Awesome GitHub RepositoriesPortfolio Optimization

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.

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

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

Portfolio and risk analytics in Python

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

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

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

mlfinlab est une bibliothèque Python d'apprentissage automatique pour la finance, conçue pour construire et valider des modèles utilisés dans le trading quantitatif et la gestion de portefeuille. Elle fournit une boîte à outils d'ingénierie de données financières et un framework de backtesting de stratégie quantitative pour transformer les données de marché brutes en signaux prédictifs et classes cibles. La bibliothèque inclut un générateur de données financières synthétiques pour créer des jeux de données artificiels qui imitent les propriétés statistiques des actifs réels pour les tests de résistance. Elle fournit également des outils spécialisés pour l'étiquetage et l'échantillonnage des séries temporelles financières afin d'éviter les fuites de données dans les marchés non stationnaires. Le projet couvre une large surface de capacités quantitatives, notamment l'ingénierie des caractéristiques, l'analyse de la codépendance des actifs pour la diversification de portefeuille et le dimensionnement des paris ajusté au risque pour l'allocation de capital. Il fournit en outre des utilitaires pour l'optimisation des modèles via le clustering et la validation croisée afin d'évaluer la robustesse des stratégies de trading.

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

Eiten is an AI-powered market analysis platform and quantitative toolset designed to translate statistical market data and options flow into investment strategies. It provides a suite of specialized financial tools, including an analysis platform driven by large language models, a quantitative portfolio optimizer, and a trading strategy backtester. The project distinguishes itself through the use of random matrix theory to filter covariance noise and mathematical algorithms for portfolio optimization. It integrates these capabilities with a financial data bot for delivery of real-time researc

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

QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing

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A program for financial portfolio management, analysis and optimisation.

Common financial risk and performance metrics. Used by zipline and pyfolio.

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

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

Collection of algorithms for online portfolio selection

Portfolio and risk analytics in Python