This repository provides over 150 Jupyter notebooks accompanying the second edition of "Machine Learning for Algorithmic Trading." It offers a comprehensive, practical guide for developers and quantitative analysts looking to integrate ML into trading strategies, covering data sourcing, feature engineering, supervised/unsupervised learning, NLP, deep learning, and reinforcement learning for algorithmic trading.

How It Works

The project demonstrates an end-to-end ML for trading workflow, from idea generation and data collection to model evaluation and strategy backtesting. It emphasizes practical application using Python libraries like pandas, scikit-learn, TensorFlow, and PyTorch, and includes implementations of recent research papers using CNNs, autoencoders, and GANs for financial time series.

Quick Start & Requirements

• Installation instructions are available for Docker and various conda environments.
• Key dependencies include Python, pandas, scikit-learn, TensorFlow, PyTorch, and a customized version of Zipline.
• Data sources are managed via a create_datasets script.
• Official documentation and setup guides are linked within the repository.

Highlighted Details

• Covers a broad spectrum of ML techniques, from linear regression to deep reinforcement learning.
• Includes practical applications for sentiment analysis from text data (SEC filings, news) and image data (satellite imagery).
• Replicates several recent academic papers on CNNs for time series, autoencoders for asset pricing, and GANs for synthetic data.
• Features a new chapter on strategy backtesting and an appendix detailing over 100 alpha factors.

Maintenance & Community

• The repository is associated with Packt Publishing.
• Specific community channels or active maintenance contributors are not detailed in the README.

Licensing & Compatibility

• The repository itself is not explicitly licensed, but the content is tied to a published book.
• Compatibility for commercial use or closed-source linking would depend on the book's licensing terms.

Limitations & Caveats

The repository contains notebooks for a published book, implying that the code is primarily for educational and illustrative purposes rather than a production-ready trading framework. The setup and data acquisition may require significant effort.