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 state-of-the-art Python structures and procedures (Python …

Package backtesting. Manuals. Quick Start User Guide; Tutorials. The tutorials encompass most framework features, so it's important and advisable to go through all of them. They are short. Library of Utilities and Composable Base Strategies; Multiple Time Frames; Parameter Heatmap & Optimization; Trading with Machine Learning

Initialize a backtest. Requires data and a strategy to test. After initialization, you can call method Backtest.run() to run a backtest instance, or Backtest.optimize() to optimize it.

Module backtesting.lib. Collection of common building blocks, helper auxiliary functions and composable strategy classes for reuse. Intended for simple missing-link procedures, not reinventing of better-suited, state-of-the-art, fast libraries, such as TA-Lib, Tulipy, PyAlgoTrade, NumPy, SciPy …

This tutorial will show how to optimize strategies with multiple parameters and how to examine and reason about optimization results. It is assumed you're already familiar with basic backtesting.py usage. First, let's again import our helper moving average function.

Best trading strategies that rely on technical analysis might take into account price action on multiple time frames. 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 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 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 machine learning in general.

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