Backtrader is Python based backtesting/trading platform for developing home cooked indicators and trading strategies.


  • Live Data Feed and Trading with
    • Interactive Brokers (needs IbPy and benefits greatly from an installed pytz)
    • Visual Chart (needs a fork of comtypes until a pull request is integrated in the release and benefits from pytz)
    • Oanda (needs oandapy)
  • Data feeds from csv/files, online sources or from pandas and blaze
  • Filters for datas (like breaking a daily bar into chunks to simulate intraday)
  • Multiple data feeds and multiple strategies supported
  • Multiple timeframes at once
  • Integrated Resampling and Replaying
  • Step by Step backtesting or at once (except in the evaluation of the Strategy)
  • Integrated battery of indicators
  • TA-Lib indicator support
  • Easy development of custom indicators
  • Analyzers (for example: TimeReturn, Sharpe Ratio, SQN) and pyfolio integration
  • Flexible definition of commission schemes
  • Integrated broker simulation with Market, Close, Limit, Stop and StopLimit orders, slippage and continuous cash adjustmet for future-like instruments
  • Plotting (requires matplotlib)

The platform has 2 main objectives:

  1. Ease of use
  2. Go back to 1

Loosely based on the Karate (Kid) rules by Mr. Miyagi.

The basics of running this platform:

  • Create a Strategy
    • Decide on potential adjustable parameters
    • Instantiate the Indicators you need in the Strategy
    • Write down the logic for entering/exiting the market

Or alternatively:

  • Prepare some indicators to work as long/short signals

And then

  • Create a Cerebro Engine

    • First:

      • Inject the Strategy


      • Inject the Signals

    And then:

    • Load and Inject a Data Feed
    • And do
    • For visual feedback execute: cerebro.plot()

The platform is highly configurable

Let’s hope you, the user, find the platform useful and fun to work with.