抄底的艺术——量化交易之路

from datetime import datetime

import backtrader as bt
from backtrader_plotting import Bokeh
from backtrader_plotting.schemes import Tradimo

from learn.strategy1 import Strategy1

if __name__ == '__main__':
    start = datetime(2020, 1, 1)
    end = datetime(2021, 4, 1)
    # 加载数据
    data = bt.feeds.BacktraderCSVData(dataname='G:\\python\\stock\\feed\\601318_中国平安.csv', fromdate=start, todate=end)
    # 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(Strategy1)
    # cerebro.optstrategy(TestStrategy, maperiod=range(3, 30))
    cerebro.addobserver(bt.observers.DrawDown)
    # 设置初始资本为10,000
    cerebro.broker.setcash(100000)
    # 设置交易手续费为 0.2%
    cerebro.broker.setcommission(commission=0.002)
    # Add a FixedSize sizer according to the stake
    cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
    cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name="AR")
    cerebro.addanalyzer(bt.analyzers.DrawDown, _name="DD")
    cerebro.addanalyzer(bt.analyzers.Returns, _name="RE")
    cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="TA")

    start_money = cerebro.broker.getvalue()
    # 运行回测系统
    result = cerebro.run()
    end_money = cerebro.broker.getvalue()
    print('初始资产: %.2f' % start_money)
    print('最后资产: %.2f' % end_money)
    rate = round((end_money - start_money) / start_money * 100, 2)
    print('收益率: %.2f' % rate, '%')
    # 输出分析结果
    for year, ret in result[0].analyzers.AR.get_analysis().items():
        print("年化收益：\t {} {}%, ".format(year, round(ret * 100, 2)))
    print("最大回撤：{}，最大损失：{}".format(result[0].analyzers.DD.get_analysis()['max']['drawdown'],
                                   result[0].analyzers.DD.get_analysis()['max']['moneydown']))
    ta_data = result[0].analyzers.TA.get_analysis()
    print("开仓：{}次，当前开仓的数据：{}，平仓：{}次".format(ta_data['total']['total'], ta_data['total']['open'],
                                            ta_data['total']['closed']))
    print("开仓：{}次，当前开仓的数据：{}，平仓：{}次".format(ta_data['total']['total'], ta_data['total']['open'],
                                            ta_data['total']['closed']))
    print("盈利：{}次,总共盈利：{}，平均盈利：{}，最大盈利：{}".format(ta_data['won']['total'], ta_data['won']['pnl']['total'],
                                                  ta_data['won']['pnl']['average'], ta_data['won']['pnl']['max']))
    print("损失：{}次,总共损失：{}，平均损失：{}，最大损失：{}".format(ta_data['lost']['total'], ta_data['lost']['pnl']['total'],
                                                  ta_data['lost']['pnl']['average'], ta_data['lost']['pnl']['max']))
    print("共有{}根k线，每{}根k线会发生一次交易，最大间隔{}根k线，最小间隔{}根k线".format(ta_data['len']['total'], ta_data['len']['average'],
                                                             ta_data['len']['max'], ta_data['len']['min'], ))
    # 可视化
    b = Bokeh(style='bar', plot_mode='single', scheme=Tradimo())
    cerebro.plot(b)
import backtrader as bt


class Strategy1(bt.Strategy):
    def next(self):
        weekday = self.datas[0].datetime.date(0).weekday()
        # 周三卖 1代表是周二，周二下单，周三以开盘价买
        if weekday == 1 and self.position:
            self.sell(size=1000)
        # 周三卖 2代表是周三，周三下单，周四以收盘价卖
        if weekday == 2 and not self.position:
            self.buy(size=1000)

class Strategy1(bt.Strategy):
    def __init__(self):
        self.num = 0

    def next(self):
        weekday = self.datas[0].datetime.date(0).weekday()
        # 周三卖 1代表是周二，周二下单，周三以开盘价买
        if weekday == 1 and self.num >= 52:
            self.close()
            self.num = 0
        # 周三卖 2代表是周三，周三下单，周四以收盘价卖
        if weekday == 2 and self.num < 52:
            self.buy(size=100)
            self.num = self.num + 1

import tushare as ts

ts.get_hist_data('600848') #一次性获取全部日k线数据

             open    high   close     low     volume    p_change  ma5 \
date
2012-01-11   6.880   7.380   7.060   6.880   14129.96     2.62   7.060
2012-01-12   7.050   7.100   6.980   6.900    7895.19    -1.13   7.020
2012-01-13   6.950   7.000   6.700   6.690    6611.87    -4.01   6.913
2012-01-16   6.680   6.750   6.510   6.480    2941.63    -2.84   6.813
2012-01-17   6.660   6.880   6.860   6.460    8642.57     5.38   6.822
2012-01-18   7.000   7.300   6.890   6.880   13075.40     0.44   6.788
2012-01-19   6.690   6.950   6.890   6.680    6117.32     0.00   6.770
2012-01-20   6.870   7.080   7.010   6.870    6813.09     1.74   6.832

             ma10    ma20      v_ma5     v_ma10     v_ma20     turnover
date
2012-01-11   7.060   7.060   14129.96   14129.96   14129.96     0.48
2012-01-12   7.020   7.020   11012.58   11012.58   11012.58     0.27
2012-01-13   6.913   6.913    9545.67    9545.67    9545.67     0.23
2012-01-16   6.813   6.813    7894.66    7894.66    7894.66     0.10
2012-01-17   6.822   6.822    8044.24    8044.24    8044.24     0.30
2012-01-18   6.833   6.833    7833.33    8882.77    8882.77     0.45
2012-01-19   6.841   6.841    7477.76    8487.71    8487.71     0.21
2012-01-20   6.863   6.863    7518.00    8278.38    8278.38     0.23