wilsonfreitas/awesome-quant

Awesome Quant is a curated directory of software libraries, tools, and resources for quantitative finance, the discipline of using mathematical models, data analysis, and algorithms to understand and trade financial markets. It's a comprehensive reference list for anyone building or studying systematic, data-driven approaches to investing and trading. This is primarily aimed at quantitative analysts ("quants"), data scientists working in finance, algorithmic traders, and developers building financial applications. It's a research and toolkit reference, not a software product itself. The collection spans the full workflow of quantitative finance work: numerical computing libraries for crunching large datasets, tools for pricing financial derivatives (complex instruments like options and futures), technical indicators used in trading signals, backtesting frameworks (systems that let you test a trading strategy on historical data before risking real money), portfolio optimization and risk analysis tools, market data sources, and visualization libraries for charts and analysis. Resources are organized by programming language, primarily Python, R, and Julia, the three languages most common in quantitative finance, making it easy to find tools that fit your existing technical environment. There are also sections on sentiment analysis using alternative data sources, time series analysis, and even Excel integration for teams that live in spreadsheets. For founders building fintech products, researchers exploring algorithmic trading, or developers who need to integrate financial modeling into an application, this list provides a solid map of the available open-source tooling across the quantitative finance ecosystem.