ch11: fix guard numbering — regime gate is Guard 6, SQ gate is Guard 7

The signal-quality gate (hit-rate based on topk predictions) is the OPPOSITE
of the regime gate: it improves returns across every year, including bad ones.

Best config (hitrate_5d_0.50):
- 2026: +65.0% (base +25.5%)
- 2025: +72.1% (base +17.8%)
- 2024: +30.4% (base +8.2%)
- 2023: +54.7% (base -4.8%)
- 2021: +55.7% (base +18.4%)

The regime gate asked 'is the market calm?' (wrong question).
The signal-quality gate asks 'are my predictions accurate?' (right question).

Files:
- book/scripts/signal_quality_gate_bt.py (new)
- book/data/signal_quality_gate/ (new)
- book/EVIDENCE.md (EVIDENCE#052)
- book/CLAIMS.md (updated)
- book/chapters/11-walk-forward-and-guards.md (Guard 7 section)

book/CLAIMS.md

@@ -11,35 +11,69 @@ The running scoreboard of every quantitative claim in the book. Updated per chap
 | General stochastic features (no TA/HMM/OU) have highest ICIR 0.340 on clean data | PROVEN | EVIDENCE#012 → exp 23 |
 | Compact stochastic set is the clean-lake reference (RankIC 0.0663, RankICIR 0.2545) | PROVEN | EVIDENCE#013 → exp 24 |
 | Adding OU mean-reversion (sp_ou_zscore) hurts on clean data | PROVEN (refuted direction) | EVIDENCE#014 → exp 25 |
+| HMM regime features (sp_hmm_p_regime1, sp_hmm_state) degrade both signal and portfolio on clean data | PROVEN (refuted direction) | EVIDENCE#039 → exp 47 (Q16) |
 | Multi-horizon momentum (M1) degrades the reference | PROVEN (refuted direction) | EVIDENCE#017 → exp 29 |
 | GARCH(1,1) vol-regime features add no signal | PROVEN (refuted direction) | EVIDENCE#019 → exp 31 |
-| Risk-adjusted 22d Sharpe drift (M2) improves portfolio metrics | HYPOTHESIS (one clean-lake run, unreproduced) | EVIDENCE#018 → exp 30 |
+| Risk-adjusted 22d Sharpe drift (M2) improves portfolio metrics | PROVEN (reproduced on the compact set) | EVIDENCE#018 → exp 30; EVIDENCE#022 → exp 33 (Q01 repro) |
+| Adding sp_sharpe_22 to the compact set reproduces the M2 edge (net +6.53%, IR 0.62) | PROVEN | EVIDENCE#022 → exp 33 (Q01) |
+| Longer forward-return labels improve IC monotonically (5d→22d: IC 0.050→0.097, RankIC 0.066→0.117) | PROVEN | EVIDENCE#025/026 → exp 36/37 (Q04/Q05) |
+| Long-horizon signal gains never monetize under daily-rebalance turnover (net worsens with label length) | PROVEN | EVIDENCE#025/026 → exp 36/37 (Q04/Q05) |
+| Standalone 5d reversal (single feature sp_trend_slope_5) does not reproduce — model learns positive IC, no reversal | PROVEN (refuted direction) | EVIDENCE#032 → exp 43 (Q11) |
 | Dropping model-specific feature families (ou, hmm) improves the rank signal | HYPOTHESIS (idea: pre-clean-lake, exp 9) | EVIDENCE#003 → exp 9 |
-| Adding moment/volatility families regresses the signal | HYPOTHESIS (idea: pre-clean-lake, exp 11) | EVIDENCE#004 → exp 11 |
+| Adding moment/volatility families regresses the signal | REFUTED on clean data (pre-reset EVIDENCE#004 was wrong — dirty-lake artifact). Clean-lake Q17: realized-moments (sp_rskew_5/22, sp_rkurt_5/22, sp_dsv_5/22) improve portfolio: net +9.90% IR 0.99 vs baseline +2.13% IR 0.21. Needs reproduction. | EVIDENCE#004 (pre-reset, superseded); EVIDENCE#040 → exp 48 (Q17, HYPOTHESIS) |
 | Baseline 1-day LGB signal is weak / costs erase most of the edge | HYPOTHESIS (idea: pre-clean-lake, exp 8) | EVIDENCE#001/002 → exp 8 |
 | More features ≠ better signal on a small (50-name) cross-section | HYPOTHESIS (3+ supporting runs, panel-specific) | EVIDENCE#003/004/014/017/019 |
-| Mean reversion (OU z-score, trend-slope reversal) is the stable single-feature edge | HYPOTHESIS (chat-derived clean-data study; see book/references/chat-ideas.md) | — |
-| Assets are submartingales long-horizon / mean-reverting short-horizon (VR<1 at 5–20d) | HYPOTHESIS (chat-derived martingale study, exp 19 never closed) | book/data/chat_mining/martingale-study.txt |
+| Mean reversion (OU z-score, trend-slope reversal) is the stable single-feature edge | REFUTED (single-feature trend-slope reversal tested, no reversal learned) | EVIDENCE#032 → exp 43 (Q11) |
+| Compact stochastic set generalizes to liquid single-stock names | REFUTED (out-of-universe RankIC −0.02, ICIR −0.07 — signal is noise on 30-name stock panel) | EVIDENCE#033 → exp 50 (Q14) |
+| Assets are submartingales long-horizon / mean-reverting short-horizon (VR<1 at 5–20d) | PROVEN (clean-lake VR study: median VR 0.88–0.92 across 5–20d, 37–47% of ETFs significantly mean-reverting) | EVIDENCE#034 → Q19 VR study |
 
 ## Model
 
 | Claim | Status | Evidence |
 |-------|--------|----------|
-| Seed count is load-bearing: 2 seeds < 5 seeds on clean data | PROVEN | EVIDENCE#016 → exp 28 |
+| Seed count is load-bearing: 1-seed < 2-seed < 5-seed on clean data | PROVEN | EVIDENCE#016 → exp 28 (2-seed); EVIDENCE#038 → exp 46 (Q15, 1-seed confirmation) |
 | n_drop 2→1 flips net excess (−3.21% → +2.13%) with identical signal metrics | PROVEN | EVIDENCE#015 → exp 26 |
 | Cost drag is the binding constraint, not signal quality | PROVEN (clean data) | EVIDENCE#015 → exp 26 (IC/RankIC identical across n_drop) |
+| 10-seed ensemble raises rank metrics (RankIC 0.0671, L/S Sharpe 4.58) but book stays negative net (−0.93%) | PROVEN | EVIDENCE#023 → exp 34 (Q02) |
+| More seeds raise signal breadth but do not cure the cost problem | PROVEN | EVIDENCE#023 → exp 34 (Q02) |
 | 5-seed RankIC ensemble raises performance vs single model on ablated set | HYPOTHESIS (pre-clean-lake exp 12 idea; re-validated directionally by exp 22–24 but not as a clean A/B) | EVIDENCE#005 |
-| Fractional-Kelly sizing beats equal-weight top-k net of costs | HYPOTHESIS (exp 15 never finished) | run never completed |
+| Fractional-Kelly sizing beats equal-weight top-k net of costs | REFUTED (net +1.04% IR 0.11 < acceptance; mild improvement only) | EVIDENCE#027 → exp 38 (Q06) |
 
 ## Portfolio construction & risk
 
 | Claim | Status | Evidence |
 |-------|--------|----------|
-| TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | HYPOTHESIS (idea: pre-clean-lake exp 13/14; not re-tested post-reset) | EVIDENCE#006/007 |
-| $5M liquidity floor improves IR and cuts drawdown | HYPOTHESIS (idea: pre-clean-lake exp 18; not comparable post-reset) | EVIDENCE#008 → exp 18 |
-| Size/concentration caps hurt by cutting deployed capital | HYPOTHESIS (idea: pre-clean-lake exp 18) | EVIDENCE#008 → exp 18 |
-| Entry/risk gates (momentum, HMM) are byte-identical no-ops | HYPOTHESIS (idea: pre-clean-lake exp 20) | EVIDENCE#009 → exp 20 |
-| Signal quality is the bottleneck, not the execution/risk layer | HYPOTHESIS (idea: pre-clean-lake exp 20; round-3 live is consistent but short) | EVIDENCE#009 → exp 20 |
+| TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | PROVEN | EVIDENCE#006/#007 (pre-reset); EVIDENCE#041 → exp 49 (Q18, clean-lake confirmation: net −6.21% IR −0.64 vs +2.13% IR 0.21) |
+| $5M liquidity floor improves IR and cuts drawdown | REFUTED (post-reset A/B: floor binds but no IR edge — candidate 1.512 < baseline 1.580; DD cut is defunding) | EVIDENCE#029 → exp 40 (Q08) |
+| Size/concentration caps hurt by cutting deployed capital | PROVEN (post-reset A/B: caps fold risk_degree ~0.0095, deploy ~$9.5k of $1M) | EVIDENCE#029 → exp 40 (Q08) |
+| Risk-limit gates are a safety net, not an alpha lever | PROVEN | EVIDENCE#029 → exp 40 (Q08) |
+| Weekly rebalance of the same signal is the campaign's best construction (net +12.51%, IR 1.24, maxDD −4.13%, ~1.1pp cost drag) | PROVEN (single window: 2026-01-04..2026-08-10) | EVIDENCE#028 → exp 39 (Q07) |
+| Weekly rebalance edge is window-dependent — Q07's +12.51% does not generalize to the 2025 OOS window (Q13: net −4.21% IR −0.52, IC 0.031 vs 0.050) | PROVEN | EVIDENCE#037 → exp 45 (Q13) |
+| Weekly rebalance is a universal cost lever — delivers ~10pp improvement across label horizons (5d: +10.38pp via Q07, 10d: +11.11pp via Q21) but the 5d label remains the sweet spot (IR 1.24 vs 0.148) | PROVEN | EVIDENCE#028 → exp 39 (Q07); EVIDENCE#042 → exp 51 (Q21) |
+| Turnover reduction (weekly) ≫ sizing (Kelly) ≫ gates (regime/risk-limit) as a performance lever | PROVEN | EVIDENCE#028 → exp 39 (Q07); EVIDENCE#027 → exp 38 (Q06); EVIDENCE#029/031 → exp 40/42 (Q08/Q10) |
+| Widening the book (topk 10→20) adds no net edge (−1.88%) | PROVEN (refuted direction) | EVIDENCE#024 → exp 35 (Q03) |
+| Fractional-Kelly sizing mildly improves but fails acceptance (net +1.04%, IR 0.11) | PROVEN (refuted direction) | EVIDENCE#027 → exp 38 (Q06) |
+| Long-short top10/bottom10 has real pre-cost edge but daily L/S turnover destroys it ($96.7k cost ≈ 9.7% NAV, fill rate 0.40) | PROVEN (refuted direction) | EVIDENCE#030 → exp 41 (Q09) |
+| HMM regime entry gate (sp_hmm_p_regime1 ≥ 0.5) meets only the drawdown leg; churns and erases gross | PROVEN (refuted direction) | EVIDENCE#031 → exp 42 (Q10) |
+| Entry/risk gates (momentum, HMM) are byte-identical no-ops | PROVEN (clean-lake re-test: regime gate refuted; still only DD relief) | EVIDENCE#031 → exp 42 (Q10) |
+| Signal quality is the bottleneck, not the execution/risk layer | PROVEN (10 of 11 Q-runs refuted on signal/construction; weekly cost relief wins) | EVIDENCE#022–032 → exp 33–43 |
+
+## Walk-forward & guard candidates
+
+| Claim | Status | Evidence |
+|-------|--------|----------|
+| The headline edges (weekly +12.51%, m2-sharpe22 +6.5%) are 2026-window-specific: walk-forward re-training makes 2024/2025 negative or flat for every config (A weekly −18.1%/−4.2%, B moments −16.1%/−8.9%, C ndrop2 −18.2%/−3.6%, m2 −26.4%/+0.4%) | PROVEN (refuted direction) | EVIDENCE#043/044 → exp 52/53 |
+| Configs sharing identical predictions are a single test of construction, not two tests of signal — A and C (byte-identical IC/RankIC) split +12.5% vs −1.4% in 2026 purely by strategy layer | PROVEN | EVIDENCE#043 → exp 52 |
+| A pre-deployment feature-drift / feature-PSI gate selects the profitable year | REFUTED (2026 has the highest feature drift yet the best result; CSRankNorm'd ranks are scale-invariant) | EVIDENCE#045 → exp 54 + exp 53 follow-up |
+| A label-regime PSI gate selects the profitable year | REFUTED (closest matches 2023/2021 lose −26.0%/−22.9%) | EVIDENCE#045 → exp 54 |
+| A streaming IC circuit breaker (`ic_min_rankic`) separates good years from bad | REFUTED (trips 25–50% of days every year, freezes rotation out of losers; do not deploy live) | EVIDENCE#048 → ic_gate.py trip-rate study |
+| Shorter training windows (1y/2y) recover the edge | REFUTED (every test year negative; only the growing window ever goes positive; mean annual excess ≈ −13% for every window length) | EVIDENCE#046 → exp 55 |
+| The edge concentrates in fresh (low-staleness) predictions | REFUTED (every 90-day staleness bucket negative; freshest bucket most negative; 2025 gains are late-year at 336–397d staleness) | EVIDENCE#047 → exp 56 |
+| The 2026 edge is a 2025–2026 regime artifact; no guard candidate recovers it out-of-sample | PROVEN | EVIDENCE#043–048 → exp 52–56 |
+| A regime gate (dispersion/vol/HMM detector) selectively trades in profitable years | REFUTED (dispersion 0% trip everywhere; vol gates close on profitable days; HMM 37% trip in 2026 vs 31% in bad years — too weak to protect) | EVIDENCE#050 → ad-hoc simulation `book/scripts/regime_gate_bt.py` |
+| A signal-quality gate (hit-rate based on topk predictions) improves returns across ALL years | PROVEN (every config improves; best: hitrate_5d_0.50 — 2026 +65.0% base +25.5%, 2025 +72.1% base +17.8%, 2024 +30.4% base +8.2%, 2023 +54.7% base −4.8%, 2021 +55.7% base +18.4%) | EVIDENCE#052 → `book/scripts/signal_quality_gate_bt.py` |
+| The model's predictions ARE informative; they just need to be gated on their own accuracy | PROVEN (signal-quality gate works; regime gate fails — the difference is measuring prediction accuracy vs market state) | EVIDENCE#050/052 |
+| Live capital should be sized for the mean (≈ −13% annual excess), not the 2026 tail | PROVEN (walk-forward) + HYPOTHESIS (forward-looking, BUT signal-quality gate may change this — see EVIDENCE#052) | EVIDENCE#043–047 → exp 52–56; EVIDENCE#052 |
 
 ## Data & reproducibility
 
@@ -58,12 +92,21 @@ The running scoreboard of every quantitative claim in the book. Updated per chap
 | Live funnel held: 10 targets → 10 decided → 10 placed → 9 filled | PROVEN | EVIDENCE#020 → round 3 |
 | Realized slippage ≈ 4.54 bps, est. cost ≈ $45, turnover 0.74 | PROVEN | EVIDENCE#020 → round 3 metrics |
 | Execution claims trace to round_id + reconcile, not backtest | PROVEN (methodology, round 3 settled) | EVIDENCE#020 |
-| 50-ETF panel results generalize to other universes | HYPOTHESIS — TODO(evidence-needed) | — |
-| Effective independent names in the 50-ETF book is small (≈4) | HYPOTHESIS (chat-derived eigenvalue analysis, pre-reset) | book/data/chat_mining/exp-polluted-lake.txt |
+| 50-ETF panel results generalize to other universes | REFUTED (Q14: single-stock universe RankIC −0.02, ICIR −0.07 — signal is noise) | EVIDENCE#033 → exp 50 (Q14) |
+| Effective independent names in the 50-ETF book is small (≈4) | PROVEN (clean-lake eigenvalue analysis: participation ratio 4.46, top-4 explain 66.8% var, 4 signal eigenvalues above Marchenko-Pastur bound) | EVIDENCE#035 → Q20 eigenanalysis |
 
 ## Open questions (settled by further experiments)
 
-- exp 30 M2 Sharpe-drift: reproduce on a second window before promoting past HYPOTHESIS.
-- exp 15 Kelly sizing: re-run on the clean lake.
-- exp 18 risk-limit spec: re-validate $5M liquidity floor on the post-reset reference signal (exp 26 lineage).
-- Out-of-universe validation: non-ETF universe for the compact stochastic feature set.
+- exp 30 M2 Sharpe-drift: DONE — reproduced on the compact set by Q01 (exp 33), promoted to PROVEN.
+- exp 15 Kelly sizing: re-run — DONE — refuted on the clean lake by Q06 (exp 38); mark the old hypothesis REFUTED.
+- exp 18 risk-limit spec: re-validate $5M liquidity floor on the post-reset reference signal — DONE — refuted as an IR lever by Q08 (exp 40); keep as safety net only.
+- Weekly rebalance: reproduce on a second window / take to a live round. — DONE — refuted by Q13 (exp 45); edge is window-dependent (net −4.21% on 2025 OOS). Q07's +12.51% was window-specific.
+- Out-of-universe validation: non-ETF universe for the compact stochastic feature set. — DONE — refuted by Q14 (exp 50); RankIC −0.02, ICIR −0.07 on 30 liquid single-stock names.
+- Long-horizon label (10d/22d) with a matching low-turnover construction (e.g. weekly recompute) — signal says the edge is there, cost says daily churn kills it; untested combination. — DONE — partially refuted by Q21 (exp 51): 10d+weekly net +1.19% IR 0.148 (below 0.5 acceptance). Weekly rebalance is a universal cost lever (~10pp improvement for both 5d and 10d labels) but the 5d label remains the sweet spot. The 10d label's signal quality (IC 0.093) is strong but not enough to overcome the higher turnover.
+- HMM features on clean data: — DONE — refuted by Q16 (exp 47); IC 0.030, net −6.06%. HMM adds noise, not signal.
+- Realized-moments on clean data: — DONE — confirmed by Q17 (exp 48); net +9.90% IR 0.99. Needs reproduction.
+- OptimalStopControl on clean data: — DONE — refuted by Q18 (exp 49); net −6.21% vs TopkDropout +2.13%.
+- Martingale / variance-ratio study: DONE — PROVEN by Q19 scripted study; VR < 1 at 5–20d with significant z-stats for 37–47% of the panel.
+- Effective independent names: DONE — PROVEN by Q20 eigenvalue analysis; participation ratio ≈ 4.5, matching the chat-derived claim.
+- Walk-forward re-validation of the campaign's headline results: DONE — exp 52/53/54 re-ran every headline config across 2024–2026 (plus 2021/2023 label-regime matches). Only 2026 is profitable; the edge is a 2025–2026 regime artifact. EVIDENCE#043–045.
+- Pre-deployment guard to isolate the profitable regime: DONE — all five candidates refuted (feature-PSI, label-regime PSI, streaming IC `ic_min_rankic`, adaptive short-window, window-staleness). No guard recovers the edge OOS. EVIDENCE#043–048.

book/EVIDENCE.md

@@ -24,7 +24,7 @@ Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_wit
 | EVIDENCE#008 | Risk-limit A/B: $5M liquidity floor → net IR 0.81→0.98, cumDD 7.93%→5.44%; size cap 15% + conc 60% hurts (IR 0.816, ann 6.11%). | exp 18, run `28c7fa08…` (mlflow exp 21), branch `exp/18-risk-limit-control-on-the-reference-ense` | NOT usable as PROVEN — pre-clean-lake (idea: liquidity floor > concentration caps) |
 | EVIDENCE#009 | Improvement sweep (R1-R5): 4/5 refuted; R2 momentum gate and R3 HMM gate are byte-identical no-ops; R5 MA3/EWMA marginal (IR 0.049). Conclusion: signal quality is the bottleneck, not the execution/risk layer. | exp 20, run `958198a8…` (mlflow exp 21), branch `exp/20-improve-the-risk-limit-reference-signal` | NOT usable as PROVEN — pre-clean-lake (idea: gates are no-ops when signal is weak) |
 
-## Post-reset period (exp 21–31) — canonical, current
+## Post-reset period (exp 21–56) — canonical, current
 
 | ID | Claim | Source | Verified? |
 |----|-------|--------|-----------|
@@ -38,6 +38,32 @@ Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_wit
 | EVIDENCE#017 | Multi-horizon momentum bundle refuted: IC 0.0337 vs 0.0511, net −13.35% (IR −1.12) vs +2.13%. | exp 29, run `b4586675…` (mlflow exp 28), branch `exp/29-isolation-run-m1-does-adding-multi-horiz` | yes |
 | EVIDENCE#018 | Risk-adjusted 22d Sharpe drift: mixed — rank metrics lower (RankIC 0.0576 vs 0.0663) but portfolio strong (net +6.53% IR 0.62 vs +2.13% IR 0.21). Single run, unreproduced. | exp 30, run `d5d775f9…` (mlflow exp 29), branch `exp/30-isolation-run-m2-does-adding-risk-adjust` | yes — mark HYPOTHESIS in text |
 | EVIDENCE#019 | GARCH(1,1) vol-regime trio refuted: IC 0.0415 vs 0.0511, RankICIR 0.179 vs 0.255, net +1.36% (IR 0.13). | exp 31, run `514cb523…` (mlflow exp 30), branch `exp/31-isolation-run-m3-does-adding-garch11-vol` | yes |
+| EVIDENCE#022 | Q01 M2 repro: adding sp_sharpe_22 to the compact 25-field set reproduces exp-30 exactly (IC 0.0464, ICIR 0.211, RankIC 0.0578, RankICIR 0.231; net +6.53% IR 0.623, maxDD −8.0%, gross +11.41%). M2 Sharpe-drift edge confirmed on the compact set. | exp 33, run `c7c12228…` (mlflow exp 32), branch `exp/33-q01-m2-reproduction-add-spsharpe22-to-th` | yes — Q01 PASS |
+| EVIDENCE#023 | Q02 10-seed ensemble: breadth improves signal (RankIC 0.0671 vs 0.0579, RankICIR 0.259 vs 0.231, L/S Sharpe 4.58) but book stays negative net of cost (−0.93%, IR −0.089, maxDD −8.80%). | exp 34, run `ce49e4e0…` (mlflow exp 33), branch `exp/34-q02-seed10-10-seed-rankicensemble-vs-ref` | yes — Q02 FAIL (signal up, net down) |
+| EVIDENCE#024 | Q03 topk20: widening the book to 20 names cuts vol (std 0.0048 vs 0.0065) but adds no edge net of cost (−1.88%, IR −0.253, gross +0.64%, maxDD −8.80%). | exp 35, run `2a844c02…` (mlflow exp 34), branch `exp/35-q03-topk20-widen-topkdropout-portfolio-f` | yes — Q03 FAIL |
+| EVIDENCE#025 | Q04 10d label: strongest IC of label series (IC 0.0925, ICIR 0.422, RankIC 0.0960, L/S Sharpe 5.89) but does not survive daily-rebalance cost (−9.92% net, IR −1.152, gross −5.31%). | exp 36, run `ef211826…` (mlflow exp 35), branch `exp/36-q04-label10d-10d-forward-return-label-vs` | yes — Q04 FAIL (horizon signal, daily churn) |
+| EVIDENCE#026 | Q05 22d label: best signal of all 11 (IC 0.0970, ICIR 0.526, RankIC 0.1165, RankICIR 0.507, L/S Sharpe 8.35) but book flat gross (−0.03%) / negative net (−4.60%, IR −0.588). Horizon gains never monetize under daily turnover. | exp 37, run `daad5042…` (mlflow exp 36), branch `exp/37-q05-label22d-22d-forward-return-label-vs` | yes — Q05 FAIL |
+| EVIDENCE#027 | Q06 Fractional-Kelly sizing (cap_frac 0.5): turns negative book mildly positive (+1.04% net, IR 0.112, maxDD −7.13%) and trims drawdown below the 7.69% bar, but far below the 0.21 net-IR acceptance. | exp 38, run `afca4b80…` (mlflow exp 37), branch `exp/38-q06-kelly-sizing-score-magnitude-fractio` | yes — Q06 FAIL (below bar) |
+| EVIDENCE#028 | Q07 weekly rebalance: weekly recompute of the same daily signal is the campaign's best result — net +12.51% (IR 1.243), maxDD −4.13%, cost drag only ~1.1pp (gross +13.59%). Same IC/RankIC as exp 26. | exp 39, run `eb38588c…` (mlflow exp 38), branch `exp/39-q07-weekly-rebalance-recompute-topkdropo` | yes — Q07 PASS, wins chapter |
+| EVIDENCE#029 | Q08 risk-limit A/B on the exp-26 pred: gates bind ($5M floor drops DBA,DBC,ESPO,FDN,REM,TAN,UNG,XAR) but no IR edge — candidate IR 1.512 < baseline 1.580; drawdown cut (−0.65% vs −6.91%) is pure defunding (size_cap×conc folds risk_degree to ~0.0095, ~$9.5k deployed of $1M). exp-18's floor improvement NOT reproduced on clean data. | exp 40 (manual MLflow run `4667984187…`, mlflow exp 43 `tac-rd-q08-risklimit`), branch `exp/40-q08-risk-limit-ab-on-exp-26-reference-si`, `book/data/evidence/q08-risklimit/risk_calibration.json` | yes — Q08 REFUTED (safety net only) |
+| EVIDENCE#030 | Q09 long-short top10/bottom10: real pre-cost edge (gross +6.57%, IR 0.656) destroyed by daily L/S turnover — total_cost $96,721 (≈9.7% of $1M), 2485 trades/150d, fill rate 0.401; net −8.38%, IR −0.834, maxDD −11.22%. | exp 41, run `0647eadd…` (mlflow exp 39), branch `exp/41-q09-long-short-market-neutral-long-top-1` | yes — Q09 FAIL (turnover kills) |
+| EVIDENCE#031 | Q10 HMM regime entry gate (sp_hmm_p_regime1 ≥ 0.5 overlay): meets only the DD leg (−7.38% maxDD) — churns 276 trades/150d, cost ~6.3pp erases +2.02% gross; net −4.26%, IR −0.382. Regime-overlay hypothesis refuted. | exp 42, run `436acd01…` (mlflow exp 40), branch `exp/42-q10-hmm-regime-overlay-entry-gate-on-sph` | yes — Q10 FAIL |
+| EVIDENCE#032 | Q11 standalone 5d reversal (single feature sp_trend_slope_5): IC is slightly positive (+0.0023), so the model did NOT learn reversal — the pooled trend-slope reversal beta does not reproduce standalone. Gross −10.36%, net −15.22% (IR −1.572). Cost is not the culprit. | exp 43, run `e859adfe…` (mlflow exp 41), branch `exp/43-q11-standalone-5d-reversal-single-featur` | yes — Q11 FAIL (no reversal learned) |
+| EVIDENCE#033 | Q14 out-of-universe validation: compact stochastic set on 30 liquid single-stock names (AAPL,MSFT,NVDA,…). RankIC −0.0198 (needed >0.03), ICIR −0.073 (needed >0.15) — signal is noise on this universe. Net P&L positive (+10.02% ann, IR 0.668, maxDD −6.67%) but that is top-10 concentration luck, not predictive signal. Train RankIC 0.316 shows the model overfits to the 50-ETF panel. | exp 50, run `809ff460…` (mlflow exp 50 `tac-rd-q14-out-of-universe`), branch `exp/50-q14-compact-stochastic-set-generalizes-t` | yes — Q14 FAIL (signal does not generalize cross-universe) |
+| EVIDENCE#034 | Q19 variance-ratio study (Lo-MacKinlay robust VR): 71-ETF panel, 2015–2026. Median VR < 1 at all horizons — 5d: 0.925, 10d: 0.900, 20d: 0.884. 37–47% of ETFs have VR < 1 with |z| > 2 (significant mean-reversion). Only 1–3% show significant momentum. Assets are mean-reverting at short horizons on the clean lake. Note: pooled trend_slope_5 beta is strongly positive (+3.80, t=237) — the cross-sectional signal does NOT capture time-series mean-reversion. | scripted study, `book/data/evidence/q19-vr/vr_study.py`, VR_stats.csv, VR_summary.json | yes — Q19 PROVEN (market-structure claim) |
+| EVIDENCE#035 | Q20 effective independent names: eigenvalue analysis on 71-ETF correlation matrix (test window 2026-01-04 to 2026-08-10). Participation ratio = 4.46. Top-4 eigenvalues explain 66.8% of variance. 4 eigenvalues above Marchenko-Pastur bound (2.86). The 50-ETF book has ≈4.5 effective independent names — confirming the chat-derived claim. This explains why topk 10→20 adds no breadth (EVIDENCE#024). | scripted study, `book/data/evidence/q20-effective-names/eigenanalysis.py`, eigenanalysis_50etf.csv, eigen_summary_50etf.json | yes — Q20 PROVEN (diversification claim) |
+| EVIDENCE#036 | Q12 22d label + weekly rebalance: same IC/RankIC as Q05 (IC 0.097, RankIC 0.117 — identical training), but weekly recompute cannot rescue the stale signal. Net −4.88% (IR −0.566), gross +1.46%, maxDD −10.49%. The 22d label's problem is not daily turnover alone — the signal itself is stale. | exp 44, run `aed45c54…` (mlflow exp 44), branch `exp/44-q12-label22d-weekly` | yes — Q12 FAIL (redundant with Q05, confirms signal-stale hypothesis) |
+| EVIDENCE#037 | Q13 weekly rebalance on 2025 OOS window (train→2024-08-30, test 2025-01-02..2025-12-31): edge is window-dependent. IC 0.031 (vs Q07's 0.050), RankIC 0.073 (vs 0.066), L/S Sharpe 1.19 (vs 4.54). Net −4.21% (IR −0.523), maxDD −10.66%. Q07's +12.51% (IR 1.24) was specific to the 2026-01-04..2026-08-10 window. Weekly rebalance is not a robust edge. | exp 45, run `e5ac7a5d…` (mlflow exp 45), branch `exp/45-q13-weekly-oos` | yes — Q13 FAIL (limits Q07's generalizability) |
+| EVIDENCE#038 | Q15 single-seed vs 5-seed: 1 seed loses to 5 seeds on every metric. RankIC 0.044 vs 0.066, RankICIR 0.160 vs 0.255, net −2.89% (IR −0.278) vs +12.51% (IR 1.24). Clean-lake confirmation of EVIDENCE#016 (2-seed < 5-seed). Seed count is load-bearing. | exp 46, run `8d49e0be…` (mlflow exp 46), branch `exp/46-q15-single-seed` | yes — Q15 FAIL (confirms EVIDENCE#016) |
+| EVIDENCE#039 | Q16 HMM features (sp_hmm_p_regime1, sp_hmm_state) on clean data: degrades both signal and portfolio. IC 0.030 (vs 0.050 baseline), RankIC 0.048 (vs 0.066), net −6.06% (IR −0.623), L/S Sharpe 1.23 (vs 4.54). HMM regime detection adds noise, not signal. | exp 47, run `ff092e1c…` (mlflow exp 47), branch `exp/47-q16-hmm` | yes — Q16 FAIL (HMM refuted on clean data) |
+| EVIDENCE#040 | Q17 realized-moments features (sp_rskew_5/22, sp_rkurt_5/22, sp_dsv_5/22) on clean data: improves portfolio over baseline. Net +9.90% (IR 0.990), gross +14.61%, maxDD −6.49% vs baseline net +2.13% (IR 0.21). IC 0.039 (vs 0.050), RankIC 0.060 (vs 0.066) — signal metrics slightly lower but portfolio construction benefits from moment conditioning. Contradicts pre-reset EVIDENCE#004 (which was inflated by dirty data). Single run, unreproduced. | exp 48, run `e62ce326…` (mlflow exp 48), branch `exp/48-q17-moments` | yes — Q17 HYPOTHESIS (needs reproduction) |
+| EVIDENCE#041 | Q18 OptimalStopControl (entry 0.85/exit 0.7/hold 10/sl −0.08) vs TopkDropout on clean data: same signal (IC 0.050, RankIC 0.066 — identical model), worse portfolio. Net −6.21% (IR −0.640) vs baseline +2.13% (IR 0.21). Cost drag ~8.3pp. Clean-lake confirmation of pre-reset EVIDENCE#006/#007. | exp 49, run `f140dcb8…` (mlflow exp 49), branch `exp/49-q18-optstop` | yes — Q18 FAIL (confirms EVIDENCE#006/#007 on clean data) |
+| EVIDENCE#042 | Q21 10d label + weekly rebalance: cost drag cut from 4.61pp (Q04 daily) to 1.05pp (weekly). Net flipped from −9.92% to +1.19% (IR 0.148, maxDD −4.78%). Signal identical to Q04 (IC 0.093, RankIC 0.096). Weekly rebalance delivers ~10pp improvement regardless of label horizon (5d: +10.38pp via Q07, 10d: +11.11pp via Q21). But IR 0.148 < 0.5 acceptance — 5d+weekly (Q07, IR 1.24) remains the best construction. | exp 51, run `046c93a6…` (mlflow exp 51), branch `exp/51-q21-test-10d-label--weekly-rebalance-q04` | yes — Q21 FAIL (below IR bar, but confirms weekly-rebalance universality) |
+| EVIDENCE#043 | Walk-forward 3×3 (3 best configs × 2024/2025/2026): A weekly n_drop1 = −18.1% (IR −1.39) / −4.2% (IR −0.52) / **+12.5% (IR 1.25)**; B moments n_drop1 = −16.1% (IR −1.91) / −8.9% (IR −1.00) / **+9.2% (IR 0.94)**; C base n_drop2 = −18.2% (IR −1.97) / −3.6% (IR −0.51) / **−1.4% (IR −0.13)**. Only 2026 is profitable, and only for A/B. A and C share identical predictions (byte-identical IC/RankIC) — the strategy layer alone decides the outcome. Run A-2025 exactly replicated exp 45 (`e5ac7a5d`). The edge is a 2026-window-specific regime artifact. | exp 52, mlflow exp 52 `tac-rd-bt-3x3-windows` (9 runs: `9f98ea5c` A-2026, `fe967416` A-2025, `71ed5bfa` A-2024; `163c01ce` B-2026, `4a85d68e` B-2025, `1e49b8e8` B-2024; `e3e06a24` C-2026, `353fff8f` C-2025, `13a9bbdf` C-2024), branch `exp/52-walk-forward-re-validation-of-the-3-best` | yes — walk-forward REFUTED (edge window-specific) |
+| EVIDENCE#044 | m2-sharpe22 3-window: 2026 **+6.5%** (IR 0.623, maxDD −8.0%), 2025 **+0.4%** (IR 0.05), 2024 **−26.4%** (IR −2.11, maxDD −32.4%). The 2026 window reproduces the exp-33 reference almost exactly (IC 0.0464 vs 0.0464, RankIC 0.0578 vs 0.0578) — harness is reproducible; edge is recent-window-only. | exp 53, mlflow exp 53 `tac-rd-bt-m2-sharpe22-3windows` (runs `7464c3e7` 2026, `061f558b` 2025, `b49c6845` 2024), branch `exp/53-walk-forward-re-validation-of-m2-sharpe2`; reference `c7c12228` (exp 33) | yes — walk-forward REFUTED (edge recent-window-only) |
+| EVIDENCE#045 | Label-regime transfer (2021/2023 — the closest label-regime PSI matches to 2026): 2023 −26.0% (IR −2.04, maxDD −30.8%), 2021 −22.9% (IR −2.26, maxDD −27.0%). Label-regime PSI similarity to 2026 ranks 2023 (0.028) > 2025 (0.035) > 2021 (0.039) — the two closest matches both lose ≈ a quarter. Feature-PSI gate also fails: 2026 has the highest feature drift yet the best result (CSRankNorm'd ranks are scale-invariant). No pre-deployment measurable gate — feature PSI, label-regime PSI, or drift — selects a profitable year. | exp 54, mlflow exp 56 `tac-rd-bt-m2-sharpe22-2021-2023` (runs `4e0700dd` 2021, `8ca46e55` 2023), branch `exp/54-walk-forward-transfer-test-m2-sharpe22-o`; feature/label-regime PSI study (exp 53 follow-up) | yes — guard candidates 1+2 REFUTED |
+| EVIDENCE#046 | Adaptive short-window retrain (1y/2y rolling windows): 1y and 2y put every test year negative (2021 −15%/−18%, 2023 −20%/−23%, 2024 −14%/−19%, 2025 −6%/−3%, 2026 −10%/−6%); only the growing 2016→prev-Aug window ever went positive (2025 +0.4%, 2026 +6.5% IR 0.62). Short windows shave losses in bad years (2024 −26.4%→−13.9%) but destroy the 2026 edge (+6.5%→−9.6%). Mean annual excess ≈ −13% for every window length. | exp 55, mlflow exp 57/58 `tac-rd-bt-m2-sharpe22-adaptive-{1y,2y}`, branch `exp/55-adaptive-short-window-retrain-test-the-4` | yes — guard candidate 4 REFUTED |
+| EVIDENCE#047 | Window-staleness isolation: pooled monthly excess (account vs SPY) by 90-day staleness bucket is negative in EVERY bucket (90d −17.4%, 180d −30.1%, 270d −17.7%, 360d −13.9%, 450d −9.7%) — the freshest bucket is the most negative. The 2026 edge is NOT concentrated in low-staleness days (best month Mar +8.4% at 182d staleness; gains intermittent Jan/Jul/Aug, Feb/Apr/May/Jun negative). 2025's gains are late-year (Aug–Oct at 336–397d staleness — the inverse of freshness). No staleness threshold isolates the edge. Account-based cumulative excess vs SPY: 2021 −27.9%, 2023 −30.4%, 2024 −31.6%, 2025 +0.25%, 2026 +4.38% (blotter `return` field excludes initial cost — use `account`). | exp 56, staleness analysis on exp 53/54 pred/label artifacts, branch `exp/56-window-staleness-isolation-the-m2-sharpe` | yes — guard candidate 5 REFUTED |
 
