Revolutionize Your Trading: Discover the Top Python Repositories for Quantitative Trading on GitHub

Pybacktest

It is a Python quantitative backtesting framework. You can use this Python framework to extract trading signals and prices. The framework provides dictionary-like objects to backtest trading strategies. The framework supports pandas vector operations and executes very quickly.

Pyalgotrade

It is another framework for backtesting and trading; it supports panda vector operation and automatic parameter optimization and has a built-in stock technical indicator library. By using this framework, you are able to use TensorFlow, PyTorch, and Keras. This framework also supports multi-variety, multi-strategy, and multi-period trading strategies.

TradingWithPython

This framework has a rich collection of quantitative trading functions to help traders develop and test their strategies.

Quantdigger

This Python trading framework is most suitable for strategy developers. With this framework, you can perform stock arbitrage and portfolio strategies. This framework also helps you with future backtesting strategies.

QuantSoftware Toolkit

If you want to perform tasks such as portfolio construction and management, this framework is best suited for you. This library is specially designed for students from computer science, finance, and quantitative analysts. With this framework, you can perform modeling analysis, backtesting analysis, and trading.

Zipline

This open-source library is developed by Quantopian and provides you with many common statistical functions. This library is easy to use, and it is the complete quantitative trading backtesting engine.

Trade

This framework is unique because it provides an event-driven approach to trading. With this framework, you can use themes and events to determine the trading results.

TA-Lib

This framework is used for technical indicators of market data. With this framework, you will quickly calculate and analyze market data.

Pynance

If you want to analyze data from stock and derivatives markets, this library is ideal for you. With the support of this framework, you can fetch data from stock and derivatives markets. Additionally, you can use this library for machine learning algorithms, create your own customized trading strategies.