momentum devandClaude Opus 4.8
275c56b545
research/regime.py: Step 0 probe (labelable-session/SPY-coverage STOP gate),
Step 1 build-features (regime_features: pre-open breadth/tape + prior-close
SPY momentum/vol + trailing bot expR, label = mean _sim_baseline_r over the
session's setup fires) + walk-forward threshold_v1 rule, Step 2 walk-forward
meta_v1 (shallow sklearn GBM), Step 4 OOS/leakage-audit helpers
(oos_split/sequential_folds/session_expr_delta/full_sample_fit_threshold).
Step 3 (HMM) deferred per scope — statsmodels stays absent.
replay.py: new iv_regime_gate intervention (keep-or-zero a fire on a
pre-computed session regime score, fail-open on no score row) wired into
_compute_fire + run(), mirroring rank_one_bullet's shape.
experiments/regime_threshold_v1.json + regime_meta_v1.json register the two
model variants against the shared population/bars/fidelity contract.
All new code is unit-tested against isolated EventStore(":memory:") schemas
only — no production DB read or written. Live-trading paths untouched.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
9 lines
607 B
JSON
9 lines
607 B
JSON
{
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"id": "regime_meta_v1",
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"hypothesis": "A shallow walk-forward GradientBoostingClassifier metalabeling P(our ORB makes money THIS session) from the same free pre-open features beats the threshold_v1 rule net, out-of-sample, and zeroing the ORB on unfavorable sessions raises whole-population expR.",
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"population": {"min_entry_gap_pct": 0.10, "min_day_volume": 300000, "price_min": 2.0, "price_max": 20.0},
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"intervention": {"kind": "regime_gate", "model_version": "meta_v1"},
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"bars": {"expR_delta_min": 0.05, "min_n": 300},
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"fidelity": {"atr_frac": 0.25, "spread_mult": 3, "adverse_ticks": 1}
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}
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