Open-source alternatives to Pyql

Portfolio and risk analytics in Python

ffn - a financial function library for Python

QuantLib is a quantitative finance library and analysis engine built in C++ for executing complex financial calculations and simulations. It serves as a framework for quantitative finance modeling and trading risk management, providing the tools necessary to calculate fair values and risk metrics for diverse financial assets. The project focuses on financial instrument modeling and the evaluation of potential losses and exposure levels to inform portfolio management decisions. It provides a system for modeling financial instruments and managing trading risk through quantitative mathematical m

An Open Source Portfolio Backtesting Engine for Everyone | 面向所有人的开源投资组合回测引擎

R interface to the QuantLib library

Riskfolio-Lib is a Python portfolio optimization library and convex risk management tool. It provides a framework for calculating optimal asset allocations using convex risk measures and mathematical programming solvers, supporting linear, quadratic, and semidefinite programming. The library features a hierarchical risk parity framework and financial asset clustering tools to group similar instruments and improve diversification. It includes a portfolio backtesting engine for simulating investment strategies using historical data and cross-validation. The system covers a broad range of quant

Common financial technical indicators implemented in Pandas.

QuantStats is an open-source Python library that calculates risk and return metrics from a portfolio return series and generates comprehensive HTML tear sheets. It computes dozens of financial statistics—including Sharpe ratio, drawdown, and volatility—in a single pass over the input data, using vectorized pandas operations for efficiency. The library distinguishes itself by combining portfolio performance analysis with Monte Carlo simulation, which models thousands of random return paths to estimate the probability of reaching financial targets or hitting loss thresholds. It produces self-co

GPU-accelerated Factors analysis library and Backtester

JQuantLib is a library for Quantitative Finance written in 100% Java

Technical analysis library for Rust language

PyPortfolioOpt is a comprehensive portfolio optimization library for Python that provides a full suite of methods for constructing and analyzing investment portfolios. At its core, the library implements mean-variance optimization, the Black-Litterman Bayesian model, and Hierarchical Risk Parity, giving users multiple approaches to asset allocation. It includes a complete covariance estimation toolkit with interchangeable estimators such as sample, exponential, shrinkage, and minimum-covariance-determinant methods, along with expected return estimation using historical mean, exponential weight

This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The

quant finance in pure haskell

credule

QuantLib with AAD

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.

A JavaScript library for common financial calculations

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.

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

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

A fixed income library for pricing bonds and bond futures, and derivatives such as interest rate swaps (IRS), cross-currency swaps (XCS) and FX swaps. Contains tools for full curveset construction with market standard optimisers and automatic differentiation (AD) and risk sensitivity calculations including delta and cross-gamma.