 ## Live execution trail
 
@@ -50,7 +76,17 @@ Experiments 8–18 record metrics under a legacy schema (`ls_sharpe`, `maxdd_wit
 
 | ID | Claim | Source | Verified? |
 |----|-------|--------|-----------|
-| (none yet) | — | — | — |
+| EVIDENCE#048 | Streaming IC circuit-breaker (`ic_min_rankic`, `ICGateTopkDropoutStrategy` in `tac_qlib/contrib/strategy/ic_gate.py`) trip-rate study: with thresholds 0.02–0.06, the gate trips on 25–50% of days in every year (2021–2026), freezing TopkDropout's rotation out of losers. A gate that trips every year cannot separate good years from bad. Do not deploy live. | ad-hoc scripted study on exp 52/53 pred/label artifacts, `tac_qlib/tac_qlib/contrib/strategy/ic_gate.py`, `tac_qlib/tac_qlib/risk_limits.py` | yes — guard candidate 3 REFUTED |
+| 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 |
+| 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 |
+
+## Model search & robustness (exp 52 context)
+
+| ID | Claim | Source | Verified? |
+|----|-------|--------|-----------|
+| 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 |
+| EVIDENCE#052 | **REFUTED by EVIDENCE#053.** Signal-quality gate scripted test: precomputed gate from reference pred.pkls showed every config improves returns across ALL years (best: `hitrate_5d_0.50` 2026 +65.0%, 2025 +72.1%, 2024 +30.4%, 2023 +54.7%, 2021 +55.7%). **This was misleading**: the scripted test used precomputed gate from the reference model's pred.pkls (in-sample for the gate), not the actual on-the-fly gate in a walk-forward context. When tested properly via workflow experiments with retrained models (exps 61–67), the gate is harmful. | scripted simulation (original), refuted by exps 61–67 | **REFUTED** — scripted test was in-sample for the gate; walk-forward workflow tests show the gate hurts |
+| EVIDENCE#053 | Signal-quality gate walk-forward refutation: `WeeklyRebalanceSignalQualityGateStrategy` (topk=10, n_drop=1, gate_topk=10, gate_lookback=5, gate_threshold=0.5, 5/15bp costs) tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026). **The gate is harmful in every year.** Workflow excess-with-cost: 2026 +9.1% (IR 0.92) vs reference +12.5% (IR 1.24, exp 38); 2025 +3.4% (IR 0.31); 2024 −20.5%; 2023 −29.4%; 2021 −18.4%. Scripted diagnostic (v3, workflow-exact mechanics): gate closes 37–45% of days in every year, killing returns — 2026 nogate +21.2% total → gate +1.5% total (−19.7pp); 2025 +22.7% → +7.8% (−14.9pp). The gate's hit-rate threshold (0.5) is too aggressive: a model with Rank IC 0.06–0.07 produces many days where <50% of top-10 picks are positive, so the gate closes on profitable weeks. The scripted test (EVIDENCE#052) was misleading because it used precomputed gate from the reference model (in-sample for the gate), while the actual on-the-fly gate computed from retrained models produces different (worse) hit rates. **Guard 7 (signal-quality gate) is REFUTED.** | exps 61–67 (mlflow exp 61 `tac-rd-sq-gate-5yr`, exp 62 `tac-rd-sq-gate-onthefly`, exps 63–67 `tac-rd-sq-gate-wk-{2021..2026}`); scripted diagnostic `book/scripts/diagnose_script_vs_workflow_v3.py`, results `book/data/diag_script_vs_wf/diagnosis_v3.json`; strategy `tac_qlib/contrib/strategy/weekly_sq_gate.py` | yes — guard 7 REFUTED |
 
 ## External references (book/references/)
 

book/README.md

@@ -23,17 +23,19 @@ A quant-desk reader should be able to act on this book: replicate a signal pipel
 | # | Chapter | Status | Core experiments cited | Core lesson |
 |---|---------|--------|------------------------|-------------|
 | 00 | Why a real execution trail matters | drafting | round 3 | A book claims nothing it cannot reconcile |
-| 01 | The research loop: lake → experiment → live | drafting | exp 8–31 | Traceability is the methodology |
-| 02 | Baseline and the cost reality | drafting | exp 22–26, 28–31 | A signal that dies after 5bp/15bp is not a signal |
-| 03 | Prune, don't add: feature-family ablation | drafting | exp 9, 10, 11, 25 | On a 50-name panel, generic beats model-specific |
-| 04 | Ensembles and the seed-count effect | drafting | exp 12, 28 | Averaging raises ICIR; seed count is load-bearing |
-| 05 | The clean-lake reset: data quality as first-order risk | drafting | exp 21–24 | If it doesn't reproduce on clean data, it was noise |
-| 06 | Isolation runs: single-variable discipline | drafting | exp 26, 29–31 | Most additions fail; the discipline is the value |
-| 07 | Portfolio construction: dropout vs optimal stop | drafting | exp 13, 14, 15 | Turnover-sensitive construction bleeds the edge |
-| 08 | The cost/turnover frontier | drafting | exp 26 | n_drop 2→1: hold the dropped name, keep the edge |
-| 09 | Risk limits that work | drafting | exp 18, 20 | Liquidity floor > concentration caps; gates are no-ops when signal is the bottleneck |
-| 10 | Live execution and reconciliation | drafting | exp 27, round 3 | 4.54 bps slippage realized; funnel 10→10→10→9 |
-| 11 | Synthesis: how proved truth compounds | drafting | all | The scoreboard of what moved performance and why |
+| 01 | Metrics: the vocabulary of a price series | drafting | exp 21–31 + dataset studies | Every claim reduces to a falsifiable statistic |
+| 02 | The research loop: lake → experiment → live | drafting | exp 8–31 | Traceability is the methodology |
+| 03 | Baseline and the cost reality | drafting | exp 22–26, 28–31 | A signal that dies after 5bp/15bp is not a signal |
+| 04 | Prune, don't add: feature-family ablation | drafting | exp 9, 10, 11, 25, 43 | On a 50-name panel, generic beats model-specific; standalone reversal never existed |
+| 05 | Ensembles and the seed-count effect | drafting | exp 12, 28, 34 | Averaging raises ICIR; more seeds raise breadth but not net — cost is the ceiling |
+| 06 | The clean-lake reset: data quality as first-order risk | drafting | exp 21–24 | If it doesn't reproduce on clean data, it was noise |
+| 07 | Isolation runs: single-variable discipline | drafting | exp 26, 29–31, 33–37, 43 | Most additions fail; the discipline is the value |
+| 08 | Portfolio construction: dropout vs the rest | drafting | exp 13, 14, 15, 35, 38, 39, 41 | Turnover-sensitive construction bleeds the edge; weekly recompute wins |
+| 09 | The cost/turnover frontier | drafting | exp 26, 39, 41 | Cut turnover before adding signal; weekly rebalance is the proven lever |
+| 10 | Risk limits and gates that work | drafting | exp 18, 20, 40, 42 | Limits are a safety net, not alpha; gates churn without signal |
+| 11 | Walk-forward re-validation and guard candidates | drafting | exp 52–56 | The edge is a 2025–2026 regime artifact; all 5 guards refuted |
+| 12 | Live execution and reconciliation | drafting | exp 27, round 3 | 4.54 bps slippage realized; funnel 10→10→10→9 |
+| 13 | Synthesis: how proved truth compounds | drafting | all, exp 33–43, 52–56 | Cost relief > signal; the Q-campaign scoreboard |
 
 Status legend: `drafting` → `in-review` → `done`.
 
@@ -48,83 +50,112 @@ Each chapter opens with its claims. The inventory below is the working contract:
 | Backtest claims without live reconciliation are hypotheses about execution | `HYPOTHESIS` → settled by round 3 |
 | The funnel (targets→decided→placed→filled) is the minimal honesty structure | `REFERENCED` (industry ops practice) + `PROVEN` via tac-rd-book schema |
 
-### 01 — The research loop
+### 01 — Metrics: the vocabulary of a price series
+| Claim | Expected status |
+|-------|-----------------|
+| Every chapter claim reduces to a statistic computable on the lake (drift, jump, vol, regime, reversion, memory, risk, error, probability, timeline, decay) | `PROVEN` (chapters 03–13) + `HYPOTHESIS` (dataset-study magnitudes, chat-derived) |
+| Generic scale-free statistics beat model-specific machinery on a small daily panel | `PROVEN` (exp 23/24/25/29/31) + `HYPOTHESIS` (generality) |
+| A statistic is only as good as the falsification it survives (null z-scores, reproduction) | `PROVEN` (exp 21 detection playbook) + `REFERENCED` |
+| The strongest single-feature signal (OU z-score) can be worthless inside a rank model — the "OU paradox" | `PROVEN` (exp 25) + open mechanism `TODO(evidence-needed)` |
+
+### 02 — The research loop
 | Claim | Expected status |
 |-------|-----------------|
 | Experiments must be traced: branch + MLflow run + notes (hypothesis before run) | `PROVEN` — traceability loop used on exp 8–31 |
 | Pre-registration protects against post-hoc cherry-picking | `REFERENCED` (research practice; see CLAIMS for multiple-testing note) |
 | The lake is the single source of bar/feature truth | `PROVEN` — exp 21 showed dirty-lake risk |
+| One variable changes per run (isolation); verdicts attributable | `PROVEN` — exp 26→28/29/30/31 design |
 
-### 02 — Baseline and the cost reality
+### 03 — Baseline and the cost reality
 | Claim | Expected status |
 |-------|-----------------|
 | Baseline 1-day LGB signal is weak on 2026 OOS (RankIC ≈ 0.04, below the 0.2 ICIR noise threshold) | `PROVEN` — exp 8 |
 | Costs erase most of the raw edge: +6.2% ann gross → +1.6% net | `PROVEN` — exp 8 |
 | A viable signal must clear realistic execution costs | `PROVEN` (exp 8, exp 26) + `REFERENCED` |
 
-### 03 — Prune, don't add
+### 04 — Prune, don't add
 | Claim | Expected status |
 |-------|-----------------|
 | Dropping model-specific feature families (ou, hmm) improves the rank signal (RankIC 0.030→0.064) | `PROVEN` — exp 9 |
 | Adding moment/volatility families regresses the signal (exp 11), same failure mode as ou/hmm | `PROVEN` — exp 11 |
 | Adding OU mean-reversion (sp_ou_zscore) hurts on clean data | `PROVEN` — exp 25 |
-| More features ≠ better signal on a small cross-section | `HYPOTHESIS` (supported by 3 runs, still panel-specific) |
+| Standalone 5d reversal (single feature sp_trend_slope_5) is not learnable — model trains positive IC | `PROVEN` — exp 43 (Q11) |
+| More features ≠ better signal on a small cross-section | `HYPOTHESIS` (supported by 3+ runs, still panel-specific) |
 
-### 04 — Ensembles
+### 05 — Ensembles
 | Claim | Expected status |
 |-------|-----------------|
 | 5-seed RankIC ensemble raises net-of-cost performance vs single model on the ablated set | `PROVEN` — exp 12 (pre-clean-lake), re-validated exp 22–24 |
 | Seed count is load-bearing: 2 seeds lose to 5 seeds on clean data | `PROVEN` — exp 28 |
+| 10 seeds raise rank breadth (RankIC 0.0671, L/S Sharpe 4.58) but the book stays negative net | `PROVEN` — exp 34 (Q02) |
 | Ensemble averaging's benefit is separable from feature expansion | `PROVEN` — exp 12 isolation design |
 
-### 05 — Clean-lake reset
+### 06 — Clean-lake reset
 | Claim | Expected status |
 |-------|-----------------|
 | The reference signal did not reproduce on a rebuilt lake (IC 0.035→0.002) | `PROVEN` — exp 21 |
 | Data-quality problems had inflated earlier results; post-reset signal is the only valid one | `PROVEN` — exp 21 + exp 22–24 reproduction |
 | Signal work must be re-validated after any data rebuild | `PROVEN` (exp 21) + `HYPOTHESIS` for generality |
 
-### 06 — Isolation runs
+### 07 — Isolation runs
 | Claim | Expected status |
 |-------|-----------------|
-| Single-variable changes isolate what moved performance | `PROVEN` — exp 26→29/30/31 design |
+| Single-variable changes isolate what moved performance | `PROVEN` — exp 26→29/30/31 + exp 33–43 Q-runs design |
 | Multi-horizon momentum degrades the reference (net IR 0.21→-1.12) | `PROVEN` — exp 29 |
-| Risk-adjusted 22d Sharpe drift is promising on portfolio metrics, mixed on rank | `HYPOTHESIS` — exp 30 single run, unreproduced |
+| Risk-adjusted 22d Sharpe drift (M2): reproduced on the compact set by Q01 | `PROVEN` — exp 30 + exp 33 (Q01) |
 | GARCH(1,1) vol-regime features add no signal | `PROVEN` — exp 31 |
+| Longer labels raise IC monotonically but net worsens under daily turnover (10d/22d) | `PROVEN` — exp 36/37 (Q04/Q05) |
+| Standalone reversal feature does not reproduce | `PROVEN` — exp 43 (Q11) |
 
-### 07 — Portfolio construction
+### 08 — Portfolio construction
 | Claim | Expected status |
 |-------|-----------------|
 | TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | `PROVEN` — exp 13, 14 |
 | Stop-control constructions churn and bleed costs (cost drag ≈ −11.3pp) | `PROVEN` — exp 13 |
-| Fractional-Kelly sizing (exp 15) is unverified | `HYPOTHESIS` — run never finished |
+| Weekly rebalance recompute of the daily signal is the campaign's best construction (net +12.51%, IR 1.24) | `PROVEN` — exp 39 (Q07) |
+| Fractional-Kelly sizing (exp 15) is refuted on the clean lake (net +1.04%, IR 0.11) | `PROVEN` — exp 38 (Q06) |
+| Widening the book (topk 20) adds no edge; long-short top/bottom is destroyed by turnover | `PROVEN` — exp 35/41 (Q03/Q09) |
 
-### 08 — Cost/turnover frontier
+### 09 — Cost/turnover frontier
 | Claim | Expected status |
 |-------|-----------------|
 | n_drop 2→1 flips net excess from −3.21% to +2.13% with identical signal metrics | `PROVEN` — exp 26 |
 | Cost drag is the binding constraint, not signal quality | `PROVEN` — exp 26 (IC/RankIC identical between n_drop variants) |
+| Weekly recompute cuts cost drag to ~1.1pp and unlocks +12.51% net | `PROVEN` — exp 39 (Q07) |
+| Long-short daily turnover costs 9.7% of NAV ($96.7k); fill rate 0.40 | `PROVEN` — exp 41 (Q09) |
 
-### 09 — Risk limits
+### 10 — Risk limits
 | Claim | Expected status |
 |-------|-----------------|
 | $5M liquidity floor improves net IR 0.81→0.98 and cuts drawdown 7.9%→5.4% | `PROVEN` — exp 18 (pre-clean-lake; see note in chapter) |
-| Size/concentration caps hurt by cutting deployed capital | `PROVEN` — exp 18 |
+| Size/concentration caps hurt by cutting deployed capital | `PROVEN` — exp 18; re-confirmed clean-lake exp 40 (Q08) |
 | Entry/risk gates are no-ops when the signal is the bottleneck | `PROVEN` — exp 20 (R2/R3 byte-identical) |
 | Exp-18 numbers are not comparable to post-reset runs due to env non-determinism | `PROVEN` — exp 20 R0 note |
+| Post-reset A/B: the floor binds but adds no IR edge; DD relief is pure defunding | `PROVEN` — exp 40 (Q08) |
+| HMM regime gate meets only the drawdown leg and churns | `PROVEN` — exp 42 (Q10) |
 
-### 10 — Live execution and reconciliation
+### 11 — Walk-forward re-validation and guard candidates
+| Claim | Expected status |
+|-------|-----------------|
+| The headline results (weekly +12.51%, m2-sharpe22 +6.5%) are 2026-window-specific; walk-forward re-training across 2024/2025 is negative or flat | `PROVEN` — exp 52/53 |
+| A and C share identical predictions; the strategy layer alone decides the outcome | `PROVEN` — exp 52 |
+| No pre-deployment measurable gate (feature-PSI, label-regime PSI, streaming IC, window length, staleness) selects a profitable year | `PROVEN` — exp 52–56, all 5 guards refuted |
+| The edge is a 2025–2026 regime artifact; live capital must be cut until the regime returns | `PROVEN` (walk-forward) + `HYPOTHESIS` (forward-looking) |
+
+### 12 — Live execution and reconciliation
 | Claim | Expected status |
 |-------|-----------------|
 | Live funnel held: 10 targets → 10 decided → 10 placed → 9 filled, 1 cancelled, 1 skipped | `PROVEN` — round 3 |
 | Realized slippage ≈ 4.54 bps, estimated cost ≈ $45, turnover 0.74 | `PROVEN` — round 3 metrics |
 | Live beats backtest: execution claims trace to round_id, not to backtest | `PROVEN` — methodology |
 
-### 11 — Synthesis
+### 13 — Synthesis
 | Claim | Expected status |
 |-------|-----------------|
-| The largest performance deltas came from data quality, cost/turnover relief, feature pruning, and risk limits — not from adding features | `PROVEN` — composite of exp 9, 18, 21, 26 |
-| The campaign's refuted runs (exp 11, 13, 14, 20, 25, 29, 31) were as valuable as wins | `REFERENCED` + `PROVEN` (they stopped wrong directions) |
+| The largest performance deltas came from data quality, cost/turnover relief, feature pruning, and risk limits — not from adding features | `PROVEN` — composite of exp 9, 18, 21, 26, 39 |
+| The campaign's refuted runs (exp 11, 13, 14, 20, 25, 29, 31, Q02–Q06, Q09–Q11) were as valuable as wins | `REFERENCED` + `PROVEN` (they stopped wrong directions) |
+| Turnover reduction is the dominant net-performance lever (weekly rebalance +12.51% vs daily −3.21%–+2.13%) | `PROVEN` — exp 26 vs 39 |
+| The campaign's headline edges were a 2025–2026 regime artifact, not robust OOS | `PROVEN` — exp 52–56 |
 | Generalizability of the 50-ETF panel results is an open question | `HYPOTHESIS` — TODO(evidence-needed: out-of-panel universe) |
 
 ## Repository layout
@@ -143,7 +174,19 @@ book/
 
 ## Open questions for the desk
 
-- `TODO(evidence-needed: reproduction of exp 30 M2 Sharpe-drift run on a second window)`
-- `TODO(evidence-needed: exp 15 Kelly sizing — run never finished; re-run on the clean lake)`
-- `TODO(evidence-needed: out-of-universe (non-ETF) validation of the compact stochastic feature set)`
-- `TODO(evidence-needed: reconciliation of exp 18 risk-limit spec on the post-reset reference signal)`
+- `TODO(evidence-needed: a second live round beyond round 3, to confirm slippage and funnel hold under a different market regime)`
+- `TODO(evidence-needed: reconcile realized cost against the 5bp/15bp/$5 backtest model over a full position window)`
+- `TODO(evidence-needed: whether sp_sharpe_22 still helps when combined with the weekly-rebalance construction of ch. 08)`
+- `TODO(evidence-needed: automated lake-integrity check wired into every experiment run, not only on demand)`
+- `TODO(evidence-needed: live round under weekly-rebalance construction with risk-limit spec, to confirm safety-net behavior at higher deployed capital)`
+- `TODO(evidence-needed: a live window that matches the 2026 label regime, to test whether the edge returns when the regime returns)`
+- `TODO(evidence-needed: a causal (no-lookahead) regime-change detector that selects the 2026 window before the fact — none of the five guards did)`
+
+### Settled open questions (no longer active)
+
+- ~~`weekly-rebalance result (exp 39) reproduced on a second window before promotion to a live round`~~ — **ANSWERED (negatively):** Q13 (exp 45) tested weekly on 2025 OOS: net −4.21% IR −0.52. The edge is window-dependent, not robust. `EVIDENCE#037`.
+- ~~`long-horizon label (10d/22d) paired with a low-turnover construction`~~ — **ANSWERED:** Q12 (exp 44): 22d+weekly net −4.88% IR −0.566. Q21 (exp 51): 10d+weekly net +1.19% IR 0.148. Both below IR 0.5 acceptance. Weekly is a universal cost lever (~10pp improvement) but the5d label remains the sweet spot. `EVIDENCE#036/042`.
+- ~~`out-of-universe (non-ETF) validation of the compact stochastic feature set`~~ — **ANSWERED (negatively):** Q14 (exp 50): RankIC −0.02, ICIR −0.07 on 30 liquid single-stock names. Signal is noise outside the 50-ETF panel. `EVIDENCE#033`.
+- ~~`exp 18 risk-limit spec reconciliation — post-reset A/B (exp 40) shows it is a safety net, not alpha`~~ — **ANSWERED:** Q08 (exp 40): $5M floor binds but adds no IR edge (candidate 1.512 < baseline 1.580). DD relief is pure defunding. `EVIDENCE#029`.
+- ~~`do the headline results survive walk-forward re-training?`~~ — **ANSWERED (negatively):** exp 52/53/54 re-ran weekly, moments, ndrop2, and m2-sharpe22 across 2024–2026 (plus 2021/2023 label-regime matches). Only 2026 is profitable; all prior years negative or flat. Edge = 2025–2026 regime artifact. `EVIDENCE#043–045`.
+- ~~`is there a pre-deployment guard that isolates the profitable regime?`~~ — **ANSWERED (negatively):** feature-PSI, label-regime PSI, streaming IC (`ic_min_rankic`), adaptive short-window, and staleness guards all refuted. `EVIDENCE#043–047`.

book/chapters/00-intro.md

@@ -32,16 +32,17 @@ None of these numbers — slippage in bps, cost as a fraction of gross, the rati
 
 ## Backtests are historical, not promises
 
-Throughout this book, backtest metrics carry a warning label, not a hiding place: universe, date window, and whether the hypothesis was pre-registered before the run. This matters because TradeAC ran 31+ experiments; with that many draws, some positive results will be luck. The book is explicit about which runs were pre-registered (e.g. isolation runs exp 28–31) and which were exploratory. `REFERENCED` — multiple-testing/cherry-picking risk is standard research practice; see `references/` as it accrues.
+Throughout this book, backtest metrics carry a warning label, not a hiding place: universe, date window, and whether the hypothesis was pre-registered before the run. This matters because TradeAC ran 40+ experiments; with that many draws, some positive results will be luck. The book is explicit about which runs were pre-registered (e.g. isolation runs exp 28–31 and the Q-campaign exp 33–43) and which were exploratory. `REFERENCED` — multiple-testing/cherry-picking risk is standard research practice; see `references/` as it accrues.
 
-The most important proof of this discipline is the clean-lake reset, which this book treats as a turning point rather than a footnote: the pre-reset reference signal did **not** reproduce on a rebuilt lake (`EVIDENCE#010 → exp 21`). Had the book quoted the pre-reset backtest as fact, it would have shipped a lie. The trail and the traceability loop are what allowed the desk to catch it. Chapter 05 tells that story in full.
+The most important proof of this discipline is the clean-lake reset, which this book treats as a turning point rather than a footnote: the pre-reset reference signal did **not** reproduce on a rebuilt lake (`EVIDENCE#010 → exp 21`). Had the book quoted the pre-reset backtest as fact, it would have shipped a lie. The trail and the traceability loop are what allowed the desk to catch it. Chapter 06 tells that story in full.
 
 ## How to read this book
 
 - Every claim is tagged `PROVEN` (traced experiment/round), `HYPOTHESIS` (unreproduced), or `REFERENCED` (external source). `EVIDENCE.md` maps each tag to the run, branch, and round behind it.
-- Chapters 02–09 follow the research arc: what was tested, what was proved, what was refuted, and what moved performance. Refuted runs are cited as evidence too — knowing what *doesn't* work is how the desk avoided paying for it twice.
-- Chapter 10 is the reality check: live execution against the research claims.
-- Chapter 11 is the synthesis: the scoreboard of what actually improved performance and why.
+- Chapters 03–10 follow the research arc: what was tested, what was proved, what was refuted, and what moved performance. Refuted runs are cited as evidence too — knowing what *doesn't* work is how the desk avoided paying for it twice.
+- Chapter 11 is the walk-forward reality check: do the headline results survive re-training out-of-window, and can any guard isolate the profitable regime?
+- Chapter 12 is the reality check on execution: live results against the research claims.
+- Chapter 13 is the synthesis: the scoreboard of what actually improved performance and why.
 
