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The KS statistic applies the Kolmogorov-Smirnov test to compare return distributions across time periods, flagging when a model's behaviour has shifted from its validated baseline. It is a non-parametric measure that works alongside Jensen-Shannon Divergence, Wasserstein Distance, and PSI in darwintIQ's distribution similarity assessment, helping traders identify structural drift before it shows up in headline figures.

Standard deviation measures how far a trading model's returns spread around their average, revealing the consistency behind headline performance. It underpins risk-adjusted metrics including the Sharpe and Sortino Ratios and helps identify whether a model's results are genuinely reliable or deceptively volatile. In darwintIQ, it forms part of a broader performance dashboard alongside drawdown, Expected Value, and distribution metrics.

An adaptive trading system continuously updates its assessment of which models are suited to current market conditions, rather than deploying a fixed strategy based on historical optimisation. In darwintIQ, this adaptation is achieved through continuous model evaluation on a rolling window — meaning model rankings reflect what is working right now, not what worked at some point in the past.

Mean reversion and trend following are the two dominant systematic trading approaches. They succeed in almost opposite market conditions, which means neither has a permanent advantage. Understanding how market regimes create and destroy edge for each approach is the foundation of building models that remain functional across changing conditions.

Trending and ranging markets represent the two fundamental states that price can occupy, and they call for entirely different trading approaches. Understanding the distinction — and recognising when a market transitions between the two — is foundational to evaluating why trading models behave as they do and when their edge is most likely to hold.