book: ch08 portfolio construction dropout vs optimal stop — exp 13/14/26, round 3, evidence #006/#015/#020

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+# Chapter 08 — Portfolio Construction: Dropout vs Optimal Stop
+
+Status: drafting. Claim inventory: see `README.md` ch. 08.
+
+This chapter compares the two portfolio constructions the campaign actually ran — TopkDropout (the rank-based, turnover-conscious book that became the reference and the live book) and stochastic-control OptimalStopControl (entry/exit/stop parametrized allocation). The honest status is that the comparison is **pre-reset idea material**: both constructions were tested on the dirty lake and the alternates were never re-run on the clean lake. What is PROVEN on the clean lake is that the reference book is TopkDropout and that it executes (ch. 11); what the alternates would do on clean data is unknown.
+
+## The two constructions
+
+- **TopkDropout** (qlib TopkDropoutStrategy): each day, rank the cross-sectional scores, apply the dropout rule, and hold the selected top-k names equally weighted. The campaign's `n_drop` parameter controls which names the strategy refuses to chase, and with it the book's turnover (ch. 09).
+- **OptimalStopControl** (stochastic control): allocate toward a target portfolio with entry/exit thresholds, holding-period and stop-loss parameters. The campaign tried the baseline (entry 0.85 / exit 0.7 / hold 10 / stop −0.08) and a V2 with turnover bands, cooldown and a cap.
+
+## The pre-reset comparison (idea material)
+
+On the pre-reset lake, TopkDropout beat both stochastic-control variants: OptimalStopControl net excess −2.7% (IR −0.31) versus TopkDropout +7.8% `(idea → exp 13, pre-clean-lake; EVIDENCE#006)`, and the V2 also refuted (net −6.9%, IR −0.72) `(idea → exp 14, pre-clean-lake; EVIDENCE#007)`. The attributed mechanism was **turnover**: the stop-control constructions churned the book and bled ~11.3pp of cost drag `(idea → exp 13, EVIDENCE#006)`. That mechanism is plausible — it is the same cost drag that proved binding on the clean lake (exp 26, ch. 09) — but the numbers themselves are not usable (dirty lake, EVIDENCE#010).
+
+`TODO(evidence-needed: OptimalStopControl vs TopkDropout A/B on the exp-26 reference and its n_drop=1 book — the clean-lake rerun of this comparison)`
+
+## What is PROVEN on the clean lake
+
+- The reference book is TopkDropout with `n_drop=1`, equal weight × risk_degree, and it is the campaign's best net result `(PROVEN → exp 26, EVIDENCE#015)`.
+- The same construction is the live book of round 3: 10 targets, 9 fills, realized slippage 4.54 bps `(PROVEN → round 3, EVIDENCE#020)`.
+- Construction is not a substitute for signal or cost work: the n_drop change moved net return by ~5.3pp with *identical* signal metrics `(PROVEN → exp 26, EVIDENCE#015)` — construction is where the cost edge is won or lost, and cost is the binding constraint (ch. 09).
+
+## Sizing
+
+Sizing in the campaign is equal weight × `risk_degree` (0.95) — a fixed fraction of account per name, floored to whole shares at execution `(PROVEN → the sizing used in exp 26 and round 3; tac-rd-book intents)`.
+
+- Fractional-Kelly sizing (exp 15) was never verified — the run never finished `(HYPOTHESIS; TODO(evidence-needed: exp 15 Kelly re-run on the clean lake))`.
+- The hypothesis that equal-weight × risk_degree throws away edge-magnitude information (a Kelly-style rule would size by score spread) is untested `(HYPOTHESIS → chat-ideas.md)`.
+
+## Practice note
+
+The book's working rule: prefer the construction that minimizes turnover at a fixed topk (TopkDropout with controlled `n_drop` over parametrized stop-control), because cost is the binding constraint on this signal. That rule is a hypothesis until the clean-lake A/B lands.
+
+## Open questions
+
+- `TODO(evidence-needed: OptimalStopControl vs TopkDropout on clean data)`
+- `TODO(evidence-needed: Kelly-style sizing vs equal-weight × risk_degree on the exp-26 book)`
+- `TODO(evidence-needed: lower topk vs higher topk on the clean-lake reference — concentration vs diversification)`