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.

License

GlacierAlgo/backtesting.py

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