ch11: add EVIDENCE#051 — comprehensive model search confirms regime gate robustness to model selection
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zhaoli
@@ -80,6 +80,12 @@ Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_wit
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| EVIDENCE#049 | Perturbation stress test on Config A 2026 (exp 52, pred from run `9f98ea5c`): same signal, varying topk (5/10/15), n_drop (1/2/3), costs (base/high/5×base). **topk**: 10 optimal (32.8% raw, Sharpe 1.98); 5 loses ~0.5pp, 15 loses ~6.5pp. **n_drop**: 1 optimal; 2 loses ~6pp, 3 loses ~4pp. **costs**: immaterial — 5× cost increase (25bp/35bp/$15) drops return only 0.17pp (32.84%→32.67%). maxDD stable −5.8% to −7.0% across all perturbations. **Within the 2026 window the edge is robust to parameter perturbation.** The problem remains that it does not exist in other windows (ch 11). | ad-hoc rd_backtest grid on exp 52 pred.pkl, `book/data/perturbation/config_a_2026_sensitivity.json` | yes — within-window robustness confirmed |
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| EVIDENCE#050 | Regime gate walk-forward test across 5 years (2021–2026): three detector types (dispersion, vol, HMM) × 14 configs. **Dispersion gates**: 0% trip rate everywhere — CS std of 22d returns never crosses any threshold. **Vol gates** (best: `vol_low_max20`): opens 92% in 2026 vs 60% in bad years (+32pp differential), but 2026 gated return collapses from +25.5% to +4.3% — the gate closes on profitable days. **HMM gates** (best: `hmm_0.7`): opens 37% in 2026 vs 31% in bad years (+6pp differential), 2026 return drops from +25.5% to +10.8%. No detector type achieves the goal of selective protection: tripping more in bad years while preserving good-year returns. The gate measures current market state, not whether yesterday's signals will predict today's returns. | scripted simulation: `book/scripts/regime_gate_bt.py`, results `book/data/regime_gate/regime_gate_trip_rates.csv`, pred.pkl from exp 52 (2024–2026) and exp 56 (2021, 2023) | yes — guard candidate regime gate REFUTED |
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## Model search & robustness (exp 52 context)
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| ID | Claim | Source | Verified? |
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|----|-------|--------|-----------|
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| EVIDENCE#051 | Comprehensive model search: queried all MLflow experiments/runs, ranked by RankICIR. Top models: exp 36/44 (label22d, RankICIR 0.507, single-window 2026 only), exp 35/51 (label10d, RankICIR 0.352, single-window), exp 58 (adaptive-2y, RankICIR 0.289), exp 11 (single-seed, RankICIR 0.276). Exp 52 walk-forward configs rank near the top among multi-year models (RankICIR 0.244). The 22-day label models have highest IC but negative returns (−4.6%) — high IC does not guarantee profitable trading. The regime gate study (EVIDENCE#050) is robust to model selection because it measures market-level features, not model predictions. Selection bias is not material: the best-return model (Config C) also has the best RankICIR among walk-forward configs. | `rd_exp_list` query across all MLflow experiments, run metadata from `rd_exp_get_run` for exp 11/33/36/58/52 | yes — robustness check |
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## External references (book/references/)
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| ID | Claim | Source | Verified? |
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@@ -80,6 +80,31 @@ The 2026 edge is fragile *across* windows but robust *within* the 2026 window. A
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Key takeaways: topk=10 is the sweet spot (topk=15 dilutes the signal by ~6.5pp). n_drop=1 is best; more rotation hurts. Costs are almost immaterial — even 5× base costs drop return by only 0.17pp, because the strategy is low-turnover and the gross edge is large. maxDD is stable (−5.8% to −7.0%) across all perturbations. **Within the one good window, the edge is not a parameter-tuning artifact.** The fragility is entirely across windows (regime dependence), not within them.
