All Posts (Page 20)

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.

DAX currently reads bearish, with regime and model leadership broadly aligned.

Win rate is the most intuitive trading statistic and the most misleading one in isolation. A model can win the large majority of its trades and still be unprofitable if the losers are bigger than the winners. This article explains why net pips and per-trade expectancy are the figures that actually measure edge, and how to read win rate correctly as one input among several.

In an evolutionary trading system, the fitness function defines what 'good' means. darwintIQ scores models with a multi-factor fitness that combines expectancy, profit factor, and return stability, then adjusts the result with walk-forward and local-robustness multipliers. This article explains why a single-metric objective produces fragile models and how a composite objective steers the search toward structural quality.

EURUSD currently reads constructive bearish, with regime and model leadership broadly aligned.