With the rise of artificial intelligence (AI) and machine learning in finance, there has been a proliferation of open-source Ai powered investment platforms on github. These platforms aim to make algorithmic trading and quantitative analysis more accessible. In this article, we will explore some of the most popular github repositories for Ai-driven investment research and trading. From backtesting frameworks like zipline to end-to-end deep learning platforms like Qlib, github has become a treasure trove for both retail and institutional investors looking to integrate AI into their investment process. With hundreds of repositories to choose from, it can be difficult to identify the optimal open-source tools for your needs. By highlighting some of the starred repositories and their key capabilities, this article provides a guide to kickstart your Ai-powered investing github journey.
Zipline – Pythonic Algorithmic Trading Library
Zipline from Quantopian is one of the most popular Ai powered backtesting frameworks on github, with over 15k stars. It features a robust event-driven architecture for backtesting and supports fetching market data for US equities. While it may require some additional work to adapt for Chinese stocks, zipline offers a solid foundation for researching and building algorithmic trading strategies.
Qlib – AI-oriented Quantitative Investment Platform
With over 9k stars, Qlib from Microsoft stands out as one of the most comprehensive end-to-end platforms for AI-driven investment research on github. It covers the full pipeline from data to model evaluation, providing tools for data management, feature engineering, model training and backtesting. Qlib natively supports both Chinese and US market data. The project also includes hundreds of predefined factors and a framework for alpha research.
JoinQuant – Chinese Market Data API
For Chinese market data, the JoinQuant SDK is a popular choice with over 4k github stars. It provides a clean interface to access historical data from various Chinese exchanges. The API coverage is quite extensive including stocks, indices, futures across multiple frequencies. Though not a full trading framework itself, the JoinQuant SDK is a solid building block for China-focused investment research.
Backtrader – Python Backtesting Library
With nearly 10k github stars, backtrader stands out for its simplicity and extensive documentation. It adopts an event-driven architecture akin to zipline but is designed to be more modular and extensible. Strategies can be coded in a Pythonic manner while still allowing optimization with Cython and Numba. While not focused specifically on AI integration, backtrader provides a battle-tested foundation for systematic trading.
Freqtrade – Crypto Trading Bot
For crypto trading bots, Freqtrade is a popular open-source option with 5k+ stars. It boasts support for algorithmic trading across many major exchanges along with backtesting capabilities. Freqtrade is implemented in Python and integrates smoothly with data science stacks. The project incorporates optimization and hyperparameter tuning to simplify strategy development.
Github has become a focal point for AI-centric investment research due to its wealth of high-quality open-source libraries and platforms. For both Chinese and US markets, there are robust tools covering data management, backtesting, live trading and model optimization. With its strong community and documentation, Ai powered investment repositories on github offer retail and institutional investors an ideal entry point to integrate algorithmic trading and machine learning into their investment process.