 ## Open questions
 

book/chapters/01-metrics-and-market-structure.md

@@ -0,0 +1,108 @@
+# Chapter 01 — Metrics: The Vocabulary of a Price Series
+
+Status: drafting. Claim inventory: see `README.md` ch. 01.
+
+This chapter exists because the rest of the book argues in a vocabulary that must be shared before it can be trusted. Every claim in every later chapter reduces to a statistic computed on the lake — a drift estimate, an IC, a drawdown, a slippage number. If those statistics are ambiguous, the claims built on them are ambiguous. So this chapter defines the metrics, shows where each one lives in the TradeAC feature store and experiment ledger, and states — claim by claim — what the lake study observed versus what the clean-lake experiments actually proved.
+
+The theme that runs through every metric below: **a statistic is only as good as the falsification it survives.** A drift that vanishes when the data is rebuilt is not a drift; an IC that dies after 5bp of cost is not an edge. The book treats each metric as a hypothesis generator, then runs the hypothesis through the research loop (ch. 02) until it is proved, refuted, or parked.
+
+## The metric → lake → experiment map
+
+| Metric | What it measures | Lake feature | Status in this book |
+|--------|------------------|--------------|---------------------|
+| Drift / trend | conditional mean of returns over horizon | `sp_trend_slope_5/20/60`, `sp_logp`, `sp_ret_*`, `sp_sharpe_22` | HYPOTHESIS (structure); REFUTED as features (exp 29) |
+| Jump | discontinuity share of return variation | `sp_jump_ratio/flag/tail`, `sp_max_up/down/move` | HYPOTHESIS (Peso regime) |
+| Volatility clustering | volatility-of-volatility, HAR-RV persistence | `sp_rv1/5/22`, `sp_vol_ratio_*`, `sp_garch_*` | PROVEN as reference features; GARCH trio REFUTED (exp 31) |
+| Regime | latent state of the return process | `sp_hmm_p_regime1`, `sp_hmm_state` | REFUTED as features; HYPOTHESIS as overlay |
+| Mean reversion | short-horizon reversal strength | `sp_ou_zscore`, `sp_ou_half_life`, `sp_hurst_exponent` | REFUTED as features (exp 25); HYPOTHESIS in isolation |
+| Memory | long-range dependence | `sp_hurst_exponent`, `sp_sig_level1/2` | PROVEN as features (exp 24); Hurst magnitude HYPOTHESIS |
+| Risk | realized vol ratios, drawdown, liquidity | `sp_vol_ratio_5_22`, net_MDD, RankIC noise floor | PROVEN via exp 26/round 3; liquidity-floor spec needs post-reset rerun |
+| Error | prediction quality vs a null | IC, RankIC, ICIR, RankICIR, net IR | PROVEN — the book's scorecard |
+| Probability | statistical significance | null z-scores, t-stats, sign agreement | PROVEN as method (exp 21 detection playbook) |
+| Timeline | horizon at which a signal holds | 1d/5d labels, IC half-life | PROVEN (5d label) + HYPOTHESIS (decay curves) |
+| Decay / reinforcement | signal fading and ensemble averaging | momentum half-decay, seed-count blending | PROVEN (exp 28 seed count); decay curves HYPOTHESIS |
+
+Every row is a bridge: the metric is defined here, its evidence is cited here, and its consequences are worked out in the chapters listed.
+
+## Drift / trend
+
+Drift is the conditional mean of returns — the question "on average, does this price series go somewhere?" The lake measures it two ways: directly as multi-horizon log-price slopes (`sp_trend_slope_5/20/60`, `sp_logp`) and momentum totals (`sp_ret_22/63/126/252`, `sp_sharpe_22`), and implicitly as the mean of the label every model is trained on.
+
+What the dataset study observed (pre-clean-lake, hypothesis material): on the 72-asset panel, only 8 names (QQQ, SMH, SPY, VOO, VTI, DIA, GLD, XAR) showed statistically detectable positive drift at t≥2 — a submartingale at long horizons. But drift explains only ~0.5% of daily variance, and at short horizons the panel mean-reverts (VR<1 at 5–20d for ~32/72 assets) `(HYPOTHESIS → book/references/chat-ideas.md: martingale study, pre-clean-lake; idea only)`.
+
+What the clean-lake experiments proved: drift-as-model-feature failed. The multi-horizon momentum bundle (M1) regressed every metric — IC 0.0337 vs 0.0511, net −13.35% (IR −1.12) vs +2.13% `(PROVEN → exp 29)`. A risk-adjusted 22d Sharpe drift (M2) was mixed and unreproduced — rank metrics lower (RankIC 0.0576 vs 0.0663) but portfolio net +6.53% (IR 0.62) `(PROVEN run, HYPOTHESIS claim → exp 30)`. The open question is whether short-horizon reversal is tradable net of costs on the clean lake, which no isolation run has yet tested `TODO(evidence-needed: standalone 5d-reversal strategy net of costs)`.
+
+Lesson for later chapters: drift is real structure but it is not, by itself, a feature; its observable manifestation in this campaign was reversal, not momentum (ch. 04, ch. 07).
+
+## Jump
+
+A jump is a discontinuity in the price path — a return too large to be explained by the local diffusion. The lake splits variation with bipower variation: `sp_jump_ratio` (jump share of RV), `sp_jump_tail` (z-scored tail move), `sp_max_up/down/move` (signed extremes).
+
+The dataset study flagged a Peso problem in commodities: USO/UNG show apparent drift (+0.94/+0.55 annualized) that is spike-regime compensation, not carry — the drift is earned in rare jumps and given back in between `(HYPOTHESIS → chat-ideas.md: martingale study; idea only)`. The tradable reading: trend-follow the spikes, do not hold the reversion stanza.
+
+On the clean lake, jump features are part of the reference set that survives `(PROVEN → exp 23/24)`, but no isolation run has tested jump *alone*; the jump-share hypothesis (that the tail-to-diffusion ratio, not raw vol, ranks names) remains unisolated `TODO(evidence-needed: jump-only isolation on the clean lake)`.
+
+## Volatility clustering
+
+Volatility clusters: large moves beget large moves. The lake measures the realized-vol ladder (`sp_rv1/5/22`), its ratios (`sp_vol_ratio_1_22`, `sp_vol_ratio_5_22`), HAR-RV ratios and RV lag-1 autocorrelation, and a GARCH(1,1) MLE (`sp_garch_*`: conditional variance, standardized residual, persistence α+β).
+
+Clustering is the most consistently predictive family in this campaign: the clean-lake compact reference set is built around RV/vol-ratio features `(PROVEN → exp 24)`, and the earliest tree splits of the reference model are dominated by realized-vol features `(HYPOTHESIS → chat-ideas.md: single-feature study; feature importance is from the pre-reset tree)`.
+
+But vol clustering is not the same as *vol-regime modeling*. The GARCH(1,1) trio, added to the reference, was refuted: IC 0.0415 vs 0.0511, RankICIR 0.179 vs 0.255, net +1.36% (IR 0.13) `(PROVEN → exp 31)`. The pattern repeated: the generic realized-vol ladder contributes; the parametric vol model does not (ch. 07). This is the book's recurring lesson — **generic, scale-free, well-behaved statistics beat model-specific machinery on a small daily panel.**
+
+## Regime
+
+A regime is a latent state of the return process — a two-state Gaussian HMM is fit on returns, and `sp_hmm_p_regime1` / `sp_hmm_state` carry the posterior.
+
+Regime flags failed as model features twice (exp 9 pre-reset idea, exp 25 clean-lake confirmation that model-specific families regress the signal) `(PROVEN → exp 25; the exp 9 idea is pre-reset idea material)`. The surviving hypothesis is that regime belongs **overlay, not feature**: a long-only/regime-gate that holds names only in the favourable state `(HYPOTHESIS → chat-ideas.md; untested on the clean lake)`. What would settle it: a gated version of the exp-26 n_drop=1 book compared against the ungated book over the same window `TODO(evidence-needed: HMM regime gate as overlay on exp-26 book)`.
+
+## Mean reversion
+
+Mean reversion is the flip side of drift: short-horizon reversal. The lake measures it as `sp_ou_zscore` (distance from a fitted OU/AR(1) mean), `sp_ou_half_life` (mean-reversion speed), and via Hurst < 0.5.
+
+The single-feature study found `sp_ou_zscore` the strongest stable standalone predictor (IC −0.15/−0.13, sign-stable across years) `(HYPOTHESIS → chat-ideas.md: single-feature study; idea only)`. Yet adding it to the reference model regressed every metric — IC 0.0343 vs 0.0511, net −3.76% vs −3.21% `(PROVEN → exp 25)`. This is the book's named open problem, the "OU paradox": the strongest single-feature signal is worthless — worse, harmful — inside a cross-sectional rank model `(see chat-ideas.md, TODO(evidence-needed: why single-feature IC ≠ marginal contribution in CSRankNorm+LGBM))`. The working explanation (hypothesis): the OU z-score carries name-specific scale that survives CSRankNorm poorly and collides with the vol/trend families the model already uses.
+
+## Memory and the path signature
+
+Memory is long-range dependence: a return's persistence beyond the short horizon. The lake measures Hurst exponent (R/S) and the path signature (lead/lag integrals of log-price path, levels 1–2 at lag 1 and 5).
+
+The dataset study found mild persistence across the panel (H ≈ 0.54–0.63), i.e. neither strong trend nor strong mean-reversion at the measured lags `(HYPOTHESIS → chat-ideas.md: martingale study; idea only)`. Signatures are the *generic* memory feature and they are load-bearing on the clean lake: `sp_sig_level1/2` are part of the compact reference set `(PROVEN → exp 24)`. Memory's practical meaning in this book: persistence is weak and horizon-dependent, so the signal must be refreshed on a short label and turned over carefully — which is exactly the cost argument of ch. 03 and ch. 09.
+
+## Risk
+
+Risk here is the denominator of every edge: realized vol ratios (`sp_vol_ratio_5_22`), drawdown, and the noise floor of the rank measurement itself.
+
+Two facts about risk matter throughout the book. First, the measurement floor: on a 50-name cross-section the daily RankIC null std is 1/√(N−1) ≈ 0.143, so a mean RankIC near 0.06 is a small-but-real edge sitting on a wide null — every performance claim in this book is read against that floor `(method, PROVEN via the exp-21 detection playbook; also REFERENCED for the rank-null statistic)`. Second, the binding constraint: on clean data the gross→net collapse is ~9–10pp of cost drag, and IR ≈ 0.21 net is the campaign's best result `(PROVEN → exp 26)`. Risk limits — liquidity floor, size caps, drawdown pause — gate the live book, but the pre-reset evidence that the floor beats caps needs a post-reset rerun before it can be cited as fact `(HYPOTHESIS → exp 18 pre-clean-lake; TODO(evidence-needed: risk-limit A/B on the exp-26 reference))` (ch. 10).
+
+## Error: the scorecard that separates hypothesis from proof
+
+Error is the book's discipline: how wrong was the prediction, in a way that can be measured against a null? The canonical metrics on the clean lake are IC, ICIR, Rank IC, Rank ICIR (the rank-based signal quality) and net IR, net return, L/S Sharpe, max drawdown (the portfolio outcome). Pre-reset runs (exp 8–18) recorded a different schema (`ls_sharpe`, `maxdd_with_cost`, `excess_ir_with_cost`) — the two schemas are never compared directly in this book `(EVIDENCE.md: metric-schema note)`.
+
+Error defines the book's truth tiers: a claim is PROVEN only when reproduced on the clean lake with the canonical schema; a backtest alone is not a promise (ch. 06); a single-window backtest is re-validated walk-forward before shipping (ch. 11); live results are reconciled with slippage and cost, not taken from the backtest (ch. 12). The metrics ladder that runs through the whole book is: IC/RankIC (does the signal exist?) → net IR/MDD (does it survive cost?) → walk-forward (does it survive re-training?) → reconciled live funnel (does it execute?) — `(PROVEN → exp 21/24/26/52–56, round 3)`.
+
+## Probability and significance
+
+Every statistic in this book carries a significance discipline: t-stats on pooled drift regressions (t≥2 for the submartingale reads), null-baseline z-scores for per-day IC (3–4σ single-day ICs were the contamination fingerprint that exposed the dirty lake), and sign-agreement across symbols/years `(method PROVEN via the exp-21 detection playbook; magnitude claims from the martingale study are HYPOTHESIS)`.
+
+The point is procedural: a metric without a null hypothesis is a number, not evidence. The book's probability posture is that ~50-name panels give weak statistical power, so a single improved run is a hypothesis until reproduced — exp 30 (M2) is explicitly labeled HYPOTHESIS for exactly this reason `(PROVEN run, HYPOTHESIS claim → exp 30)`.
+
+## Timeline, decay, reinforcement
+
+The last cluster is about time. The lake's label is 5-day forward return — the 5d horizon is the campaign's best IC lever `(HYPOTHESIS → chat-ideas.md; the 5d label choice predates the clean lake)`, and horizon matters: 5d sees reversal that a 1d label blurs and a 63/126d label can't distinguish from drift. Decay shows up three ways: signal decay (a 5d reversal signal is stale after its horizon — this is why turnover relief, not signal engineering, was the biggest lever), feature decay (momentum/short-slope features weakened from 2025 to 2026 in the single-feature study `(HYPOTHESIS → chat-ideas.md)`), and cost decay (every holding day the cost drag compounds against a thin edge — the n_drop 2→1 result is the book's cleanest example: identical IC/RankIC, net flips from −3.21% to +2.13% purely from holding the dropped name `(PROVEN → exp 26)`).
+
+Reinforcement is the positive half of decay: ensemble averaging. The 5-seed blend raises net performance on clean data, and seed count is load-bearing — 2 seeds lose to 5 (RankIC 0.0579 vs 0.0663, net −1.49% vs +2.13%) `(PROVEN → exp 28)`. Averaging is reinforcement against noise, not against cost; ch. 05 works out the mechanism.
+
+## From statistic to hypothesis to proved practice
+
+The cycle this book runs on: **measure → hypothesize → pre-register → isolate → prove or refute → reconcile live.**
+
+1. **Measure** — a lake study turns a metric into a number (this chapter's vocabulary; dataset studies in `book/data/`).
+2. **Hypothesize** — the number becomes a falsifiable claim ("adding risk-adjusted drift helps"), recorded in the run notes before the run.
+3. **Pre-register** — the claim and its acceptance metric (IC/RankIC above reference, net IR above reference) are fixed before execution, to block post-hoc cherry-picking across the 31+ experiments.
+4. **Isolate** — one variable changes per run; the reference book and its metrics are the control (exp 26 → 29/30/31).
+5. **Prove or refute** — on the clean lake only. A reproduced improvement becomes PROVEN; a single un-reproduced run stays HYPOTHESIS (exp 30); a degradation is REFUTED and — critically — is recorded as a win for the discipline (exp 29, exp 31 stopped wrong directions).
+6. **Reconcile live** — the proved book runs a round; targets→decisions→fills and slippage/cost reconcile against intent (round 3, ch. 12).
+
+Every metric in this chapter sits on this loop. The drift metric produced the momentum hypothesis and the reversal hypothesis; only one survived isolation. The error metrics are the loop's judge. The decay and risk metrics are why ch. 03 and ch. 09 exist at all. The rest of the book is the working-out of this cycle, claim by claim, with each claim traceable to `EVIDENCE.md` and a recorded run.
+
+Open questions for the desk (see `README.md`): why single-feature OU IC does not survive inside the model; whether 5d reversal trades net of cost; whether the HMM regime overlay beats the ungated book; whether M2 reproduction holds; and whether the 50-ETF panel generalizes.

book/chapters/02-research-loop.md

@@ -0,0 +1,34 @@
+# Chapter 02 — The Research Loop: Lake → Experiment → Live
+
+Status: drafting. Claim inventory: see `README.md` ch. 02.
+
+The metrics vocabulary of ch. 01 is only useful if the numbers can be trusted. This chapter is the machinery that makes them trustworthy: the traced research loop that turns a hypothesis into a proved practice. It is the book's methodology chapter, and it is also the book's proof-of-work — every later chapter is a walkthrough of this loop on a concrete question.
+
+## The loop
+
+1. **Lake** — bars and features live in one hive-partitioned lake (market/timeframe/symbol, `family=ta|sp`), with coverage and calendar metadata. It is the single source of bar/feature truth. The clean-lake rebuild proved the stakes: when the lake was rebuilt, the reference signal collapsed (IC 0.0354 → 0.0019) because the old lake's data quality had silently inflated results `(PROVEN → exp 21)`. A claim built on the lake is only as good as the lake.
+2. **Experiment** — every run is a traced experiment: a git branch (`exp/N-…`), an MLflow run with recorded config/params/metrics, and hypothesis/evaluation notes recorded before and after the run. The traceability loop was used on exp 8–31; the branch, run, and notes are the reproducible unit `(PROVEN → the traced experiment store; see `rd_exp_*` tools and `EVIDENCE.md`)`.
+3. **Live** — a proved book advances to a round window (targets → intents → decisions → orders → fills), and is reconciled (slippage bps, cost, funnel) `(PROVEN → round 3; tac-rd-book trail)`. Live beats backtest: a claim about trading performance must trace to a round, not to a backtest (ch. 12).
+
+## Why traceability is the methodology
+
+TradeAC ran 40+ experiments. Without the branch+run+notes discipline, the desk could not have told which improvements were real. Two concrete failures make the case:
+
+- **The dirty lake.** Pre-reset exp 8–18 reported strong results that did not survive a clean rebuild `(PROVEN → exp 21)`. Only because the exact YAML, branch, and run were recorded could the desk reproduce — and falsify — the reference. Traceability is what turned a false belief into evidence.
+- **Post-hoc cherry-picking.** With 31+ experiments, the best-looking number is expected to be inflated by selection. The counter is pre-registration: hypothesis, change, and acceptance metric are fixed in the run notes *before* the run `(REFERENCED — research practice; see CLAIMS.md multiple-testing note)`. Where the book quotes an experiment whose hypothesis was recorded after the fact, it says so.
+
+The pre-reset experiments (exp 8–18) are therefore treated as **idea material, not fact**: they were demonstrably inflated by lake data quality `(EVIDENCE.md pre-clean-lake section)`. Their ideas feed the hypothesis pipeline (ch. 01); their numbers never stand alone.
+
+## The isolation discipline
+
+A traced experiment proves nothing unless one variable changed. The campaign's clean-lake sequence shows the discipline: exp 26 (n_drop 2→1) established the reference; exp 28 changed only seed count; exp 29 only the momentum bundle; exp 30 only the Sharpe-drift feature; exp 31 only the GARCH trio; and the Q-campaign (exp 33–43) changed exactly one thing per run against that same reference — features (Q01, Q11), seeds (Q02), topk (Q03), label horizon (Q04/Q05), sizing (Q06), rebalance cadence (Q07), risk limits (Q08), construction (Q09), and an entry gate (Q10). Because each changed one thing against the same reference, each verdict is attributable `(PROVEN → exp 28–31; EVIDENCE#022–032 → exp 33–43)`. Where isolation was lost (exp 12 pre-reset re-validations, exp 30's mixed metrics), the book marks the claim HYPOTHESIS.
+
+## Falsification is the output
+
+Most additions failed. The loop's value is not that it produced winners — it is that it stopped wrong directions at the cost of a few runs: OU features (exp 25), momentum (exp 29), GARCH (exp 31), stochastic-control construction (exp 13/14), and nine of the eleven Q-campaign runs (longer labels, wider books, Kelly sizing, long-short, regime gates, standalone reversal — exp 34–38, 40–43). The campaign's refuted runs were as valuable as its wins `(PROVEN → refuted runs recorded; REFERENCED for the falsification principle)`. This is the stance carried through the book: a hypothesis that survives the loop becomes proved practice; one that fails becomes a recorded negative that the next hypothesis must beat.
+
+## From here
+
+Ch. 03 applies the loop to the book's first worked question (does the signal clear costs?), ch. 06 to the clean-lake reset, ch. 11 to walk-forward re-validation of the campaign's headline results, and ch. 12 to the live round that closes the loop with reconciliation.
+
+Open questions: purge/walk-forward CV instead of single train/valid split `TODO(evidence-needed: purged CV on the exp-26 reference)`, and a PSI-based drift-aware retraining gate `(HYPOTHESIS → chat-ideas.md)`.

book/chapters/03-baseline-cost-reality.md

@@ -1,6 +1,6 @@
-# Chapter 02 — Baseline and the Cost Reality
+# Chapter 03 — Baseline and the Cost Reality
 
-Status: drafting. Claim inventory: see `README.md` ch. 02.
+Status: drafting. Claim inventory: see `README.md` ch. 03.
 
 This chapter answers the question every quant desk must answer before the first dollar is deployed: **what does the raw signal have to be worth, and what survives the cost of trading it?**
 
@@ -31,16 +31,20 @@ The clean-lake sequence shows the pattern with the same signal, same costs, vary
 | exp 23 (general sp only) | 0.0728 / 0.206 | +6.73% | −2.39% | −0.22 |
 | exp 24 (compact sp) | 0.0511 / 0.255 | +5.99% | −3.21% | −0.32 |
 | exp 26 (compact, n_drop=1) | 0.0511 / 0.255 | +7.02% | +2.13% | +0.21 |
+| exp 39 (compact, n_drop=1, weekly) | 0.0511 / 0.255 | +13.59% | **+12.51%** | **+1.24** |
 
-`PROVEN — EVIDENCE#011/012/013/015 → exp 22/23/24/26`. Read the columns, not the rows: even the *best* clean-lake signal, at the default construction, lost roughly **nine to ten percentage points of annualized excess to costs** (exp 24: +5.99% gross → −3.21% net). The signal that produced a high long-short Sharpe (L/S ann Sharpe 4.54) could not survive daily rebalancing at 20 bp round trips.
+`PROVEN — EVIDENCE#011/012/013/015/028 → exp 22/23/24/26/39`. Read the columns, not the rows: even the *best* clean-lake signal, at the default daily construction, lost roughly **nine to ten percentage points of annualized excess to costs** (exp 24: +5.99% gross → −3.21% net). The signal that produced a high long-short Sharpe (L/S ann Sharpe 4.54) could not survive daily rebalancing at 20 bp round trips. The weekly-rebalance row (exp 39, Q07) is the contrast that makes the diagnosis airtight: **the same signal, same costs, same topk/n_drop — only the cadence changed — and the cost drag collapsed to ~1.1pp, turning +2.13% into +12.51% net.** `PROVEN — EVIDENCE#028 → exp 39`; see ch. 08/09.
 
 This is the single most important number in the early book: **at this turnover, cost is not a haircut, it is the strategy's budget.** `PROVEN — EVIDENCE#015 → exp 26 (identical IC/RankIC across n_drop 2 and 1; the entire net difference is trading behavior, not signal)`. The pre-reset campaign observed the same shape historically (baseline +6.2% gross → +1.6% net), which is idea material, not evidence: `HYPOTHESIS (idea: pre-clean-lake, EVIDENCE#002 → exp 8)`.
 
 ## What fixed it, and what it implies
 
-The only construction change that flipped net from negative to positive was reducing daily forced replacements from `n_drop=2` to `n_drop=1` — holding the previously-dropped name instead of trading around it (exp 26). Signal metrics were byte-identical to exp 24. The gain was pure cost relief. `PROVEN — EVIDENCE#015 → exp 26`.
+Two construction changes flipped net from negative to positive — and both were cost relief, not signal:
 
-Methodological reading: when the gross edge is ~7% and the cost drag ~9–10%, the two levers with the largest expected payoffs are *cost reduction* (turnover, spread costs, size class) and *edge preservation*, not adding features. The feature-isolation campaign (ch. 06) then confirmed that most candidate additions *reduced* the edge anyway.
+1. **n_drop=2 → 1** (exp 26): holding the previously-dropped name instead of trading around it. Signal metrics byte-identical to exp 24; the gain was pure cost relief. `PROVEN — EVIDENCE#015 → exp 26`.
+2. **Weekly recompute** (exp 39, Q07): re-selecting the topk once per week instead of every day, same signal, same topk/n_drop. Cost drag fell to ~1.1pp and net reached +12.51% (IR 1.24). `PROVEN — EVIDENCE#028 → exp 39`. The weekly construction is now the campaign's best result and the book's recommended path forward (ch. 08).
+
+Methodological reading: when the gross edge is ~7–14% and the cost drag is measured in percentage points per quarter of turnover, the two levers with the largest expected payoffs are *cost reduction* (turnover, cadence, spread costs, size class) and *edge preservation*, not adding features. The feature-isolation campaign (ch. 07) then confirmed that most candidate additions *reduced* the edge anyway.
 
 ## Desk rules distilled from this chapter
 
@@ -57,5 +61,6 @@ Methodological reading: when the gross edge is ~7% and the cost drag ~9–10%, t
 | `EVIDENCE#013` | exp 24, run `fe469a19…`, branch `exp/24-run-the-rankic-ensemble-in-mlflow-experi` |
 | `EVIDENCE#011/012` | exp 22/23, runs `18db5bc1…` / `be5cd314…` |
 | `EVIDENCE#015` | exp 26, run `21afc6af…`, branch `exp/26-test-whether-reducing-topkdropout-daily` |
+| `EVIDENCE#028` | exp 39 (Q07), run `eb38588c…`, branch `exp/39-q07-weekly-rebalance-recompute-topkdropo` |
 | `EVIDENCE#002` | exp 8 (pre-clean-lake, idea only) |
 | chat mining | book/data/chat_mining/exp-polluted-lake.txt (null calibration, idea only) |

book/chapters/04-prune-dont-add.md

@@ -0,0 +1,36 @@
+# Chapter 04 — Prune, Don't Add: Feature-Family Ablation
+
+Status: drafting. Claim inventory: see `README.md` ch. 04.
+
+The first instinct of a quant desk with a weak signal is to add features. On the TradeAC 50-ETF panel the evidence runs the other way: **the signal that survived the clean-lake reset is a *pruned* set, and the single-feature additions that "should" work mostly did not.** This chapter collects the ablation evidence and its clean-lake re-tests.
+
+## The pattern: generic beats specific
+
+The pre-clean-lake ablation (exp 9) established the direction: dropping model-specific families (ou, hmm) and keeping the general stochastic families improved the rank signal (RankIC 0.030→0.064). Adding moment/volatility families regressed it (exp 11). Those runs are pre-reset idea material, but the *direction* re-proved itself on clean data: OU reversion hurts (exp 25, `EVIDENCE#014`), GARCH adds nothing (exp 31, `EVIDENCE#019`), and the compact general stochastic set is the reference (exp 24, `EVIDENCE#013`). `HYPOTHESIS (pre-clean-lake) → PROVEN (clean-lake direction, exp 24/25/31)`.
+
+## Two clean-lake additions, one prune (Q01, Q11)
+
+The Q-campaign tested the two extremes of the feature axis against the reference:
+
+- **Add (Q01):** `sp_sharpe_22` — the 22-day risk-adjusted Sharpe drift from exp 30's M2 — reproduced exactly on the compact set: IC 0.0464, RankIC 0.0578, net +6.53% (IR 0.62), maxDD −8.0%. This is a **warranted addition**: it carries the M2 edge into the pruned set. `PROVEN — EVIDENCE#022 → exp 33`.
+- **Prune to one (Q11):** the single "obvious" mean-reversion feature `sp_trend_slope_5` alone (plus raw OHLCV) — the hypothesis was that a standalone 5d reversal exists. The model trained **positive** IC (+0.0023), so it did not learn reversal at all; gross −10.4%, net −15.2%. `PROVEN — EVIDENCE#032 → exp 43`. The "mean reversion is the stable single-feature edge" claim is refuted on the clean lake; the pooled trend-slope reversal beta does not survive as a standalone.
+
+The lesson is the pair taken together: on a 50-name daily panel, one principled addition (Sharpe drift) helped and reproduced, while the "obvious" reversal feature did not exist standalone. Feature decisions need isolation runs, not intuition (ch. 07).
+
+## What this means for a reader
+
+- Treat "more features" as a hypothesis, tested one at a time against the reference.
+- Prefer scale-free general statistics (volatility, jump, trend, signature) over model-specific machinery (HMM/OU states) on a small cross-section.
+- A feature that fails as a bundle member is not necessarily dead (OU); a feature that fails standalone is not necessarily live in a bundle — test both directions (Q01 = bundle→isolated-addition PASS, Q11 = standalone FAIL).
+- `TODO(evidence-needed: whether sp_sharpe_22 still helps when combined with the weekly-rebalance construction of ch. 08)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#022` | exp 33 (Q01), run `c7c12228…`, branch `exp/33-q01-m2-reproduction-add-spsharpe22-to-th` |
+| `EVIDENCE#032` | exp 43 (Q11), run `e859adfe…`, branch `exp/43-q11-standalone-5d-reversal-single-featur` |
+| `EVIDENCE#014` | exp 25, run `57450d1a…`, branch `exp/25-test-the-clean-data-hypothesis-that-addi` |
+| `EVIDENCE#019` | exp 31, run `514cb523…`, branch `exp/31-isolation-run-m3-does-adding-garch11-vol` |
+| `EVIDENCE#013` | exp 24, run `fe469a19…`, branch `exp/24-run-the-rankic-ensemble-in-mlflow-experi` |
+| pre-clean ideas | exp 9/11, EVIDENCE#003/004 |

book/chapters/05-ensembles-seed-count.md

@@ -0,0 +1,30 @@
+# Chapter 05 — Ensembles and the Seed-Count Effect
+
+Status: drafting. Claim inventory: see `README.md` ch. 05.
+
+TradeAC's reference signal is a **5-seed RankIC ensemble** of LightGBM rankers. This chapter records what seed count is worth — and, from the Q-campaign, what it is *not* worth.
+
+## What averaging buys (and its limits)
+
+- **2 < 5 seeds (proven):** on the clean lake the 2-seed ensemble lost to the 5-seed on every rank metric and net (RankIC 0.0579 vs 0.0663; net −1.49% IR −0.14 vs +2.13% IR +0.21). `PROVEN — EVIDENCE#016 → exp 28`.
+- **5 seeds = the reference** (compact stochastic set): RankIC 0.0663, RankICIR 0.2545. `PROVEN — EVIDENCE#013 → exp 24`.
+- **5→10 seeds (new, Q02):** the Q-campaign tested whether more breadth keeps paying. The 10-seed ensemble (parallel 10, seeds `42,7,2026,99,123,17,3,2020,88,55` — the recorded config artifact is authoritative over the trace's prose note) raised the rank metrics: RankIC 0.0671, RankICIR 0.259, L/S Sharpe 4.58 (vs 5-seed 4.54). But the book stayed **negative net of cost**: −0.93%, IR −0.089, maxDD −8.80%. `PROVEN — EVIDENCE#023 → exp 34`.
+
+## The verdict on seed count
+
+More seeds buy a small, real improvement in signal breadth — the RankICIR nudges up and the long-short Sharpe ticks up — but the added breadth **does not cross the cost barrier** (ch. 09). At 5 seeds the ensemble benefit has already done its work; 10 seeds add breadth without changing the construction's economics. Seed count is load-bearing up to ~5 and asymptotically irrelevant beyond, on this panel and cost model. `PROVEN — EVIDENCE#016/023`.
+
+## Desk rules distilled from this chapter
+
+1. Use a small multi-seed ensemble (3–5) as the standard, not a single model — the 2→5 step is the reproducible gain.
+2. Do not chase seed count past the point of signal saturation; breadth past ~5 seeds did not pay net of cost.
+3. Verify the recorded config artifact for seeds/parallel — the trace prose note disagreed with the YAML for Q02; the config artifact is authoritative.
+4. `TODO(evidence-needed: whether the 10-seed breadth improves the *weekly-rebalance* construction (ch. 08), where cost is not the bottleneck)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#023` | exp 34 (Q02), run `ce49e4e0…`, branch `exp/34-q02-seed10-10-seed-rankicensemble-vs-ref` |
+| `EVIDENCE#016` | exp 28, run `c4ab1d01…`, branch `exp/28-isolate-the-seed-count-effect-on-the-ndr` |
+| `EVIDENCE#013` | exp 24, run `fe469a19…`, branch `exp/24-run-the-rankic-ensemble-in-mlflow-experi` |

book/chapters/06-clean-lake-reset.md

@@ -1,6 +1,6 @@
-# Chapter 05 — The Clean-Lake Reset: Data Quality as First-Order Risk
+# Chapter 06 — The Clean-Lake Reset: Data Quality as First-Order Risk
 
-Status: drafting. Claim inventory: see `README.md` ch. 05.
+Status: drafting. Claim inventory: see `README.md` ch. 06.
 
 Every number quoted before this chapter was a warning shot. This chapter is the impact. On 2026-08-18 the TradeAC team rebuilt the data lake and re-executed its best reference experiment with byte-identical configuration. The signal collapsed. This is the most important methodological result in the book: **a positive backtest that does not reproduce on clean data was not a strategy, it was a data-quality artifact** — and the tools that caught it were the same traceability tools the book is built on.
 

book/chapters/07-isolation-runs.md

@@ -0,0 +1,58 @@
+# Chapter 07 — Isolation Runs: Single-Variable Discipline
+
+Status: drafting. Claim inventory: see `README.md` ch. 07.
+
+The research loop's discipline (ch. 02) is that one variable changes per run. This chapter runs that discipline across the clean-lake campaign and the Q-series (exp 33–43), and shows that most additions fail — the discipline is the value, not the win rate.
+
+## The design
+
+The reference is the compact stochastic set, 5-seed RankIC ensemble, topk=10, n_drop=1, 5-day forward label, 50-ETF panel, train 2016-01-04→2025-09-01 / valid →2026-01-03 / test 2026-01-04→2026-08-10, account $1M, benchmark SPY, cost 5bp open / 15bp close / $5 min. Acceptance bar (from the reference): **net_IR ≥ 0.21, net_ann ≥ +2.13%, maxDD ≤ 7.69%** `(PROVEN — exp 24/26 baseline)`. Every Q-run changed exactly one thing against this reference.
+
+| Q | One variable changed | Verdict |
+|---|---------------------|---------|
+| Q01 (exp 33) | features +1: `sp_sharpe_22` | PASS — reproduces M2 |
+| Q02 (exp 34) | seeds 5→10 (parallel 10) | FAIL — signal up, book negative |
+| Q03 (exp 35) | topk 10→20 | FAIL — no edge, lower vol |
+| Q04 (exp 36) | label 5d→10d | FAIL — IC up, net collapses |
+| Q05 (exp 37) | label 5d→22d | FAIL — best IC, flat gross |
+| Q06 (exp 38) | sizing → fractional Kelly (cap 0.5) | FAIL — below bar |
+| Q07 (exp 39) | rebalance daily→weekly | PASS — campaign best |
+| Q08 (exp 40) | risk-limit gates on | FAIL as alpha (safety net) |
+| Q09 (exp 41) | construction → long-short | FAIL — turnover kills |
+| Q10 (exp 42) | regime entry gate on | FAIL — churns |
+| Q11 (exp 43) | features → single `sp_trend_slope_5` | FAIL — no reversal learned |
+
+`PROVEN — EVIDENCE#022–032 → exp 33–43, all pre-registered in trace start + workflow YAML before each run`.
+
+## What isolation bought
+
+Because each Q-run changed one thing, the verdicts attribute cleanly:
+
+- **Feature axis (Q01, Q11):** adding the risk-adjusted Sharpe-drift feature is reproducible and positive `(EVIDENCE#022 → exp 33, Q01)`; stripping to a single mean-reversion feature is not learnable — the model trained *positive* IC (+0.0023), meaning there is no standalone reversal to find in the pooled cross-section `(EVIDENCE#032 → exp 43, Q11)`. The "mean reversion is the stable single-feature edge" hypothesis is **refuted** on the clean lake.
+- **Label axis (Q04, Q05):** longer forward-return labels monotonically *improve* the signal — 10d: IC 0.0925 / RankIC 0.0960; 22d: IC 0.0970 / RankIC 0.1165 — yet net-of-cost performance *worsens* (10d: −9.92% IR −1.15; 22d: −4.60% IR −0.59). `PROVEN — EVIDENCE#025/026 → exp 36/37`. Horizon signal and daily-turnover construction are incompatible.
+- **Model axis (Q02):** 10 seeds raise rank breadth (RankIC 0.0671, L/S Sharpe 4.58) but the book stays negative net (−0.93%) — the added breadth never crosses the cost barrier. `PROVEN — EVIDENCE#023 → exp 34`.
+- **Construction axis (Q03, Q06, Q07, Q09):** see ch. 08 — weekly recompute (Q07) is the only change that clears the bar by a wide margin.
+- **Risk/gate axis (Q08, Q10):** see ch. 10 — both met at most a drawdown leg; neither adds alpha.
+
+## The discipline is the output
+
+Only 2 of 11 Q-runs passed. That is not a failure of the campaign — it is the mechanism doing its job. Each FAIL closed a candidate direction at the cost of one run, and the two PASSes (Q01 reproducing the M2 feature, Q07 the weekly construction) are the campaign's forward path. The campaign's refuted runs were as valuable as its wins: knowing that a 22d label has an IC of 0.097 *and still loses money daily* is exactly the kind of fact a desk must not learn twice. `REFERENCED (falsification) + PROVEN (recorded negatives) — EVIDENCE#022–032`.
+
+## Desk rules distilled from this chapter
+
+1. Fix the reference and the acceptance bar *before* the series; change one variable per run.
+2. Record signal metrics and net-of-cost metrics side by side — a signal gain that does not clear costs is not a strategy gain (Q02, Q04, Q05).
+3. A single-feature "obvious" edge must be tested standalone before being trusted in a bundle (Q11 refuted it).
+4. ~~`TODO(evidence-needed: reproduce Q07 weekly rebalance on a second window, and Q01's sp_sharpe_22 in a live round)`~~ — Weekly rebalance second window: **answered (negatively)** by Q13 (exp 45, net −4.21% IR −0.52, edge is window-dependent). sp_sharpe_22 in a live round: still open.
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#022` | exp 33 (Q01), run `c7c12228…`, branch `exp/33-q01-m2-reproduction-add-spsharpe22-to-th` |
+| `EVIDENCE#023` | exp 34 (Q02), run `ce49e4e0…`, branch `exp/34-q02-seed10-10-seed-rankicensemble-vs-ref` |
+| `EVIDENCE#024` | exp 35 (Q03), run `2a844c02…`, branch `exp/35-q03-topk20-widen-topkdropout-portfolio-f` |
+| `EVIDENCE#025` | exp 36 (Q04), run `ef211826…`, branch `exp/36-q04-label10d-10d-forward-return-label-vs` |
+| `EVIDENCE#026` | exp 37 (Q05), run `daad5042…`, branch `exp/37-q05-label22d-22d-forward-return-label-vs` |
+| `EVIDENCE#032` | exp 43 (Q11), run `e859adfe…`, branch `exp/43-q11-standalone-5d-reversal-single-featur` |
+| reference | exp 24/26 (compact set / n_drop=1), runs `fe469a19…`/`21afc6af…` |

book/chapters/08-portfolio-construction.md

@@ -0,0 +1,56 @@
+# Chapter 08 — Portfolio Construction: Dropout, Sizing, and Cadence
+
+Status: drafting. Claim inventory: see `README.md` ch. 08.
+
+This chapter asks how a given signal should be turned into a book. The answer the TradeAC campaign converged on is that **construction is the performance lever** — more than features, more than seeds — and the best construction found on the clean lake is weekly recompute of a daily signal.
+
+## The construction space tested
+
+All runs share the compact stochastic signal, 5-seed ensemble, 5d label, $1M / SPY benchmark / 5bp·15bp·$5 costs. Only the construction varies:
+
+| Construction | Run | Net ann | Net IR | MaxDD | Gross ann | Note |
+|--------------|-----|---------|--------|-------|-----------|------|
+| Topk10 n_drop1, daily (reference) | exp 26 | +2.13% | +0.21 | −7.69% | +7.02% | baseline |
+| Topk20, daily | Q03 (exp 35) | −1.88% | −0.253 | −8.80% | +0.64% | wider, no edge |
+| Fractional-Kelly (cap 0.5) | Q06 (exp 38) | +1.04% | +0.112 | −7.13% | +5.43% | sizing, below bar |
+| **Weekly recompute** | **Q07 (exp 39)** | **+12.51%** | **+1.243** | **−4.13%** | **+13.59%** | **wins chapter** |
+| Long-short top10/bottom10, daily | Q09 (exp 41) | −8.38% | −0.834 | −11.22% | +6.57% | $96.7k cost |
+
+`PROVEN — EVIDENCE#024/027/028/030 → exp 35/38/39/41`.
+
+## Weekly recompute: the campaign's best result
+
+The reference strategy recomputes the topk book **daily** from the same 5-day-label predictions. Q07 kept the signal, topk, n_drop, and risk_degree identical and changed only the rebalance cadence to **weekly** (ISO-week, recompute topk from the freshest score each week). Result:
+
+- net +12.51% (IR **1.24**) vs +2.13% (IR 0.21) daily;
+- maxDD −4.13% vs −7.69%;
+- cost drag collapsed to ~1.1pp (gross +13.59% → net +12.51%), versus the ~5–9pp drags that dominated every daily construction;
+- signal metrics byte-identical to exp 26 (IC 0.0502, RankIC 0.0660).
+
+`PROVEN — EVIDENCE#028 → exp 39`. The prediction is a 5-day-ahead cross-sectional rank; holding it weekly instead of churning it daily lets the edge survive the 20bp round-trip. This is the strongest single construction result in the book — `TODO(evidence-needed: reproduce on a second window, then take to a live round)`.
+
+## What failed, and why
+
+- **Wider book (Q03):** topk 10→20 halves per-name size and cuts book vol (std 0.0048 vs 0.0065) but adds no edge net of cost (−1.88%). Spreading the same signal thinner does not create value.
+- **Fractional Kelly (Q06):** sizing by score magnitude at half-Kelly (cap_frac 0.5) turned the negative daily book mildly positive (+1.04%, IR 0.11) and trimmed maxDD to −7.13% — but it is a weak paste-over of the turnover problem, not a fix, and lands far below the 0.21 acceptance bar.
+- **Long-short (Q09):** the top10/bottom10 market-neutral construction has a genuine *pre-cost* edge (gross +6.57%, IR 0.656) — the signal does rank longs over shorts — but daily long-short turnover is prohibitive: **total cost $96,721 ≈ 9.7% of a $1M book**, 2485 trades in ~150 days, fill rate 0.40, net −8.38%. `PROVEN — EVIDENCE#030 → exp 41`. The same weekly cadence that fixed Q07 was deliberately *not* applied here; the pair is a controlled comparison of cadence on the same signal family.
+
+Pre-clean-lake context: stochastic-control OptimalStopControl constructions (exp 13/14) bled ~11pp to cost — the same turnover mechanism, different strategy class. Those are idea material only. `HYPOTHESIS (idea: pre-clean-lake) — EVIDENCE#006/007`.
+
+## Desk rules distilled from this chapter
+
+1. Construction is a first-class lever: identical signal, +10pp of net annual difference between daily and weekly recompute (exp 26 vs 39).
+2. Before changing the signal, ask whether turnover is the binding constraint — weekly cadence buys more than most feature additions.
+3. Market-neutral structures are only worth the cost if the long-short spread clears two-sided turnover; on this panel it does not.
+4. `TODO(evidence-needed: weekly + long-short combination — the pre-cost edge of Q09 may clear costs at weekly cadence)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#028` | exp 39 (Q07), run `eb38588c…`, branch `exp/39-q07-weekly-rebalance-recompute-topkdropo` |
+| `EVIDENCE#024` | exp 35 (Q03), run `2a844c02…`, branch `exp/35-q03-topk20-widen-topkdropout-portfolio-f` |
+| `EVIDENCE#027` | exp 38 (Q06), run `afca4b80…`, branch `exp/38-q06-kelly-sizing-score-magnitude-fractio` |
+| `EVIDENCE#030` | exp 41 (Q09), run `0647eadd…`, branch `exp/41-q09-long-short-market-neutral-long-top-1` |
+| reference | exp 26, run `21afc6af…`, branch `exp/26-test-whether-reducing-topkdropout-daily` |
+| pre-clean idea | exp 13/14, EVIDENCE#006/007 |

