Hands-On Machine Learning for Algorithmic Trading

Hierarchical risk parity

The key idea of hierarchical risk parity is to use hierarchical clustering on the covariance matrix in order to be able to group assets with similar correlations together, and reduce the number of degrees of freedom by only considering similar assets as substitutes when constructing the portfolio (see notebook and Python files in the hierarchical_risk_parity subfolder for details).

The first step is to compute a distance matrix that represents proximity for correlated assets and meets distance metric requirements. The resulting matrix becomes an input to the SciPy hierarchical clustering function which computes the successive clusters using one of several available methods discussed so far:

def get_distance_matrix(corr): ...