With the rise of big data and machine learning, open source data science and quantitative libraries on GitHub have become invaluable tools for researching and developing investment strategies. Ranging from backtesting frameworks to deep learning and reinforcement learning models, these libraries enable both retail and institutional investors to leverage cutting-edge techniques.
Zipline and Quantopian for Python-based Backtesting
Zipline and Quantopian are two of the most popular Python-based backtesting libraries with over 7,000 stars on GitHub. They provide a full pipeline for fetching data, defining alpha factors, constructing optimal portfolios, and analyzing performance. The libraries are well-documented, frequently updated, and have active user communities.
DeepDow for Deep Learning Optimization
As a novel Python library, DeepDow connects deep neural networks with portfolio optimization to find optimal weights. With TensorFlow 2.0 integration, it brings the power of deep learning to finance for discovering complex patterns and delivering stable returns.
bt for Flexible Event-driven Backtesting
The bt library delivers event-driven backtesting functionality for Python, making it easy to test strategies across different time frames and markets. With support for multiple data sources and customizable metrics, bt provides a flexible foundation for quantitative research.
Stable Baselines for Reinforcement Learning
Stable Baselines is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. With scalable TensorFlow integration, it enables researchers to experiment with state-of-the-art RL techniques on financial and market data.
In summary, GitHub contains a wealth of open-source data science libraries for investment research, ranging from backtesting frameworks to deep learning and reinforcement learning. Libraries like Zipline, Quantopian, DeepDow, bt, and Stable Baselines provide powerful tools to uncover alpha signals and develop systematic trading strategies.