Every backtest statistic is an estimate, and the fewer trades it rests on, the noisier that estimate is. A model can post a stellar win rate on a handful of trades purely by chance. This article explains why sample size determines how much weight a result deserves, and why darwintIQ enforces minimum-trade thresholds — including on its out-of-sample holdout — before treating a result as meaningful.
Intraday markets behave very differently across the Asian, London, and New York sessions. A backtest window short enough to sit inside one session lets a model look excellent simply by fitting that session’s character — a failure mode that only shows up when conditions change. This article explains session overfitting and why darwintIQ extended its evaluation window to span multiple sessions.