Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
The QuantLib C++ library
High-performance TensorFlow library for quantitative finance.
A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
- risk
- pricing
- risk-management
- asset-allocation
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Technical Analysis Library for Golang
- technical-analysis
- golang
- go
- stock
Go library containing a collection of financial functions for time value of money (annuities), cash flow, interest rate conversions, bonds and depreciation calculations.
Streaming-first technical indicators with a Rust core: native Python, Node.js and WebAssembly bindings plus a C ABI hub for C, C++, C#, Go, Java and R. 514 indicators, O(1) per-tick updates, no system dependencies. Drop-in TA-Lib replacement.