Awesome AI in Finance
Section: Framework · High-performance TensorFlow library for quantitative finance.
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Appears in 5 awesome lists
High-performance TensorFlow library for quantitative finance from Google
Section: Framework · High-performance TensorFlow library for quantitative finance.
Section: Financial Instruments & Pricing · Python - High-performance TensorFlow library for quantitative finance.
Section: Pricing · High-performance TensorFlow library for quantitative finance from Google
Section: Mathematics · High-performance TensorFlow library for quantitative finance.
Section: Pricing · High-performance TensorFlow library for quantitative finance from Google
| Python, MCP | - AI-powered multi-market stock analysis with transparent multi-factor scoring for 73 stocks across US, HK, and A-share markets. EU AI Act Art.50 compliant. MCP server + REST API.
| Python | - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
| Python, Cython | - Python wrapper of the famous pricing library QuantLib
| Python | - A financial function library for Python
Rust - Quantitative finance library written in Rust.
Python - Python toolkit for quantitative finance.
| Julia | - Quantlib implementation in pure Julia.
| Python | - Fundamentally a swig/python wrapper around Peter Jaeckel's lets_be_rational. lets_be_rational focuses exclusively on Black76, while Vollib extends this to add support for Black-Scholes and Black-Scholes-Merton.