book/chapters/09-cost-turnover-frontier.md

@@ -0,0 +1,49 @@
+# Chapter 09 — The Cost/Turnover Frontier
+
+Status: drafting. Claim inventory: see `README.md` ch. 09.
+
+This chapter is the empirical core of the book's cost argument: **turnover, not signal, is the binding constraint.** Ch. 03 established the gross→net collapse on the reference. Ch. 08 showed the fix. This chapter quantifies the frontier — what turnover costs at 20bp round-trips and what the trade-off looks like when you cut it.
+
+## The frontier on the clean lake
+
+The 50-ETF panel, $1M book, 5bp open / 15bp close / $5 minimum. The same underlying signal (compact stochastic set, 5-seed ensemble, 5d label — IC 0.050, RankIC 0.066) expressed at different turnover levels:
+
+| Construction | Turnover character | Cost drag | Net ann | Net IR | Source |
+|--------------|--------------------|-----------|---------|--------|--------|
+| daily topk10, n_drop 2 | daily forced replacement | ~9–10pp | −3.21% | −0.32 | exp 24 |
+| daily topk10, n_drop 1 | daily, hold dropped name | ~5pp | +2.13% | +0.21 | exp 26 |
+| **weekly recompute** | **weekly refresh** | **~1.1pp** | **+12.51%** | **+1.24** | **exp 39 (Q07)** |
+| daily long-short top10/b10 | two-sided daily | ~9.7% of NAV | −8.38% | −0.83 | exp 41 (Q09) |
+
+`PROVEN — EVIDENCE#015 (exp 26), #028 (exp 39), #030 (exp 41)`.
+
+The n_drop 2→1 step (exp 26) already showed the mechanism with byte-identical signal metrics — the entire net gain was cost relief `(EVIDENCE#015)`. The weekly step (Q07) went further: same signal, same topk/n_drop, cadence only, and cost drag fell to ~1.1pp while net went to +12.51%.
+
+## The long-short lesson
+
+Q09 is the cleanest demonstration that cost, not signal, is the frontier: the long-short construction had a *positive* pre-cost excess (+6.57%, IR 0.656) — the signal genuinely separates longs from shorts — yet cost **$96,721 ≈ 9.7% of NAV** in ~150 days (2485 trades, fill rate 0.40) and net was −8.38%. `PROVEN — EVIDENCE#030 → exp 41`. A construction that spends ~10% of the book annually on two-sided turnover cannot be rescued by signal alone.
+
+## Where the frontier bends
+
+- **Cadence (proven).** Weekly recompute of a 5-day signal is the single biggest lever found: ~1.1pp drag, +12.51% net. `PROVEN — EVIDENCE#028 → exp 39`.
+- **Dropped-name policy (proven).** n_drop 2→1 (hold, don't re-trade) bought ~5pp. `PROVEN — EVIDENCE#015 → exp 26`.
+- **Sizing (weak).** Kelly-style sizing scaled exposure but did not change the turnover bill (Q06, +1.04% net). `PROVEN — EVIDENCE#027 → exp 38`.
+- **Label horizon (counterintuitive).** Longer labels improve the *signal* monotonically (22d IC 0.097, RankIC 0.117) but *worsen net* under daily churn (Q05: −4.60%). The horizon gain is real but unmonetized. `PROVEN — EVIDENCE#026 → exp 37`. ~~`TODO(evidence-needed: long-horizon label at weekly cadence — the combination is untested and is the book's most promising open cell)`~~ — **ANSWERED:** Q12 (exp 44): 22d+weekly net −4.88% IR −0.566. Q21 (exp 51): 10d+weekly net +1.19% IR 0.148. Both below IR 0.5 acceptance. Weekly is a universal cost lever (~10pp improvement) but the5d label remains the sweet spot. `EVIDENCE#036/042`.
+
+## Desk rules distilled from this chapter
+
+1. Compute cost drag as a share of NAV before believing any net number; at 20bp round-trips, 1% NAV per quarter is easy to spend.
+2. Rank construction changes by cost drag first: cadence > dropped-name policy > sizing > gates.
+3. Report gross and net side by side in every experiment; a positive-gross/negative-net run is a turnover problem, not a signal verdict.
+4. `TODO(evidence-needed: realized-cost comparison of the weekly construction against the 5bp/15bp/$5 model once it trades live)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#015` | exp 26, run `21afc6af…`, branch `exp/26-test-whether-reducing-topkdropout-daily` |
+| `EVIDENCE#028` | exp 39 (Q07), run `eb38588c…`, branch `exp/39-q07-weekly-rebalance-recompute-topkdropo` |
+| `EVIDENCE#030` | exp 41 (Q09), run `0647eadd…`, branch `exp/41-q09-long-short-market-neutral-long-top-1` |
+| `EVIDENCE#026` | exp 37 (Q05), run `daad5042…`, branch `exp/37-q05-label22d-22d-forward-return-label-vs` |
+| `EVIDENCE#027` | exp 38 (Q06), run `afca4b80…`, branch `exp/38-q06-kelly-sizing-score-magnitude-fractio` |
+| `EVIDENCE#013` | exp 24, run `fe469a19…`, branch `exp/24-run-the-rankic-ensemble-in-mlflow-experi` |

book/chapters/10-risk-limits-and-gates.md

@@ -0,0 +1,54 @@
+# Chapter 10 — Risk Limits and Gates: Safety Net, Not Alpha
+
+Status: drafting. Claim inventory: see `README.md` ch. 10.
+
+Every desk wants to believe risk controls are a performance lever. On the TradeAC clean lake the evidence says otherwise: **risk limits are a safety net, and overlay gates mostly churn.** This chapter separates the two claims — what a liquidity floor does (defund) and what a regime gate does (churn) — on the post-reset signal.
+
+## The post-reset A/B (Q08)
+
+The reference pred (exp 26, `21afc6af…`) was run through `rd_risk_calibrate` with the live spec `{liquidity_floor_adv: $5M, size_cap_pct: 0.12, concentration_cap_pct: 0.95, drawdown_pause_pct: 0.10}` versus no limits, same window (2026-01-04→2026-08-10), topk10/n_drop1, SPY, $1M:
+
+| Spec | ann return | IR | maxDD |
+|------|-----------|-----|-------|
+| baseline (no limits) | +27.50% | **1.5804** | −6.91% |
+| candidate (5M floor + caps) | +2.20% | **1.5121** | **−0.65%** |
+
+`PROVEN — EVIDENCE#029 → exp 40`. Read the columns carefully:
+
+- **The floor binds.** The $5M ADV floor drops DBA, DBC, ESPO, FDN, REM, TAN, UNG, XAR (8 of 50 names) — it does real work on this panel.
+- **No IR edge.** Candidate IR 1.5121 < baseline 1.5804. Gating does not improve the risk-adjusted return; the floor removes small-AVD names but the surviving book has no better rank.
+- **The drawdown cut is pure defunding.** size_cap 0.12 × concentration_cap 0.95 folds the effective risk_degree to ≈ 0.0095 — about **$9.5k deployed of a $1M book**. maxDD falls to −0.65% because there is almost nothing at risk, not because risk was managed well.
+
+The pre-clean-lake claim that "$5M liquidity floor improves IR 0.81→0.98" (exp 18) is **not reproduced** on the clean-lake signal. That number stays idea material `(EVIDENCE#008 → exp 18, pre-clean-lake)`. `PROVEN (refutation) — EVIDENCE#029 → exp 40`.
+
+## The regime gate (Q10)
+
+A HMM regime overlay (`sp_hmm_p_regime1 ≥ 0.5` entry gate, `RegimeGateDropoutStrategy`) on the same daily signal:
+
+- net −4.26%, IR −0.382, maxDD −7.38% — meets the drawdown leg (7.38% < 7.69%) but far below the net-IR acceptance;
+- the gate churned 276 trades in ~150 days; ~6.3pp of cost erased the +2.02% gross;
+- signal metrics byte-identical to the reference (IC 0.0502, RankIC 0.0660).
+
+`PROVEN — EVIDENCE#031 → exp 42`. A regime gate that flips exposure on a regime posterior priced into the features already just adds turnover. This clean-lake re-test refutes the "gates are a free drawdown cut" idea carried from exp 20 (pre-clean-lake, byte-identical no-ops there) `(EVIDENCE#009)`.
+
+## The synthesis
+
+- **Risk limits**: keep them as a live harness (the round-3 live round used the same spec and the funnel held — EVIDENCE#020), but never market them as alpha. On this signal they defund, not improve. `PROVEN — EVIDENCE#029/020`.
+- **Gates**: regime/momentum overlays on top of features the model already sees add turnover, not edge. `PROVEN — EVIDENCE#031/009`.
+- **Where risk does earn its keep**: as a *cap on damage*, not a return source. The drawdown pause and floor are the reason the live round stays disciplined; their value is the tail, not the mean. `REFERENCED (risk-management practice) + PROVEN (round-3 funnel held under the spec)`.
+
+## Desk rules distilled from this chapter
+
+1. A/B any risk-limit spec against no-limits on the same pred before shipping it; if IR does not improve, it is defunding.
+2. Report deployed capital alongside maxDD — a smaller drawdown with 100x less risk is not a risk win.
+3. Prefer limits that bind rarely but cap hard (liquidity floor, drawdown pause) over gates that churn every day (regime overlay).
+4. `TODO(evidence-needed: a live round under the weekly-rebalance construction with the risk-limit spec, to confirm the safety-net behavior at higher deployed capital)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#029` | exp 40 (Q08), MLflow run `4667984187…` (exp `tac-rd-q08-risklimit`, id 43), branch `exp/40-q08-risk-limit-ab-on-exp-26-reference-si`, `book/data/evidence/q08-risklimit/risk_calibration.json` |
+| `EVIDENCE#031` | exp 42 (Q10), run `436acd01…`, branch `exp/42-q10-hmm-regime-overlay-entry-gate-on-sph` |
+| `EVIDENCE#020` | round 3, trace 27, branch `exp/27-scheduled-algo-retrain-on-2026-08-17-tac` |
+| `EVIDENCE#009/008` | exp 20/18 (pre-clean-lake, idea material) |

book/chapters/11-walk-forward-and-guards.md

@@ -0,0 +1,207 @@
+# Chapter 11 — Walk-Forward Re-validation and Guard Candidates: The Edge Is a Regime Artifact
+
+Status: drafting. Claim inventory: see `README.md` ch. 11.
+
+This chapter answers the question every desk must ask before shipping a backtest result: **does the edge survive re-training on a different window?** The TradeAC campaign's headline results — weekly rebalance (exp 39, Q07), realized-moments features (exp 48, Q17), and the m2-sharpe22 reference (exp 33, Q01) — were all measured on a single 2026 window. This chapter re-runs them walk-forward across 2024–2026 (and 2021/2023 for the label-regime matches), then tests seven guard candidates that would plausibly have isolated the good years. **Every one of them is refuted.** The edge is a 2025–2026 regime artifact; no pre-deployment measurable gate selects it.
+
+## The walk-forward re-validation (exp 52, 53, 54)
+
+Three independent walk-forward sweeps, all on the clean lake, all with the same 5-seed RankIC ensemble (`42,7,2026,99,123`, 5d label, SPY benchmark, 5bp/15bp/$5 costs, 50-ETF panel):
+
+### 3×3 sweep (exp 52) — the three best configs across 2024/2025/2026
+
+Nine runs (3 configs × 3 windows), train/valid shifted per window to avoid overlap. Config A = weekly-rebalance TopkDropout topk10/n_drop1 (exp 39, Q07); Config B = TopkDropout topk10/n_drop1 with realized-moments features (exp 48, Q17); Config C = TopkDropout topk10/n_drop2 base features (exp 26 reference). Excess = annualized return over SPY, net of cost.
+
+| Config | 2024 (test) | 2025 (test) | 2026 (test) |
+|--------|-------------|-------------|-------------|
+| **A** weekly, n_drop=1 | −18.1% (IR −1.39, maxDD −22.7%) | −4.2% (IR −0.52, maxDD −10.3%) | **+12.5% (IR 1.25, maxDD −4.1%)** |
+| **B** moments, n_drop=1 | −16.1% (IR −1.91, maxDD −20.0%) | −8.9% (IR −1.00, maxDD −9.8%) | **+9.2% (IR 0.94, maxDD −6.5%)** |
+| **C** base, n_drop=2 | −18.2% (IR −1.97, maxDD −23.3%) | −3.6% (IR −0.51, maxDD −6.3%) | **−1.4% (IR −0.13, maxDD −9.8%)** |
+
+`PROVEN — EVIDENCE#043 → exp 52`. Read the table carefully:
+
+- **The 2026 window is the only profitable one**, and only for A (+12.5%) and B (+9.2%); C goes negative even in 2026.
+- **A and C share identical predictions** — same model, same features, byte-identical IC/RankIC in every window (e.g. 2026 IC 0.0494, RankIC 0.0637 for both). The strategy layer alone (weekly recompute vs daily n_drop2) differentiates the outcome. This is the cleanest possible demonstration that construction, not signal, separated A from C in 2026.
+- **The harness is reproducible**: run 2 (Config A, 2025) exactly replicated exp 45 (`e5ac7a5d`, net −4.21%, IR −0.52) and Config A's 2026 run replicated exp 39 (`eb38588c`, +12.51%, IR 1.24).
+
+### m2-sharpe22 3-window (exp 53)
+
+The m2-sharpe22 reference (exp 33/Q01, `c7c12228`) re-run on the same 2024/2025/2026 scheme: 2026 **+6.5%** (IR 0.623, maxDD −8.0%), 2025 **+0.4%** (IR 0.05), 2024 **−26.4%** (IR −2.11, maxDD −32.4%). The 2026 window reproduces the reference almost exactly (IC 0.0464 vs 0.0464, RankIC 0.0578 vs 0.0578) — same edge, same window, same config. `PROVEN — EVIDENCE#044 → exp 53`. The edge is recent-window-only.
+
+### Label-regime transfer (exp 54) — 2021 and 2023
+
+The 2025 feature-drift check had already shown a naive feature-PSI gate does **not** predict walk-forward performance — 2026 has the highest feature drift yet the best result (the model consumes CSRankNorm'd ranks, so raw feature drift is scale-invariant noise). Exp 54 instead tested the *label/return regime*: high cross-sectional 5d-label dispersion → good ranking year (2026 disp 0.0302, +6.5%); fat right tail / high skew → topk blowup (2024 skew +29, −26.4%). Label-regime PSI similarity to 2026 ranks 2023 (0.028) > 2025 (0.035) > 2021 (0.039) — the two untested closest matches were run:
+
+- **2023**: −26.0% (IR −2.04, maxDD −30.8%)
+- **2021**: −22.9% (IR −2.26, maxDD −27.0%)
+
+`PROVEN — EVIDENCE#045 → exp 54`. Both closest label-regime matches are as bad as the 2024 tail. **No pre-deployment measurable gate — feature PSI, label-regime PSI, or drift — selects a profitable year.** 2023 had decent dispersion but negative skew (−4.7) and still lost 26%; label dispersion alone does not protect against blowups.
+
+## The seven guard candidates — all refuted
+
+With the walk-forward sweep showing the edge is 2026-window-specific, the desk tested seven guards that could plausibly have preserved the good years and cut the bad ones. All seven were pre-registered as hypotheses (traced experiments), all seven failed:
+
+| # | Guard | Test | Result |
+|---|-------|------|--------|
+| 1 | **Feature-PSI gate** | halt when the live feature distribution drifts from the training distribution (exp 52/53 feature-drift study) | REFUTED — 2026 has the *highest* drift yet the *best* result; CSRankNorm'd ranks make raw drift scale-invariant. |
+| 2 | **Label-regime gate** | trade only when the live label regime matches the profitable 2026 regime (PSI on 5d-label dispersion/skew/vol) | REFUTED — closest matches (2023, 2021) both ≈ −26%/−23%; 2023 had decent dispersion and still blew up. |
+| 3 | **Streaming IC circuit breaker** (`ic_min_rankic`) | pause new buys while trailing realized RankIC (computed causally from lake bars) is below a threshold | REFUTED — trips 25–50% of days *every year*, freezing TopkDropout's rotation out of losers; implemented in `tac_qlib/contrib/strategy/ic_gate.py`, do not deploy live. |
+| 4 | **Adaptive short-window retrain** (exp 55) | retrain on rolling 1y/2y windows instead of the growing 2016→prev-Aug window | REFUTED — 1y and 2y put **every** test year negative (2021 −15%/−18%, 2023 −20%/−23%, 2024 −14%/−19%, 2025 −6%/−3%, 2026 −10%/−6%); only the growing window ever went positive (2025 +0.4%, 2026 +6.5% IR 0.62). Short windows shave losses in bad years (2024 −26.4%→−13.9%) but destroy the 2026 edge (+6.5%→−9.6%). Mean annual excess ≈ −13% for *every* window length. |
+| 5 | **Window-staleness isolation** (exp 56) | gate on days-since-training-cutoff; the hypothesis was that the edge concentrates in fresh (low-staleness) predictions and bad years bleed when the model is stale | REFUTED — pooled monthly excess (account vs SPY) by 90-day staleness bucket is negative in **every** bucket (90d −17.4%, 180d −30.1%, 270d −17.7%, 360d −13.9%, 450d −9.7%): the *freshest* bucket is the *most* negative. The 2026 edge is NOT concentrated in low-staleness days (best month Mar +8.4% at 182d staleness; gains intermittent Jan/Jul/Aug; Feb/Apr/May/Jun negative). 2025's gains are late-year (Aug–Oct at 336–397d staleness — the inverse of freshness). Bad years bleed at all staleness levels including their freshest months. No staleness threshold isolates the edge. |
+| 6 | **Regime gate** (dispersion / vol / HMM) | daily boolean gate based on market state (low vol, HMM posterior, dispersion) | REFUTED — see below; regime gate closes on the wrong days (hmm_0.7 destroys 2026: +25.5% → +10.8%) |
+| 7 | **Signal-quality gate** (hit-rate) | gate on whether the model's recent topk predictions were correct (5-day rolling hit rate > 0.50) | REFUTED — scripted test (EVIDENCE#052) was in-sample for the gate (precomputed from reference pred.pkls); walk-forward workflow tests (EVIDENCE#053) show the gate is harmful in every year: 2026 +9.1% vs +12.5% reference (−3.4pp), 2025 +3.4% vs +3.7% (−0.3pp), 2024 −20.5% vs −19.4% (−1.1pp). A model with Rank IC 0.06–0.07 produces too many days where <50% of top-10 picks are positive — the 0.5 threshold is too aggressive, closing on profitable weeks. |
+
+Guards 1–3 are documented across exp 52/53/54 and the `ic_gate.py` implementation; guard 4 = `PROVEN (refuted) — EVIDENCE#046 → exp 55`; guard 5 = `PROVEN (refuted) — EVIDENCE#047 → exp 56`; guard 6 = `PROVEN (refuted) — EVIDENCE#050`; guard 7 = `REFUTED — EVIDENCE#052 → EVIDENCE#053`.
+
+## The account-level truth
+
+The blotter's daily `account` field is the authoritative measure (the `return` field excludes initial cost and does not compound to the final account). Cumulative excess vs SPY, account-based: **2021 −27.9%, 2023 −30.4%, 2024 −31.6%, 2025 +0.25%, 2026 +4.38%**. This reconciles with the recorded metrics — 2026 `excess_return_with_cost` annualized +6.5% (IR 0.62; without cost +11.4%, IR 1.09) — the same sign and order of magnitude on a shorter window. `PROVEN — EVIDENCE#047 → exp 56` (account curves from the exp 53/54 runs' blotter artifacts).
+
+## The synthesis
+
+- **The headline results were window-specific.** Weekly rebalance (+12.51%, IR 1.24) and m2-sharpe22 (+6.5%, IR 0.62) are 2026-only. Retrained out-of-window, every config is negative or flat: the Q-campaign's "wins" (Q01/Q07) were a 2025–2026 regime artifact, exactly as Q13 (exp 45) first suggested. `PROVEN — EVIDENCE#043/044`.
+- **No guard candidate recovers the edge out-of-sample.** Feature drift, label-regime match, streaming IC, training-window length, staleness, and signal-quality gating all fail to separate the profitable years from the bleeding ones. A guard that cannot identify the good regime in hindsight cannot protect it live. The signal-quality gate (Guard 6) was initially promising in scripted tests but refuted by walk-forward workflow experiments — the scripted test was in-sample for the gate. `PROVEN — EVIDENCE#043–053`.
+- **Construction still matters inside the good regime.** A and C share identical predictions; weekly recompute captured the 2026 upside that daily n_drop2 missed. But that capture is regime-dependent too — the same strategy lost 18% in 2024.
+- **Live implication:** size for the mean, not the tail. The mean annual excess across every window length is ≈ −13%. Until a live window demonstrably matches the 2026 calm-high-dispersion label regime (disp ≈ 0.030, near-zero skew, moderate vol), deployed capital must be cut — the default assumption is the edge is absent, and any positive live result is evidence against that assumption, not proof it is safe.
+
+## Within-window robustness (perturbation stress test)
+
+The 2026 edge is fragile *across* windows but robust *within* the 2026 window. A perturbation grid on Config A's predictions (exp 52, run `9f98ea5c`, same pred.pkl, varying only backtest parameters):
+
+| Perturbation | Config | Ann. return | Sharpe | maxDD |
+|-------------|--------|-------------|--------|-------|
+| **Baseline** | topk=10, n_drop=1, costs 5/15/$5 | 32.8% | 1.98 | −5.8% |
+| topk=5 | concentration ↑ | 32.3% | 1.75 | −7.0% |
+| topk=15 | concentration ↓ | 26.3% | 1.61 | −6.9% |
+| n_drop=2 | rotation ↑ | 26.7% | 1.59 | −6.5% |
+| n_drop=3 | rotation ↑↑ | 28.6% | 1.66 | −6.7% |
+| costs 3× (15/25/$10) | cost stress | 32.8% | 1.97 | −5.8% |
+| costs 5× (25/35/$15) | cost stress ↑↑ | 32.7% | 1.97 | −5.8% |
+
+`PROVEN — EVIDENCE#049` (ad-hoc rd_backtest grid on exp 52 pred.pkl, `book/data/perturbation/config_a_2026_sensitivity.json`).
+
+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.
+
+## Model search & robustness of the regime gate finding
+
+The regime gate study (Guard 7, 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:
+
+| Rank | Exp | Test Window | RankICIR | Net Return | Notes |
+|------|-----|-------------|----------|------------|-------|
+| 1 | 36 | 2026 only | **0.507** | −4.6% | 22d label, single-window |
+| 2 | 44 | 2026 only | **0.507** | −4.9% | Same pred as #1 |
+| 3 | 35 | 2026 only | **0.352** | −9.9% | 10d label |
+| 4 | 51 | 2026 only | **0.352** | +1.2% | Same pred as #3 |
+| 5 | 58 | 2025 only | **0.289** | −3.4% | Adaptive 2y |
+| 6 | 11 | 2026 only | **0.276** | +3.1% | Single seed |
+| 7 | 33 | 2026 only | **0.259** | −0.9% | 10 seeds |
+| 8 | **52-C** | **2024–2026** | **0.244** | **+12.5%** | Walk-forward, weekly |
+| 9 | **52-A** | **2024–2026** | **0.244** | **−1.4%** | Walk-forward, TopkDrop |
+
+`PROVEN — EVIDENCE#051` (comprehensive `rd_exp_list` query, run metadata).
+
+Key observations:
+
+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.
+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).
+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.
+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.
+
+## Signal-quality gate (Guard 7): refuted
+
+`REFUTED — EVIDENCE#052 → EVIDENCE#053`
+
+The regime gate (Guard 6) failed because it answered the wrong question: *"Is the market calm?"* The signal-quality gate asks a better question: *"Are my predictions accurate?"* — but when tested properly, it still doesn't work.
+
+**Logic:** For each day t, look at the topk symbols from yesterday (t-1). Compute the hit rate — the fraction of those symbols that had positive returns today. If the hit rate is above a threshold, keep trading; otherwise, go to cash. This is a retrospective gate — it measures prediction accuracy, not market state.
+
+**Initial scripted test (EVIDENCE#052):** Precomputed gate from reference pred.pkls showed every config improves returns across ALL years (best: `hitrate_5d_0.50` 2026 +65.0%, 2025 +72.1%, 2024 +30.4%, 2023 +54.7%, 2021 +55.7%). This was **misleading** — the scripted test used precomputed gate from the reference model's pred.pkls (in-sample for the gate), not the actual on-the-fly gate in a walk-forward context.
+
+**Walk-forward workflow test (EVIDENCE#053):** `WeeklyRebalanceSignalQualityGateStrategy` (topk=10, n_drop=1, gate_topk=10, gate_lookback=5, gate_threshold=0.5, 5/15bp costs) tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026), retraining the model each year. **The gate is harmful in every year:**
+
+| Year | Workflow excess w/cost (gate) | Reference excess w/cost (nogate) | Delta |
+|------|------------------------------|----------------------------------|-------|
+| 2026 | +9.1% (IR 0.92) | +12.5% (IR 1.24) | **−3.4pp** |
+| 2025 | +3.4% (IR 0.31) | +3.7% (IR 0.33) | **−0.3pp** |
+| 2024 | −20.5% | −19.4% | **−1.1pp** |
+| 2023 | −29.4% | −29.6% | +0.2pp |
+| 2021 | −18.4% | −21.2% | +2.8pp |
+
+**Why the scripted test was wrong:** The diagnostic (v3, workflow-exact mechanics) reveals the gate closes 37–45% of days in every year, killing returns:
+
+| Year | Script total (nogate) | Script total (gate) | Delta | Gate open% |
+|------|----------------------|--------------------|-------|-----------|
+| 2026 | +21.2% | +1.5% | −19.7pp | 56% |
+| 2025 | +22.7% | +7.8% | −14.9pp | 63% |
+| 2024 | +4.0% | −2.6% | −6.6pp | 59% |
+
+A model with Rank IC 0.06–0.07 produces many days where <50% of top-10 picks are positive — the gate's 0.5 threshold is too aggressive, closing on profitable weeks. The scripted test inflated returns because it used precomputed gate from the reference model (in-sample for the gate), while the actual on-the-fly gate computed from retrained models produces different (worse) hit rates.
+
+**Why this still fails:** The gate answers *"did my predictions work yesterday?"* — but with a 0.06–0.07 Rank IC, yesterday's hit rate is mostly noise. A weak signal needs more days to accumulate statistical significance; gating on a 5-day rolling hit rate at 0.5 threshold is too noisy, too aggressive, and destroys the strategy's ability to capture the good days that compensate for the bad ones.
+
+**Caveat:** The gate is retrospective (yesterday's hit rate → today's trades, no look-ahead). The problem is not look-ahead — it's that the signal is too weak for a 0.5 threshold on a 5-day window to be informative.
+
+## Desk rules distilled from this chapter
+
+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.
+2. Treat identical-prediction configs as a single test of construction, not two tests of signal — A-vs-C is a strategy-layer comparison, not a model comparison.
+3. Do not ship a guard that cannot select the good regime in hindsight. Feature PSI, label-regime PSI, streaming IC, window length, staleness, and signal-quality gating all failed on this panel. The signal-quality gate was particularly instructive: a scripted test using precomputed gate from the reference model showed +65% in 2026, but walk-forward workflow experiments showed the gate is harmful — the scripted test was in-sample for the gate.
+4. Report account-based curves, not the blotter `return` field — the latter excludes initial cost and does not compound to the account.
+5. When the mean annual excess is negative in every configuration, cut size until the live window demonstrates the regime is back.
+
+### Guard 6: Regime gate (dispersion / vol / HMM)
+
+`PROVEN — EVIDENCE#050`
+
+If the edge is regime-dependent, the most direct guard is a regime detector that opens on good years and closes on bad years. We test three detector types, each producing a daily boolean (trade / don't trade):
+
+| Detector | Logic |
+|----------|-------|
+| **dispersion** | CS std of 22-day rolling returns < threshold (low dispersion → calm market → trade) |
+| **vol** | CS mean of 22-day rolling realized vol within a band (mid-range vol → trade) |
+| **HMM** | 2-state Gaussian HMM posterior for regime 1 (productive regime) > threshold |
+
+Each detector is applied as a daily gate on top of the weekly-rebalance TopkDropout (topk=10, n_drop=1, yesterday's scores). We run 14 configs across 5 walk-forward windows (2021–2026), tracking trip rate (fraction of days gate is open) and gated return.
+
+**Trip rates (2026 vs bad years 2021/2023/2024):**
+
+| Gate | 2026 trip | Bad-years avg | Differential |
+|------|-----------|---------------|-------------|
+| `vol_low_max20` | 92% | 60% | +32pp |
+| `vol_low_max25` | 63% | 16% | +47pp |
+| `hmm_0.7` | 37% | 31% | +6pp |
+| All dispersion | 0% | 0% | 0pp |
+
+The vol gates show the largest trip differential — they open on more days in 2026 than in bad years. But the gate **closes on the wrong days**: when the gate is open only 63% of the time (vol_low_max25), the 2026 return collapses from +25.5% to −1.6%. The gate eliminates the profitable days along with the bad ones.
+
+**Gated returns:**
+
+| Gate | 2026 base | 2026 gated | 2023 base | 2023 gated | 2025 base | 2025 gated |
+|------|-----------|------------|-----------|------------|-----------|------------|
+| `vol_low_max20` | +25.5% | +4.3% | −4.8% | −5.3% | +17.8% | +14.5% |
+| `hmm_0.7` | +25.5% | +10.8% | −4.8% | +0.6% | +17.8% | +26.8% |
+
+`hmm_0.7` has the most interesting profile: it **improves** 2023 (−4.8% → +0.6%) and 2025 (+17.8% → +26.8%), but **destroys** 2026 (+25.5% → +10.8%). The gate's Sharpe is inflated (1.78 in 2021) because it spends most of its time in cash — the Sharpe measures "active days only" and ignores the flat periods.
+
+**Why none of these gates work:** The gate answers *"is the market calm right now?"* — but the right question is *"will today's signal be profitable tomorrow?"* These are different questions. A calm market can produce bad signals (low vol but wrong factor regime), and a volatile market can produce good signals (high vol but correct factor direction). The gate needs to predict **signal quality**, not **market state**. See Guard 7 (signal-quality gate, EVIDENCE#053) for a gate that was tested on this principle — and still failed.
+
+## Open questions
+
+- `TODO(evidence-needed: a live window that matches the 2026 label regime, to test whether the edge returns when the regime returns)`
+- `TODO(evidence-needed: understanding the script-vs-workflow gap for signal-quality gate — scripted test shows gate destroying ~20pp more return than workflow, despite identical parameters; root cause is pred date alignment differences between precomputed and on-the-fly gate computation)`
+
+## Evidence cited in this chapter
+
+| Tag | Source |
+|-----|--------|
+| `EVIDENCE#043` | exp 52, mlflow exp 52 `tac-rd-bt-3x3-windows` (9 runs: `9f98ea5c` A-2026, `fe967416` A-2025, `71ed5bfa` A-2024; `163c01ce` B-2026, `4a85d68e` B-2025, `1e49b8e8` B-2024; `e3e06a24` C-2026, `353fff8f` C-2025, `13a9bbdf` C-2024), branch `exp/52-walk-forward-re-validation-of-the-3-best` |
+| `EVIDENCE#044` | exp 53, mlflow exp 53 `tac-rd-bt-m2-sharpe22-3windows` (runs `7464c3e7` 2026, `061f558b` 2025, `b49c6845` 2024), branch `exp/53-walk-forward-re-validation-of-m2-sharpe2`; reference run `c7c12228` (exp 33, Q01) |
+| `EVIDENCE#045` | exp 54, mlflow exp 56 `tac-rd-bt-m2-sharpe22-2021-2023` (runs `4e0700dd` 2021, `8ca46e55` 2023), branch `exp/54-walk-forward-transfer-test-m2-sharpe22-o`; feature/label-regime PSI study (exp 53 follow-up) |
+| `EVIDENCE#046` | exp 55, mlflow exp 57/58 `tac-rd-bt-m2-sharpe22-adaptive-{1y,2y}`, branch `exp/55-adaptive-short-window-retrain-test-the-4` |
+| `EVIDENCE#047` | exp 56, staleness analysis on the exp 53/54 pred/label artifacts, branch `exp/56-window-staleness-isolation-the-m2-sharpe` |
+| 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 |
+| `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` |
+| `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` |
+| `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 |
+| `EVIDENCE#052` | Signal-quality gate scripted test: precomputed gate from reference pred.pkls showed every config improves returns across ALL years. **REFUTED by EVIDENCE#053** — scripted test was in-sample for the gate. |
+| `EVIDENCE#053` | Signal-quality gate walk-forward refutation: `WeeklyRebalanceSignalQualityGateStrategy` tested via `rd_train` + `rd_run_workflow` on 5 walk-forward windows (2021–2026). Gate harmful in every year: 2026 +9.1% vs +12.5% reference (−3.4pp), 2025 +3.4% vs +3.7% (−0.3pp). Scripted diagnostic (v3) confirms gate closes 37–45% of days in every year, killing returns. |

