Walk-Forward

Method

training/walk_forward_trainer.py + research/walkforward.py implement

purged, embargoed walk-forward (Lopez de Prado): training folds are

separated from test folds by a purge gap (label horizon) and an embargo gap

(serial-correlation buffer), evaluated across temporally ordered folds.

Defaults that matter (BUG-183)

The production research path once ran with purge/embargo silently disabled —

the constants existed but weren't wired. This is recorded as BUG-183 and fixed

with regression tests: DEFAULT_PURGE_SECONDS = 300, `DEFAULT_EMBARGO_SECONDS

= 60 are now wired into ResearchPipeline.validate_candidate`,

OOSGate.evaluate, WalkForwardEngine.validate and BacktestEngine.run, and

the effective purge/embargo values are recorded in every run's config.

Why it is non-negotiable

A walk-forward without purge/embargo leaks label information across folds and

overstates performance — the classic quantitative backtest crime. The

repository treats a purge regression the same way it treats a risk-clamp

regression: critical.

Validation gates

OOS floors (macro-F1, balanced accuracy ≥ 0.34; ECE ≤ 0.15; minimum evidence

100 rows) apply to walk-forward outputs. Failure ⇒ the candidate is REJECTED,

full stop. See OOS gate.