Model selection¶

Financial samples overlap and trend; naive K-fold cross-validation leaks future information into training folds. cpz_quant.portfolio.model_selection ships splitters built for time series.

Walk-forward¶

from cpz_quant.portfolio import WalkForward, cross_validate, hierarchical_risk_parity

cv = cross_validate(
    lambda train: hierarchical_risk_parity(train).weights,
    returns,
    cv=WalkForward(n_splits=5, test_size=63, expanding=True),
)
print(cv.oos_sharpe)          # out-of-sample annualised Sharpe
print(cv.per_split_sharpe)    # fold-by-fold
print(cv.stability())         # fraction of folds with positive OOS Sharpe

expanding=True grows the training window each fold; expanding=False rolls it.

Combinatorial purged cross-validation¶

CombinatorialPurgedCV(n_splits, n_test_splits, purge) evaluates every combination of test blocks and purges observations adjacent to the test boundary, so serial correlation cannot leak between train and test. This is the same machinery behind the PBO certification test.

Grid search¶

from cpz_quant.portfolio import grid_search, WalkForward

result = grid_search(
    lambda **p: (lambda train: my_allocator(train, **p)),
    {"lookback": [60, 120, 252], "risk_aversion": [0.5, 1.0, 2.0]},
    returns,
    WalkForward(n_splits=4),
)
print(result.best_params, result.best_score)

Every grid cell is a trial

The more configurations you evaluate, the higher the chance the best one is a fluke. Feed the per-trial returns into probability_of_backtest_overfitting and check the Deflated Sharpe Ratio before believing any grid search winner.

scikit-learn interoperability¶

With pip install "cpz-quant[sklearn]", the estimator wrappers work with sklearn's own model-selection tooling (Pipeline, GridSearchCV) when you prefer that ecosystem.