If the backtrader strategy optimization result is output gracefully

Backtrader technical tutorial we wrote

The backtrader backtest results are stored in various analyzers. The result is in the form of a dictionary. If you want to output it in the form of a table and save it to a csv file, you can refer to the following example.

This example comes from Optimizing Strategy Backtesting in Python with Backtrader , using backtrader to optimize strategy parameters. The strategy is a simple double moving average strategy. What needs to be optimized is the time window size of the two moving averages. The following code obtains stock market data from yahoo online api and can be run directly.

analyzeroutputcsv.py

import backtrader as bt
import backtrader.analyzers as btanalyzers
import pandas as pd
import matplotlib
from datetime import datetime

# 定义双均线策略
class MaCrossStrategy(bt.Strategy):
 
    params = (
        ('fast_length', 10),
        ('slow_length', 50)
    )
     
    def __init__(self):
        ma_fast = bt.ind.SMA(period = self.params.fast_length)
        ma_slow = bt.ind.SMA(period = self.params.slow_length)
         
        self.crossover = bt.ind.CrossOver(ma_fast, ma_slow)
 
    def next(self):
        if not self.position:
            if self.crossover > 0: 
                self.buy()
        elif self.crossover < 0: 
            self.close()

if __name__ == "__main__":
    
    cerebro = bt.Cerebro()

  
    # 从yahoo在线api取得股票AAPL的日线数据
    data = bt.feeds.YahooFinanceData(dataname = 'AAPL', fromdate = datetime(2018, 1, 1), todate = datetime(2020, 1, 1))
    cerebro.adddata(data)

    #策略优化
    cerebro.optstrategy(
            MaCrossStrategy,
            fast_length = range(1, 11, 5), 
            slow_length = range(25, 35, 5))
    


    cerebro.broker.setcash(1000000.0)
    
    cerebro.addsizer(bt.sizers.PercentSizer, percents = 10)
    cerebro.addanalyzer(btanalyzers.SharpeRatio, _name = "sharpe")
    cerebro.addanalyzer(btanalyzers.DrawDown, _name = "drawdown")
    cerebro.addanalyzer(btanalyzers.Returns, _name = "returns")
    
    # 运行优化，由于每个参数组合运行一次策略，所以back是返回的策略实例列表（每个实例对应一组参数值）
    back = cerebro.run()


    # 每个策略实例的结果以列表的形式保存在列表中。
    # 优化运行模式下，返回值是列表的列表,内列表只含一个元素，即策略实例
    par_list = [[x[0].params.fast_length, 
                x[0].params.slow_length,
                x[0].analyzers.returns.get_analysis()['rnorm100'], 
                x[0].analyzers.drawdown.get_analysis()['max']['drawdown'],
                x[0].analyzers.sharpe.get_analysis()['sharperatio']
                ] for x in back]

    # 结果转成dataframe
    par_df = pd.DataFrame(par_list, columns = ['length_fast', 'length_slow', 'return', 'dd', 'sharpe'])

    print(par_df.head())

    par_df.to_csv('result.csv')

 

Edited on 11-25