Skip to main content

Backtest trading strategies in Python

Project description

Backtesting.py

Build Status Code Coverage Backtesting on PyPI

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):
        Close = self.data.Close
        self.ma1 = self.I(SMA, Close, 10)
        self.ma2 = self.I(SMA, Close, 20)

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


bt = Backtest(GOOG, SmaCross,
              cash=10000, commission=.002)
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 [%]                            94.29
Equity Final [$]                     69665.12
Equity Peak [$]                      69722.15
Return [%]                             596.65
Buy & Hold Return [%]                  703.46
Max. Drawdown [%]                      -33.61
Avg. Drawdown [%]                       -5.68
Max. Drawdown Duration      689 days 00:00:00
Avg. Drawdown Duration       41 days 00:00:00
# Trades                                   93
Win Rate [%]                            53.76
Best Trade [%]                          56.98
Worst Trade [%]                        -17.03
Avg. Trade [%]                           2.44
Max. Trade Duration         121 days 00:00:00
Avg. Trade Duration          32 days 00:00:00
Expectancy [%]                           6.92
SQN                                      1.77
Sharpe Ratio                             0.22
Sortino Ratio                            0.54
Calmar Ratio                             0.07
_strategy                            SmaCross

plot of trading simulation

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

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 0.1.2
Filename, size File type Python version Upload date Hashes
Filename, size Backtesting-0.1.2.tar.gz (158.4 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page