hierarchical-risk-parity

Cluster-based portfolio allocation on an explicit, inspectable tree: hierarchical risk parity, Schur complementary allocation and hierarchical 1/N

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.

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.

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.

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

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

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.

Open-source quantitative portfolio optimization, risk analytics, and anti-overfitting strategy certification in Python. The research core of the CPZAI systematic trading operating system.

Comparison of classical and modern portfolio optimization methods using BIST-30 stocks.

Hierarchical Risk Parity (Lopez de Prado, 2016) implemented from scratch — clustering, quasi-diagonalization, recursive bisection, plus a five-task S&P 500 sector study.

Modern portfolio optimization using constrained Kelly, HRP, K-Fold cross-validation, and Marčenko-Pastur denoising to improve risk-adjusted returns.

Motor de portafolios modelo multi-perfil con Hierarchical Risk Parity sobre un universo curado de FICs colombianos (datos.gov.co). Compara Markowitz, Risk Parity y HRP con backtesting walk-forward fuera de muestra.

Equity research and portfolio-construction engine: 8-source ingest into a 23-table schema, 7-category composite scoring, walk-forward backtesting with bootstrap confidence intervals, and three portfolio optimisers (MPT / HRP / Black-Litterman).

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

Robust portfolio construction using Ledoit-Wolf covariance shrinkage and Hierarchical Risk Parity (HRP) for stable, risk-aware asset allocation

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

Verified implementations of VPIN, Hierarchical Risk Parity and the Probabilistic Sharpe Ratio: runnable code, synthetic ground truth, dated verification reports of defects found and fixed. Free.

An AI-powered Indian stock market investment platform with quantitative portfolio optimization (HRP), macro threat intelligence, target profit & sell-date prediction, and geopolitical stress testing — all in ₹ INR for NSE/BSE investors.

Project for the Quantitative Finance PhD course at Scuola Normale Superiore (SNS): MATLAB empirical backtesting, code, and slides demonstrating the out-of-sample limitations of Marcos López de Prado's paper "Building Diversified Portfolios that Outperform Out-of-Sample".