In recent years, with the development of artificial intelligence and financial technology, AI investment research has become a hot topic. More and more developers and researchers are open-sourcing AI quant libraries and tools on Github, which provides great convenience for AI investment research. This article will introduce several popular Github open-source AI quant libraries for stock trading, crypto trading, etc, helping investors find the right tools to improve their investment research and strategies. There are some great open-source AI quant libraries on Github like Backtrader, Zipline, Qlib etc, which support backtesting, machine learning model research and live trading. With the help of these AI libraries, investors can develop automated trading strategies based on quantitative models and historical data analysis.
Backtrader – Feature-rich Python backtesting library
Backtrader is an open-source Python library focused on backtesting trading strategies. It is event-driven and supports replay, optimization, machine learning integration. The library is designed to be flexible and extensible to let users customize behaviors. Some key features of Backtrader:
– Data feeds from CSV, online sources or Pandas DataFrames
– Multiple data timeframes like ticks, daily etc.
– Common technical indicators like SMA, RSI etc.
– Strategy optimization through machine learning
– Visualization of backtest results
– Live trading integration
Backtrader is actively maintained and has detailed documentation and community support. It is one of the most popular open-source backtesting libraries with over 10k stars on Github.
Zipline – Python backtesting for US stocks
Zipline is another Python library for backtesting trading algorithms against historical data. It is developed by Quantopian specifically for US equities and options trading. Zipline supports fetch of market data, order management, risk analysis etc. It can run locally using Pandas data sources and also be deployed on Quantopian’s cloud platform. Key capabilities of Zipline:
– Fetch US stock pricing data from Yahoo Finance, CSV
– Data structures like order book, performance tracking
– Risk analysis of portfolio metrics like volatility, beta
– Visualization of strategy performance
– Seamless live trading integration
As an open-source project from Quantopian, Zipline has good documentation and community support. It also integrates well with related libraries like Pyfolio for performance analysis.
Qlib – AI & quant investment platform from Microsoft
Qlib is an open-source AI investment platform from Microsoft, designed for quant researchers and developers. It aims to make AI quant research simple and efficient. Qlib supports the full pipeline from data processing, model research, backtesting to live trading. Some key capabilities of Qlib:
– China and US stock market data sources
– Dataset with OHLCV, technical indicators
– Automated data preprocessing
– Model integration frameworks like PyTorch, TensorFlow
– Backtesting and visualization
– Model serving for live trading
Qlib also provides built-in datasets of alpha factors, risk models and benchmark trading strategies. With Microsoft’s continued development, Qlib has become one of the most popular AI quant libraries on Github.
Freqtrade – Crypto trading bot in Python
For crypto trading, Freqtrade is a popular open-source automated trading bot written in Python. It supports backtesting trading strategies and live trading through exchange APIs. Freqtrade is designed to support major crypto exchanges like Binance, Bitfinex etc. Some key features:
– Backtesting with data from exchange API
– Customizable TA indicators and trading strategies
– Automated order execution through CCXT library
– Telegram bot for notifications and controlling
– Plotting tools to analyze performance
– Multi-pair and multi-exchange trading
As an actively maintained project, Freqtrade has detailed documentation and many strategy examples for developers to reference when building own bots.
In summary, Github has become a hub for AI quant developers and researchers to share useful libraries and tools on AI investment research. Popular OSS libraries like Backtrader, Zipline, Qlib provide convenient frameworks for strategy research, backtesting, automation on different assets like stocks and crypto. By taking advantage of these open-source libraries, AI quant investors can significantly improve their research efficiency and uncover alpha-generating investment strategies.