This repository is a curated list of resources for applying AI, particularly LLMs and deep reinforcement learning, to financial markets. It targets quantitative analysts, researchers, and developers interested in automated trading, portfolio management, and financial analysis. The benefit is a comprehensive overview of relevant papers, tools, libraries, and strategies for leveraging AI in finance.

How It Works

The list categorizes resources across various AI applications in finance, including LLMs for financial statement analysis and trading strategy generation, deep reinforcement learning for portfolio management and automated trading, and technical analysis tools. It highlights specific libraries like FinRL for DRL in quantitative finance and mentions LLM projects like PIXIU and MarS for financial market simulation and analysis.

Quick Start & Requirements

• Installation: Primarily involves cloning repositories and installing Python dependencies (e.g., pip install -r requirements.txt).
• Prerequisites: Python 3.x, common data science libraries (NumPy, Pandas), deep learning frameworks (TensorFlow, PyTorch), and potentially specific exchange APIs or backtesting environments (e.g., Zipline, backtrader).
• Resources: Many projects require significant historical financial data and computational resources for training DRL agents or LLMs.
• Links:
  • FinRL: https://github.com/gentlesavior/FinRL
  • OpenBB: https://github.com/OpenBB-finance/OpenBBTerminal
  • Zipline: https://github.com/quantopian/zipline

Highlighted Details

• Extensive coverage of LLM applications in finance, including GPT-4 for earnings prediction and LLM-based financial advisors.
• Strong emphasis on Deep Reinforcement Learning (DRL) for automated trading and portfolio optimization, with multiple libraries and research papers.
• Includes resources for high-frequency trading (HFT) and event-driven strategies.
• Features a wide array of data sources, research tools, and backtesting/trading system frameworks.

Maintenance & Community

This is a curated list, not a single project with active development. The primary maintainer is georgezouq. Community interaction is likely through individual linked projects.

Licensing & Compatibility

Licenses vary significantly across the linked projects, ranging from permissive (MIT, Apache) to more restrictive. Users must check the license of each individual repository or tool for compatibility with commercial or closed-source applications.

Limitations & Caveats

As a curated list, it does not represent a single, cohesive, or actively maintained project. The quality, documentation, and maintenance status of individual linked resources can vary greatly. Many advanced AI trading strategies require substantial domain expertise and computational resources.