book/chapters/13-synthesis.md

@@ -0,0 +1,58 @@
+# Chapter 13 — Synthesis: How Proved Truth Compounds
+
+Status: drafting. Claim inventory: see `README.md` ch. 13.
+
+This chapter is the scoreboard. It collects everything the campaign proved, in order of what actually moved performance — and why the Q-campaign's 9 refutations were as informative as its 2 passes.
+
+## The scoreboard (clean lake, exp 21–43)
+
+| Lever | Evidence | Net effect |
+|-------|----------|-----------|
+| **Data quality** | exp 21 (collapse), 22–24 (fix + revalidation) | The single largest swing: pre-reset +7.77% became −20.6% on the same config, then reappeared as a real signal. Everything before the reset is void. |
+| **Cost relief / construction** | exp 26 (n_drop 2→1): −3.21%→+2.13%; exp 39 (weekly, Q07): **+12.51%, IR 1.24, maxDD −4.13%** | The dominant *positive* lever. Same signal, cadence changed. |
+| **Feature pruning** | exp 9, 24, 25, 31, 33 (Q01), 43 (Q11) | Generic pruned set > model-specific; one warranted addition (sp_sharpe_22, Q01); standalone reversal refuted (Q11). |
+| **Ensemble** | exp 28 (2<5 seeds), exp 34 (Q02: 10 seeds) | Real but bounded: breadth saturates ~5 seeds; extra seeds don't clear costs. |
+| **Risk limits** | exp 40 (Q08) | Safety net only: floor binds, no IR edge, DD relief is defunding. |
+| **Gates** | exp 20, 42 (Q10) | Refuted: regime overlay churns, adds cost, no edge. |
+| **Sizing** | exp 38 (Q06) | Weak: half-Kelly mildly positive, below bar. |
+| **Long-short** | exp 41 (Q09) | Refuted by turnover: pre-cost edge +6.6% destroyed by $96.7k cost. |
+
+`PROVEN — EVIDENCE#010–032`.
+
+## What the Q-campaign settled
+
+Eleven pre-registered runs, two passes:
+
+1. **Q01 PASS** — M2's risk-adjusted 22d Sharpe-drift feature reproduces on the compact set (net +6.53%, IR 0.62). Promotes exp-30's lone result from HYPOTHESIS to PROVEN. `EVIDENCE#022`.
+2. **Q07 PASS** — weekly recompute is the campaign's best construction (net +12.51%, IR 1.24). The forward path. `EVIDENCE#028`.
+3. **Nine refutations** — seed breadth (Q02), wider book (Q03), long labels under daily churn (Q04/Q05), Kelly sizing (Q06), risk-limit-as-alpha (Q08), long-short (Q09), regime gate (Q10), standalone reversal (Q11). Each closed a direction the desk had been considering, at one-run cost each. `EVIDENCE#023–027, 029–032`.
+
+The refuted runs were as valuable as the passes: the label-horizon result (22d label, IC 0.097, yet net negative) is exactly the kind of counterintuitive fact a desk must not re-learn. `REFERENCED (falsification) + PROVEN (recorded negatives)`.
+
+## The order of operations a reader should copy
+
+1. **Fix data first** — re-validate the lake before any run (ch. 06).
+2. **Attack turnover before signal** — cadence and dropped-name policy are the proven levers (ch. 09).
+3. **Test features one at a time** against the reference (ch. 07); prune, don't add (ch. 04).
+4. **Use a small ensemble** (5 seeds) and stop there (ch. 05).
+5. **A/B risk limits** before shipping; keep them as a safety net (ch. 10).
+6. **Reconcile live** — the funnel and slippage are the only claims that count (ch. 00/12).
+7. **Re-validate walk-forward before shipping** — a single-window edge is a regime artifact until it survives re-training out-of-window (ch. 11).
+
+## Open questions
+
+- `TODO(evidence-needed: a second live round beyond round 3, to confirm slippage and funnel hold under a different market regime)`
+- `TODO(evidence-needed: reconcile realized cost against the 5bp/15bp/$5 backtest model over a full position window)`
+- `TODO(evidence-needed: whether sp_sharpe_22 still helps when combined with the weekly-rebalance construction of ch. 08)`
+
+### Settled open questions
+
+- ~~`reproduce exp 39 weekly rebalance on a second window, then a live round`~~ — **ANSWERED (negatively):** Q13 (exp 45) tested weekly on 2025 OOS: net −4.21% IR −0.52. The edge is window-dependent, not robust. `EVIDENCE#037`.
+- ~~`long-horizon label at weekly cadence — the proven signal edge with the proven low-turnover construction`~~ — **ANSWERED:** Q12 (exp 44): 22d+weekly net −4.88% IR −0.566. Q21 (exp 51): 10d+weekly net +1.19% IR 0.148. Both below IR 0.5 acceptance. Weekly is a universal cost lever (~10pp improvement) but the5d label remains the sweet spot. `EVIDENCE#036/042`.
+- ~~`out-of-universe (non-ETF) validation of the compact stochastic set`~~ — **ANSWERED (negatively):** Q14 (exp 50): RankIC −0.02, ICIR −0.07 on 30 liquid single-stock names. Signal is noise outside the 50-ETF panel. `EVIDENCE#033`.
+- ~~`do the campaign's headline results (Q01 m2-sharpe22, Q07 weekly) survive walk-forward re-training?`~~ — **ANSWERED (negatively):** exp 52/53/54 re-ran every headline config across 2024–2026 (plus 2021/2023 label-regime matches). Only 2026 is profitable (+6.5% m2, +12.5% weekly); all prior years are negative or flat. **The edge is a 2025–2026 regime artifact.** `EVIDENCE#043–045`.
+- ~~`is there a pre-deployment guard that isolates the profitable regime?`~~ — **ANSWERED (negatively):** five guard candidates (feature-PSI, label-regime PSI, streaming IC `ic_min_rankic`, adaptive short-window, window-staleness) were pre-registered and all refuted; none selects the good years in hindsight. `EVIDENCE#043–047`; see ch. 11.
+
+## Evidence cited in this chapter
+
+Composite of `EVIDENCE#010–032`; see the per-chapter evidence tables (ch. 04–10) and `EVIDENCE.md` for run/branch level citations.

book/data/diag_script_vs_wf/diagnosis.json

@@ -0,0 +1,117 @@
+[
+  {
+    "year": "2026",
+    "script_weekly_100": {
+      "ann_ret": 0.0921,
+      "sharpe": 0.5512,
+      "maxDD": -0.0947
+    },
+    "script_weekly_95": {
+      "ann_ret": 0.088,
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+    },
+    "daily_topk_100": {
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+      "sharpe": -0.5241,
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+    },
+    "daily_topk_95": {
+      "ann_ret": -0.0863,
+      "sharpe": -0.5214,
+      "maxDD": -0.1376
+    }
+  },
+  {
+    "year": "2025",
+    "script_weekly_100": {
+      "ann_ret": 0.1774,
+      "sharpe": 0.9416,
+      "maxDD": -0.1809
+    },
+    "script_weekly_95": {
+      "ann_ret": 0.1688,
+      "sharpe": 0.9431,
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+    },
+    "daily_topk_100": {
+      "ann_ret": -0.2439,
+      "sharpe": -0.8798,
+      "maxDD": -0.2807
+    },
+    "daily_topk_95": {
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+      "sharpe": -0.8794,
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+    }
+  },
+  {
+    "year": "2024",
+    "script_weekly_100": {
+      "ann_ret": 0.0316,
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+    },
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+      "maxDD": -0.0768
+    },
+    "daily_topk_100": {
+      "ann_ret": -0.041,
+      "sharpe": -0.2846,
+      "maxDD": -0.1248
+    },
+    "daily_topk_95": {
+      "ann_ret": -0.0385,
+      "sharpe": -0.2815,
+      "maxDD": -0.1188
+    }
+  },
+  {
+    "year": "2023",
+    "script_weekly_100": {
+      "ann_ret": 0.1025,
+      "sharpe": 0.7325,
+      "maxDD": -0.1237
+    },
+    "script_weekly_95": {
+      "ann_ret": 0.0976,
+      "sharpe": 0.7345,
+      "maxDD": -0.1178
+    },
+    "daily_topk_100": {
+      "ann_ret": 0.1847,
+      "sharpe": 1.2305,
+      "maxDD": -0.1314
+    },
+    "daily_topk_95": {
+      "ann_ret": 0.1753,
+      "sharpe": 1.2295,
+      "maxDD": -0.1252
+    }
+  },
+  {
+    "year": "2021",
+    "script_weekly_100": {
+      "ann_ret": 0.1458,
+      "sharpe": 0.8808,
+      "maxDD": -0.1115
+    },
+    "script_weekly_95": {
+      "ann_ret": 0.1387,
+      "sharpe": 0.8824,
+      "maxDD": -0.1061
+    },
+    "daily_topk_100": {
+      "ann_ret": 0.0009,
+      "sharpe": 0.0051,
+      "maxDD": -0.129
+    },
+    "daily_topk_95": {
+      "ann_ret": 0.0016,
+      "sharpe": 0.0095,
+      "maxDD": -0.1228
+    }
+  }
+]

book/data/diag_script_vs_wf/diagnosis_v2.json

@@ -0,0 +1,142 @@
+[
+  {
+    "year": "2026",
+    "weekly_100_zc": {
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+      "maxDD": -0.0947
+    },
+    "weekly_95_zc": {
+      "ann_ret": 0.088,
+      "sharpe": 0.5545,
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+    },
+    "weekly_95_10bp": {
+      "ann_ret": 0.0647,
+      "sharpe": 0.4077,
+      "maxDD": -0.0945
+    },
+    "daily_100_zc": {
+      "ann_ret": -0.0914,
+      "sharpe": -0.5241,
+      "maxDD": -0.1446
+    },
+    "daily_100_10bp": {
+      "ann_ret": -0.1581,
+      "sharpe": -0.9069,
+      "maxDD": -0.1765
+    }
+  },
+  {
+    "year": "2025",
+    "weekly_100_zc": {
+      "ann_ret": 0.1774,
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+    },
+    "weekly_95_zc": {
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+      "maxDD": -0.1725
+    },
+    "weekly_95_10bp": {
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+    },
+    "daily_100_zc": {
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+      "sharpe": -0.8798,
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+    },
+    "daily_100_10bp": {
+      "ann_ret": -0.2975,
+      "sharpe": -1.0722,
+      "maxDD": -0.314
+    }
+  },
+  {
+    "year": "2024",
+    "weekly_100_zc": {
+      "ann_ret": 0.0316,
+      "sharpe": 0.232,
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+    },
+    "weekly_95_zc": {
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+    },
+    "weekly_95_10bp": {
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+    },
+    "daily_100_zc": {
+      "ann_ret": -0.041,
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+    },
+    "daily_100_10bp": {
+      "ann_ret": -0.1126,
+      "sharpe": -0.7819,
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+    }
+  },
+  {
+    "year": "2023",
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+    },
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+    },
+    "weekly_95_10bp": {
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+      "sharpe": 0.5449,
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+    },
+    "daily_100_zc": {
+      "ann_ret": 0.1847,
+      "sharpe": 1.2305,
+      "maxDD": -0.1314
+    },
+    "daily_100_10bp": {
+      "ann_ret": 0.0967,
+      "sharpe": 0.6449,
+      "maxDD": -0.1481
+    }
+  },
+  {
+    "year": "2021",
+    "weekly_100_zc": {
+      "ann_ret": 0.1279,
+      "sharpe": 0.7781,
+      "maxDD": -0.1122
+    },
+    "weekly_95_zc": {
+      "ann_ret": 0.1219,
+      "sharpe": 0.7803,
+      "maxDD": -0.1068
+    },
+    "weekly_95_10bp": {
+      "ann_ret": 0.0971,
+      "sharpe": 0.6218,
+      "maxDD": -0.1079
+    },
+    "daily_100_zc": {
+      "ann_ret": -0.0098,
+      "sharpe": -0.0564,
+      "maxDD": -0.1296
+    },
+    "daily_100_10bp": {
+      "ann_ret": -0.089,
+      "sharpe": -0.509,
+      "maxDD": -0.1869
+    }
+  }
+]

book/data/diag_script_vs_wf/diagnosis_v3.json

@@ -0,0 +1,142 @@
+[
+  {
+    "year": "2026",
+    "ideal_100_zc": {
+      "ann_ret": 0.0921,
+      "sharpe": 0.5512,
+      "maxDD": -0.0947
+    },
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+      "maxDD": -0.0901
+    },
+    "ideal_95_10bp": {
+      "ann_ret": 0.0647,
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+      "maxDD": -0.0945
+    },
+    "wf_exact_95_nogate": {
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+      "maxDD": -0.1039
+    },
+    "wf_exact_95_gate": {
+      "ann_ret": 0.0146,
+      "sharpe": 0.1372,
+      "maxDD": -0.0668
+    }
+  },
+  {
+    "year": "2025",
+    "ideal_100_zc": {
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+    },
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+      "maxDD": -0.1725
+    },
+    "ideal_95_10bp": {
+      "ann_ret": 0.1436,
+      "sharpe": 0.8026,
+      "maxDD": -0.1754
+    },
+    "wf_exact_95_nogate": {
+      "ann_ret": 0.2273,
+      "sharpe": 1.1963,
+      "maxDD": -0.1864
+    },
+    "wf_exact_95_gate": {
+      "ann_ret": 0.0777,
+      "sharpe": 0.8239,
+      "maxDD": -0.0773
+    }
+  },
+  {
+    "year": "2024",
+    "ideal_100_zc": {
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+      "sharpe": 0.232,
+      "maxDD": -0.0807
+    },
+    "ideal_95_zc": {
+      "ann_ret": 0.0304,
+      "sharpe": 0.2353,
+      "maxDD": -0.0768
+    },
+    "ideal_95_10bp": {
+      "ann_ret": 0.0051,
+      "sharpe": 0.0393,
+      "maxDD": -0.0796
+    },
+    "wf_exact_95_nogate": {
+      "ann_ret": 0.0616,
+      "sharpe": 0.4145,
+      "maxDD": -0.0951
+    },
+    "wf_exact_95_gate": {
+      "ann_ret": -0.0261,
+      "sharpe": -0.2782,
+      "maxDD": -0.1157
+    }
+  },
+  {
+    "year": "2023",
+    "ideal_100_zc": {
+      "ann_ret": 0.1025,
+      "sharpe": 0.7325,
+      "maxDD": -0.1237
+    },
+    "ideal_95_zc": {
+      "ann_ret": 0.0976,
+      "sharpe": 0.7345,
+      "maxDD": -0.1178
+    },
+    "ideal_95_10bp": {
+      "ann_ret": 0.0724,
+      "sharpe": 0.5449,
+      "maxDD": -0.1238
+    },
+    "wf_exact_95_nogate": {
+      "ann_ret": 0.0327,
+      "sharpe": 0.2259,
+      "maxDD": -0.1517
+    },
+    "wf_exact_95_gate": {
+      "ann_ret": 0.0094,
+      "sharpe": 0.0968,
+      "maxDD": -0.1036
+    }
+  },
+  {
+    "year": "2021",
+    "ideal_100_zc": {
+      "ann_ret": 0.1279,
+      "sharpe": 0.7781,
+      "maxDD": -0.1122
+    },
+    "ideal_95_zc": {
+      "ann_ret": 0.1219,
+      "sharpe": 0.7803,
+      "maxDD": -0.1068
+    },
+    "ideal_95_10bp": {
+      "ann_ret": 0.0971,
+      "sharpe": 0.6218,
+      "maxDD": -0.1079
+    },
+    "wf_exact_95_nogate": {
+      "ann_ret": 0.0476,
+      "sharpe": 0.0429,
+      "maxDD": -0.3489
+    },
+    "wf_exact_95_gate": {
+      "ann_ret": -0.0227,
+      "sharpe": -0.0318,
+      "maxDD": -0.2398
+    }
+  }
+]

book/data/evidence/q08-risklimit/RESULT.md

@@ -0,0 +1,35 @@
+# Q08 — Risk-limit A/B re-validation (trace 40)
+
+**Status:** DONE (verdict: REFUTED as an IR edge; safety-net value retained)
+
+## Input
+- Reference signal: exp-26 pred, run `21afc6afdb674a399b59dd76c97628ce` (mlflow exp 25)
+- Window: 2026-01-04 → 2026-08-10, Topk10 n_drop1, SPY benchmark, $1M, 5/15bp/$5
+- Tool: `rd_risk_calibrate` (A/B + sensitivity grid). Full JSON: `risk_calibration.json`
+
+## Candidate spec (round-3 live spec)
+`{"liquidity_floor_adv": 5000000, "size_cap_pct": 0.12, "concentration_cap_pct": 0.95, "drawdown_pause_pct": 0.10}`
+
+## Results (net, with cost)
+| Config | IR | Ann. return | Max DD |
+|---|---|---|---|
+| baseline (no limits) | 1.5804 | +27.50% | −6.91% |
+| **candidate (5M floor + caps)** | **1.5121** | +2.20% | **−0.65%** |
+| liquidity $10M | 1.5457 | +2.25% | −0.64% |
+
+## Findings
+- **Floor binds, not a no-op**: $5M liquidity floor dropped 8 symbols —
+  `DBA, DBC, ESPO, FDN, REM, TAN, UNG, XAR`.
+- **No IR edge from the gate**: candidate IR (1.512) is BELOW baseline (1.580).
+  The exp-18 direction (floor IR 0.81→0.98) does NOT reproduce on the clean-lake
+  reference signal.
+- **Drawdown cut is pure defunding**: size_cap 0.12 × concentration 0.95 fold
+  the effective risk_degree to ~0.0095 → ~$9.5k deployed of $1M (~100x less).
+  Sensitivity grid shows both caps are no-ops (conc 20–50% identical,
+  size_cap 5–20% identical); only the liquidity floor moves returns, marginally.
+- **Conclusion**: keep the live spec as a safety net; there is no risk-limit
+  gate IR edge to harvest when the signal is the bottleneck (exp-20 pattern).
+
+## Artifacts on this branch
+- `evidence/q08-risklimit/risk_calibration.json` — full calibration dump
+- `queue/designs/q08_risk_limit_ab.md` — the pre-registered design doc

book/data/evidence/q08-risklimit/risk_calibration.json

@@ -0,0 +1,401 @@
+{
+  "rows": [
+    {
+      "label": "baseline (no limits)",
+      "mean": 0.001155,
+      "std": 0.011279,
+      "annualized_return": 0.274989,
+      "information_ratio": 1.580427,
+      "max_drawdown": -0.069145
+    },
+    {
+      "label": "liquidity $10,000,000",
+      "mean": 9.4e-05,
+      "std": 0.000942,
+      "annualized_return": 0.022464,
+      "information_ratio": 1.545736,
+      "max_drawdown": -0.006389
+    },
+    {
+      "label": "conc 20%",
+      "mean": 0.000115,
+      "std": 0.001168,
+      "annualized_return": 0.027285,
+      "information_ratio": 1.513718,
+      "max_drawdown": -0.008104
+    },
+    {
+      "label": "conc 30%",
+      "mean": 0.000115,
+      "std": 0.001168,
+      "annualized_return": 0.027285,
+      "information_ratio": 1.513718,
+      "max_drawdown": -0.008104
+    },
+    {
+      "label": "conc 40%",
+      "mean": 0.000115,
+      "std": 0.001168,
+      "annualized_return": 0.027285,
+      "information_ratio": 1.513718,
+      "max_drawdown": -0.008104
+    },
+    {
+      "label": "conc 50%",
+      "mean": 0.000115,
+      "std": 0.001168,
+      "annualized_return": 0.027285,
+      "information_ratio": 1.513718,
+      "max_drawdown": -0.008104
+    },
+    {
+      "label": "candidate {\"liquidity_floor_adv\": 5000000.0, \"size_cap_pct\": 0.12, \"concentration_cap_pct\": 0.95, \"drawdown_pause_pct\": 0.1}",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "size_cap 5%",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "size_cap 10%",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "size_cap 15%",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "size_cap 20%",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "liquidity $5,000,000",
+      "mean": 9.2e-05,
+      "std": 0.000943,
+      "annualized_return": 0.021991,
+      "information_ratio": 1.512051,
+      "max_drawdown": -0.00653
+    },
+    {
+      "label": "liquidity $1,000,000",
+      "mean": 9.1e-05,
+      "std": 0.000929,
+      "annualized_return": 0.021625,
+      "information_ratio": 1.508748,
+      "max_drawdown": -0.006376
+    },
+    {
+      "label": "liquidity $2,500,000",
+      "mean": 7.1e-05,
+      "std": 0.000918,
+      "annualized_return": 0.017,
+      "information_ratio": 1.199721,
+      "max_drawdown": -0.007158
+    }
+  ],
+  "runs": {
+    "baseline": {
+      "risk": {
+        "mean": 0.0011554172081987572,
+        "std": 0.01127853762493476,
+        "annualized_return": 0.27498929555130425,
+        "information_ratio": 1.5804272791471323,
+        "max_drawdown": -0.06914515336341577
+      },
+      "applied": {}
+    },
+    "candidate": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "size_cap 5%": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "size_cap 10%": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "size_cap 15%": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "size_cap 20%": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "conc 20%": {
+      "risk": {
+        "mean": 0.00011464156491316718,
+        "std": 0.0011683839517000441,
+        "annualized_return": 0.027284692449333788,
+        "information_ratio": 1.5137180903503433,
+        "max_drawdown": -0.008103887185240407
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "conc 30%": {
+      "risk": {
+        "mean": 0.00011464156491316718,
+        "std": 0.0011683839517000441,
+        "annualized_return": 0.027284692449333788,
+        "information_ratio": 1.5137180903503433,
+        "max_drawdown": -0.008103887185240407
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "conc 40%": {
+      "risk": {
+        "mean": 0.00011464156491316718,
+        "std": 0.0011683839517000441,
+        "annualized_return": 0.027284692449333788,
+        "information_ratio": 1.5137180903503433,
+        "max_drawdown": -0.008103887185240407
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "conc 50%": {
+      "risk": {
+        "mean": 0.00011464156491316718,
+        "std": 0.0011683839517000441,
+        "annualized_return": 0.027284692449333788,
+        "information_ratio": 1.5137180903503433,
+        "max_drawdown": -0.008103887185240407
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $1,000,000": {
+      "risk": {
+        "mean": 9.086210454881382e-05,
+        "std": 0.0009290831160004576,
+        "annualized_return": 0.021625180882617688,
+        "information_ratio": 1.508747982736451,
+        "max_drawdown": -0.006376134679664126
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "ESPO"
+        ]
+      }
+    },
+    "liquidity $2,500,000": {
+      "risk": {
+        "mean": 7.142665167642606e-05,
+        "std": 0.0009184775632266332,
+        "annualized_return": 0.016999543098989402,
+        "information_ratio": 1.1997208834083914,
+        "max_drawdown": -0.0071582979845040825
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "REM",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $5,000,000": {
+      "risk": {
+        "mean": 9.239707947451976e-05,
+        "std": 0.0009427144352738658,
+        "annualized_return": 0.0219905049149357,
+        "information_ratio": 1.5120514373488407,
+        "max_drawdown": -0.006530482262119444
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $10,000,000": {
+      "risk": {
+        "mean": 9.438545151345752e-05,
+        "std": 0.0009420158170657147,
+        "annualized_return": 0.02246373746020289,
+        "information_ratio": 1.5457360696934006,
+        "max_drawdown": -0.006388809561209335
+      },
+      "applied": {
+        "dropped_liquidity": [
+          "DBA",
+          "DBC",
+          "ESPO",
+          "FDN",
+          "ICLN",
+          "ITA",
+          "MDY",
+          "REM",
+          "SHY",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    }
+  },
+  "candidate": {
+    "liquidity_floor_adv": 5000000.0,
+    "size_cap_pct": 0.12,
+    "concentration_cap_pct": 0.95,
+    "drawdown_pause_pct": 0.1
+  }
+}

book/data/evidence/q19-vr/VR_stats.csv

@@ -0,0 +1,72 @@
+symbol,n_days,VR_5d,z_5d,p_5d,VR_10d,z_10d,p_10d,VR_20d,z_20d,p_20d
+AGG,2924,0.9097,-2.34,0.0194,0.8609,-2.4,0.0163,0.8397,-1.91,0.0567
+ARKK,2924,0.9864,-0.34,0.7357,0.9354,-1.07,0.284,0.9442,-0.63,0.532
+BIL,2658,0.7672,-6.26,0.0,0.5346,-9.73,0.0,0.1342,-24.56,0.0
+BND,2924,0.8574,-3.8,0.0001,0.8383,-2.83,0.0047,0.8216,-2.14,0.0322
+DBA,2921,1.055,1.32,0.1868,0.9978,-0.04,0.9715,0.9522,-0.53,0.5945
+DBC,2923,1.021,0.51,0.6078,1.0126,0.2,0.8414,1.0324,0.35,0.7294
+DIA,2924,0.8656,-3.57,0.0004,0.8399,-2.8,0.0051,0.8225,-2.13,0.0329
+EEM,2924,0.8788,-3.19,0.0014,0.8293,-3.01,0.0027,0.7882,-2.6,0.0093
+EFA,2924,0.9588,-1.04,0.2987,0.9409,-0.98,0.329,0.8853,-1.33,0.1838
+EMB,2924,1.056,1.35,0.1785,1.0907,1.39,0.164,1.1128,1.17,0.2435
+ESPO,1958,0.6191,-9.78,0.0,0.5412,-8.12,0.0,0.5161,-5.96,0.0
+EWA,2672,0.8081,-5.02,0.0,0.814,-3.13,0.0017,0.8024,-2.28,0.0226
+EWG,2672,1.0308,0.72,0.4744,1.0339,0.51,0.6103,1.0125,0.13,0.8972
+EWJ,2672,0.9185,-2.0,0.0451,0.8644,-2.23,0.0258,0.7457,-3.05,0.0023
+EWU,2672,0.9757,-0.58,0.5624,0.9338,-1.05,0.2954,0.8919,-1.19,0.2352
+EWY,2672,0.8729,-3.21,0.0013,0.8266,-2.92,0.0035,0.8406,-1.8,0.0711
+EWZ,2672,0.8651,-3.42,0.0006,0.8822,-1.92,0.0548,0.9524,-0.51,0.6115
+FDN,2923,0.9523,-1.21,0.2275,0.8836,-1.98,0.0473,0.8566,-1.69,0.0916
+FXI,2672,0.8852,-2.87,0.004,0.8312,-2.82,0.0047,0.7516,-2.96,0.003
+GDX,2672,0.9159,-2.07,0.0384,0.8388,-2.69,0.0071,0.8019,-2.28,0.0226
+GLD,2924,0.9712,-0.72,0.4704,0.9144,-1.43,0.152,0.8802,-1.37,0.1695
+HYG,2924,1.0529,1.27,0.2031,0.9766,-0.38,0.7043,0.9166,-0.95,0.3418
+IBB,2924,0.9412,-1.49,0.1359,0.8999,-1.68,0.0921,0.7981,-2.44,0.0146
+ICLN,2924,1.0298,0.73,0.4682,1.0343,0.54,0.5891,1.1145,1.18,0.2369
+IEF,2924,0.8899,-2.88,0.004,0.8662,-2.3,0.0215,0.8838,-1.34,0.18
+IGV,2924,0.9889,-0.28,0.7822,0.9954,-0.07,0.9415,0.9952,-0.05,0.9582
+INDA,2672,0.7818,-5.82,0.0,0.7803,-3.81,0.0001,0.816,-2.12,0.0343
+ITA,2908,0.9747,-0.63,0.528,0.9891,-0.18,0.8606,0.9957,-0.05,0.9626
+ITB,2672,1.0153,0.36,0.7198,0.9751,-0.39,0.6999,1.002,0.02,0.9837
+IWM,2924,0.9602,-1.0,0.3163,0.9483,-0.85,0.3949,0.9469,-0.6,0.5518
+IWV,2665,0.808,-5.03,0.0,0.7791,-3.82,0.0001,0.7672,-2.75,0.006
+JNK,2924,1.1001,2.36,0.0184,1.0612,0.95,0.341,1.0189,0.2,0.8382
+KRE,2672,0.9209,-1.94,0.0518,0.9524,-0.75,0.4555,0.9833,-0.18,0.861
+KWEB,2672,0.9051,-2.35,0.0186,0.8517,-2.46,0.0139,0.8147,-2.14,0.0326
+LQD,2924,1.0826,1.96,0.0499,1.037,0.58,0.5606,0.9921,-0.09,0.9312
+MDY,2924,0.9161,-2.17,0.0304,0.8976,-1.73,0.0832,0.8979,-1.18,0.24
+QQQ,2924,0.8369,-4.4,0.0,0.7887,-3.81,0.0001,0.7654,-2.92,0.0035
+REM,2920,1.2256,5.03,0.0,1.2122,3.09,0.002,1.3822,3.54,0.0004
+SHY,2924,0.8209,-4.88,0.0,0.7856,-3.87,0.0001,0.7764,-2.76,0.0058
+SLV,2924,1.0275,0.67,0.5034,0.9628,-0.61,0.544,0.9051,-1.08,0.2794
+SMH,2924,0.8999,-2.6,0.0092,0.8786,-2.08,0.0379,0.8669,-1.56,0.1191
+SOXX,2924,0.9364,-1.62,0.105,0.933,-1.11,0.266,0.9293,-0.8,0.4238
+SPY,2491,0.9373,-1.48,0.1401,0.8716,-2.03,0.0419,0.7884,-2.4,0.0165
+TAN,2924,1.0551,1.32,0.1856,1.0388,0.61,0.5414,1.035,0.38,0.7075
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+XME,2672,0.9908,-0.22,0.828,0.9907,-0.14,0.8873,1.041,0.41,0.6787
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book/data/evidence/q19-vr/VR_summary.json

@@ -0,0 +1,35 @@
+{
+  "panel_size": 71,
+  "date_range": "2015-01-02 to 2026-08-19",
+  "n_trading_days": 2924,
+  "horizons": {
+    "5d": {
+      "mean_vr": 0.9248,
+      "median_vr": 0.9248,
+      "frac_lt1": 0.789,
+      "frac_sig_revert_z2": 0.465,
+      "frac_sig_momentum_z2": 0.028
+    },
+    "10d": {
+      "mean_vr": 0.8925,
+      "median_vr": 0.8999,
+      "frac_lt1": 0.859,
+      "frac_sig_revert_z2": 0.423,
+      "frac_sig_momentum_z2": 0.014
+    },
+    "20d": {
+      "mean_vr": 0.8733,
+      "median_vr": 0.8838,
+      "frac_lt1": 0.845,
+      "frac_sig_revert_z2": 0.366,
+      "frac_sig_momentum_z2": 0.014
+    }
+  },
+  "trend_slope_5_beta": {
+    "mean": 3.7952,
+    "se": 0.016,
+    "t_stat": 237.3,
+    "n_negative": 0,
+    "n_total": 71
+  }
+}

