BT (Backtesting Toolkit) is a Python library for backtesting trading strategies. It is designed to be a flexible and reusable framework for building and testing trading strategies. One of the main features of BT is its use of strategy blocks, which are reusable and flexible blocks of strategy logic that can be combined to create complex trading strategies. These strategy blocks can be used to define various aspects of a trading strategy, such as entry and exit conditions, risk management techniques, and portfolio management techniques.

• Vectorbt
• Backtrader
• PyAlgoTrade
• Pinkfish
• Finmarketpy
• QuantStart QsTrader
• Pysystemtrade
• QTPyLib
• Gemini
• Quantdom
• Clairvoyant
• optopsy

BT also provides a range of tools for analyzing and visualizing the results of backtesting. It allows you to output detailed statistics about your trading strategy, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts, such as equity curves and performance graphs, to help you visualize the performance of your trading strategy.

In addition to these features, BT also supports multiple instruments and allows you to easily incorporate transaction costs and slippage into your backtests. It is a powerful and flexible tool for backtesting trading strategies in Python.

Vectorbt

Vectorbt is a Python library for analyzing and backtesting trading strategies at scale. It is built on top of the popular pandas library and provides a range of tools and functions for analyzing and backtesting trading strategies on large datasets.

One of the main features of vectorbt is its use of vectorized operations, which allow you to perform calculations and analysis on large datasets in a fast and efficient way. This makes it well-suited for analyzing and backtesting trading strategies on large datasets, such as minute-by-minute or tick-by-tick data.

In addition to its vectorized operations, vectorbt also provides a range of tools and functions for analyzing and visualizing the performance of trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

Vectorbt is a powerful and efficient tool for analyzing and backtesting trading strategies at scale in Python.

Backtrader

Backtrader is a Python library for backtesting and live algotrading. It is a feature-rich framework that provides a wide range of tools and functions for building and testing trading strategies.

One of the main features of Backtrader is its support for both backtesting and live algotrading. It allows you to test your trading strategies on historical data and then deploy them in a live trading environment, using a few select brokers.

Backtrader also provides a range of tools and features for building and testing trading strategies. It allows you to easily define entry and exit conditions, as well as incorporate risk management techniques and portfolio management techniques. It also provides a range of functions for analyzing and visualizing the performance of your trading strategies, including functions for calculating key performance metrics and creating useful charts and plots.

In addition to these features, Backtrader is a pure-Python library and is easy to install and use. It is a popular choice for backtesting and algotrading in Python.

PyAlgoTrade

PyAlgoTrade is a Python library for event-driven algorithmic trading. It is focused on backtesting and supports live trading through a few select brokers.

One of the main features of PyAlgoTrade is its event-driven design, which allows you to easily build and test trading strategies that respond to real-time market events. It provides a range of tools and functions for defining entry and exit conditions, as well as incorporating risk management techniques and portfolio management techniques.

PyAlgoTrade also provides a range of functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

In addition to these features, PyAlgoTrade is easy to install and use, making it a popular choice for event-driven algorithmic trading in Python.

Pinkfish

Pinkfish is a Python library for backtesting intraday trading strategies on daily data. It is a lightweight library that provides a simple and easy-to-use interface for backtesting intraday strategies.

One of the main features of Pinkfish is its focus on intraday trading strategies. It allows you to test your trading strategies on daily data and evaluate their performance over short time periods, such as a few hours or a few days.

Pinkfish also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

In addition to these features, Pinkfish is a lightweight and easy-to-use library, making it a good choice for backtesting intraday trading strategies on daily data in Python.

Finmarketpy

finmarketpy is a Python library for analyzing financial market data. It provides a range of tools and functions for analyzing and visualizing financial data, including stock prices, futures prices, options prices, and more.

One of the main features of finmarketpy is its ability to retrieve and analyze financial market data from a variety of sources, including Yahoo Finance, Google Finance, and Quandl. It provides functions for downloading and loading financial data into Python, as well as functions for cleaning and preprocessing the data.

In addition to its data handling capabilities, finmarketpy also provides a range of tools and functions for analyzing and visualizing financial data. It allows you to calculate key performance metrics, such as the Sharpe ratio and drawdown, and provides a range of useful charts and plots, such as candlestick charts and performance graphs, to help you visualize the performance of financial assets.

