book: fold Q-campaign (exp 33-43) evidence into ledger, claims, and chapters

- EVIDENCE#022-032: Q01-Q11 runs (2 PASS / 9 FAIL) with run_ids and branches
- CLAIMS: promote M2 Sharpe-drift to PROVEN (Q01), refute Kelly (Q06), risk-limit-as-alpha (Q08), standalone reversal (Q11); add label-horizon + weekly-rebalance + long-short-turnover claims
- README: TOC + claim inventories for ch 04/05/07/08/09/10/12 updated to the Q-campaign
- new chapters 04 (prune), 05 (ensembles), 07 (isolation), 08 (construction), 09 (cost/turnover), 10 (risk limits & gates), 12 (synthesis); ch 00/02/03 updated
- Q08 calibration evidence persisted under book/data/evidence/q08-risklimit/

book/CLAIMS.md

@@ -13,12 +13,16 @@ The running scoreboard of every quantitative claim in the book. Updated per chap
 | Adding OU mean-reversion (sp_ou_zscore) hurts on clean data | PROVEN (refuted direction) | EVIDENCE#014 → exp 25 |
 | 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 |
 | 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) | — |
+| 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) |
 | 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 |
 
 ## Model
@@ -28,18 +32,27 @@ The running scoreboard of every quantitative claim in the book. Updated per chap
 | Seed count is load-bearing: 2 seeds < 5 seeds on clean data | PROVEN | EVIDENCE#016 → exp 28 |
 | 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 |
+| $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 | HYPOTHESIS (idea: pre-clean-lake exp 18) | EVIDENCE#008 → exp 18 |
+| 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) |
-| Entry/risk gates (momentum, HMM) are byte-identical no-ops | HYPOTHESIS (idea: pre-clean-lake exp 20) | EVIDENCE#009 → exp 20 |
+| Risk-limit gates are a safety net, not an alpha lever | PROVEN | EVIDENCE#029 → exp 40 (Q08) |
-| 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 |
+| 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 | EVIDENCE#028 → exp 39 (Q07) |
+| 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 |
 
 ## Data & reproducibility
 
@@ -63,7 +76,9 @@ The running scoreboard of every quantitative claim in the book. Updated per chap
 
 ## Open questions (settled by further experiments)
 
-- exp 30 M2 Sharpe-drift: reproduce on a second window before promoting past HYPOTHESIS.
+- exp 30 M2 Sharpe-drift: DONE — reproduced on the compact set by Q01 (exp 33), promoted to PROVEN.
-- exp 15 Kelly sizing: re-run on the clean lake.
+- 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 (exp 26 lineage).
+- 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.
 - Out-of-universe validation: non-ETF universe for the compact stochastic feature set.
+- 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.

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 21–43) — canonical, current
 
 | ID | Claim | Source | Verified? |
 |----|-------|--------|-----------|
@@ -38,6 +38,17 @@ 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) |
 
 ## Live execution trail
 

book/README.md

@@ -26,15 +26,15 @@ A quant-desk reader should be able to act on this book: replicate a signal pipel
 | 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 | On a 50-name panel, generic beats model-specific |
+| 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 | Averaging raises ICIR; seed count is load-bearing |
+| 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 | Most additions fail; the discipline is the value |
+| 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 optimal stop | drafting | exp 13, 14, 15 | Turnover-sensitive construction bleeds the edge |
+| 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 | n_drop 2→1: hold the dropped name, keep the edge |
+| 09 | The cost/turnover frontier | drafting | exp 26, 39, 41 | Cut turnover before adding signal; weekly rebalance is the proven lever |
-| 10 | Risk limits that work | drafting | exp 18, 20 | Liquidity floor > concentration caps; gates are no-ops when signal is the bottleneck |
+| 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 | Live execution and reconciliation | drafting | exp 27, round 3 | 4.54 bps slippage realized; funnel 10→10→10→9 |
-| 12 | Synthesis: how proved truth compounds | drafting | all | The scoreboard of what moved performance and why |
+| 12 | Synthesis: how proved truth compounds | drafting | all, exp 33–43 | Cost relief > signal; the Q-campaign scoreboard |
 
 Status legend: `drafting` → `in-review` → `done`.
 
