Le
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Rolling train/test folds over 3 years of data auto-optimize the three cheap-to-tune trading parameters (STRONG/BUY score thresholds, max hold time) via grid search on each fold's train window, then evaluate purely on the held-out test window. Stitching all out-of-sample results gives an honest performance estimate uninflated by tuning against the same data used to score it. Split signal_scoring.py's expensive 13-algorithm scoring from its cheap final threshold classification so grid search can replay many parameter combinations without recomputing indicators each time. Moved the backtest engine (fetch/precompute/simulate) out of the API layer into app/services/backtest_engine.py so both /backtest/run and the new walk-forward optimizer share one implementation instead of drifting copies — same rationale as the earlier signal_service.py split (item h). Also merges two long-diverged Alembic migration heads discovered while adding the walk_forward_results table, so `alembic upgrade head` has a single target again. New: POST/GET/DELETE /walk-forward/* endpoints, a Walk-Forward tab on the Backtest page (fold table, out-of-sample equity curve, run history). 19 new backend tests (153 total, all passing). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>