Open-source alternatives to Empyrical

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

Alphalens is a quantitative alpha factor analysis library designed to measure the predictive power of financial factors. It serves as a computational toolset for processing financial time series and calculating performance metrics to evaluate quantitative trading hypotheses. The library distinguishes itself through the use of quantile-based data binning to analyze return distributions across different factor strength levels. It aligns historical alpha signals with forward-looking price changes to isolate predictive effects and transforms these metrics into heatmaps and time-series charts for

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

AkShare is a Python financial data library and programmatic interface designed for fetching real-time and historical stock, currency, and economic market data. It serves as a quantitative data acquisition tool for gathering the large-scale financial datasets required for economic research and quantitative analysis. The library provides a unified interface to retrieve datasets from various official and commercial providers, removing the need to write custom scrapers for individual financial sources. It maps standardized function calls to diverse third-party sources to normalize varying respons

This project is a quantitative finance library providing implementations of numerical methods for financial engineering. It focuses on derivative pricing, portfolio optimization, stochastic simulation, and volatility calibration. The library includes tools for calculating option values using Monte Carlo simulations, binomial trees, and Fourier inversion. It provides a framework for fitting volatility smiles to market data and a simulation engine for generating asset price paths via geometric Brownian motion and jump-diffusion models. The codebase covers broader numerical analysis capabilitie

QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti

PyPortfolioOpt is a Python library for financial portfolio optimization that implements mean-variance optimization, Black-Litterman models, and Hierarchical Risk Parity methods. It provides a complete toolkit for constructing risk-adjusted asset portfolios by combining expected return estimation, covariance modeling, constraint handling, and discrete allocation into a single optimization framework. The library distinguishes itself through its integration of multiple optimization approaches within a unified interface. It includes a Black-Litterman Bayesian framework that blends market equilibr

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.

CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using natural mathematical syntax. It is built on a disciplined convex programming framework that automatically enforces convexity rules, ensuring that problems formulated by the user are valid for convex solvers. The project also functions as a multi-solver optimization interface, abstracting away backend details and dispatching problems to specialized solvers like ECOS, SCS, and Gurobi without manual configuration. Beyond standard convex optimization, CVXPY extends its reach to geometri

Python library to download market data via Bloomberg, Eikon, Quandl, Yahoo etc.

A portfolio rebalancing tool that runs in both the terminal and the browser from a single codebase, powered by ink-web. Read the blog post.

portfolio construction and quantitative analysis

Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.

This is a pandas-based technical analysis library and financial feature engineering tool. It serves as a vectorized indicator calculator that transforms raw price and volume data into derived metrics for time series analysis. The library uses a NumPy-based engine to perform mathematical operations across entire arrays, avoiding iterative loops to maintain high performance. It organizes technical indicators into a modular class hierarchy with a consistent interface, allowing for bulk feature generation and the direct appending of results as new columns to a pandas DataFrame. The system covers

Risk tools for commodities trading and finance

Open-source framework for agentic quantitative finance research.

Cython QuantLib wrappers

A Julia quantitative portfolio analytics (risk / performance) via online algorithms

A program for financial portfolio management, analysis and optimisation.

Design of Portfolio of Stocks to Track an Index

Ghostfolio is a self-hosted portfolio tracker designed for personal finance tracking and wealth management. It allows users to record investment transactions and monitor asset holdings across multiple financial accounts in a single private environment. The system provides a financial performance analyzer to calculate investment returns and generate growth charts. It includes an investment risk auditor that performs static analysis on asset holdings to identify financial vulnerabilities and diversification gaps. The platform covers broader capabilities for multi-account management and financi

Contingency Random Number Generator — numbers with controllable fat tails, volatility clustering, and scale convergence

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

Technical analysis library for Rust language

Design of Risk Parity Portfolios