book/data/evidence/q19-vr/vr_study.py

@@ -0,0 +1,217 @@
+"""
+Q19 — Variance-ratio study on the 50-ETF panel (clean lake).
+
+Tests whether assets are submartingales long-horizon / mean-reverting
+short-horizon (VR < 1 at 5–20d). Uses the Lo–MacKinlay heteroskedasticity-
+robust VR statistic.
+
+Output: VR_stats.csv + stdout summary.
+"""
+import pathlib, json, sys
+import numpy as np
+import pandas as pd
+from scipy import stats
+
+LAKE = pathlib.Path("/home/data/lake/market=US/timeframe=1d")
+OUT  = pathlib.Path(__file__).parent
+
+# --- 50-ETF panel (all non-single-stock names in the lake) ---
+SINGLE_STOCKS = {
+    "AAPL","MSFT","NVDA","AMZN","GOOGL","META","TSLA","AVGO","AMD",
+    "JPM","UNH","PG","JNJ","MA","V","WMT","DIS","HD","KO","PEP",
+    "BAC","XOM","MCD","ABBV","COST","CRM","NFLX","ORCL","IBM","T",
+}
+
+def load_etf_bars(start="2015-01-01", end="2026-08-19"):
+    frames = []
+    for f in sorted(LAKE.glob("symbol=*.parquet")):
+        sym = f.stem.replace("symbol=", "")
+        if sym in SINGLE_STOCKS:
+            continue
+        df = pd.read_parquet(f)
+        if len(df) < 100:
+            continue
+        df.columns = [c.lower() for c in df.columns]
+        # Lake uses 'c' for close, 'date' column for date
+        close_col = "c" if "c" in df.columns else "close"
+        if close_col not in df.columns:
+            continue
+        if "date" in df.columns:
+            df = df.set_index("date")
+        elif "datetime" in df.columns:
+            df = df.set_index("datetime")
+        df.index = pd.to_datetime(df.index)
+        df = df.loc[start:end]
+        if len(df) < 200:
+            continue
+        frames.append(df[close_col].rename(sym))
+    return pd.DataFrame(frames).T.sort_index()
+
+def variance_ratio(series, q):
+    """
+    Lo-MacKinlay variance ratio with heteroskedasticity-robust z-stat.
+    VR(q) = Var(q-period returns) / (q * Var(1-period returns))
+    H0: VR = 1 (random walk).
+    VR < 1 => mean reversion; VR > 1 => momentum / trending.
+    """
+    y = series.dropna().values
+    n = len(y)
+    if n < q + 10:
+        return np.nan, np.nan, np.nan
+    rets = np.diff(np.log(y))
+    n_ret = len(rets)
+    mu = np.mean(rets)
+    # 1-period variance (with heteroskedasticity correction)
+    m2 = np.sum((rets - mu) ** 2) / (n_ret - 1)
+    # q-period returns
+    rq = np.array([np.sum(rets[i:i+q]) for i in range(n_ret - q + 1)])
+    vq = np.var(rq, ddof=1)
+    vr = vq / (q * m2) if m2 > 0 else np.nan
+    # Robust z-stat (heteroskedasticity-robust, Lo-MacKinlay 1988 Eq. 18)
+    # Under H0: VR=1, z ~ N(0,1)
+    T = n_ret
+    # Sum of autocovariances for q-period returns
+    mu_q = np.mean(rq)
+    # Omega_1 (heteroskedasticity-robust variance of VR estimate)
+    # Simplified: use the asymptotic variance under heteroskedasticity
+    delta = np.zeros(q)
+    for j in range(1, q):
+        rho_j = np.corrcoef(rets[j:], rets[:-j])[0, 1] if len(rets) > j + 1 else 0
+        delta[j] = 2 * (1 - j/q) * rho_j
+    omega2 = np.sum(delta)
+    # z-stat
+    se_vr = np.sqrt(max((2 * (2*q - 1) * (q-1)) / (3 * q * T) * (1 + omega2), 1e-15))
+    z = (vr - 1) / se_vr if se_vr > 0 else 0
+    pval = 2 * (1 - stats.norm.cdf(abs(z)))
+    return vr, z, pval
+
+def main():
+    print("Loading 50-ETF daily bars from lake...")
+    prices = load_etf_bars()
+    print(f"Loaded {prices.shape[1]} symbols, {prices.shape[0]} trading days ({prices.index[0].date()} to {prices.index[-1].date()})")
+
+    horizons = [5, 10, 20]
+    results = []
+
+    for sym in prices.columns:
+        s = prices[sym].dropna()
+        if len(s) < 500:
+            continue
+        row = {"symbol": sym, "n_days": len(s)}
+        for q in horizons:
+            vr, z, p = variance_ratio(s, q)
+            row[f"VR_{q}d"] = round(vr, 4)
+            row[f"z_{q}d"] = round(z, 2)
+            row[f"p_{q}d"] = round(p, 4)
+        results.append(row)
+
+    df = pd.DataFrame(results)
+
+    # --- Summary ---
+    print("\n=== Variance Ratio Summary (50-ETF Panel, 2015-01-01 to 2026-08-19) ===")
+    for q in horizons:
+        vr_col = f"VR_{q}d"
+        valid = df[vr_col].dropna()
+        frac_lt1 = (valid < 1).mean()
+        frac_sig_revert = ((valid < 1) & (df[f"z_{q}d"].abs() > 2)).mean()
+        frac_sig_momentum = ((valid > 1) & (df[f"z_{q}d"].abs() > 2)).mean()
+        print(f"\n  Horizon {q}d:")
+        print(f"    Mean VR: {valid.mean():.4f}, Median VR: {valid.median():.4f}")
+        print(f"    Std VR:  {valid.std():.4f}")
+        print(f"    Fraction VR < 1: {frac_lt1:.1%} ({(valid < 1).sum()}/{len(valid)})")
+        print(f"    Fraction VR < 1 & |z|>2 (mean-revert): {frac_sig_revert:.1%}")
+        print(f"    Fraction VR > 1 & |z|>2 (momentum):    {frac_sig_momentum:.1%}")
+        print(f"    Min VR: {valid.min():.4f}, Max VR: {valid.max():.4f}")
+
+    # --- Cross-check: pooled trend-slope beta ---
+    print("\n=== Cross-check: sp_trend_slope_5 regression ===")
+    # Compute log-price momentum slope for each symbol
+    betas = []
+    for sym in prices.columns:
+        s = prices[sym].dropna()
+        if len(s) < 100:
+            continue
+        logp = np.log(s.values)
+        # 5-day rolling slope (regress logp on [0,1,2,3,4] for each window)
+        slopes = []
+        for i in range(len(logp) - 4):
+            y_win = logp[i:i+5]
+            x_win = np.arange(5)
+            # OLS slope
+            slope = (5 * np.sum(x_win * y_win) - np.sum(x_win) * np.sum(y_win)) / (5 * np.sum(x_win**2) - np.sum(x_win)**2)
+            slopes.append(slope)
+        # Future 5-day return
+        rets_5d = np.array([np.log(s.values[i+5] / s.values[i]) for i in range(len(s) - 5)])
+        slopes_arr = np.array(slopes[:len(rets_5d)])
+        if len(slopes_arr) < 50:
+            continue
+        # Regression: future 5d return ~ beta * trend_slope_5
+        valid_mask = np.isfinite(slopes_arr) & np.isfinite(rets_5d)
+        if valid_mask.sum() < 50:
+            continue
+        slope_valid = slopes_arr[valid_mask]
+        ret_valid = rets_5d[valid_mask]
+        # OLS
+        X = np.column_stack([np.ones(len(slope_valid)), slope_valid])
+        beta_hat = np.linalg.lstsq(X, ret_valid, rcond=None)[0]
+        betas.append({"symbol": sym, "beta": beta_hat[1], "n": valid_mask.sum()})
+
+    beta_df = pd.DataFrame(betas)
+    if len(beta_df) > 0:
+        pooled_beta = beta_df["beta"].mean()
+        pooled_se = beta_df["beta"].std() / np.sqrt(len(beta_df))
+        t_stat = pooled_beta / pooled_se if pooled_se > 0 else 0
+        print(f"  Panel ({len(beta_df)} symbols): mean slope-beta = {pooled_beta:.4f}, SE = {pooled_se:.4f}, t = {t_stat:.2f}")
+        print(f"  Beta range: [{beta_df['beta'].min():.4f}, {beta_df['beta'].max():.4f}]")
+        n_negative = (beta_df["beta"] < 0).sum()
+        print(f"  Symbols with negative beta (mean-revert): {n_negative}/{len(beta_df)} ({n_negative/len(beta_df):.1%})")
+
+    # --- Save ---
+    df.to_csv(OUT / "VR_stats.csv", index=False)
+    summary = {
+        "panel_size": len(df),
+        "date_range": f"{prices.index[0].date()} to {prices.index[-1].date()}",
+        "n_trading_days": len(prices),
+        "horizons": {},
+    }
+    for q in horizons:
+        valid = df[f"VR_{q}d"].dropna()
+        summary["horizons"][f"{q}d"] = {
+            "mean_vr": round(float(valid.mean()), 4),
+            "median_vr": round(float(valid.median()), 4),
+            "frac_lt1": round(float((valid < 1).mean()), 3),
+            "frac_sig_revert_z2": round(float(((valid < 1) & (df[f"z_{q}d"].abs() > 2)).mean()), 3),
+            "frac_sig_momentum_z2": round(float(((valid > 1) & (df[f"z_{q}d"].abs() > 2)).mean()), 3),
+        }
+    if len(beta_df) > 0:
+        summary["trend_slope_5_beta"] = {
+            "mean": round(float(pooled_beta), 4),
+            "se": round(float(pooled_se), 4),
+            "t_stat": round(float(t_stat), 2),
+            "n_negative": int(n_negative),
+            "n_total": len(beta_df),
+        }
+    with open(OUT / "VR_summary.json", "w") as f:
+        json.dump(summary, f, indent=2)
+
+    print(f"\nSaved: {OUT / 'VR_stats.csv'}")
+    print(f"Saved: {OUT / 'VR_summary.json'}")
+
+    # --- Verdict ---
+    print("\n=== VERDICT ===")
+    vr5 = summary["horizons"]["5d"]
+    vr10 = summary["horizons"]["10d"]
+    vr20 = summary["horizons"]["20d"]
+    any_revert = any(h["frac_sig_revert_z2"] > 0.1 for h in [vr5, vr10, vr20])
+    all_lt1_median = all(h["median_vr"] < 1 for h in [vr5, vr10, vr20])
+    if all_lt1_median and any_revert:
+        print("  SUPPORTS mean-reversion hypothesis: median VR < 1 at all horizons,")
+        print("  material fraction with significant mean-reversion (|z| > 2).")
+    elif all_lt1_median:
+        print("  PARTIAL: median VR < 1 at all horizons, but few significant z-stats.")
+    else:
+        print("  REFUTES strict mean-reversion: median VR >= 1 at some horizons.")
+    print("  See VR_stats.csv for per-symbol detail.")
+
+if __name__ == "__main__":
+    main()

book/data/evidence/q20-effective-names/eigen_summary.json

@@ -0,0 +1,36 @@
+{
+  "N_symbols": 149,
+  "T_days": 72,
+  "q_ratio": 0.48,
+  "mp_bound": 5.9466,
+  "n_signal_eigenvalues": 6,
+  "top_eigenvalues": [
+    38.3649,
+    19.6496,
+    16.599,
+    10.3877,
+    9.5385,
+    6.5538,
+    5.7046,
+    5.2692,
+    3.6853,
+    3.5121
+  ],
+  "top_pct_variance": [
+    25.7,
+    13.2,
+    11.1,
+    7.0,
+    6.4,
+    4.4,
+    3.8,
+    3.5,
+    2.5,
+    2.4
+  ],
+  "participation_ratio": 8.84,
+  "eigenvalues_for_80pct_var": 10,
+  "eigenvalues_for_90pct_var": 17,
+  "cumulative_var_top4": 57.0,
+  "cumulative_var_top10": 80.0
+}

book/data/evidence/q20-effective-names/eigen_summary_50etf.json

@@ -0,0 +1,37 @@
+{
+  "universe": "50-ETF trading panel",
+  "N_symbols": 71,
+  "T_days": 149,
+  "q_ratio": 2.1,
+  "mp_bound": 2.8571,
+  "n_signal_eigenvalues": 4,
+  "top_eigenvalues": [
+    31.815,
+    7.406,
+    4.4638,
+    3.7723,
+    2.8325,
+    2.198,
+    1.9641,
+    1.5672,
+    1.2612,
+    1.1654
+  ],
+  "top_pct_variance": [
+    44.8,
+    10.4,
+    6.3,
+    5.3,
+    4.0,
+    3.1,
+    2.8,
+    2.2,
+    1.8,
+    1.6
+  ],
+  "participation_ratio": 4.46,
+  "eigenvalues_for_80pct_var": 9,
+  "eigenvalues_for_90pct_var": 17,
+  "cumulative_var_top4": 66.8,
+  "cumulative_var_top10": 82.3
+}

book/data/evidence/q20-effective-names/eigenanalysis.csv

@@ -0,0 +1,150 @@
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book/data/evidence/q20-effective-names/eigenanalysis.py

@@ -0,0 +1,165 @@
+"""
+Q20 — Effective independent names in the 50-ETF book (clean lake).
+
+Eigenvalue analysis on the 50-ETF correlation matrix to determine
+how many effective independent names exist in the book.
+
+Output: eigenanalysis.csv + eigenvalue_spectrum.png + stdout summary.
+"""
+import pathlib, json
+import numpy as np
+import pandas as pd
+from scipy import linalg
+
+LAKE = pathlib.Path("/home/data/lake/market=US/timeframe=1d")
+OUT  = pathlib.Path(__file__).parent
+
+SINGLE_STOCKS = {
+    "AAPL","MSFT","NVDA","AMZN","GOOGL","META","TSLA","AVGO","AMD",
+    "JPM","UNH","PG","JNJ","MA","V","WMT","DIS","HD","KO","PEP",
+    "BAC","XOM","MCD","ABBV","COST","CRM","NFLX","ORCL","IBM","T",
+}
+
+def load_etf_returns(start="2026-01-04", end="2026-08-10"):
+    frames = []
+    for f in sorted(LAKE.glob("symbol=*.parquet")):
+        sym = f.stem.replace("symbol=", "")
+        if sym in SINGLE_STOCKS:
+            continue
+        df = pd.read_parquet(f)
+        df.columns = [c.lower() for c in df.columns]
+        close_col = "c" if "c" in df.columns else "close"
+        if close_col not in df.columns:
+            continue
+        if "date" in df.columns:
+            df = df.set_index("date")
+        elif "datetime" in df.columns:
+            df = df.set_index("datetime")
+        df.index = pd.to_datetime(df.index)
+        df = df.loc[start:end]
+        if len(df) < 20:
+            continue
+        rets = df[close_col].pct_change().dropna()
+        if len(rets) < 20:
+            continue
+        frames.append(rets.rename(sym))
+    return pd.DataFrame(frames).T.sort_index()
+
+def marchenko_pastur_bound(N, T, q=None):
+    """
+    Marchenko-Pastur upper bound for eigenvalues of a random correlation matrix.
+    q = T/N ratio. Eigenvalues above this bound are 'signal'.
+    """
+    if q is None:
+        q = T / N
+    sigma2 = 1.0  # correlation matrix has unit diagonal
+    lambda_plus = sigma2 * (1 + 1/np.sqrt(q))**2
+    return lambda_plus
+
+def participation_ratio(eigenvalues):
+    """Participation ratio: (sum(lambda))^2 / sum(lambda^2). Equals N for identity."""
+    lam = eigenvalues[eigenvalues > 0]
+    return (np.sum(lam))**2 / np.sum(lam**2)
+
+def main():
+    print("Loading 50-ETF daily returns (test window: 2026-01-04 to 2026-08-10)...")
+    rets = load_etf_returns()
+    N = rets.shape[0]  # symbols (rows)
+    T = rets.shape[1]  # trading days (columns)
+    print(f"Loaded {N} ETFs, {T} trading days")
+    print(f"Note: N={N} symbols (rows), T={T} days (columns) in return matrix")
+
+    # Drop any ETFs with too many NaNs
+    rets = rets.dropna(axis=0, thresh=int(T * 0.8))
+    N = rets.shape[0]
+    rets = rets.fillna(0)
+    print(f"After dropping high-NaN ETFs: {N} symbols")
+
+    # Correlation matrix
+    corr = rets.T.corr()
+    print(f"Correlation matrix: {corr.shape}")
+
+    # Eigendecomposition
+    eigvals_raw = linalg.eigvalsh(corr.values)
+    eigvals = np.sort(eigvals_raw)[::-1]  # descending
+
+    # Marchenko-Pastur bound
+    q_ratio = T / N
+    mp_bound = marchenko_pastur_bound(N, T, q_ratio)
+    n_signal = int(np.sum(eigvals > mp_bound))
+
+    print(f"\n=== Eigenvalue Analysis ===")
+    print(f"  N (ETFs): {N}")
+    print(f"  T (days):  {T}")
+    print(f"  q = T/N:   {q_ratio:.2f}")
+    print(f"  Marchenko-Pastur upper bound: {mp_bound:.4f}")
+    print(f"  Eigenvalues above MP bound (signal): {n_signal}")
+    print(f"\n  Top 10 eigenvalues:")
+    for i, ev in enumerate(eigvals[:10]):
+        pct = ev / eigvals.sum() * 100
+        marker = " * SIGNAL" if ev > mp_bound else ""
+        print(f"    λ_{i+1:2d} = {ev:8.4f}  ({pct:5.1f}% var){marker}")
+
+    # Cumulative variance share
+    cumvar = np.cumsum(eigvals) / eigvals.sum()
+    print(f"\n  Cumulative variance explained by top-k components:")
+    for k in [1, 2, 3, 4, 5, 10, 15, 20]:
+        if k <= len(cumvar):
+            print(f"    Top {k:2d}: {cumvar[k-1]*100:5.1f}%")
+
+    # Effective rank measures
+    pr = participation_ratio(eigvals)
+    # 80% variance count
+    var_80 = int(np.searchsorted(cumvar, 0.80) + 1)
+    # 90% variance count
+    var_90 = int(np.searchsorted(cumvar, 0.90) + 1)
+
+    print(f"\n  Participation ratio (effective rank): {pr:.2f}")
+    print(f"  Eigenvalues needed for 80% variance: {var_80}")
+    print(f"  Eigenvalues needed for 90% variance: {var_90}")
+
+    # --- Save ---
+    eigen_df = pd.DataFrame({
+        "rank": range(1, len(eigvals) + 1),
+        "eigenvalue": eigvals,
+        "pct_variance": eigvals / eigvals.sum() * 100,
+        "cumulative_pct": cumvar * 100,
+        "above_mp_bound": eigvals > mp_bound,
+    })
+    eigen_df.to_csv(OUT / "eigenanalysis.csv", index=False)
+
+    summary = {
+        "N_symbols": N,
+        "T_days": T,
+        "q_ratio": round(q_ratio, 2),
+        "mp_bound": round(float(mp_bound), 4),
+        "n_signal_eigenvalues": n_signal,
+        "top_eigenvalues": [round(float(ev), 4) for ev in eigvals[:10]],
+        "top_pct_variance": [round(float(ev / eigvals.sum() * 100), 1) for ev in eigvals[:10]],
+        "participation_ratio": round(float(pr), 2),
+        "eigenvalues_for_80pct_var": var_80,
+        "eigenvalues_for_90pct_var": var_90,
+        "cumulative_var_top4": round(float(cumvar[3] * 100), 1) if len(cumvar) > 3 else None,
+        "cumulative_var_top10": round(float(cumvar[9] * 100), 1) if len(cumvar) > 9 else None,
+    }
+    with open(OUT / "eigen_summary.json", "w") as f:
+        json.dump(summary, f, indent=2)
+
+    print(f"\nSaved: {OUT / 'eigenanalysis.csv'}")
+    print(f"Saved: {OUT / 'eigen_summary.json'}")
+
+    # --- Verdict ---
+    print(f"\n=== VERDICT ===")
+    if var_80 <= 5:
+        print(f"  CONFIRMED: top-{var_80} components explain 80%+ of variance.")
+        print(f"  The 50-ETF book has ≈{var_80} effective independent names.")
+        print(f"  This explains why topk 10→20 adds no breadth (EVIDENCE#024).")
+    elif var_80 <= 10:
+        print(f"  PARTIAL: top-{var_80} for 80% variance — moderate concentration.")
+        print(f"  Participation ratio = {pr:.1f}, suggesting ~{pr:.0f} effective names.")
+    else:
+        print(f"  REFUTED: need {var_80} components for 80% variance — book is well-diversified.")
+        print(f"  The 'only ~4 effective names' claim is overstated.")
+
+if __name__ == "__main__":
+    main()

book/data/evidence/q20-effective-names/eigenanalysis_50etf.csv

@@ -0,0 +1,72 @@
+rank,eigenvalue,pct_variance,cumulative_pct,above_mp_bound
+1,31.815034542104435,44.8099078057809,44.8099078057809,True
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book/data/perturbation/config_a_2026_sensitivity.json

@@ -0,0 +1,37 @@
+{
+  "description": "Perturbation stress test on Config A (exp 52, run 9f98ea5c) — 2026 window (2026-01-04 to 2026-08-19). Same pred.pkl, varying backtest parameters. Raw returns (not excess over SPY).",
+  "pred_source": "exp 52, run 9f98ea5c550a409f87b56a6cd8fee343",
+  "test_window": ["2026-01-04", "2026-08-19"],
+  "trading_days": 157,
+  "topk_sensitivity": {
+    "description": "vary topk, n_drop=1, costs=base (5bp/15bp/$5)",
+    "results": [
+      {"topk": 5,  "n_drop": 1, "ann_return": 0.3230, "sharpe": 1.745, "maxDD": -0.0695},
+      {"topk": 10, "n_drop": 1, "ann_return": 0.3284, "sharpe": 1.978, "maxDD": -0.0581},
+      {"topk": 15, "n_drop": 1, "ann_return": 0.2630, "sharpe": 1.611, "maxDD": -0.0689}
+    ]
+  },
+  "ndrop_sensitivity": {
+    "description": "vary n_drop, topk=10, costs=base",
+    "results": [
+      {"topk": 10, "n_drop": 1, "ann_return": 0.3284, "sharpe": 1.978, "maxDD": -0.0581},
+      {"topk": 10, "n_drop": 2, "ann_return": 0.2674, "sharpe": 1.586, "maxDD": -0.0652},
+      {"topk": 10, "n_drop": 3, "ann_return": 0.2861, "sharpe": 1.658, "maxDD": -0.0667}
+    ]
+  },
+  "cost_sensitivity": {
+    "description": "vary costs, topk=10, n_drop=1",
+    "results": [
+      {"open_cost": 0.0005, "close_cost": 0.0015, "min_cost": 5,  "ann_return": 0.3284, "sharpe": 1.978, "maxDD": -0.0581},
+      {"open_cost": 0.0015, "close_cost": 0.0025, "min_cost": 10, "ann_return": 0.3275, "sharpe": 1.974, "maxDD": -0.0580},
+      {"open_cost": 0.0025, "close_cost": 0.0035, "min_cost": 15, "ann_return": 0.3267, "sharpe": 1.971, "maxDD": -0.0580}
+    ]
+  },
+  "findings": {
+    "topk": "topk=10 is optimal. topk=5 loses ~0.5pp (concentration risk), topk=15 loses ~6.5pp (signal dilution). Edge is moderate-sensitivity to topk.",
+    "n_drop": "n_drop=1 is best. n_drop=2 loses ~6pp, n_drop=3 loses ~4pp. More rotation hurts in this window.",
+    "costs": "Almost irrelevant. Even at 5x base costs (25bp/35bp/$15), return drops only 0.17pp (32.84% → 32.67%). Low turnover + large gross edge makes cost assumptions immaterial.",
+    "maxDD": "Stable across all perturbations: range -5.8% to -7.0%. No blowup risk from parameter changes.",
+    "overall": "The 2026 edge is robust WITHIN the window. The problem is it doesn't exist in other windows (ch 11)."
+  }
+}

book/data/regime_gate/regime_gate_trip_rates.csv

@@ -0,0 +1,71 @@
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book/data/signal_quality_gate/signal_quality_gate_results.csv

@@ -0,0 +1,46 @@
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book/data/signal_quality_gate/signal_quality_gate_results.json

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+reference,2024,hitrate_5d_0.70,2024-01-02,2024-12-31,253,19,234,0.9249,0.08229,0.5594,-0.10685,0.143125,3.377,-0.003364
+reference,2024,hitrate_10d_0.50,2024-01-02,2024-12-31,253,157,96,0.3794,0.08229,0.5594,-0.10685,0.277421,2.5767,-0.043146
+reference,2024,hitrate_10d_0.60,2024-01-02,2024-12-31,253,37,216,0.8538,0.08229,0.5594,-0.10685,0.207161,3.6083,-0.011122
+reference,2024,hitrate_10d_0.70,2024-01-02,2024-12-31,253,7,246,0.9723,0.08229,0.5594,-0.10685,0.031785,1.7408,-0.002083
+reference,2024,hitrate_20d_0.40,2024-01-02,2024-12-31,253,247,6,0.0237,0.08229,0.5594,-0.10685,0.078007,0.5342,-0.10685
+reference,2024,hitrate_20d_0.50,2024-01-02,2024-12-31,253,177,76,0.3004,0.08229,0.5594,-0.10685,0.210672,1.8469,-0.056207
+reference,2024,hitrate_20d_0.60,2024-01-02,2024-12-31,253,13,240,0.9486,0.08229,0.5594,-0.10685,0.005953,0.3039,-0.014443
+reference,2023,hitrate_5d_0.50,2023-01-03,2023-12-29,250,139,111,0.444,-0.047644,-0.2738,-0.197856,0.546654,4.2124,-0.035676
+reference,2023,hitrate_5d_0.60,2023-01-03,2023-12-29,250,79,171,0.684,-0.047644,-0.2738,-0.197856,0.556291,5.1026,-0.025449
+reference,2023,hitrate_5d_0.70,2023-01-03,2023-12-29,250,34,216,0.864,-0.047644,-0.2738,-0.197856,0.404269,4.4477,-0.013842
+reference,2023,hitrate_10d_0.50,2023-01-03,2023-12-29,250,148,102,0.408,-0.047644,-0.2738,-0.197856,0.459869,3.5211,-0.046921
+reference,2023,hitrate_10d_0.60,2023-01-03,2023-12-29,250,62,188,0.752,-0.047644,-0.2738,-0.197856,0.368232,4.0148,-0.022983
+reference,2023,hitrate_10d_0.70,2023-01-03,2023-12-29,250,13,237,0.948,-0.047644,-0.2738,-0.197856,0.091481,2.1324,-0.010866
+reference,2023,hitrate_20d_0.40,2023-01-03,2023-12-29,250,237,13,0.052,-0.047644,-0.2738,-0.197856,0.06639,0.3895,-0.146704
+reference,2023,hitrate_20d_0.50,2023-01-03,2023-12-29,250,149,101,0.404,-0.047644,-0.2738,-0.197856,0.366016,2.5969,-0.063998
+reference,2023,hitrate_20d_0.60,2023-01-03,2023-12-29,250,40,210,0.84,-0.047644,-0.2738,-0.197856,0.149844,2.2645,-0.032267
+reference,2021,hitrate_5d_0.50,2021-01-04,2021-12-31,252,145,107,0.4246,0.18367,1.0979,-0.102651,0.556615,5.9167,-0.028062
+reference,2021,hitrate_5d_0.60,2021-01-04,2021-12-31,252,68,184,0.7302,0.18367,1.0979,-0.102651,0.351867,5.8468,-0.011638
+reference,2021,hitrate_5d_0.70,2021-01-04,2021-12-31,252,26,226,0.8968,0.18367,1.0979,-0.102651,0.148417,3.9593,-0.0041
+reference,2021,hitrate_10d_0.50,2021-01-04,2021-12-31,252,163,89,0.3532,0.18367,1.0979,-0.102651,0.465023,4.2342,-0.040569
+reference,2021,hitrate_10d_0.60,2021-01-04,2021-12-31,252,51,201,0.7976,0.18367,1.0979,-0.102651,0.21802,4.7978,-0.011134
+reference,2021,hitrate_10d_0.70,2021-01-04,2021-12-31,252,13,239,0.9484,0.18367,1.0979,-0.102651,0.06108,2.7055,-0.000262
+reference,2021,hitrate_20d_0.40,2021-01-04,2021-12-31,252,252,0,0.0,0.18367,1.0979,-0.102651,0.18367,1.0979,-0.102651
+reference,2021,hitrate_20d_0.50,2021-01-04,2021-12-31,252,166,86,0.3413,0.18367,1.0979,-0.102651,0.289756,2.5787,-0.049244
+reference,2021,hitrate_20d_0.60,2021-01-04,2021-12-31,252,38,214,0.8492,0.18367,1.0979,-0.102651,0.104685,2.394,-0.015084

book/references/chat-ideas.md

@@ -2,7 +2,7 @@
 
 Source: opencode chat transcripts under `book/data/chat_mining/` (historical context, pre-clean-lake). Per the evidence contract these are **idea material only** — none may be cited as `PROVEN`. Each idea below is a hypothesis to be tested on the clean lake (exp 21+).
 
-## Data-quality failure classes (feed ch. 05)
+## Data-quality failure classes (feed ch. 06)
 
 These are the *classes* of failure documented across `exp-polluted-lake.txt`, `exp-dirty-lake.txt`, `cleaned-lake.txt`. Durable lessons even though exact numbers are pre-reset.
 
@@ -13,7 +13,7 @@ These are the *classes* of failure documented across `exp-polluted-lake.txt`, `e
 5. **Mid-experiment regeneration.** Feature parquet mtimes showed regeneration at 00:56 and 02:50 (Aug 17) — after exp-18 but before R0 — so reference and R0 ran on different feature files.
 6. **Detection playbook** (the valuable part): byte-identical-config reproduction; prediction-distribution comparison (pred_std, rank correlation, top-10 overlap); null-baseline IC z-scores (daily RankIC null std = 1/√(N−1) ≈ 0.143 for 50 names); per-day IC outlier fingerprints (3–4σ single-day ICs are contamination, not signal); feature-vs-bar alignment checks; file-mtime forensics; same-environment baselines.
 
-## Market-structure hypotheses (feed ch. 03/06; from martingale study + clean-data study)
+## Market-structure hypotheses (feed ch. 04/07; from martingale study + clean-data study)
 
 - **Submartingale at long horizons, mean-reverting at short horizons.** Drift compounds but explains ~0.5% of daily variance; short-horizon reversal (VR<1 at 5–20d for ~32/72 assets) is the tradable deviation.
 - **5-day momentum strongly reverses** (pooled regression: `sp_trend_slope_5` β = −0.53, t = −24). Fade 5-day strength; the repo's 5-day label is the best IC lever.
@@ -21,7 +21,7 @@ These are the *classes* of failure documented across `exp-polluted-lake.txt`, `e
 - **HMM regime gating as an overlay, not a feature.** Regime flags failed as model features (exp 9, exp 25) but the long-only/regime-gate overlay idea survives untested.
 - **Edge is long-short, not long-only** (drift is mostly common/market-wide).
 
-## Feature methodology hypotheses (feed ch. 03/06)
+## Feature methodology hypotheses (feed ch. 04/07)
 
 - **Panel width vs feature count:** three independent feature expansions (ou/hmm, realized moments, TA) regressed; the minimal generic set won repeatedly. Hypothesis: on ~50-name daily panels, cross-sectional features dilute CSRankNorm+LGBM.
 - **Single-feature time-series IC ≠ marginal contribution in a cross-sectional rank model.** `sp_ou_zscore` was the strongest stable single-feature predictor (IC −0.15/−0.13) yet hurt the model (IC 0.051→0.034). Measurement mismatch unresolved. TODO(evidence-needed).

book/scripts/diagnose_script_vs_workflow.py

@@ -0,0 +1,191 @@
+"""Diagnose exactly why the scripted test and workflow give different results.
+
+Compares the same pred.pkl through:
+  1. Script logic (weekly rebalance, equal-weight, hold-through-week, zero cost)
+  2. Workflow logic (PortAnaRecord daily backtest, TopkDropout-like)
+
+Isolates the effect of:
+  A. Weekly vs daily position evaluation
+  B. Equal weight vs risk_degree sizing
+  C. Hold-through-week vs daily top-k re-ranking
+"""
+
+from __future__ import annotations
+import json, pathlib
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
+
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
+]
+
+SYMS = [
+    "SPY","QQQ","DIA","IWM","MDY","VTI","VOO","VEA","VWO","VT","EFA","EEM",
+    "TLT","IEF","SHY","AGG","BND","LQD","HYG","JNK","EMB","GLD","SLV",
+    "USO","UNG","DBA","DBC","XLK","XLF","XLE","XLV","XLI","XLY","XLP",
+    "XLU","XLB","XLRE","ARKK","SMH","SOXX","IBB","XBI","ITA","XAR",
+    "ICLN","TAN","FDN","IGV","ESPO","REM",
+]
+
+
+def load_pred(path):
+    df = pd.read_pickle(path)
+    s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def load_closes(start, end):
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
+    closes = {}
+    for sym in SYMS:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists(): continue
+        try:
+            df = pd.read_parquet(p)
+        except: continue
+        if not len(df): continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def strategy_script(pred, closes, start, end, topk=10, risk_degree=1.0):
+    """Mimics the scripted test: weekly rebalance, hold all week."""
+    ret_df = closes.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    equity = 1_000_000.0
+    holdings = []
+    prev_week = None
+    daily_eq = []
+    for d in trade_dates:
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(equity)
+            prev_scores = None
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1])
+        if cur_week != prev_week or not holdings:
+            holdings = list(day_scores.index[:topk])
+        ret_row = ret_df.loc[d] if d in ret_df.index else None
+        if ret_row is not None and holdings:
+            wts = np.array([risk_degree / len(holdings)] * len(holdings))
+            rets = ret_row.reindex(holdings).fillna(0).values
+            equity *= (1 + (wts * rets).sum())
+        daily_eq.append(equity)
+        prev_week = cur_week
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def strategy_daily_topk(pred, closes, start, end, topk=10, risk_degree=1.0):
+    """Mimics PortAnaRecord: re-rank every day, hold top-k."""
+    ret_df = closes.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    equity = 1_000_000.0
+    daily_eq = []
+    for d in trade_dates:
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(equity)
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        holdings = list(day_scores.index[:topk])
+        ret_row = ret_df.loc[d] if d in ret_df.index else None
+        if ret_row is not None and holdings:
+            wts = np.array([risk_degree / len(holdings)] * len(holdings))
+            rets = ret_row.reindex(holdings).fillna(0).values
+            equity *= (1 + (wts * rets).sum())
+        daily_eq.append(equity)
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def metrics(eq):
+    if len(eq) < 2:
+        return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
+    rets = eq.pct_change().dropna()
+    ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+    vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+    sharpe = ann_ret / vol if vol > 0 else 0
+    peak = eq.cummax()
+    dd = (eq - peak) / peak
+    return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
+
+
+def main():
+    OUT.mkdir(parents=True, exist_ok=True)
+    results = []
+    for w in WINDOWS:
+        print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
+        pred = load_pred(w["pred"])
+        closes = load_closes(w["start"], w["end"])
+        print(f"  pred dates: {pred.index.get_level_values(0).min()} to {pred.index.get_level_values(0).max()}")
+        print(f"  close dates: {closes.index.min()} to {closes.index.max()}")
+        print(f"  symbols in close: {closes.shape[1]}")
+
+        # Script: weekly, equal weight (risk_degree=1.0)
+        eq_weekly_100 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0)
+        m_weekly_100 = metrics(eq_weekly_100)
+
+        # Script: weekly, 95% risk degree
+        eq_weekly_95 = strategy_script(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95)
+        m_weekly_95 = metrics(eq_weekly_95)
+
+        # Daily top-k: re-rank daily, equal weight
+        eq_daily_100 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0)
+        m_daily_100 = metrics(eq_daily_100)
+
+        # Daily top-k: re-rank daily, 95%
+        eq_daily_95 = strategy_daily_topk(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95)
+        m_daily_95 = metrics(eq_daily_95)
+
+        row = {
+            "year": w["label"],
+            "script_weekly_100": m_weekly_100,
+            "script_weekly_95": m_weekly_95,
+            "daily_topk_100": m_daily_100,
+            "daily_topk_95": m_daily_95,
+        }
+        results.append(row)
+        print(f"  Script weekly 100%: ann={m_weekly_100['ann_ret']:+.1%} sharpe={m_weekly_100['sharpe']:.2f} maxDD={m_weekly_100['maxDD']:.1%}")
+        print(f"  Script weekly  95%: ann={m_weekly_95['ann_ret']:+.1%} sharpe={m_weekly_95['sharpe']:.2f} maxDD={m_weekly_95['maxDD']:.1%}")
+        print(f"  Daily topk   100%: ann={m_daily_100['ann_ret']:+.1%} sharpe={m_daily_100['sharpe']:.2f} maxDD={m_daily_100['maxDD']:.1%}")
+        print(f"  Daily topk    95%: ann={m_daily_95['ann_ret']:+.1%} sharpe={m_daily_95['sharpe']:.2f} maxDD={m_daily_95['maxDD']:.1%}")
+
+    with open(OUT / "diagnosis.json", "w") as f:
+        json.dump(results, f, indent=2, default=str)
+    print(f"\nSaved to {OUT / 'diagnosis.json'}")
+
+
+if __name__ == "__main__":
+    main()

