mean-variance-optimization

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

Mean Variance (Markowitz) Portfolio Optimization and Beyond

Open-source investment analytics platform bridging academic research and retail finance. Features include portfolio risk decomposition [Fama-French Five Factor Model], retirement sustainability modeling [Block Bootstrap Monte Carlo], max drawdown/CVaR dashboards, and risk-return optimisation [Markowitz, Ledoit-Wolf] via an intuitive user interface.

Mean-Variance Optimization using DL (pytorch)

Assets' Risk Management Using Mean-Variance Opt Based On Mult-Factors Trending Prediction

Portfolio optimisation library for Julia. Over 50 risk measures (CVaR, EVaR, RLVaR, drawdown, OWA), hierarchical risk parity, HERC, nested clustered optimisation, risk budgeting, near-optimal centering, four Black-Litterman variants, entropy pooling, factor and high-order priors, denoising, and JuMP-backed convex and non-convex optimization.

Enhanced Portfolio Optimization (EPO)

DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.

A MATLAB Realisation of Regime Switching Asset Allocation Strategy

Cross asset allocation with mean-variance and mean-CVaR (Expected Shortfall) optimization methods

Machine Learning portfolio optimization : Ridge, LogisticRegressions,LightGBM, XGBoost , Black-litterman , CVaR Efficient , Sortino, calmar, Sharpe maximization. Python/scikit-learn/PyPortfolioOpt

Streamlit app to simulate/optimize different portfolio allocations based on mathematical methods.

Flexible Python library for asset allocation and investor view integration

Portfolio optimization : Markowitz's mean-variance optimization technique using Pyportfolioopt package.

This repository of codes includes in the R and Python programs used in the six chapters of my published book titled "Analysis and Forecasting of Financial Time Series: Selected Cases". The book is published by Cambridge Scholars Publishing, New Casle upon Tyne, United Kindoam, in 2022.

Reproducibility repository for 'Beyond De Prado and Cotton: Hierarchical and Iterative Methods for General Mean-Variance Portfolios' (Wuebben): Python code and result artifacts for HRP-μ, HRP-Σμ, and the CRISP iterative shrinkage solver.

End-to-End Python implementation of Ang et al's (2026) Agentic 'Self-Driving Portfolio'. Implements: Black-Litterman equilibrium priors, Grinold-Kroner building blocks, Campbell-Shiller CAPE analysis, Ledoit-Wolf covariance shrinkage, Risk Parity, Hierarchical Risk Parity, and Robust Mean-Variance optimization across 18 asset classes.

Using Monte-Carlo simulation in order to find the optimal portfolio weights according to several criteras (Sharpe ratio, max drawdown, mean-variance).

Optimizing equities portfolios using Mean-Variance Optimization, Robust Mean-Variance Optimization, Risk-Parity (ERC), and One-Fund Theorem

Selecting and optimizing portfolios involving FTSE 100 top 10 stocks. Comparison of each strategy performance over time with rolling statistics and tail risk metrics.