QuantStart QsTrader

QuantStart QSTrader is a Python library for backtesting systematic trading strategies. It is a modular, schedule-driven framework that is designed specifically for long-short equities and ETF-based trading strategies.

One of the main features of QuantStart QSTrader is its focus on systematic trading strategies. It provides a range of tools and functions for building and testing systematic trading strategies, including support for long-short equities and ETFs.

QuantStart QSTrader also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

Pysystemtrade

pysystemtrade is a Python library for backtesting trading strategies. It is the open-source version of Robert Carver’s backtesting engine, which implements trading systems according to his book “Systematic Trading: A unique new method for designing trading and investing systems.”

One of the main features of pysystemtrade is its focus on systematic trading strategies. It provides a range of tools and functions for building and testing systematic trading strategies, including support for long-short equities and ETFs.

pysystemtrade also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

QTPyLib

QTPyLib is a Python library for event-driven algorithmic trading. It is a versatile library that provides a range of tools and functions for building and testing trading strategies that respond to real-time market events.

One of the main features of QTPyLib is its event-driven design, which allows you to easily build and test trading strategies that respond to real-time market events. It provides a range of tools and functions for defining entry and exit conditions, as well as incorporating risk management techniques and portfolio management techniques.

QTPyLib also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

Gemini

Gemini is a Python library for backtesting cryptocurrency trading strategies. It is a backtester that focuses specifically on the cryptocurrency market and provides a range of tools and functions for building and testing cryptocurrency trading strategies.

One of the main features of Gemini is its focus on the cryptocurrency market. It provides a range of tools and functions for retrieving and analyzing cryptocurrency market data, including support for multiple exchanges and multiple cryptocurrencies.

Gemini also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

Quantdom

Quantdom is a Python library for building and testing financial trading strategies. It is a Qt-based framework that provides a range of tools and functions for modeling financial strategies, portfolio management, and analyzing backtests.

One of the main features of Quantdom is its focus on modeling financial strategies. It provides a range of tools and functions for defining and testing financial trading strategies, including support for long-short equities and ETFs.

Quantdom also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown. It also provides a range of useful charts and plots, such as equity curves and performance graphs, to help you visualize the performance of your trading strategies.

Clairvoyant

Clairvoyant is software for identifying and monitoring social and historical cues for short-term stock movement. It is designed to help traders and investors make more informed decisions by providing insights into market trends and sentiment.

One of the main features of Clairvoyant is its ability to identify and monitor social and historical cues for short-term stock movement. It uses machine learning algorithms to analyze large amounts of data from social media, news articles, and other sources to identify trends and patterns that may impact stock prices.

Clairvoyant also provides a range of tools and functions for analyzing and visualizing the performance of your trading strategies. It allows you to calculate key performance metrics, such as the Sharpe ratio, drawdown, and maximum drawdown.

optopsy

optopsy is a Python library for backtesting options trading strategies. It is a nimble library that provides a range of tools and functions for building and testing options trading strategies quickly and efficiently.

One of the main features of optopsy is its focus on options trading. It provides a range of tools and functions for analyzing and modeling options trading strategies, including support for a variety of options types and expiration dates.

• Python Backtesting Frameworks – vegibit (vegibit.com)
• backtesting-frameworks · GitHub Topics · GitHub (github.com)
• List of Most Extensive Backtesting Frameworks Available (tradewithpython.com)
• Backtesting.py – A complete guide | Greyhound Analytics (greyhoundanalytics.com)
• Python Backtesting Frameworks: Six Options to Consider (pipekit.io)
• Master the art of backtesting with Python: A step-by-step guide (medium.datadriveninvestor.com)
• Python Backtesting Primer using backtesting.py – Medium (medium.com)
• Python Trading Toolbox: a gentle introduction to (towardsdatascience.com)
• Best Backtesting Library for Python – QMR (www.qmr.ai)
• Backtrader for Backtesting (Python) – A Complete (algotrading101.com)
• Build a Backtester in Python in 10 Minutes – DEV Community (blankly.finance)
• Best Backtesting Framework (python) : r/algotrading – Reddit (www.reddit.com)
• A multi-asset backtesting framework with python – Python Awesome (pythonawesome.com)
• Python Trading Strategy (Backtesting, List, And (www.quantifiedstrategies.com)
• backtesting API documentation – GitHub Pages (kernc.github.io)