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

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- 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/
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book/chapters/03-baseline-cost-reality.md
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@@ -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) |