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Markets constantly change their behavior. This article explains why trading models lose effectiveness over time and why adapting to regime shifts is more important than optimizing for the past.

Jensen–Shannon Divergence is a metric used in darwintIQ to evaluate how stable the statistical behaviour of a trading model remains over time. Instead of looking only at profit, it helps detect whether the distribution of returns or behaviour of a model is changing.

Expected Value (EV) measures the statistical edge of a trading model by combining win probability, average win, and average loss into a single expectation per trade. Unlike raw profit, EV reveals whether performance is structurally stable or driven by outliers. In darwintIQ, Expected Value is evaluated over rolling market windows and used alongside profitability, drawdown, and distribution metrics to identify models with durable adaptive behavior under current conditions.

In darwintIQ, fitness measures how well a trading model is adapted to current market conditions. It reflects stability, consistency, and behavioral quality — not just profit.

Traditional quant trading often relies on static models optimized on historical data. Genetic algorithms were meant to improve adaptability — but are usually still applied as one-time optimizers. darwintIQ takes a different approach: continuously evolving populations of trading models evaluated on the most recent market conditions. Instead of searching for the best historical strategy, darwintIQ measures which models currently retain their edge in a drifting market.