Walk-Forward Validator

Whether an optimization generalizes out of sample. You give it a return series; it slides in-sample / out-of-sample windows across the series and reports each window's in-sample and out-of-sample Sharpe plus an aggregate walk-forward efficiency. It does not see your strategy's parameters — it works from the returns you supply.

The visualizer shows window-by-window detail interactively. The validator runs the full walk-forward and reports per-window Sharpes plus an aggregate efficiency verdict band. Use the validator for a quick robust-versus-fragile read; use the visualizer to diagnose which windows fail.

There is no strategy-parameter check, median rule, or lifetime-P&L rule. The verdict is a band on two aggregate numbers: walk-forward efficiency (mean out-of-sample Sharpe divided by mean in-sample Sharpe) and the aggregate out-of-sample Sharpe. Efficiency below 0.4 flags likely overfitting, 0.4 to 0.7 is some decay, above 0.7 is robust; an aggregate out-of-sample Sharpe below 0.3 flags a weak edge regardless.

You don't set a window count directly — the number of windows falls out of your in-sample length, out-of-sample length, and step versus the length of the series. More windows give a steadier efficiency estimate; a handful is enough for a rough read but leaves the aggregate Sharpe noisy.

If you tune until validation passes, you've used the validator as just another in-sample fit, which defeats the purpose. The validator should be a final gate, not an iterative optimizer. Use a held-out 'final' window that's never seen by the validator until the strategy is otherwise locked.