This repository is a comprehensive, curated list of open-source libraries, packages, and resources for quantitative finance professionals. It covers a wide array of programming languages and frameworks, aiming to be a central hub for quants to discover tools for data analysis, financial modeling, trading, risk management, and research.

How It Works

The list is organized by functional area, such as numerical libraries, financial instruments and pricing, trading and backtesting, risk analysis, and data sources. It highlights tools written in Python, R, Julia, Java, JavaScript, C++, and other languages, providing links to official documentation and GitHub repositories for each entry. The curation emphasizes libraries that are actively maintained and widely adopted within the quantitative finance community.

Quick Start & Requirements

• Installation typically involves pip install <library_name> for Python packages, or following specific instructions for other languages.
• Prerequisites vary widely, but common dependencies include Python (often 3.7+), NumPy, Pandas, and sometimes specialized libraries like TA-Lib or QuantLib. GPU/CUDA support is mentioned for specific machine learning-focused libraries.
• Setup time and resource requirements depend on the complexity of the chosen library, ranging from minutes for simple data downloaders to hours for setting up complex trading backtesting frameworks.
• Official documentation and GitHub links are provided for each listed resource.

Highlighted Details

• Extensive coverage of Python libraries for data manipulation (NumPy, Pandas, Polars), modeling (PyMC3, TensorFlow Quant Finance), and backtesting (Zipline, Backtrader, vectorbt).
• Includes resources for R, Julia, Java, and C++ for quantitative finance tasks, offering cross-language options.
• Features specialized sections for technical analysis indicators, algorithmic trading frameworks, portfolio optimization, risk management, and data sourcing from various providers (e.g., Yahoo Finance, Bloomberg, Alpha Vantage).

Maintenance & Community

• The list is community-driven and curated, with contributions from various individuals and organizations.
• Specific community links (Discord, Slack) or active maintenance signals are not consistently provided for every entry but are often available on the linked GitHub repositories.

Licensing & Compatibility

• Licenses vary significantly across the listed projects, including MIT, BSD, Apache, GPL, and proprietary licenses.
• Users must carefully check the license of each individual library for compatibility with commercial use or closed-source projects.

Limitations & Caveats

• As a curated list, the quality and maintenance status of individual projects can vary; some entries may be archived or less actively developed.
• The sheer volume of resources means users need to perform their own due diligence on specific libraries for suitability and reliability.