feat(quantlib): add Hierarchical Risk Parity (HRP) portfolio allocation - #1196
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Implement Hierarchical Risk Parity (López de Prado 2016) with tree clustering, quasi-diagonalization, inverse-variance allocation, and cluster variance calculation. Signed-off-by: santhreal <64453045+santhreal@users.noreply.github.com>
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Summary
Implements Hierarchical Risk Parity (HRP - López de Prado 2016) and inverse-variance weighting for robust portfolio allocation without covariance matrix inversion.
Changes
agent/src/quantlib/portfolio.pywithhierarchical_risk_parity,inverse_variance_weights,correlation_distance,quasi_diagonalize, andcluster_variance.QuantlibCallToolinagent/src/tools/quantlib_tool.py.agent/tests/quantlib/test_portfolio.pyverifying distance metrics, weight normalization, and allocation properties.Signed-off-by: santhreal 64453045+santhreal@users.noreply.github.com