Open-source alternatives to Jquantlib

QuantLib with AAD

Fast, easy automatic differentiation in C++

Rust library for quantitative finance.

A Julia package for quantitative finance

Python implementation of the R package fOptions for use in energy trading. Changes include coverting the package to OOP as well as Finite Difference Methods for Option greeks for all Options.

QoX is a fast and accurate quant library written in Rust, designed to work in production environments. These samples demonstrate its performance and ease of use.

credule

quant finance in pure haskell

Toolkit for integration and analysis

Scala Quantitative Finance Library

A library for financial options pricing written in Python.

Kelly Criterion calculation

A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.

A JavaScript library for common financial calculations

Applications of Monte Carlo methods to financial engineering projects, in Python.

Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.

Package for time value of money calculation, time series analysis and computational finance

Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.

Python SDK for the FlashAlpha options analytics API — live options screener, gamma exposure (GEX), DEX/VEX/CHEX, options flow, 0DTE, VRP, volatility surfaces, greeks

Manipulates Stock / ETF Data

gs-quant is a quantitative finance library and financial data analytics toolkit. It serves as a framework for analyzing financial data, developing systematic trading strategies, and managing risk exposure for derivative products in global markets. The project provides tools for quantitative financial analysis, quantitative portfolio modeling, and the development of systematic trading strategies. It enables the calculation of risk for derivative products to structure and hedge positions across markets.

This is a quantitative finance library built on TensorFlow for financial engineering, asset pricing, and risk management. It serves as a financial derivative pricing engine, a model calibration tool, and a hardware-accelerated math library for numerical tasks. The library provides specialized capabilities for pricing financial assets using standard models and American option logic, as well as calibrating pricing models to market data through local volatility. It includes tools for constructing yield curves via bootstrapping algorithms and monotone convex interpolation. The framework covers a

Lightweight Python library for assembling and analysing financial data

Financial Market Building Blocks

Vanilla and exotic option pricing library to support quantitative R&D. Focus on pricing interesting/useful models and contracts (including and beyond Black-Scholes), as well as calibration of financial models to market data.