A curated, categorized list of open-source libraries, packages and resources for quantitative finance, spanning pricing, backtesting, data and more.
Awesome Quant is an awesome-style list that collects open-source libraries, packages and resources used in quantitative finance. Entries are grouped by topic, including numerical libraries, financial instruments and pricing, technical indicators, trading and backtesting, portfolio optimization and risk analysis, factor analysis, time series, market data sources, visualization, and cross-language frameworks. Most entries cover Python and R, with additional sections for Julia, Java, Rust, TypeScript and other languages.
Each entry is a one-line description with a link to the project and, where relevant, its language. The list also includes sections on reproducible research and books, as well as commercial and proprietary services, and a section for archived projects. It is useful as a starting point when looking for a tool for a specific quant task, such as options pricing, a backtesting engine, or market data access.
Summary generated by AI from the project's README. GitHub stats refresh daily.
AI-oriented quantitative investment platform from Microsoft covering data, model training, backtesting and order execution for quant research.
Rust-native, event-driven trading engine with Python control plane for backtesting and live trading across crypto, FX, equities, futures, options and betting venues.
Open-source Python data platform that connects financial and other data sources and exposes them to Python, Excel, AI agents and REST APIs.
Native C++20 desktop terminal for financial research, with Qt6 UI, embedded Python analytics, AI agents and 100+ market data connectors.
An open-source Python framework for applying deep reinforcement learning to automated stock trading and portfolio tasks.
Config-driven backtesting framework built on Backtrader for testing trading strategies across stocks, crypto, and forex, with an MCP server for AI assistants.