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A backtesting protocol that generates multiple out-of-sample paths through C(N, k) combinations of purged folds.
A backtesting protocol that generates multiple out-of-sample paths through C(N, k) combinations of purged folds.
CPCV, also from López de Prado (2018), produces multiple non-overlapping backtest paths from a single labeled dataset. Each path is a sequence of test folds, with purging and embargo applied at each boundary. The distribution of path outcomes estimates the strategy's probability of backtest overfitting (PBO) and provides a more robust view of generalization than a single walk-forward pass.
Number of backtest paths = C(N, k) where N is the number of folds.
Probability of Backtest Overfitting (PBO) is the fraction of paths with negative logit performance.
Theoretically grounded in combinatorial cross-validation for dependent observations.
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).