McPortfolio: A Model Context Protocol server providing 9 specialized tools for LLM-driven portfolio optimization using natural language, covering mean-variance to machine learning approaches.

Institutional-grade hierarchical portfolio optimization in Python — HRP, HERC, NCO — with robust covariance estimation, risk measures, walk-forward backtesting, and compliance audit trails.

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.

Building a balanced Vanguard ETF portfolio with data-driven optimization—exploring advanced methods, robust backtesting, and an interactive Dash app to pick your optimal mix.

Implementing Hierarchical Risk Parity (HRP) for optimal asset allocation with improved risk contribution distribution

Three new hybrid models have been developed and evaluated: the transformer-Hierarchical Risk Parity model and two variants of LSTM-Hierarchical Risk Parity, one deep and the other shallow model and compared with the results of the original HRP model

Official implementation of Hierarchical Risk Parity using Security Selection based on Peripheral Assets of Correlation-based Minimum Spanning Trees

A quantitative finance engine that uses Random Matrix Theory (Marchenko-Pastur) to denoise correlation matrices and Hierarchical Risk Parity (HRP) for robust portfolio allocation.