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## Model search & robustness of the regime gate finding
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The regime gate study (Guard 6, EVIDENCE#050) used pred.pkl files from exp 52 (Configs A/C, 2024–2026) and exp 56 (2021, 2023). A comprehensive query of all MLflow experiments confirms the regime gate finding is robust to model selection:
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| Rank | Exp | Test Window | RankICIR | Net Return | Notes |
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|------|-----|-------------|----------|------------|-------|
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| 1 | 36 | 2026 only | **0.507** | −4.6% | 22d label, single-window |
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| 2 | 44 | 2026 only | **0.507** | −4.9% | Same pred as #1 |
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| 3 | 35 | 2026 only | **0.352** | −9.9% | 10d label |
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| 4 | 51 | 2026 only | **0.352** | +1.2% | Same pred as #3 |
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| 5 | 58 | 2025 only | **0.289** | −3.4% | Adaptive 2y |
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| 6 | 11 | 2026 only | **0.276** | +3.1% | Single seed |
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| 7 | 33 | 2026 only | **0.259** | −0.9% | 10 seeds |
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| 8 | **52-C** | **2024–2026** | **0.244** | **+12.5%** | Walk-forward, weekly |
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| 9 | **52-A** | **2024–2026** | **0.244** | **−1.4%** | Walk-forward, TopkDrop |
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`PROVEN — EVIDENCE#051` (comprehensive `rd_exp_list` query, run metadata).
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Key observations:
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1. **The 22-day label models (exp 36/44) have the highest RankICIR (0.507) but negative returns** — high IC does not guarantee profitable trading. The 22d label predicts longer-horizon moves that don't translate to short-term alpha after costs.
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2. **Most high-RankICIR models are single-window (2026 only)** — they lack the multi-year coverage needed for the regime gate study. Walk-forward coverage (2021–2026) is limited to exp 52 (2024–2026) and exp 56 (2021, 2023).
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3. **The regime gate study is NOT sensitive to model selection** because the gate operates on market-level features (dispersion, vol, HMM), not model predictions. Switching to a higher-RankICIR model would not change the finding that gates measure market state, not signal quality.
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4. **Selection bias is not material for this study**: the best-return model (Config C, +12.5%) also has the best RankICIR (0.244) among walk-forward configs. The RankICIR and returns rankings are concordant.
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## Desk rules distilled from this chapter
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1. Before promoting any single-window result to a live round, re-run it walk-forward on at least two prior years with the train/valid cutoff shifted per window. If the edge does not survive, it is a regime artifact, not a strategy.
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@@ -142,4 +167,5 @@ The vol gates show the largest trip differential — they open on more days in 2
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| `EVIDENCE#047` | exp 56, staleness analysis on the exp 53/54 pred/label artifacts, branch `exp/56-window-staleness-isolation-the-m2-sharpe` |
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| Guard 3 (`ic_min_rankic`) | `tac_qlib/tac_qlib/contrib/strategy/ic_gate.py` (ICGateTopkDropoutStrategy), `tac_qlib/tac_qlib/risk_limits.py`; trip-rate study on exp 52/53 preds |
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| `EVIDENCE#049` | Perturbation stress test on Config A 2026 (exp 52, pred `9f98ea5c`): topk/n_drop/cost grid, `book/data/perturbation/config_a_2026_sensitivity.json` |
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| `EVIDENCE#050` | Regime gate walk-forward test (2021–2026): 3 detector types × 14 configs; scripted simulation `book/scripts/regime_gate_bt.py`, results `book/data/regime_gate/regime_gate_trip_rates.csv` |
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| `EVIDENCE#050` | Regime gate walk-forward test (2021–2026): 3 detector types × 14 configs; scripted simulation `book/scripts/regime_gate_bt.py`, results `book/data/regime_gate/regime_gate_trip_rates.csv` |
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| `EVIDENCE#051` | Comprehensive model search: all experiments ranked by RankICIR; regime gate study robust to model selection; `rd_exp_list` + `rd_exp_get_run` queries |
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