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A rolling re-estimation method that trains on a historical window and validates on the subsequent out-of-sample period.
A rolling re-estimation method that trains on a historical window and validates on the subsequent out-of-sample period.
Walk-forward optimization addresses the overfitting risk in parameter calibration by repeatedly re-estimating model parameters on a rolling in-sample window and evaluating on the subsequent out-of-sample window. It produces an honest, stitched out-of-sample equity curve and is the standard validation method for systematic trading strategies.
Combines estimation (in-sample fit) with validation (out-of-sample performance) in one process.
Window length and re-optimization frequency are themselves hyperparameters to be tested.
Sensitive to regime changes a strategy can degrade between walk-forward windows.
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).