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Walk-Forward Optimization

Walk-forward optimization evaluates parameter stability by training on one window and validating on the next. It reduces overfitting by emphasizing out-of-sample performance.

Window Diagram

flowchart LR
  A[In-Sample Window] --> B[Optimize Params]
  B --> C[Out-of-Sample Test]
  C --> D[Next Window]

Window Types

  • Rolling: sliding windows of fixed size.
  • Anchored: expanding in-sample windows with fixed out-of-sample windows.
  • RegimeAware: window segmentation based on regime boundaries.

Overfitting Detection

The optimizer computes an efficiency ratio and flags potential overfitting when in-sample performance is disproportionately higher than out-of-sample performance.

See guide/walkforward.md for configuration.