This repository is a curated list of resources for applying machine learning, deep learning, and neural networks to algorithmic trading. It targets researchers, engineers, and practitioners interested in leveraging AI for financial market analysis and strategy development, offering a comprehensive overview of papers, code, datasets, and guides.

How It Works

The collection categorizes resources by specific deep learning architectures (CNNs, LSTMs, GANs), application areas (high-frequency trading, portfolio management, sentiment analysis), and methodologies (reinforcement learning). This structured approach allows users to quickly find relevant research and tools for various aspects of algorithmic trading, from data processing and prediction to strategy execution and risk management.

Quick Start & Requirements

This is a curated list, not a runnable project. To implement any of the discussed techniques, users will need to consult the linked papers and repositories, which may require Python, deep learning frameworks (TensorFlow, PyTorch, Keras), and specific libraries like Zipline for backtesting.

Highlighted Details

• Extensive coverage of papers from 2016-2020, detailing applications of CNNs, LSTMs, GANs, and Reinforcement Learning in finance.
• Includes links to numerous GitHub repositories implementing various trading strategies and models.
• Features sections on datasets, simulation resources, and guides for practical implementation.
• Covers niche areas like social processing, behavioral analysis, and cryptocurrency trading.

Maintenance & Community

The repository was last updated in 2021 and is maintained by Craig Bailes. Links to his Patreon and email are provided for direct contact. There are no explicit community channels like Discord or Slack listed.

Licensing & Compatibility

The repository grants open access for use and re-use of any kind, at no cost, under either the MIT License or Creative Commons CC-BY International Public License. This allows for broad commercial and closed-source compatibility.

Limitations & Caveats

As a curated list, this repository does not provide runnable code or direct support. Users must independently evaluate and integrate the various resources, which may have their own dependencies, licenses, and varying levels of maturity.