分析策略的优劣

定投真的可行么？

上文我们写了第一个简单的策略，买入并持有指数基金。
我们通过回测发现，指数基金在中国是否能取得很好的收益，本质上是存疑的。

那有的同学可能说，那我们使用定投策略投资指数基金的话会怎么样呢？

第二个策略（定投指数基金）

假设我们每月投入1万元购买指数基金，期限从2010-1-1到2019-12-31。
我们回测下这个数据。

图形化plot

我们可能想图形化展示下我们的投资过程，各种指标的变化情况。
backtrader提供了基本的图形能力。
backtrader的图形化依赖于mataplotlib，因此必须安装该lib。

cerebo.plot()

显示效果如下：

analyzer

backtrader提供了analyzer工具，可以用来对投资过程进行分析。

比如我们熟知的sharp ratio，买卖次数，回撤情况等等。

可以在回测运行前通过addanalyzer增加分析器，然后回测后查看分析结果。

    # Analyzer    cerebro.addanalyzer(btanalyzers.SharpeRatio, _name='mysharpe')    cerebro.addanalyzer(btanalyzers.AnnualReturn, _name='myannual')    cerebro.addanalyzer(btanalyzers.DrawDown, _name='mydrawdown')    # Run over everything    thestrats = cerebro.run()    thestrat = thestrats[0]    print('Sharpe Ratio:', thestrat.analyzers.mysharpe.get_analysis())    print('Annual Return:', thestrat.analyzers.myannual.get_analysis())    print('Drawdown Info:', thestrat.analyzers.mydrawdown.get_analysis())

分析结果如下：

$python example3.pyStarting Portfolio Value: 1200000.00Final Portfolio Value: 1335469.29Sharpe Ratio: OrderedDict([('sharperatio', 0.08794344140568065)])Annual Return: OrderedDict([(2010, 0.009994798081538692), (2011, -0.06385257455175308), (2012, -0.001059256049693591), (2013, 0.0513281370753218), (2014, 0.18051195171848144), (2015, 0.2553421396965294), (2016, -0.12786696056384306), (2017, -0.0024386306434659444), (2018, -0.2973329165832902), (2019, 0.23710565581150012)])Drawdown Info: AutoOrderedDict([('len', 1110), ('drawdown', 44.11989120158515), ('moneydown', 1054413.8328226043), ('max', AutoOrderedDict([('len', 1110), ('drawdown', 56.18897640055283), ('moneydown', 1342850.8628497515)]))])

总结

从回测结果可以看出来，十年的收益可以说很一般，即使算XIRR收益估计也就是和货币基金差不多。
另外从analyzer数据看回撤也非常之大。

回测完整代码

#!/usr/bin/env python# -*- coding: utf-8; py-indent-offset:4 -*-"""计算定投某个标的的盈利情况"""from __future__ import (absolute_import, division, print_function,                        unicode_literals)import datetime  # For datetime objects# Import the backtrader platformimport backtrader as btimport backtrader.analyzers as btanalyzersimport backtrader.feeds as btfeedimport click# Create a Strateyclass TestStrategy(bt.Strategy):    def log(self, txt, dt=None):        ''' Logging function fot this strategy'''        dt = dt or self.datas[0].datetime.date(0)        print('%s, %s' % (dt.isoformat(), txt))    def __init__(self):        # Keep a reference to the "close" line in the data[0] dataseries        self.dataclose = self.datas[0].close        # To keep track of pending orders        self.month = -1    def next(self):        # Simply log the closing price of the series from the reference        # 每月进行购买一次        if self.month != self.datas[0].datetime.date(0).month:            self.month = self.datas[0].datetime.date(0).month            self.buy(size=10000/self.dataclose[0])class TSCSVData(btfeed.GenericCSVData):    params = (        ("fromdate", datetime.datetime(2010, 1, 1)),        ("todate", datetime.datetime(2019, 12, 31)),        ('nullvalue', 0.0),        ('dtformat', ('%Y-%m-%d')),        ('openinterest', -1)    )def backtest():    filename = "bs_sh.000905.csv"    cash = 1200000.0    # Create a cerebro entity    cerebro = bt.Cerebro()    # Add a strategy    cerebro.addstrategy(TestStrategy)    # Create a Data Feed    data = TSCSVData(dataname="./datas/{0}".format(filename))    # Add the Data Feed to Cerebro    cerebro.adddata(data)    # Set our desired cash start    cerebro.broker.setcash(cash)    # Print out the starting conditions    print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())    # Analyzer    cerebro.addanalyzer(btanalyzers.SharpeRatio, _name='mysharpe')    cerebro.addanalyzer(btanalyzers.AnnualReturn, _name='myannual')    cerebro.addanalyzer(btanalyzers.DrawDown, _name='mydrawdown')    # Run over everything    thestrats = cerebro.run()    # Print out the final result    print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())    thestrat = thestrats[0]    print('Sharpe Ratio:', thestrat.analyzers.mysharpe.get_analysis())    print('Annual Return:', thestrat.analyzers.myannual.get_analysis())    print('Drawdown Info:', thestrat.analyzers.mydrawdown.get_analysis())    cerebro.plot()if __name__ == '__main__':    backtest()

(9条消息) backtrader量化平台教程（三）分析策略的优劣