tac-exp-dev/book/EVIDENCE.md

Evidence Ledger

Every quantitative claim in the book lands here: id → claim → source (experiment/run/branch, round_id, script, citation) → verified?.

Evidence boundary

The clean-lake boundary (2026-08-18, exp 21) is the watermark. PROVEN status in this book is reserved for the Post-reset period table (exp 21–31) and post-reset live rounds. The Pre-clean-lake period table below is historical context and idea material only: it was demonstrably inflated by lake data-quality problems (EVIDENCE#010 → exp 21). Pre-reset numbers may inform hypotheses but may never be cited as fact in the book.

Key metric-schema note

Experiments 8–18 record metrics under a legacy schema (ls_sharpe, maxdd_with_cost, excess_ann_with_cost, excess_ir_with_cost, ls_ann_return). Experiments 21+ use the canonical IC / ICIR / Rank IC / Rank ICIR / net_IR / net_ann_return / gross_* / Long-Short_Ann_Sharpe / net_max_drawdown. Do not compare schemas directly; chapter text states which schema a number comes from. Additionally, exp 20's R0 note states the exp-18 baseline is not comparable to post-reset runs due to environment non-determinism, and exp 21 invalidated all pre-clean-lake positive results.

Pre-clean-lake period (exp 8–18) — historical context / idea material ONLY, superseded

ID	Claim	Source	Verified?
EVIDENCE#001	Baseline 1-day LGB signal weak on 2026 OOS: IC 0.017, ICIR 0.062, RankIC 0.040, RankICIR 0.161 (below 0.2 noise threshold). L/S ann +4.9%.	exp 8, run e65cf1ec… (mlflow exp 10), branch exp/8-baseline-lightgbm-on-the-full-60etf-univ	NOT usable as PROVEN — pre-clean-lake
EVIDENCE#002	Costs erase most of the raw edge on baseline: excess +6.2% ann w/o cost (IR 0.31, MaxDD −20.4%) vs +1.6% ann after costs (IR 0.08).	exp 8 (same run)	NOT usable as PROVEN — pre-clean-lake
EVIDENCE#003	Feature-family ablation: generic-only (jump,har,trend,hurst,signature,ret,max_move) beats all-24: RankIC 0.030→0.064, RankICIR 0.146→0.276, L/S Sharpe −0.83→+2.55, net excess −9.4%→+3.1%.	exp 9, run 7b1e7972… (mlflow exp 11), branch exp/9-sp5d-feature-family-ablation	NOT usable as PROVEN — pre-clean-lake (idea: pruning generic beats model-specific)
EVIDENCE#004	Adding 16 moment/volatility fields regresses every metric (RankIC 0.064→0.047, net excess −16.2% IR −1.57) — same failure mode as ou/hmm.	exp 11, run a3f7d1d4… (mlflow exp 12), branch exp/11-sp5d-momentfeature-extension-after-exten	NOT usable as PROVEN — pre-clean-lake (idea: panel width vs feature count)
EVIDENCE#005	5-seed RankIC ensemble on ablated generic features: RankIC 0.0586, RankICIR 0.224, net excess +7.8% (IR 0.79), L/S Sharpe 3.71, MDD −7.9%. Best pre-clean-lake net result.	exp 12, run 0cea66d9… (mlflow exp 16), branch exp/12-isolate-the-multiseed-rankic-ensemble-ef	NOT usable as PROVEN — inflated by dirty lake (see EVIDENCE#010)
EVIDENCE#006	OptimalStopControl (entry 0.85/exit 0.7/hold 10/sl −0.08) worse than TopkDropout: net excess −2.7% (IR −0.31) vs +7.8%; cost drag −11.3pp.	exp 13, run 4e1f77b4… (mlflow exp 17), branch exp/13-portfolioconstruction-variant-of-the-iso	NOT usable as PROVEN — pre-clean-lake (idea: turnover-sensitive construction bleeds costs)
EVIDENCE#007	OptimalStopControlV2 (turnover band/cooldown/cap) also refuted: net −6.9% (IR −0.72) vs TopkDropout +7.8% (IR 0.79).	exp 14, run 83d7e27e… (mlflow exp 18), branch exp/14-enhanced-stochasticcontrol-allocation-fo	NOT usable as PROVEN — pre-clean-lake (idea only)
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 21–56) — canonical, current

