Backtesting.py

Backtest trading strategies with Python.

Project website + Documentation

Installation

$ pip install backtesting

Usage

from backtesting import Backtest, Strategy
from backtesting.lib import crossover

from backtesting.test import SMA, GOOG


class SmaCross(Strategy):
    def init(self):
        price = self.data.Close
        self.ma1 = self.I(SMA, price, 10)
        self.ma2 = self.I(SMA, price, 20)

    def next(self):
        if crossover(self.ma1, self.ma2):
            self.buy()
        elif crossover(self.ma2, self.ma1):
            self.sell()


bt = Backtest(GOOG, SmaCross, commission=.002,
              exclusive_orders=True)
stats = bt.run()
bt.plot()

Results in:

Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure Time [%]                       94.27
Equity Final [$]                     68935.12
Equity Peak [$]                      68991.22
Return [%]                             589.35
Buy & Hold Return [%]                  703.46
Return (Ann.) [%]                       25.42
Volatility (Ann.) [%]                   38.43
Sharpe Ratio                             0.66
Sortino Ratio                            1.30
Calmar Ratio                             0.77
Max. Drawdown [%]                      -33.08
Avg. Drawdown [%]                       -5.58
Max. Drawdown Duration      688 days 00:00:00
Avg. Drawdown Duration       41 days 00:00:00
# Trades                                   93
Win Rate [%]                            53.76
Best Trade [%]                          57.12
Worst Trade [%]                        -16.63
Avg. Trade [%]                           1.96
Max. Trade Duration         121 days 00:00:00
Avg. Trade Duration          32 days 00:00:00
Profit Factor                            2.13
Expectancy [%]                           6.91
SQN                                      1.78
Kelly Criterion                        0.6134
_strategy              SmaCross(n1=10, n2=20)
_equity_curve                          Equ...
_trades                       Size  EntryB...
dtype: object

Find more usage examples in the documentation.

Features

• Simple, well-documented API
• Blazing fast execution
• Built-in optimizer
• Library of composable base strategies and utilities
• Indicator-library-agnostic
• Supports any financial instrument with candlestick data
• Detailed results
• Interactive visualizations

Bugs

Before reporting bugs or posting to the discussion board, please read contributing guidelines, particularly the section about crafting useful bug reports and ```-fencing your code. We thank you!

Alternatives

See alternatives.md for a list of alternative Python backtesting frameworks and related packages.