book/scripts/diagnose_script_vs_workflow_v2.py

@@ -0,0 +1,203 @@
+"""Diagnose the script-vs-workflow gap properly.
+
+Three strategies compared:
+  A. Script logic: weekly rebalance, equal-weight, hold through week
+  B. Weekly rebalance (qlib engine behavior): same as script but with risk_degree
+  C. Daily re-rank: re-select top-k every day (wrong model)
+
+Root cause was (C) — we were modeling daily re-ranking which neither
+the script nor the qlib engine actually does.
+"""
+
+from __future__ import annotations
+import json, pathlib
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
+
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
+]
+
+SYMS = [
+    "SPY","QQQ","DIA","IWM","MDY","VTI","VOO","VEA","VWO","VT","EFA","EEM",
+    "TLT","IEF","SHY","AGG","BND","LQD","HYG","JNK","EMB","GLD","SLV",
+    "USO","UNG","DBA","DBC","XLK","XLF","XLE","XLV","XLI","XLY","XLP",
+    "XLU","XLB","XLRE","ARKK","SMH","SOXX","IBB","XBI","ITA","XAR",
+    "ICLN","TAN","FDN","IGV","ESPO","REM",
+]
+
+
+def load_pred(path):
+    df = pd.read_pickle(path)
+    s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def load_closes(start, end):
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
+    closes = {}
+    for sym in SYMS:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists(): continue
+        try:
+            df = pd.read_parquet(p)
+        except: continue
+        if not len(df): continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def strategy_weekly(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
+    """Weekly rebalance: re-rank on first day of each ISO week, hold rest of week."""
+    ret_df = closes.pct_change(fill_method=None)
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    equity = 1_000_000.0
+    holdings = []
+    prev_week = None
+    daily_eq = []
+    for d in trade_dates:
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(equity)
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1])
+        if cur_week != prev_week:
+            # Rebalance: compute cost of turnover
+            new_holdings = list(day_scores.index[:topk])
+            if holdings and cost_bps > 0:
+                sold = set(holdings) - set(new_holdings)
+                bought = set(new_holdings) - set(holdings)
+                turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
+                equity *= (1 - turnover * cost_bps / 10000)
+            holdings = new_holdings
+        ret_row = ret_df.loc[d] if d in ret_df.index else None
+        if ret_row is not None and holdings:
+            wts = np.array([risk_degree / len(holdings)] * len(holdings))
+            rets = ret_row.reindex(holdings).fillna(0).values
+            equity *= (1 + (wts * rets).sum())
+        daily_eq.append(equity)
+        prev_week = cur_week
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def strategy_daily(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
+    """Daily re-rank: re-select top-k every day (wrong model — what we incorrectly tested)."""
+    ret_df = closes.pct_change(fill_method=None)
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    equity = 1_000_000.0
+    holdings = []
+    daily_eq = []
+    for d in trade_dates:
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(equity)
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        new_holdings = list(day_scores.index[:topk])
+        if holdings and cost_bps > 0:
+            sold = set(holdings) - set(new_holdings)
+            bought = set(new_holdings) - set(holdings)
+            turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
+            equity *= (1 - turnover * cost_bps / 10000)
+        holdings = new_holdings
+        ret_row = ret_df.loc[d] if d in ret_df.index else None
+        if ret_row is not None and holdings:
+            wts = np.array([risk_degree / len(holdings)] * len(holdings))
+            rets = ret_row.reindex(holdings).fillna(0).values
+            equity *= (1 + (wts * rets).sum())
+        daily_eq.append(equity)
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def metrics(eq):
+    if len(eq) < 2:
+        return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
+    rets = eq.pct_change().dropna()
+    ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+    vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+    sharpe = ann_ret / vol if vol > 0 else 0
+    peak = eq.cummax()
+    dd = (eq - peak) / peak
+    return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
+
+
+def main():
+    OUT.mkdir(parents=True, exist_ok=True)
+    results = []
+    for w in WINDOWS:
+        print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
+        pred = load_pred(w["pred"])
+        closes = load_closes(w["start"], w["end"])
+        print(f"  pred: {pred.index.get_level_values(0).min().date()} to {pred.index.get_level_values(0).max().date()}, "
+              f"{pred.index.get_level_values(1).nunique()} syms")
+        print(f"  close: {closes.index.min().date()} to {closes.index.max().date()}, {closes.shape[1]} syms")
+
+        row = {"year": w["label"]}
+
+        # A. Script: weekly, rd=1.0, zero cost
+        eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
+        m = metrics(eq); row["weekly_100_zc"] = m
+        print(f"  Script weekly 100% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
+
+        # B. Script: weekly, rd=0.95, zero cost
+        eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=0)
+        m = metrics(eq); row["weekly_95_zc"] = m
+        print(f"  Script weekly  95% zc: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
+
+        # C. Weekly, rd=0.95, with 5/15bp cost
+        eq = strategy_weekly(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=10)
+        m = metrics(eq); row["weekly_95_10bp"] = m
+        print(f"  Weekly 95% 10bp cost:   ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
+
+        # D. Daily re-rank, rd=1.0, zero cost (WRONG MODEL — for reference only)
+        eq = strategy_daily(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
+        m = metrics(eq); row["daily_100_zc"] = m
+        print(f"  Daily 100% zc (WRONG):  ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
+
+        # E. Daily re-rank, rd=1.0, 10bp cost
+        eq = strategy_daily(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=10)
+        m = metrics(eq); row["daily_100_10bp"] = m
+        print(f"  Daily 100% 10bp (WRONG):ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f}")
+
+        results.append(row)
+
+    with open(OUT / "diagnosis_v2.json", "w") as f:
+        json.dump(results, f, indent=2, default=str)
+    print(f"\nSaved to {OUT / 'diagnosis_v2.json'}")
+
+
+if __name__ == "__main__":
+    main()

book/scripts/diagnose_script_vs_workflow_v3.py

@@ -0,0 +1,410 @@
+"""Diagnose script-vs-workflow gap v3: replicate workflow execution mechanics exactly.
+
+Replicates the WeeklyRebalanceDropoutStrategy execution:
+  1. Weekly rebalance (first trading day of ISO week only)
+  2. TopkDropout selection: sell bottom n_drop, buy top fill
+  3. Cash-after-sells sizing: sell first, then cash * risk_degree / len(buy)
+  4. Whole-share rounding (floor)
+  5. Asymmetric costs: open_cost=5bp, close_cost=15bp, min_cost=$5 per order
+  6. Optional SQ gate (hit-rate threshold)
+
+Compares against the idealized script (fractional shares, symmetric cost).
+"""
+
+from __future__ import annotations
+import json, pathlib
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+OUT = pathlib.Path("/app/experiments/book/data/diag_script_vs_wf")
+
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
+]
+
+SYMS = [
+    "SPY","QQQ","DIA","IWM","MDY","VTI","VOO","VEA","VWO","VT","EFA","EEM",
+    "TLT","IEF","SHY","AGG","BND","LQD","HYG","JNK","EMB","GLD","SLV",
+    "USO","UNG","DBA","DBC","XLK","XLF","XLE","XLV","XLI","XLY","XLP",
+    "XLU","XLB","XLRE","ARKK","SMH","SOXX","IBB","XBI","ITA","XAR",
+    "ICLN","TAN","FDN","IGV","ESPO","REM",
+]
+
+OPEN_COST = 0.0005   # 5bp
+CLOSE_COST = 0.0015  # 15bp
+MIN_COST = 5.0       # $5 minimum per order
+
+
+def load_pred(path):
+    df = pd.read_pickle(path)
+    s = df["score"] if isinstance(df, pd.DataFrame) and "score" in df.columns else df.iloc[:, 0] if isinstance(df, pd.DataFrame) else df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def load_closes(start, end):
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
+    closes = {}
+    for sym in SYMS:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists(): continue
+        try:
+            df = pd.read_parquet(p)
+        except: continue
+        if not len(df): continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        df.index = pd.to_datetime(df.index).normalize()
+        warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def load_opens(start, end):
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
+    opens = {}
+    for sym in SYMS:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists(): continue
+        try:
+            df = pd.read_parquet(p)
+        except: continue
+        if not len(df): continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        df.index = pd.to_datetime(df.index).normalize()
+        warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup:end]
+        if len(df) >= 22:
+            opens[sym] = df["o"]
+    return pd.DataFrame(opens)
+
+
+def load_vwap(start, end):
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), "US")
+    vwaps = {}
+    for sym in SYMS:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists(): continue
+        try:
+            df = pd.read_parquet(p)
+        except: continue
+        if not len(df): continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup:end]
+        if len(df) >= 22:
+            vwaps[sym] = df["vw"]
+    return pd.DataFrame(vwaps)
+
+
+def compute_gate(pred, closes, start, end, gate_topk=10, gate_lookback=5, gate_threshold=0.5):
+    """Compute the SQ gate: rolling average hit-rate of topk predictions."""
+    ret_df = closes.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    pred_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    if len(pred_dates) < 2:
+        return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
+
+    hit_rates = {}
+    for i in range(1, len(pred_dates)):
+        day = pred_dates[i]
+        prev_day = pred_dates[i - 1]
+        try:
+            prev_scores = pred.loc[prev_day]
+        except KeyError:
+            continue
+        if isinstance(prev_scores, pd.DataFrame):
+            prev_scores = prev_scores.iloc[:, 0]
+        prev_scores = prev_scores.dropna().sort_values(ascending=False)
+        topk_syms = list(prev_scores.index[:gate_topk])
+        if day not in ret_df.index:
+            continue
+        today_ret = ret_df.loc[day]
+        topk_rets = today_ret.reindex(topk_syms).dropna()
+        if len(topk_rets) == 0:
+            continue
+        hit_rates[day] = (topk_rets > 0).sum() / len(topk_rets)
+
+    if not hit_rates:
+        return pd.Series(True, index=pd.DatetimeIndex(pred_dates))
+
+    hr_series = pd.Series(hit_rates).sort_index()
+    rolling_hr = hr_series.rolling(gate_lookback, min_periods=1).mean()
+    gate = rolling_hr >= gate_threshold
+    gate.iloc[:gate_lookback] = True
+    return gate
+
+
+def strategy_idealized(pred, closes, start, end, topk=10, risk_degree=1.0, cost_bps=0):
+    """Idealized script: fractional shares, symmetric cost, no gate."""
+    ret_df = closes.pct_change(fill_method=None)
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+    equity = 1_000_000.0
+    holdings = []
+    prev_week = None
+    daily_eq = []
+    for d in trade_dates:
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(equity)
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1])
+        if cur_week != prev_week:
+            new_holdings = list(day_scores.index[:topk])
+            if holdings and cost_bps > 0:
+                sold = set(holdings) - set(new_holdings)
+                bought = set(new_holdings) - set(holdings)
+                turnover = (len(sold) + len(bought)) / (2 * max(len(holdings), 1))
+                equity *= (1 - turnover * cost_bps / 10000)
+            holdings = new_holdings
+        ret_row = ret_df.loc[d] if d in ret_df.index else None
+        if ret_row is not None and holdings:
+            wts = np.array([risk_degree / len(holdings)] * len(holdings))
+            rets = ret_row.reindex(holdings).fillna(0).values
+            equity *= (1 + (wts * rets).sum())
+        daily_eq.append(equity)
+        prev_week = cur_week
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def strategy_workflow_exact(pred, closes, opens, start, end,
+                            topk=10, n_drop=1, risk_degree=0.95,
+                            use_gate=False, gate_series=None):
+    """Exact replication of WeeklyRebalanceDropoutStrategy execution mechanics.
+
+    - Sells first (all shares of dropped positions)
+    - Sizes buys as: cash * risk_degree / len(buy)
+    - Rounds to whole shares (floor)
+    - Asymmetric costs: open_cost on buys, close_cost on sells, $5 min per order
+    - Tracks position values for daily equity
+    """
+    ret_df = closes.pct_change(fill_method=None)
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    open_df = opens.copy()
+    open_df.index = pd.to_datetime(open_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx[(dt_idx >= start) & (dt_idx <= end)].unique())
+
+    cash = 1_000_000.0
+    positions = {}  # {sym: num_shares}
+    prev_week = None
+    daily_eq = []
+
+    for d in trade_dates:
+        # Skip non-trading days (pred may include weekends)
+        if d not in closes.index:
+            daily_eq.append(daily_eq[-1] if daily_eq else cash)
+            continue
+        try:
+            day_scores = pred.loc[d]
+        except KeyError:
+            daily_eq.append(daily_eq[-1] if daily_eq else cash)
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1])
+
+        if cur_week != prev_week:
+            # === REBALANCE DAY ===
+            # Check gate
+            if use_gate and gate_series is not None:
+                known = gate_series[gate_series.index <= d]
+                if len(known) and not bool(known.iloc[-1]):
+                    # gate closed: sell everything, go to cash
+                    for sym in list(positions.keys()):
+                        shares = positions[sym]
+                        if shares <= 0:
+                            continue
+                        sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
+                        if sell_price is None or pd.isna(sell_price):
+                            continue
+                        trade_val = shares * sell_price
+                        trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
+                        cash += trade_val - trade_cost
+                        positions[sym] = 0
+                    positions = {s: v for s, v in positions.items() if v > 0}
+                    daily_eq.append(cash)
+                    prev_week = cur_week
+                    continue
+
+            # TopkDropout selection (matching WeeklyRebalanceDropoutStrategy exactly)
+            current_syms = [s for s, v in positions.items() if v > 0]
+            last = pred.loc[d].reindex(current_syms).sort_values(ascending=False).index if current_syms else pd.Index([])
+            # buy candidates: top stocks NOT in current holdings, take n_drop + topk - len(last)
+            buy_cands = day_scores[~day_scores.index.isin(last)].sort_values(ascending=False).index
+            buy_list = list(buy_cands[:n_drop + topk - len(last)])
+            # comb = union of current holdings + buy candidates (actual strategy line 132)
+            comb = pred.loc[d].reindex(last.union(pd.Index(buy_list))).sort_values(ascending=False).index
+            # sell: items from current holdings that are in the bottom n_drop of comb
+            sell_list = list(last[last.isin(comb[-n_drop:])]) if n_drop > 0 and len(comb) >= n_drop else []
+
+            # --- SELL FIRST ---
+            for sym in sell_list:
+                if sym not in positions or positions[sym] <= 0:
+                    continue
+                shares = positions[sym]
+                sell_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
+                if sell_price is None or pd.isna(sell_price):
+                    continue
+                trade_val = shares * sell_price
+                trade_cost = max(trade_val * CLOSE_COST, MIN_COST) if trade_val > 0 else 0
+                cash += trade_val - trade_cost
+                positions[sym] = 0
+
+            # --- BUY ---
+            n_buy = len(buy_list)
+            if n_buy > 0:
+                buy_budget = cash * risk_degree / n_buy
+                for sym in buy_list:
+                    buy_price = closes.loc[d, sym] if d in closes.index and sym in closes.columns else None
+                    if buy_price is None or pd.isna(buy_price) or buy_price <= 0:
+                        continue
+                    shares_to_buy = int(buy_budget / buy_price)  # floor to whole shares
+                    if shares_to_buy <= 0:
+                        continue
+                    trade_val = shares_to_buy * buy_price
+                    trade_cost = max(trade_val * OPEN_COST, MIN_COST) if trade_val > 0 else 0
+                    total_cost = trade_val + trade_cost
+                    if total_cost > cash:
+                        shares_to_buy = int((cash - MIN_COST) / buy_price)
+                        if shares_to_buy <= 0:
+                            continue
+                        trade_val = shares_to_buy * buy_price
+                        trade_cost = max(trade_val * OPEN_COST, MIN_COST)
+                        total_cost = trade_val + trade_cost
+                    cash -= total_cost
+                    positions[sym] = positions.get(sym, 0) + shares_to_buy
+
+            positions = {s: v for s, v in positions.items() if v > 0}
+
+        # === DAILY EQUITY ===
+        eq = cash
+        if d in closes.index:
+            for sym, shares in positions.items():
+                if sym in closes.columns:
+                    px = closes.loc[d, sym]
+                    if not pd.isna(px):
+                        eq += shares * px
+        daily_eq.append(eq)
+        prev_week = cur_week
+
+    return pd.Series(daily_eq, index=trade_dates)
+
+
+def metrics(eq):
+    if len(eq) < 2:
+        return {"ann_ret": 0, "sharpe": 0, "maxDD": 0}
+    rets = eq.pct_change().dropna()
+    ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+    vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+    sharpe = ann_ret / vol if vol > 0 else 0
+    peak = eq.cummax()
+    dd = (eq - peak) / peak
+    return {"ann_ret": round(ann_ret, 4), "sharpe": round(sharpe, 4), "maxDD": round(float(dd.min()), 4)}
+
+
+def main():
+    OUT.mkdir(parents=True, exist_ok=True)
+    results = []
+    for w in WINDOWS:
+        print(f"\n=== {w['label']} ({w['start']} to {w['end']}) ===")
+        pred = load_pred(w["pred"])
+        closes = load_closes(w["start"], w["end"])
+        opens = load_opens(w["start"], w["end"])
+        print(f"  pred: {pred.index.get_level_values(0).min().date()} to {pred.index.get_level_values(0).max().date()}, "
+              f"{pred.index.get_level_values(1).nunique()} syms")
+        print(f"  close: {closes.index.min().date()} to {closes.index.max().date()}, {closes.shape[1]} syms")
+
+        row = {"year": w["label"]}
+
+        # A. Idealized: fractional shares, 10bp symmetric, no gate (diag v2 baseline)
+        eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=1.0, cost_bps=0)
+        m = metrics(eq); row["ideal_100_zc"] = m
+        print(f"  A. Ideal 100% zc:       ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
+
+        # B. Idealized: 95% invested, 10bp symmetric
+        eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=0)
+        m = metrics(eq); row["ideal_95_zc"] = m
+        print(f"  B. Ideal 95% zc:        ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
+
+        # C. Idealized: 95%, 10bp cost
+        eq = strategy_idealized(pred, closes, w["start"], w["end"], topk=10, risk_degree=0.95, cost_bps=10)
+        m = metrics(eq); row["ideal_95_10bp"] = m
+        print(f"  C. Ideal 95% 10bp:      ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
+
+        # D. Workflow-exact: whole shares, 5/15bp, $5 min, no gate
+        eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
+                                     topk=10, n_drop=1, risk_degree=0.95,
+                                     use_gate=False)
+        m = metrics(eq); row["wf_exact_95_nogate"] = m
+        print(f"  D. WF exact 95% nogate: ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}")
+
+        # E. Workflow-exact: whole shares, 5/15bp, $5 min, WITH SQ gate
+        gate = compute_gate(pred, closes, w["start"], w["end"],
+                           gate_topk=10, gate_lookback=5, gate_threshold=0.5)
+        gate_open_pct = gate.sum() / len(gate) if len(gate) > 0 else 1.0
+        eq = strategy_workflow_exact(pred, closes, opens, w["start"], w["end"],
+                                     topk=10, n_drop=1, risk_degree=0.95,
+                                     use_gate=True, gate_series=gate)
+        m = metrics(eq); row["wf_exact_95_gate"] = m
+        print(f"  E. WF exact 95% gate:   ann={m['ann_ret']:+.1%} sharpe={m['sharpe']:.2f} maxDD={m['maxDD']:.1%}  gate_open={gate_open_pct:.0%}")
+
+        # Gap analysis
+        ideal = row["ideal_95_zc"]["ann_ret"]
+        wf_nogate = row["wf_exact_95_nogate"]["ann_ret"]
+        wf_gate = row["wf_exact_95_gate"]["ann_ret"]
+        print(f"\n  Gap analysis:")
+        print(f"    Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, nogate): {ideal:+.1%} → {wf_nogate:+.1%} (gap: {wf_nogate - ideal:+.1%})")
+        print(f"    Ideal (fractional, zc) → WF exact (whole shares, 5/15bp, gate):  {ideal:+.1%} → {wf_gate:+.1%} (gap: {wf_gate - ideal:+.1%})")
+
+        results.append(row)
+
+    with open(OUT / "diagnosis_v3.json", "w") as f:
+        json.dump(results, f, indent=2, default=str)
+    print(f"\nSaved to {OUT / 'diagnosis_v3.json'}")
+
+    # Summary table
+    print("\n" + "=" * 80)
+    print("SUMMARY: Ideal vs Workflow-Exact")
+    print("=" * 80)
+    print(f"{'Year':<6} {'Ideal%zc':>10} {'WF nogate':>10} {'WF gate':>10} {'Gap(nogate)':>12} {'Gap(gate)':>12}")
+    for r in results:
+        y = r["year"]
+        i = r["ideal_95_zc"]["ann_ret"]
+        wn = r["wf_exact_95_nogate"]["ann_ret"]
+        wg = r["wf_exact_95_gate"]["ann_ret"]
+        print(f"{y:<6} {i:>+10.1%} {wn:>+10.1%} {wg:>+10.1%} {wn-i:>+12.1%} {wg-i:>+12.1%}")
+
+
+if __name__ == "__main__":
+    main()

book/scripts/regime_gate_bt.py

@@ -0,0 +1,400 @@
+"""Regime-gate walk-forward backtest grid.
+
+Precomputes regime gates (dispersion/vol/hmm × threshold grid) from lake bars,
+then runs a qlib TopkDropout backtest with each gate applied as a date-level
+trade overlay.  Uses the SAME pred.pkl from exp 52 (Config A 2026) so the
+model is trained only once.
+
+Usage:
+    cd /app && .venv/bin/python book/scripts/regime_gate_bt.py
+"""
+
+from __future__ import annotations
+
+import json
+import pathlib
+import sys
+import time
+
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+MARKET = "US"
+OUT_DIR = pathlib.Path("/app/experiments/book/data/regime_gate")
+
+# Walk-forward test windows with their pred.pkl sources
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
+]
+
+# Gate grid
+DISP_THRESHOLDS = [0.010, 0.015, 0.020, 0.025, 0.030]
+VOL_BANDS = [
+    (0.0, 0.15, "low_max15"),
+    (0.0, 0.20, "low_max20"),
+    (0.0, 0.25, "low_max25"),
+    (0.10, 0.25, "10_25"),
+    (0.10, 0.30, "10_30"),
+]
+HMM_THRESHOLDS = [0.3, 0.5, 0.7, 0.9]
+
+
+def load_pred(path: str) -> pd.Series:
+    """Load pred.pkl (MultiIndex: datetime × instrument → score), dates normalized to midnight."""
+    df = pd.read_pickle(path)
+    if isinstance(df, pd.DataFrame):
+        if "score" in df.columns:
+            s = df["score"]
+        else:
+            s = df.iloc[:, 0]
+    else:
+        s = df
+    # Normalize datetime level to date-only (midnight, no tz)
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def precompute_gates(close_df: pd.DataFrame) -> dict:
+    """Precompute all regime gate series from close prices."""
+    gates = {}
+
+    # --- dispersion gates ---
+    ret22 = close_df.pct_change(22)
+    cs_disp = ret22.std(axis=1)
+    for thr in DISP_THRESHOLDS:
+        g = cs_disp >= thr
+        g.iloc[:22] = True
+        gates[f"disp_{thr:.3f}"] = g
+
+    # --- vol gates ---
+    import numpy as np
+    log_ret = np.log(close_df / close_df.shift(1))
+    rv22 = log_ret.rolling(22).std() * (252 ** 0.5)
+    cs_vol = rv22.mean(axis=1)
+    for vlow, vhigh, tag in VOL_BANDS:
+        g = (cs_vol >= vlow) & (cs_vol <= vhigh)
+        g.iloc[:22] = True
+        gates[f"vol_{tag}"] = g
+
+    # --- HMM gates ---
+    hmm_root = pathlib.Path(LAKE_ROOT) / "features" / "market=US" / "timeframe=1d"
+    for thr in HMM_THRESHOLDS:
+        all_post = {}
+        for sym in close_df.columns:
+            for family in ("sp", "ta"):
+                fp = hmm_root / f"family={family}" / f"symbol={sym}.parquet"
+                if not fp.exists():
+                    continue
+                try:
+                    feat = pd.read_parquet(fp)
+                except Exception:
+                    continue
+                if "sp_hmm_p_regime1" not in feat.columns:
+                    continue
+                tcol = feat["t"] if "t" in feat.columns else feat["date"]
+                ts = pd.to_datetime(tcol)
+                s = pd.Series(feat["sp_hmm_p_regime1"].values, index=ts, name=sym)
+                s = s.dropna()
+                if len(s) > 0:
+                    all_post[sym] = s
+                break
+        if all_post:
+            post_df = pd.DataFrame(all_post)
+            cs_mean = post_df.mean(axis=1)
+            g = cs_mean >= thr
+        else:
+            g = pd.Series(True, index=close_df.index)
+        gates[f"hmm_{thr:.1f}"] = g
+
+    # Normalize all gate indices to date-only (no tz, no time)
+    for key in gates:
+        gates[key].index = pd.to_datetime(gates[key].index).normalize()
+
+    return gates
+
+
+def run_backtest_with_gate(
+    pred: pd.Series,
+    gate: pd.Series,
+    close_df: pd.DataFrame,
+    start: str,
+    end: str,
+    topk: int = 10,
+    n_drop: int = 1,
+) -> dict:
+    """Simulate TopkDropout with gate overlay, computing daily returns.
+
+    - On gate-open days: hold topk stocks (equal-weight), rebalance weekly
+    - On gate-closed days: liquidate to cash
+    - Tracks both gated and ungated (baseline) equity curves
+    """
+    # Ensure pred has MultiIndex (date, instrument)
+    if not isinstance(pred.index, pd.MultiIndex):
+        return {"error": "pred must have MultiIndex (date, instrument)"}
+
+    # Daily returns per symbol (close-to-close)
+    ret_df = close_df.pct_change()
+    # Normalize ret_df index to date-only for matching
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+
+    # Filter pred to window and get trade dates
+    dt_idx = pred.index.get_level_values(0)
+    window_mask = dt_idx >= pd.Timestamp(start)
+    window_mask &= dt_idx <= pd.Timestamp(end)
+    window_pred = pred.loc[window_mask]
+    if len(window_pred) == 0:
+        return {"error": "no pred data in window"}
+    trade_dates = sorted(dt_idx[window_mask].unique())
+
+    # Compute gate status per trade date
+    gate_open = {}
+    for d in trade_dates:
+        known = gate[gate.index <= d]
+        gate_open[d] = bool(known.iloc[-1]) if len(known) else True
+
+    n_total = len(trade_dates)
+    n_open = sum(1 for v in gate_open.values() if v)
+    n_closed = n_total - n_open
+
+    # Simulate: track current holdings — both base and gated use weekly rebalance
+    # Use yesterday's scores to pick today's holdings (no look-ahead)
+    holdings_base = []
+    holdings_gated = []
+    equity_gated = 1_000_000.0
+    equity_base = 1_000_000.0
+    prev_week = None
+    prev_scores = None  # yesterday's scores
+
+    daily_gated = []
+    daily_base = []
+
+    # Build a date → ret_df row map
+    ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
+
+    for i, d in enumerate(trade_dates):
+        # Get today's cross-sectional prediction
+        try:
+            day_scores = window_pred.loc[d]
+        except KeyError:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+
+        if isinstance(day_scores, pd.Series) and not isinstance(day_scores.index, pd.MultiIndex):
+            pass
+        elif isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        else:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        if len(day_scores) == 0:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+
+        ret_row = ret_by_date.get(d)
+        if ret_row is None:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = day_scores
+            continue
+
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
+        gate_val = gate_open.get(d, True)
+
+        # --- ungated baseline: weekly rebalance using yesterday's scores ---
+        if cur_week != prev_week or not holdings_base:
+            if prev_scores is not None:
+                holdings_base = list(prev_scores.index[:topk])
+        if holdings_base:
+            base_rets = ret_row.reindex(holdings_base).dropna()
+            if len(base_rets) > 0:
+                equity_base *= (1 + base_rets.mean())
+
+        # --- gated: weekly rebalance only when gate open, using yesterday's scores ---
+        if gate_val:
+            if cur_week != prev_week or not holdings_gated:
+                if prev_scores is not None:
+                    holdings_gated = list(prev_scores.index[:topk])
+            if holdings_gated:
+                hold_rets = ret_row.reindex(holdings_gated).dropna()
+                if len(hold_rets) > 0:
+                    equity_gated *= (1 + hold_rets.mean())
+        else:
+            holdings_gated = []
+
+        prev_week = cur_week
+        prev_scores = day_scores
+        daily_gated.append(equity_gated)
+        daily_base.append(equity_base)
+
+    # Compute metrics
+    g_series = pd.Series(daily_gated, index=trade_dates)
+    b_series = pd.Series(daily_base, index=trade_dates)
+
+    def _metrics(eq: pd.Series) -> dict:
+        if len(eq) < 2:
+            return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
+        rets = eq.pct_change().dropna()
+        ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+        vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+        sharpe = ann_ret / vol if vol > 0 else 0
+        peak = eq.cummax()
+        dd = (eq - peak) / peak
+        maxDD = float(dd.min())
+        return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
+
+    base_m = _metrics(b_series)
+    gated_m = _metrics(g_series)
+
+    return {
+        "trade_dates": n_total,
+        "gate_open_days": n_open,
+        "gate_closed_days": n_closed,
+        "trip_rate": round(n_closed / n_total, 4) if n_total else 0,
+        "base": base_m,
+        "gated": gated_m,
+    }
+
+
+def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
+    """Load daily close prices for all symbols in the universe."""
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
+    sp = cfg.lake_root / "symbols.parquet"
+    if sp.exists():
+        syms = pd.read_parquet(sp)
+        col = "symbol" if "symbol" in syms.columns else syms.columns[0]
+        symbols = sorted(syms[col].astype(str).str.upper().tolist())
+    else:
+        return pd.DataFrame()
+
+    closes = {}
+    for sym in symbols:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists():
+            continue
+        try:
+            df = pd.read_parquet(p)
+        except Exception:
+            continue
+        if not len(df):
+            continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        # Load a bit extra for warmup
+        warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup_start:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def main():
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+
+    # Load bars (with warmup) for the full panel
+    full_start = "2015-01-03"
+    full_end = "2026-08-19"
+    print("Loading lake bars for gate precomputation...")
+    close_df = load_bars_for_window(full_start, full_end)
+    print(f"  {close_df.shape[1]} symbols, {close_df.shape[0]} days")
+
+    print("Precomputing regime gates...")
+    gates = precompute_gates(close_df)
+    print(f"  {len(gates)} gate configs: {list(gates.keys())}")
+
+    results = []
+
+    for window in WINDOWS:
+        wl, ws, we = window["label"], window["start"], window["end"]
+        pred_path = window["pred"]
+        print(f"\n=== Window {wl} ({ws} to {we}) ===")
+
+        print(f"  Loading pred.pkl from {pred_path}...")
+        pred = load_pred(pred_path)
+        print(f"  pred shape: {pred.shape}")
+
+        for gate_name, gate_series in gates.items():
+            bt = run_backtest_with_gate(pred, gate_series, close_df, ws, we)
+            if "error" in bt:
+                print(f"  {gate_name}: {bt['error']}")
+                continue
+            row = {
+                "window": wl,
+                "gate": gate_name,
+                "start": ws,
+                "end": we,
+                "trade_dates": bt["trade_dates"],
+                "gate_open": bt["gate_open_days"],
+                "gate_closed": bt["gate_closed_days"],
+                "trip_rate": bt["trip_rate"],
+                "base_ann": bt["base"]["ann_return"],
+                "base_sharpe": bt["base"]["sharpe"],
+                "base_maxDD": bt["base"]["maxDD"],
+                "gated_ann": bt["gated"]["ann_return"],
+                "gated_sharpe": bt["gated"]["sharpe"],
+                "gated_maxDD": bt["gated"]["maxDD"],
+            }
+            results.append(row)
+            print(f"  {gate_name}: trip={bt['trip_rate']:.1%}, "
+                  f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
+                  f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f})")
+
+    # Save results
+    df = pd.DataFrame(results)
+    out_path = OUT_DIR / "regime_gate_trip_rates.csv"
+    df.to_csv(out_path, index=False)
+    print(f"\nSaved trip rates to {out_path}")
+
+    # Also save as JSON for the book
+    json_results = df.to_dict(orient="records")
+    with open(OUT_DIR / "regime_gate_trip_rates.json", "w") as f:
+        json.dump(json_results, f, indent=2, default=str)
+
+    # Print summary: trip rate differential (2026 vs bad years)
+    print("\n=== Trip Rate Summary (2026 vs bad years) ===")
+    for gate_name in gates.keys():
+        gdf = df[df["gate"] == gate_name]
+        r2026 = gdf[gdf["window"] == "2026"]["trip_rate"].values
+        r_bad = gdf[gdf["window"].isin(["2021", "2023", "2024"])]["trip_rate"].values
+        if len(r2026) and len(r_bad):
+            d = r2026[0] - np.mean(r_bad)
+            print(f"  {gate_name}: 2026 trip={r2026[0]:.1%}, bad-years avg={np.mean(r_bad):.1%}, diff={d:+.1%}")
+
+    print("\n=== Gated Return Summary (2026 vs bad years) ===")
+    for gate_name in gates.keys():
+        gdf = df[df["gate"] == gate_name]
+        r2026 = gdf[gdf["window"] == "2026"]
+        r_bad = gdf[gdf["window"].isin(["2021", "2023", "2024"])]
+        if len(r2026) and len(r_bad):
+            g26 = r2026["gated_ann"].values[0]
+            b26 = r2026["base_ann"].values[0]
+            g_bad = r_bad["gated_ann"].mean()
+            b_bad = r_bad["base_ann"].mean()
+            print(f"  {gate_name}: 2026 gated={g26:+.1%} (base={b26:+.1%}), "
+                  f"bad-years gated={g_bad:+.1%} (base={b_bad:+.1%})")
+
+
+if __name__ == "__main__":
+    main()

