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The statistical error of fitting a quantitative model to historical noise rather than true underlying relationships.
The statistical error of fitting a quantitative model to historical noise rather than true underlying relationships.
Overfitting occurs when a backtest or ML algorithm uses excessive parameters or optimization iterations, resulting in flawless historical backtest metrics that fail completely in live trading.
Single largest cause of backtest performance degradation in live trading.
Mitigated via out-of-sample testing, cross-validation, and regularized models.
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).
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).