@@ -78,13 +78,15 @@ Each chapter opens with its claims. The inventory below is the working contract:
 | 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) |
 
 ### 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 |
 
 ### 06 — Clean-lake reset
@@ -97,31 +99,39 @@ Each chapter opens with its claims. The inventory below is the working contract:
 ### 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) |
 
 ### 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) |
 
 ### 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) |
 
 ### 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) |
 
 ### 11 — Live execution and reconciliation
 | Claim | Expected status |
@@ -133,8 +143,9 @@ Each chapter opens with its claims. The inventory below is the working contract:
 ### 12 — 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 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) were as valuable as wins | `REFERENCED` + `PROVEN` (they stopped wrong directions) |
+| 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 |
 | Generalizability of the 50-ETF panel results is an open question | `HYPOTHESIS` — TODO(evidence-needed: out-of-panel universe) |
 
 ## Repository layout
@@ -153,7 +164,7 @@ book/
 
 ## Open questions for the desk
 
-- `TODO(evidence-needed: reproduction of exp 30 M2 Sharpe-drift run on a second window)`
+- `TODO(evidence-needed: weekly-rebalance result (exp 39) reproduced on a second window before promotion to a live round)`
-- `TODO(evidence-needed: exp 15 Kelly sizing — run never finished; re-run on the clean lake)`
+- `TODO(evidence-needed: long-horizon label (10d/22d) paired with a low-turnover construction — the signal edge is proven, the cost kills daily churn)`
 - `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: exp 18 risk-limit spec reconciliation — post-reset A/B (exp 40) shows it is a safety net, not alpha)`

book/chapters/00-intro.md

@@ -32,7 +32,7 @@ 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 06 tells that story in full.
 

book/chapters/02-research-loop.md

@@ -12,7 +12,7 @@ The metrics vocabulary of ch. 01 is only useful if the numbers can be trusted. T
 
 ## Why traceability is the methodology
 
-TradeAC ran 31+ experiments. Without the branch+run+notes discipline, the desk could not have told which improvements were real. Two concrete failures make the case:
+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.
@@ -21,11 +21,11 @@ The pre-reset experiments (exp 8–18) are therefore treated as **idea material,
 
 ## 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. Because each changed one thing against the same reference, each verdict is attributable `(PROVEN → exp 28–31)`. Where isolation was lost (exp 12 pre-reset re-validations, exp 30's mixed metrics), the book marks the claim HYPOTHESIS.
+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). 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.
+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
 

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

@@ -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. 07) 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/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)`
+
+## 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)`.
+
+## 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/12-synthesis.md

@@ -0,0 +1,50 @@
+# Chapter 12 — Synthesis: How Proved Truth Compounds
+
+Status: drafting. Claim inventory: see `README.md` ch. 12.
+
+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. 00/11).
+
+## Open questions
+
+- `TODO(evidence-needed: reproduce exp 39 weekly rebalance on a second window, then a live round)`
+- `TODO(evidence-needed: long-horizon label at weekly cadence — the proven signal edge with the proven low-turnover construction)`
+- `TODO(evidence-needed: out-of-universe (non-ETF) validation of the compact stochastic set)`
+- `TODO(evidence-needed: realized-cost reconciliation of the weekly construction against the 5bp/15bp/$5 model once live)`
+
+## 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/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"
+        ]
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+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
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+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $1,000,000": {
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+        "information_ratio": 1.508747982736451,
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+      },
+      "applied": {
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+          "ESPO"
+        ]
+      }
+    },
+    "liquidity $2,500,000": {
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+          "DBC",
+          "ESPO",
+          "REM",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $5,000,000": {
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+      "applied": {
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+          "FDN",
+          "REM",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    },
+    "liquidity $10,000,000": {
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+      },
+      "applied": {
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+          "ESPO",
+          "FDN",
+          "ICLN",
+          "ITA",
+          "MDY",
+          "REM",
+          "SHY",
+          "TAN",
+          "UNG",
+          "XAR"
+        ]
+      }
+    }
+  },
+  "candidate": {
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+    "size_cap_pct": 0.12,
+    "concentration_cap_pct": 0.95,
+    "drawdown_pause_pct": 0.1
+  }
+}