book/scripts/signal_quality_gate_bt.py

@@ -0,0 +1,293 @@
+"""Signal-quality gate walk-forward backtest.
+
+Gates trades based on whether the model's recent topk predictions were correct
+(hit rate). This is a retrospective gate — it measures prediction accuracy,
+not market state.
+
+Usage:
+    cd /app && .venv/bin/python book/scripts/signal_quality_gate_bt.py
+"""
+
+from __future__ import annotations
+
+import json
+import pathlib
+import sys
+import time
+
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+MARKET = "US"
+OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
+
+WINDOWS = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
+]
+
+# Signal-quality gate configs: (lookback_days, threshold, name)
+SIGNAL_GATE_CONFIGS = [
+    (5, 0.50, "hitrate_5d_0.50"),
+    (5, 0.60, "hitrate_5d_0.60"),
+    (5, 0.70, "hitrate_5d_0.70"),
+    (10, 0.50, "hitrate_10d_0.50"),
+    (10, 0.60, "hitrate_10d_0.60"),
+    (10, 0.70, "hitrate_10d_0.70"),
+    (20, 0.40, "hitrate_20d_0.40"),
+    (20, 0.50, "hitrate_20d_0.50"),
+    (20, 0.60, "hitrate_20d_0.60"),
+]
+
+
+def load_pred(path: str) -> pd.Series:
+    df = pd.read_pickle(path)
+    if isinstance(df, pd.DataFrame):
+        if "score" in df.columns:
+            s = df["score"]
+        else:
+            s = df.iloc[:, 0]
+    else:
+        s = df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays([new_dt, idx.get_level_values(1)], names=idx.names)
+    return s
+
+
+def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
+    sp = cfg.lake_root / "symbols.parquet"
+    if sp.exists():
+        syms = pd.read_parquet(sp)
+        col = "symbol" if "symbol" in syms.columns else syms.columns[0]
+        symbols = sorted(syms[col].astype(str).str.upper().tolist())
+    else:
+        return pd.DataFrame()
+    closes = {}
+    for sym in symbols:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists():
+            continue
+        try:
+            df = pd.read_parquet(p)
+        except Exception:
+            continue
+        if not len(df):
+            continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup_start:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def compute_hit_rate_series(
+    pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
+) -> pd.Series:
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx.unique())
+    hit_rates = {}
+    for i in range(1, len(trade_dates)):
+        prev_date = trade_dates[i - 1]
+        curr_date = trade_dates[i]
+        try:
+            prev_scores = pred.loc[prev_date]
+        except KeyError:
+            continue
+        if isinstance(prev_scores, pd.DataFrame):
+            prev_scores = prev_scores.iloc[:, 0]
+        prev_scores = prev_scores.dropna().sort_values(ascending=False)
+        topk_syms = list(prev_scores.index[:topk])
+        if curr_date not in ret_df.index:
+            continue
+        today_ret = ret_df.loc[curr_date]
+        topk_rets = today_ret.reindex(topk_syms).dropna()
+        if len(topk_rets) > 0:
+            hit_rate = (topk_rets > 0).mean()
+            hit_rates[curr_date] = hit_rate
+    hit_series = pd.Series(hit_rates)
+    if len(hit_series) == 0:
+        return hit_series
+    rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
+    return rolling_hr
+
+
+def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
+    if not isinstance(pred.index, pd.MultiIndex):
+        return {"error": "pred must have MultiIndex"}
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
+    window_pred = pred.loc[window_mask]
+    if len(window_pred) == 0:
+        return {"error": "no pred data in window"}
+    trade_dates = sorted(dt_idx[window_mask].unique())
+    gate_open = {}
+    for d in trade_dates:
+        known = hit_rate[hit_rate.index <= d]
+        if len(known) > 0 and not pd.isna(known.iloc[-1]):
+            gate_open[d] = bool(known.iloc[-1] >= threshold)
+        else:
+            gate_open[d] = True
+    n_total = len(trade_dates)
+    n_open = sum(1 for v in gate_open.values() if v)
+    n_closed = n_total - n_open
+    holdings_base = []
+    holdings_gated = []
+    equity_gated = 1_000_000.0
+    equity_base = 1_000_000.0
+    prev_week = None
+    prev_scores = None
+    daily_gated = []
+    daily_base = []
+    ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
+    for d in trade_dates:
+        try:
+            day_scores = window_pred.loc[d]
+        except KeyError:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        if len(day_scores) == 0:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        ret_row = ret_by_date.get(d)
+        if ret_row is None:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = day_scores
+            continue
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
+        gate_val = gate_open.get(d, True)
+        if cur_week != prev_week or not holdings_base:
+            if prev_scores is not None:
+                holdings_base = list(prev_scores.index[:topk])
+        if holdings_base:
+            base_rets = ret_row.reindex(holdings_base).dropna()
+            if len(base_rets) > 0:
+                equity_base *= (1 + base_rets.mean())
+        if gate_val:
+            if cur_week != prev_week or not holdings_gated:
+                if prev_scores is not None:
+                    holdings_gated = list(prev_scores.index[:topk])
+            if holdings_gated:
+                hold_rets = ret_row.reindex(holdings_gated).dropna()
+                if len(hold_rets) > 0:
+                    equity_gated *= (1 + hold_rets.mean())
+        else:
+            holdings_gated = []
+        prev_week = cur_week
+        prev_scores = day_scores
+        daily_gated.append(equity_gated)
+        daily_base.append(equity_base)
+    g_series = pd.Series(daily_gated, index=trade_dates)
+    b_series = pd.Series(daily_base, index=trade_dates)
+    def _metrics(eq):
+        if len(eq) < 2:
+            return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
+        rets = eq.pct_change().dropna()
+        ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+        vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+        sharpe = ann_ret / vol if vol > 0 else 0
+        peak = eq.cummax()
+        dd = (eq - peak) / peak
+        maxDD = float(dd.min())
+        return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
+    base_m = _metrics(b_series)
+    gated_m = _metrics(g_series)
+    return {
+        "trade_dates": n_total,
+        "gate_open_days": n_open,
+        "gate_closed_days": n_closed,
+        "trip_rate": round(n_closed / n_total, 4) if n_total else 0,
+        "base": base_m,
+        "gated": gated_m,
+    }
+
+
+def main():
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    full_start = "2015-01-03"
+    full_end = "2026-08-19"
+    print("Loading lake bars...")
+    close_df = load_bars_for_window(full_start, full_end)
+    print(f"  {close_df.shape[1]} symbols, {close_df.shape[0]} days")
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    results = []
+    for window in WINDOWS:
+        wl, ws, we = window["label"], window["start"], window["end"]
+        pred_path = window["pred"]
+        print(f"\n=== Window {wl} ({ws} to {we}) ===")
+        pred = load_pred(pred_path)
+        print(f"  pred shape: {pred.shape}")
+        hit_rates = {}
+        for lookback, _, name in SIGNAL_GATE_CONFIGS:
+            if lookback not in hit_rates:
+                hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
+                hit_rates[lookback] = hr
+                print(f"  lookback={lookback}: {len(hr)} days with hit rates")
+        for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
+            hr = hit_rates[lookback]
+            bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
+            if "error" in bt:
+                print(f"  {name}: {bt['error']}")
+                continue
+            row = {
+                "window": wl,
+                "gate": name,
+                "start": ws,
+                "end": we,
+                "trade_dates": bt["trade_dates"],
+                "gate_open": bt["gate_open_days"],
+                "gate_closed": bt["gate_closed_days"],
+                "trip_rate": bt["trip_rate"],
+                "base_ann": bt["base"]["ann_return"],
+                "base_sharpe": bt["base"]["sharpe"],
+                "base_maxDD": bt["base"]["maxDD"],
+                "gated_ann": bt["gated"]["ann_return"],
+                "gated_sharpe": bt["gated"]["sharpe"],
+                "gated_maxDD": bt["gated"]["maxDD"],
+            }
+            results.append(row)
+            print(f"  {name}: trip={bt['trip_rate']:.1%}, "
+                  f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
+                  f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f})")
+    df = pd.DataFrame(results)
+    out_path = OUT_DIR / "signal_quality_gate_results.csv"
+    df.to_csv(out_path, index=False)
+    with open(OUT_DIR / "signal_quality_gate_results.json", "w") as f:
+        json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
+    print(f"\nSaved to {out_path}")
+    print("\n=== Summary: Gated Return by Window ===")
+    for gate_name in df["gate"].unique():
+        gdf = df[df["gate"] == gate_name]
+        print(f"\n{gate_name}:")
+        for _, r in gdf.iterrows():
+            print(f"  {r['window']}: base={r['base_ann']:+.1%}, gated={r['gated_ann']:+.1%}, "
+                  f"trip={r['trip_rate']:.0%}, diff={r['gated_ann']-r['base_ann']:+.1%}pp")
+
+
+if __name__ == "__main__":
+    main()

book/scripts/signal_quality_gate_retrained.py

@@ -0,0 +1,343 @@
+"""Signal-quality gate walk-forward backtest — RE-TRAINED MODEL variant.
+
+Identical logic to the original scripted test (signal_quality_gate_bt.py),
+but uses pred.pkls from exp 62 (retrained LGBModel per year, same model config
+as the workflow test) instead of the reference exp 52/56 pred.pkls.
+
+This isolates whether the gate itself works when the model is the same,
+regardless of the backtest engine.
+
+Usage:
+    cd /app && .venv/bin/python book/scripts/signal_quality_gate_retrained.py
+"""
+
+from __future__ import annotations
+
+import json
+import pathlib
+
+import numpy as np
+import pandas as pd
+
+LAKE_ROOT = "/home/data/lake"
+MARKET = "US"
+OUT_DIR = pathlib.Path("/app/experiments/book/data/signal_quality_gate")
+
+# Retrained pred.pkls from exp 62 (on-the-fly gate test)
+WINDOWS_RETRAINED = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/62/3771f96eb1b74365aeae966af7aec5a3/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/c57c6a8370cc48619d7cdd2bd109b76a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/97cf5f282e6f4e699443e38d9bfb40fd/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/62/11b9b65ea4e14b3f8ce50d244da0412e/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/62/af3034e5910348a382f2ad1e1741f17c/artifacts/pred.pkl"},
+]
+
+# Original reference pred.pkls for head-to-head comparison
+WINDOWS_REFERENCE = [
+    {"label": "2026", "start": "2026-01-04", "end": "2026-08-19",
+     "pred": f"{LAKE_ROOT}/mlruns/52/9f98ea5c550a409f87b56a6cd8fee343/artifacts/pred.pkl"},
+    {"label": "2025", "start": "2025-01-02", "end": "2025-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/fe96741654df4780957a3a949999ae6a/artifacts/pred.pkl"},
+    {"label": "2024", "start": "2024-01-02", "end": "2024-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/52/71ed5bfa9984490f8bba8b222f7acc39/artifacts/pred.pkl"},
+    {"label": "2023", "start": "2023-01-03", "end": "2023-12-29",
+     "pred": f"{LAKE_ROOT}/mlruns/56/8ca46e554311444c9a42637a788226e8/artifacts/pred.pkl"},
+    {"label": "2021", "start": "2021-01-04", "end": "2021-12-31",
+     "pred": f"{LAKE_ROOT}/mlruns/56/4e0700ddab2a4e108b46efece7346ee3/artifacts/pred.pkl"},
+]
+
+SIGNAL_GATE_CONFIGS = [
+    (5, 0.50, "hitrate_5d_0.50"),
+    (5, 0.60, "hitrate_5d_0.60"),
+    (5, 0.70, "hitrate_5d_0.70"),
+    (10, 0.50, "hitrate_10d_0.50"),
+    (10, 0.60, "hitrate_10d_0.60"),
+    (10, 0.70, "hitrate_10d_0.70"),
+    (20, 0.40, "hitrate_20d_0.40"),
+    (20, 0.50, "hitrate_20d_0.50"),
+    (20, 0.60, "hitrate_20d_0.60"),
+]
+
+
+def load_pred(path: str) -> pd.Series:
+    df = pd.read_pickle(path)
+    if isinstance(df, pd.DataFrame):
+        if "score" in df.columns:
+            s = df["score"]
+        else:
+            s = df.iloc[:, 0]
+    else:
+        s = df
+    idx = s.index
+    new_dt = pd.to_datetime(idx.get_level_values(0)).normalize()
+    s.index = pd.MultiIndex.from_arrays(
+        [new_dt, idx.get_level_values(1)], names=idx.names
+    )
+    return s
+
+
+def load_bars_for_window(start: str, end: str) -> pd.DataFrame:
+    from tac_qlib.data.config import LakeConfig, resolve_lake_root
+    cfg = LakeConfig(resolve_lake_root(LAKE_ROOT), MARKET)
+    sp = cfg.lake_root / "symbols.parquet"
+    if sp.exists():
+        syms = pd.read_parquet(sp)
+        col = "symbol" if "symbol" in syms.columns else syms.columns[0]
+        symbols = sorted(syms[col].astype(str).str.upper().tolist())
+    else:
+        return pd.DataFrame()
+    closes = {}
+    for sym in symbols:
+        p = cfg.bar_path("1d", sym)
+        if not p.exists():
+            continue
+        try:
+            df = pd.read_parquet(p)
+        except Exception:
+            continue
+        if not len(df):
+            continue
+        tcol = df["t"] if "t" in df.columns else df["date"]
+        ts = pd.to_datetime(tcol)
+        df = df.assign(_t=ts).set_index("_t").sort_index()
+        warmup_start = pd.Timestamp(start) - pd.Timedelta(days=60)
+        df = df.loc[warmup_start:end]
+        if len(df) >= 22:
+            closes[sym] = df["c"]
+    return pd.DataFrame(closes)
+
+
+def compute_hit_rate_series(
+    pred: pd.Series, ret_df: pd.DataFrame, topk: int = 10, lookback: int = 10,
+) -> pd.Series:
+    dt_idx = pred.index.get_level_values(0)
+    trade_dates = sorted(dt_idx.unique())
+    hit_rates = {}
+    for i in range(1, len(trade_dates)):
+        prev_date = trade_dates[i - 1]
+        curr_date = trade_dates[i]
+        try:
+            prev_scores = pred.loc[prev_date]
+        except KeyError:
+            continue
+        if isinstance(prev_scores, pd.DataFrame):
+            prev_scores = prev_scores.iloc[:, 0]
+        prev_scores = prev_scores.dropna().sort_values(ascending=False)
+        topk_syms = list(prev_scores.index[:topk])
+        if curr_date not in ret_df.index:
+            continue
+        today_ret = ret_df.loc[curr_date]
+        topk_rets = today_ret.reindex(topk_syms).dropna()
+        if len(topk_rets) > 0:
+            hit_rate = (topk_rets > 0).mean()
+            hit_rates[curr_date] = hit_rate
+    hit_series = pd.Series(hit_rates)
+    if len(hit_series) == 0:
+        return hit_series
+    rolling_hr = hit_series.rolling(lookback, min_periods=max(1, lookback // 2)).mean()
+    return rolling_hr
+
+
+def run_backtest(pred, hit_rate, close_df, start, end, topk=10, threshold=0.5):
+    if not isinstance(pred.index, pd.MultiIndex):
+        return {"error": "pred must have MultiIndex"}
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+    dt_idx = pred.index.get_level_values(0)
+    window_mask = (dt_idx >= pd.Timestamp(start)) & (dt_idx <= pd.Timestamp(end))
+    window_pred = pred.loc[window_mask]
+    if len(window_pred) == 0:
+        return {"error": "no pred data in window"}
+    trade_dates = sorted(dt_idx[window_mask].unique())
+    gate_open = {}
+    for d in trade_dates:
+        known = hit_rate[hit_rate.index <= d]
+        if len(known) > 0 and not pd.isna(known.iloc[-1]):
+            gate_open[d] = bool(known.iloc[-1] >= threshold)
+        else:
+            gate_open[d] = True
+    n_total = len(trade_dates)
+    n_open = sum(1 for v in gate_open.values() if v)
+    n_closed = n_total - n_open
+    holdings_base = []
+    holdings_gated = []
+    equity_gated = 1_000_000.0
+    equity_base = 1_000_000.0
+    prev_week = None
+    prev_scores = None
+    daily_gated = []
+    daily_base = []
+    ret_by_date = {rd: ret_df.loc[rd] for rd in ret_df.index}
+    for d in trade_dates:
+        try:
+            day_scores = window_pred.loc[d]
+        except KeyError:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        if isinstance(day_scores, pd.DataFrame):
+            day_scores = day_scores.iloc[:, 0]
+        day_scores = day_scores.dropna().sort_values(ascending=False)
+        if len(day_scores) == 0:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = None
+            continue
+        ret_row = ret_by_date.get(d)
+        if ret_row is None:
+            daily_gated.append(equity_gated)
+            daily_base.append(equity_base)
+            prev_scores = day_scores
+            continue
+        cur_week = (d.isocalendar()[0], d.isocalendar()[1]) if hasattr(d, 'isocalendar') else None
+        gate_val = gate_open.get(d, True)
+        if cur_week != prev_week or not holdings_base:
+            if prev_scores is not None:
+                holdings_base = list(prev_scores.index[:topk])
+        if holdings_base:
+            base_rets = ret_row.reindex(holdings_base).dropna()
+            if len(base_rets) > 0:
+                equity_base *= (1 + base_rets.mean())
+        if gate_val:
+            if cur_week != prev_week or not holdings_gated:
+                if prev_scores is not None:
+                    holdings_gated = list(prev_scores.index[:topk])
+            if holdings_gated:
+                hold_rets = ret_row.reindex(holdings_gated).dropna()
+                if len(hold_rets) > 0:
+                    equity_gated *= (1 + hold_rets.mean())
+        else:
+            holdings_gated = []
+        prev_week = cur_week
+        prev_scores = day_scores
+        daily_gated.append(equity_gated)
+        daily_base.append(equity_base)
+    g_series = pd.Series(daily_gated, index=trade_dates)
+    b_series = pd.Series(daily_base, index=trade_dates)
+
+    def _metrics(eq):
+        if len(eq) < 2:
+            return {"ann_return": 0, "sharpe": 0, "maxDD": 0}
+        rets = eq.pct_change().dropna()
+        ann_ret = float((eq.iloc[-1] / eq.iloc[0]) ** (252 / max(len(eq), 1)) - 1)
+        vol = float(rets.std() * (252 ** 0.5)) if len(rets) > 1 else 0
+        sharpe = ann_ret / vol if vol > 0 else 0
+        peak = eq.cummax()
+        dd = (eq - peak) / peak
+        maxDD = float(dd.min())
+        return {"ann_return": round(ann_ret, 6), "sharpe": round(sharpe, 4), "maxDD": round(maxDD, 6)}
+
+    base_m = _metrics(b_series)
+    gated_m = _metrics(g_series)
+    return {
+        "trade_dates": n_total,
+        "gate_open_days": n_open,
+        "gate_closed_days": n_closed,
+        "trip_rate": round(n_closed / n_total, 4) if n_total else 0,
+        "base": base_m,
+        "gated": gated_m,
+    }
+
+
+def run_set(windows, close_df, tag):
+    results = []
+    for window in windows:
+        wl, ws, we = window["label"], window["start"], window["end"]
+        pred_path = window["pred"]
+        print(f"\n=== [{tag}] Window {wl} ({ws} to {we}) ===")
+        pred = load_pred(pred_path)
+        print(f"  pred shape: {pred.shape}, date range: {pred.index.get_level_values(0).min()} .. {pred.index.get_level_values(0).max()}")
+        hit_rates = {}
+        for lookback, _, name in SIGNAL_GATE_CONFIGS:
+            if lookback not in hit_rates:
+                hr = compute_hit_rate_series(pred, ret_df, topk=10, lookback=lookback)
+                hit_rates[lookback] = hr
+                print(f"  lookback={lookback}: {len(hr)} days with hit rates")
+        for lookback, threshold, name in SIGNAL_GATE_CONFIGS:
+            hr = hit_rates[lookback]
+            bt = run_backtest(pred, hr, close_df, ws, we, topk=10, threshold=threshold)
+            if "error" in bt:
+                print(f"  {name}: {bt['error']}")
+                continue
+            row = {
+                "source": tag,
+                "window": wl,
+                "gate": name,
+                "start": ws,
+                "end": we,
+                "trade_dates": bt["trade_dates"],
+                "gate_open": bt["gate_open_days"],
+                "gate_closed": bt["gate_closed_days"],
+                "trip_rate": bt["trip_rate"],
+                "base_ann": bt["base"]["ann_return"],
+                "base_sharpe": bt["base"]["sharpe"],
+                "base_maxDD": bt["base"]["maxDD"],
+                "gated_ann": bt["gated"]["ann_return"],
+                "gated_sharpe": bt["gated"]["sharpe"],
+                "gated_maxDD": bt["gated"]["maxDD"],
+            }
+            results.append(row)
+            diff = bt["gated"]["ann_return"] - bt["base"]["ann_return"]
+            print(f"  {name}: trip={bt['trip_rate']:.1%}, "
+                  f"base={bt['base']['ann_return']:+.1%} (Sharpe {bt['base']['sharpe']:.2f}), "
+                  f"gated={bt['gated']['ann_return']:+.1%} (Sharpe {bt['gated']['sharpe']:.2f}), "
+                  f"diff={diff:+.1%}pp")
+    return results
+
+
+def main():
+    OUT_DIR.mkdir(parents=True, exist_ok=True)
+    full_start = "2015-01-03"
+    full_end = "2026-08-19"
+    print("Loading lake bars...")
+    close_df = load_bars_for_window(full_start, full_end)
+    print(f"  {close_df.shape[1]} symbols, {close_df.shape[0]} days")
+    global ret_df
+    ret_df = close_df.pct_change()
+    ret_df.index = pd.to_datetime(ret_df.index).normalize()
+
+    print("\n" + "=" * 70)
+    print("RUN A: Retrained model pred.pkls (exp 62)")
+    print("=" * 70)
+    results_retrained = run_set(WINDOWS_RETRAINED, close_df, "retrained")
+
+    print("\n" + "=" * 70)
+    print("RUN B: Reference pred.pkls (exp 52/56)")
+    print("=" * 70)
+    results_reference = run_set(WINDOWS_REFERENCE, close_df, "reference")
+
+    all_results = results_retrained + results_reference
+    df = pd.DataFrame(all_results)
+
+    # Save combined results
+    out_path = OUT_DIR / "signal_quality_gate_retrained.csv"
+    df.to_csv(out_path, index=False)
+    with open(OUT_DIR / "signal_quality_gate_retrained.json", "w") as f:
+        json.dump(df.to_dict(orient="records"), f, indent=2, default=str)
+    print(f"\nSaved to {out_path}")
+
+    # Head-to-head comparison table
+    print("\n" + "=" * 70)
+    print("HEAD-TO-HEAD: Retrained vs Reference (hitrate_5d_0.50)")
+    print("=" * 70)
+    print(f"{'Year':>6} | {'Ref Base':>10} {'Ref Gated':>10} {'Ref Diff':>10} | {'Ret Base':>10} {'Ret Gated':>10} {'Ret Diff':>10}")
+    print("-" * 85)
+    for year in ["2021", "2023", "2024", "2025", "2026"]:
+        ref = df[(df["source"] == "reference") & (df["window"] == year) & (df["gate"] == "hitrate_5d_0.50")]
+        ret = df[(df["source"] == "retrained") & (df["window"] == year) & (df["gate"] == "hitrate_5d_0.50")]
+        if len(ref) > 0 and len(ret) > 0:
+            rb = ref.iloc[0]["base_ann"]
+            rg = ref.iloc[0]["gated_ann"]
+            tb = ret.iloc[0]["base_ann"]
+            tg = ret.iloc[0]["gated_ann"]
+            print(f"{year:>6} | {rb:>+9.1%} {rg:>+9.1%} {rg-rb:>+9.1%} | {tb:>+9.1%} {tg:>+9.1%} {tg-tb:>+9.1%}")
+
+
+if __name__ == "__main__":
+    main()

book/strategies/signal_quality_gate.py

@@ -0,0 +1,94 @@
+"""Signal-quality gate strategy: gate trades based on hit-rate of topk predictions.
+
+Unlike the regime gate (which asks 'is the market calm?'), the signal-quality
+gate asks 'are my predictions accurate?' and works across ALL years.
+"""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+from qlib.contrib.strategy.signal_strategy import TopkDropoutStrategy
+
+
+class SignalQualityGateStrategy(TopkDropoutStrategy):
+    """TopkDropout with signal-quality gate overlay.
+
+    The gate computes the rolling hit rate of the model's topk picks:
+    - For each day, check if yesterday's topk had positive returns
+    - Compute rolling hit rate over lookback days
+    - If hit rate >= threshold, trade; otherwise, go to cash
+
+    Parameters
+    ----------
+    signal_quality_gate_lookback : int
+        Rolling window for hit rate computation (default: 5)
+    signal_quality_gate_threshold : float
+        Hit rate threshold to keep trading (default: 0.5)
+    signal_quality_gate_topk : int
+        Number of top picks to track for hit rate (default: 10)
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.sg_lookback = kwargs.pop("signal_quality_gate_lookback", 5)
+        self.sg_threshold = kwargs.pop("signal_quality_gate_threshold", 0.5)
+        self.sg_topk = kwargs.pop("signal_quality_gate_topk", 10)
+        super().__init__(*args, **kwargs)
+        self._hit_rates = {}
+        self._trade_dates = []
+
+    def get_kick_out_day_list(self, phase, **kwargs):
+        """Override to compute hit rates and determine gate-open days."""
+        # Get the standard trade dates from parent
+        trade_dates = super().get_kick_out_day_list(phase, **kwargs)
+        if trade_dates is None:
+            return trade_dates
+
+        # We'll compute hit rates in the backtest loop
+        # For now, return all dates (gate applied in get_gated_sp)
+        self._trade_dates = trade_dates
+        return trade_dates
+
+    def compute_hit_rate(self, date_idx: int, pred_df: pd.DataFrame, ret_df: pd.DataFrame) -> float:
+        """Compute rolling hit rate up to date_idx."""
+        if date_idx < 1:
+            return 1.0  # default open when no history
+
+        hit_count = 0
+        total_count = 0
+
+        for i in range(max(1, date_idx - self.sg_lookback), date_idx):
+            if i < 1:
+                continue
+            # Get yesterday's topk
+            prev_date = self._trade_dates[i - 1] if i - 1 < len(self._trade_dates) else None
+            curr_date = self._trade_dates[i] if i < len(self._trade_dates) else None
+            if prev_date is None or curr_date is None:
+                continue
+
+            try:
+                prev_scores = pred_df.loc[prev_date]
+            except KeyError:
+                continue
+            if isinstance(prev_scores, pd.DataFrame):
+                prev_scores = prev_scores.iloc[:, 0]
+            prev_scores = prev_scores.dropna().sort_values(ascending=False)
+            topk_syms = list(prev_scores.index[:self.sg_topk])
+
+            # Get today's returns
+            if curr_date not in ret_df.index:
+                continue
+            today_ret = ret_df.loc[curr_date]
+            topk_rets = today_ret.reindex(topk_syms).dropna()
+
+            if len(topk_rets) > 0:
+                hit_count += (topk_rets > 0).sum()
+                total_count += len(topk_rets)
+
+        if total_count == 0:
+            return 1.0  # default open
+        return hit_count / total_count
+
+    def is_gate_open(self, date_idx: int, pred_df: pd.DataFrame, ret_df: pd.DataFrame) -> bool:
+        """Check if the signal-quality gate is open for this date."""
+        hr = self.compute_hit_rate(date_idx, pred_df, ret_df)
+        return hr >= self.sg_threshold

book/workflows/sq_gate_weekly/sq_gate_wk_2021.yaml

@@ -0,0 +1,119 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-2021"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2021-12-31
+                    fit_start_time: 2017-01-03
+                    fit_end_time: 2020-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2017-01-03, 2020-12-31]
+                valid: [2021-01-04, 2021-01-04]
+                test: [2021-01-04, 2021-12-31]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2017-01-03"
+                          gate_end: "2021-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2021-01-04
+                      end_time: 2021-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly/sq_gate_wk_2023.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-2023"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2023-12-29
+                    fit_start_time: 2019-01-02
+                    fit_end_time: 2022-12-30
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2019-01-02, 2022-12-30]
+                valid: [2023-01-03, 2023-01-03]
+                test: [2023-01-03, 2023-12-29]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2019-01-02"
+                          gate_end: "2023-12-29"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2023-01-03
+                      end_time: 2023-12-29
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly/sq_gate_wk_2024.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-2024"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2024-12-31
+                    fit_start_time: 2020-01-02
+                    fit_end_time: 2023-12-29
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2020-01-02, 2023-12-29]
+                valid: [2024-01-02, 2024-01-02]
+                test: [2024-01-02, 2024-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2020-01-02"
+                          gate_end: "2024-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2024-01-02
+                      end_time: 2024-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly/sq_gate_wk_2025.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-2025"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2025-12-31
+                    fit_start_time: 2021-01-04
+                    fit_end_time: 2024-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2021-01-04, 2024-12-31]
+                valid: [2025-01-02, 2025-01-02]
+                test: [2025-01-02, 2025-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2021-01-04"
+                          gate_end: "2025-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2025-01-02
+                      end_time: 2025-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly/sq_gate_wk_2026.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-2026"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2026-08-19"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly_zerocost/sq_gate_wk_2021.yaml

@@ -0,0 +1,119 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-zc-2021"
+
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2021-12-31
+                    fit_start_time: 2017-01-03
+                    fit_end_time: 2020-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2017-01-03, fit_end_time: 2020-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2017-01-03, 2020-12-31]
+                valid: [2021-01-04, 2021-01-04]
+                test: [2021-01-04, 2021-12-31]
+
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2017-01-03"
+                          gate_end: "2021-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2021-01-04
+                      end_time: 2021-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0
+                          close_cost: 0.0
+                          min_cost: 0.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly_zerocost/sq_gate_wk_2023.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-zc-2023"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2023-12-29
+                    fit_start_time: 2019-01-02
+                    fit_end_time: 2022-12-30
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2019-01-02, fit_end_time: 2022-12-30 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2019-01-02, 2022-12-30]
+                valid: [2023-01-03, 2023-01-03]
+                test: [2023-01-03, 2023-12-29]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2019-01-02"
+                          gate_end: "2023-12-29"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2023-01-03
+                      end_time: 2023-12-29
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0
+                          close_cost: 0.0
+                          min_cost: 0.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly_zerocost/sq_gate_wk_2024.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-zc-2024"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2024-12-31
+                    fit_start_time: 2020-01-02
+                    fit_end_time: 2023-12-29
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2020-01-02, fit_end_time: 2023-12-29 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2020-01-02, 2023-12-29]
+                valid: [2024-01-02, 2024-01-02]
+                test: [2024-01-02, 2024-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2020-01-02"
+                          gate_end: "2024-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2024-01-02
+                      end_time: 2024-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0
+                          close_cost: 0.0
+                          min_cost: 0.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly_zerocost/sq_gate_wk_2025.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-zc-2025"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2025-12-31
+                    fit_start_time: 2021-01-04
+                    fit_end_time: 2024-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2021-01-04, fit_end_time: 2024-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2021-01-04, 2024-12-31]
+                valid: [2025-01-02, 2025-01-02]
+                test: [2025-01-02, 2025-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2021-01-04"
+                          gate_end: "2025-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2025-01-02
+                      end_time: 2025-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0
+                          close_cost: 0.0
+                          min_cost: 0.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_weekly_zerocost/sq_gate_wk_2026.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-zc-2026"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2026-08-19"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0
+                          close_cost: 0.0
+                          min_cost: 0.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_wk_v3/sq_gate_wk_2021.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-v3"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2021-12-31
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2020-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2020-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2020-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2020-12-31]
+                valid: [2021-01-04, 2021-01-04]
+                test: [2021-01-04, 2021-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2021-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2021-01-04
+                      end_time: 2021-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_wk_v3/sq_gate_wk_2023.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-v3"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2023-12-29
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2022-12-30
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2022-12-30 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2022-12-30 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2022-12-30]
+                valid: [2023-01-03, 2023-01-03]
+                test: [2023-01-03, 2023-12-29]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2023-12-29"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2023-01-03
+                      end_time: 2023-12-29
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_wk_v3/sq_gate_wk_2024.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-v3"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2024-12-31
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2023-12-29
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2023-12-29 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2023-12-29 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2023-12-29]
+                valid: [2024-01-02, 2024-01-02]
+                test: [2024-01-02, 2024-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2024-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2024-01-02
+                      end_time: 2024-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_wk_v3/sq_gate_wk_2025.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-v3"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2025-12-31
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2024-12-31
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2024-12-31 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2024-12-31 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2024-12-31]
+                valid: [2025-01-02, 2025-01-02]
+                test: [2025-01-02, 2025-12-31]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2025-12-31"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2025-01-02
+                      end_time: 2025-12-31
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d

book/workflows/sq_gate_wk_v3/sq_gate_wk_2026.yaml

@@ -0,0 +1,115 @@
+{%- set LAKE = TAC_LAKE_DIR %}
+{%- set UNIVERSE = "SPY,QQQ,DIA,IWM,MDY,VTI,VOO,VEA,VWO,VT,EFA,EEM,TLT,IEF,SHY,AGG,BND,LQD,HYG,JNK,EMB,GLD,SLV,USO,UNG,DBA,DBC,XLK,XLF,XLE,XLV,XLI,XLY,XLP,XLU,XLB,XLRE,ARKK,SMH,SOXX,IBB,XBI,ITA,XAR,ICLN,TAN,FDN,IGV,ESPO,REM" %}
+{%- set SP_FIELDS = "sp_ret,sp_jump_ratio,sp_jump_flag,sp_jump_tail,sp_max_move,sp_rv1,sp_rv5,sp_rv22,sp_vol_ratio_5_22,sp_vol_ratio_1_22,sp_trend_slope_5,sp_trend_slope_20,sp_trend_slope_60,sp_logp,sp_hurst_exponent,sp_sig_level1_lead,sp_sig_level1_lag,sp_sig_level2_lead_lag,sp_sig_level2_lag_lead" %}
+qlib_init:
+    provider_uri: "{{ LAKE }}"
+    region: us
+    expression_cache: null
+    dataset_cache: null
+    calendar_provider:
+        class: tac_qlib.data.providers.LakeCalendarProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    instrument_provider:
+        class: tac_qlib.data.providers.LakeInstrumentProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US, markets: {} }
+    feature_provider:
+        class: tac_qlib.data.providers.LakeFeatureProvider
+        kwargs: { lake_root: "{{ LAKE }}", market: US }
+    exp_manager:
+        class: MLflowExpManager
+        module_path: qlib.workflow.expm
+        kwargs:
+            uri: "sqlite:///{{ LAKE }}/mlruns.db"
+            default_exp_name: "tac-rd-sq-gate-wk-v3"
+task:
+    model:
+        class: RankICEnsembleLGBModel
+        module_path: tac_qlib.contrib.model.rank_ensemble
+        kwargs:
+            loss: mse
+            learning_rate: 0.02
+            num_leaves: 31
+            n_estimators: 3000
+            num_boost_round: 3000
+            early_stopping_rounds: 200
+            min_data_in_leaf: 20
+            lambda_l2: 0.5
+            colsample_bytree: 0.8
+            subsample: 0.8
+            subsample_freq: 1
+            reg_alpha: 0.1
+            reg_lambda: 1.0
+            seeds: "42,7,2026,99,123"
+            parallel: 5
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: TACHandler
+                module_path: tac_qlib.contrib.data.handler
+                kwargs:
+                    instruments: "{{ UNIVERSE }}"
+                    start_time: 2015-01-03
+                    end_time: 2026-08-10
+                    fit_start_time: 2016-01-04
+                    fit_end_time: 2025-09-01
+                    freq: day
+                    lake_root: "{{ LAKE }}"
+                    market: US
+                    label: "Ref($close,-6)/Ref($close,-1)-1"
+                    feature_fields: "$open,$high,$low,$close,$vwap,$volume,{{ SP_FIELDS }}"
+                    infer_processors:
+                        - class: DropAllNaN
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: ProcessInf
+                          kwargs: {}
+                        - class: CSRankNorm
+                          kwargs: {}
+                        - class: ZScoreNorm
+                          kwargs: { fit_start_time: 2016-01-04, fit_end_time: 2025-09-01 }
+                        - class: Fillna
+                          kwargs: {}
+            segments:
+                train: [2016-01-04, 2025-09-01]
+                valid: [2025-09-03, 2026-01-03]
+                test: [2026-01-04, 2026-08-10]
+    record:
+        - class: SignalRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: {}
+        - class: SigAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs: { ana_long_short: true, ann_scaler: 252 }
+        - class: PortAnaRecord
+          module_path: qlib.workflow.record_temp
+          kwargs:
+              config:
+                  strategy:
+                      class: WeeklyRebalanceSignalQualityGateStrategy
+                      module_path: tac_qlib.contrib.strategy.weekly_sq_gate
+                      kwargs:
+                          signal: "<PRED>"
+                          lake_root: "{{ LAKE }}"
+                          gate_topk: 10
+                          gate_lookback: 5
+                          gate_threshold: 0.5
+                          gate_start: "2016-01-04"
+                          gate_end: "2026-08-19"
+                          topk: 10
+                          n_drop: 1
+                          only_tradable: true
+                          risk_degree: 0.95
+                  backtest:
+                      start_time: 2026-01-04
+                      end_time: 2026-08-10
+                      account: 1000000
+                      benchmark: SPY
+                      exchange_kwargs:
+                          codes: "{{ UNIVERSE }}"
+                          deal_price: $close
+                          freq: day
+                          open_cost: 0.0005
+                          close_cost: 0.0015
+                          min_cost: 5.0
+              risk_analysis_freq: 1d