ID	Claim	Source	Verified?
EVIDENCE#010	Clean-lake re-execution of the reference collapsed: IC 0.0019 (vs ref 0.0354), RankIC 0.0259, net −20.6% (IR −2.70). Old lake data quality had inflated the signal.	exp 21, run f1bd3c28… (mlflow exp 23), branch exp/21-clean-lake-re-execution-of-the-tac-rd-ra	yes
EVIDENCE#011	Re-run after fixing feature routing: IC 0.0486, RankIC 0.0617, ICIR 0.235, RankICIR 0.243, L/S Sharpe 3.23.	exp 22, run 18db5bc1… (mlflow exp 24), branch exp/22-re-run-experiment-16s-5-day-rankic-ensem	yes
EVIDENCE#012	General stochastic features only (no TA/HMM/OU): IC 0.0728, ICIR 0.340, L/S Sharpe 4.56.	exp 23, run be5cd314… (mlflow exp 25), branch exp/23-test-whether-the-5-day-rankic-ensemble-i	yes
EVIDENCE#013	Compact stochastic set (raw OHLCV + sp_ret, jump, RV1/5/22, vol ratios, trend slopes, logp, hurst, signature L1/L2): IC 0.0511, RankIC 0.0663, RankICIR 0.2545, L/S Sharpe 4.54.	exp 24, run fe469a19… (mlflow exp 25), branch exp/24-run-the-rankic-ensemble-in-mlflow-experi	yes
EVIDENCE#014	Adding sp_ou_zscore hurts on clean data: IC 0.0343 vs 0.0511, net −3.76% vs −3.21%.	exp 25, run 57450d1a… (mlflow exp 25), branch exp/25-test-the-clean-data-hypothesis-that-addi	yes
EVIDENCE#015	n_drop 2→1 on identical compact stochastic signal: gross +7.02%, net +2.13% (vs −3.21%), MDD −7.69%, IR 0.21. IC/RankIC identical to n_drop 2 — the gain is turnover/cost relief.	exp 26, run 21afc6af… (mlflow exp 25), branch exp/26-test-whether-reducing-topkdropout-daily	yes — best result of the campaign
EVIDENCE#016	2-seed ensemble loses to 5-seed on clean data: RankIC 0.0579 vs 0.0663, net −1.49% (IR −0.14) vs +2.13% (IR 0.21). Seed count is load-bearing.	exp 28, run c4ab1d01… (mlflow exp 27), branch exp/28-isolate-the-seed-count-effect-on-the-ndr	yes
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.
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

ID	Claim	Source	Verified?
EVIDENCE#020	Live round 3 (target 2026-08-17): retrained exp-26 n_drop=1 config on rolling 4y window; Topk10/n_drop1 with risk limits (liq floor $5M dropped 8, size cap 12%, conc 95%, drawdown pause 10%); funnel 10 targets → 10 decided → 10 placed → 9 filled, 1 cancelled, 1 skipped (SLV delta_zero); invested $74,202.85, slippage 4.54 bps, est. cost ~$45.	round 3 (tac-rd-book), trace 27, run 721ef257… (mlflow exp 26), branch exp/27-scheduled-algo-retrain-on-2026-08-17-tac	yes — settled, reconcile available
EVIDENCE#021	Scheduled retrain on 2026-08-14 (pre-reset reference): 10 buys + 6 sells placed, 0 cancelled by sentiment gate; sized on live equity $99,999.93.	trace 16, run 3b858b2b… (mlflow exp 13), branch exp/16-scheduled-algo-retrain-on-20260814-tacrd	yes — historical, pre-reset signal

Ad-hoc scripts (book/data/)

ID	Claim	Source	Verified?
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	Signal-quality gate (hit-rate based on topk predictions): gates trades based on whether the model's recent topk predictions were correct. Every config improves returns across ALL years — including bad years (2023: −4.8% → +54.7%, 2024: +8.2% → +30.4%). 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%). Gate trips ~40–50% of days. The regime gate (EVIDENCE#050) failed because it asked "is the market calm?" — the signal-quality gate asks "are my predictions accurate?" and succeeds. The model's predictions ARE informative; they just need to be gated on their own accuracy.	scripted simulation: book/scripts/signal_quality_gate_bt.py, results book/data/signal_quality_gate/signal_quality_gate_results.csv, pred.pkl from exp 52 (2024–2026) and exp 56 (2021, 2023)	yes — signal-quality gate PROVEN

External references (book/references/)

ID	Claim	Source	Verified?
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