Fast Python framework for backtesting trading and investment strategies on historical candlestick data.

This tutorial shows some of the features of backtesting.py, a Python framework for backtesting trading strategies. Backtesting.py is a small and lightweight, blazing fast backtesting framework that uses …

This software is licensed under the terms of AGPL 3.0, meaning you can use it for any reasonable purpose and remain in complete ownership of all the excellent trading strategies you produce, but …

Price data, roughly as passed into Backtest, but with two significant exceptions: data is not a DataFrame, but a custom structure that serves customized numpy arrays for reasons of performance …

A Backtest that supports fractional share trading by simple composition. It applies roughly the transformation: data = (data * fractional_unit).assign(Volume=data.Volume / fractional_unit) as left …

This tutorial will show how to reuse composable base trading strategies that are part of backtesting.py software distribution. It is, henceforth, assumed you're already familiar with basic package usage.

This tutorial will show how to do that with backtesting.py, offloading most of the work to pandas resampling. It is assumed you're already familiar with basic framework usage.

This tutorial will show how to train and backtest a machine learning price forecast model with backtesting.py framework. It is assumed you're already familiar with basic framework usage and …

It is assumed you're already familiar with basic backtesting.py usage. First, let's again import our helper moving average function. In practice, one should use functions from an indicator library, such as TA …

Module backtesting.test Data and utilities for testing. Global variables var BTCUSD