Light1YT 692f468010 feat(backtest): validate #978 Almon-ADL + #979 seasonal as OOS variants ... Extend the read-only §9.6 rate-sensitivity OOS harness with two opt-in candidate-method variants so any wiring decision is evidence-based: - --almon: evaluate_oos_almon, Almon distributed-lag (regression.fit_almon_dl), fit on TRAIN only, point-in-time sum_j beta_j*drate[t-j] prediction. - --deseasonalize: train-only month-of-year factors (normalize.seasonal_factors) divided out before log_diff, then the existing best_lag evaluator. Both pin the fit to _time_ordered_split(n_train); no look-ahead leakage (adversarial tests assert the train fit is byte-identical under test corruption). Default path (best_lag/raw) is byte-identical to before. 88 tests pass, ruff clean. Prod OOS findings (directional hit-rate, coin-flip 0.50, bar 0.55+lag-stable): - #979 deseasonalize: neutral (B 0.148->0.148, A 0.40->0.40) -> keep advisory. - #978 Almon-ADL: dominates best_lag (B 0.148->0.407 lag-stable; A 0.40->0.60, clears coin-flip+margin) -> candidate to promote from advisory.		2026-06-04 14:55:21 +05:00
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__init__.py	feat(forecasting): read-only OOS backtest of §9.6 rate-sensitivity (#978) (#1024)	2026-06-03 09:33:02 +00:00
test_backtest_rate_sensitivity.py	feat(backtest): validate #978 Almon-ADL + #979 seasonal as OOS variants	2026-06-04 14:55:21 +05:00