Skip to main content

Backtest trading strategies in Python

Project description

Build Status Code Coverage Backtesting on PyPI PyPI downloads GitHub Sponsors

Backtest trading strategies with Python.

Project website


Star the project if you use it.


$ 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.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for Backtesting, version 0.3.2
Filename, size File type Python version Upload date Hashes
Filename, size Backtesting-0.3.2.tar.gz (174.5 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page