quant-validation
Validating a financial model — the five ways the number lies
A backtest that looks excellent and loses money live is not usually a bad strategy. It is a good measurement of the wrong thing. Each section below is one mechanism by which a number becomes convincing without becoming true.
On sourcing. The methods here are standard and attributable — most of them to Marcos López de Prado's Advances in Financial Machine Learning, with the information-ratio framing from Grinold & Kahn. This file states the MECHANISM and what to check, and deliberately does not restate formulas from memory. Where an implementation needs an exact expression — the deflated Sharpe ratio in particular — verify it against the primary source before shipping a number that depends on it. A formula recalled approximately is worse here than no formula: it produces a specific, wrong, confident figure.