Building HRP Portfolios in Python:
What if your portfolio construction method could survive a market crash where traditional models fail? Hierarchical Risk Parity (HRP) offers a graph-bas...
What if your portfolio construction method could survive a market crash where traditional models fail? Hierarchical Risk Parity (HRP) offers a graph-based solution that outperforms classic mean-variance optimization when assets are highly correlated. Key fact: Hierarchical Risk Parity was developed in 2016 by Marcos López de Prado at Guggenheim Partners and Cornell University as a probabilistic alternative to the 1952 Markowitz framework. Most traders rely on standard optimization techniques that assume assets behave independently. In reality, during stress events, correlations converge toward one, causing traditional models to collapse. HRP addresses this by using discrete mathematics and machine learning to create robust portfolios. This approach is particularly valuable for algorithmic traders who need reliable risk management across diverse asset classes. The core advantage of HRP is its ability to handle high correlation without breaking down. Unlike mean-variance optimization, which is sensitive to input errors, HRP uses a clustering method to group assets by similarity.