shares".format( self.data.close[0]))print(self.position)elif self.crossover < 0:  # in the market & cross to the downsideself.close()# close long positionprint(self.position)self.sell(size=1500)print("Sale {} shares".format(self.data.close[0]))print(self.position)def bt1():# 利用 AKShare 获取股票的后复权数据，这里只获取前 6 列stock_hfq_df = ak.stock_zh_a_hist(symbol="000001", adjust="hfq").iloc[:, :6]# 处理字段命名，以符合 Backtrader 的要求stock_hfq_df.columns = ['date','open','close','high','low','volume',]# 把 date 作为日期索引，以符合 Backtrader 的要求stock_hfq_df.index = pd.to_datetime(stock_hfq_df['date'])start_date = datetime(1991, 4, 3)  # 回测开始时间end_date = datetime(2022, 6, 16)  # 回测结束时间data = bt.feeds.PandasData(dataname=stock_hfq_df, fromdate=start_date, todate=end_date)  # 加载数据# 初始化cerebro回测系统设置cerebro = bt.Cerebro()# 将数据传入回测系统cerebro.adddata(data)# 将交易策略加载到回测系统中cerebro.addstrategy(SmaCross)# 设置初始资本为10,000startcash = 10000cerebro.broker.setcash(startcash)# 设置交易手续费为 0.1%cerebro.broker.setcommission(commission=0.001)# 运行回测系统cerebro.run()# 获取回测结束后的总资金portvalue = cerebro.broker.getvalue()pnl = portvalue - startcashprint(f'净收益: {round(pnl,2)}')# 打印结果print(f'总资金: {round(portvalue,2)}')cerebro.plot(style='candlestick')if __name__ == '__main__':bt1()