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A k-fold cross-validation variant for financial time series that purges overlapping labels and applies an embargo to prevent leakage.
A k-fold cross-validation variant for financial time series that purges overlapping labels and applies an embargo to prevent leakage.
Proposed by Marcos López de Prado in Advances in Financial Machine Learning (2018), purged k-fold CV addresses the leakage that standard k-fold introduces in financial data due to overlapping labels across time. After standard k-fold splitting, observations whose labels overlap the test set are purged, and an embargo removes additional samples near the test boundary.
Prevents leakage from serially correlated labels that violate the i.i.d. assumption.
The embargo fraction is a hyperparameter that depends on label horizon and autocorrelation.
Combinatorial Purged Cross-Validation (CPCV) extends this to multiple backtest paths.
Research methodology is the set of practices that distinguish a rigorous quant research process from a hopeful one. The central topics are backtest audit, multiple testing correction, purged and combinatorial cross-validation, walk-forward optimisation, the triple-barrier labelling method, and the deflated Sharpe ratio. The dominant practitioner reference is López de Prado (2018).
Financial machine learning is the application of supervised, unsupervised, and reinforcement learning methods to financial prediction, classification, and decision problems. The defining methodological constraint is that financial data are serially correlated, not independently and identically distributed, which means that the standard machine learning toolkit must be substantially adapted. The dominant practitioner reference is López de Prado (2018).