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Backtest trading strategies in Python

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Backtest trading strategies with Python.

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$ pip install backtesting


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

from backtesting.test import SMA, GOOG

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

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

bt = Backtest(GOOG, SmaCross, commission=.002,
stats =

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
_strategy              SmaCross(n1=10, n2=20)
_equity_curve                          Equ...
_trades                       Size  EntryB...
dtype: object

plot of trading simulation

Find more usage examples in the documentation.


  • 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


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

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