queue: pre-registered experiment backlog to prove better-trading-performance hypotheses

Mined from book/ on the 'book' branch (HEAD 436692a). 11 queued runs,
each = hypothesis + one-variable change vs the exp-26 reference + acceptance
metric, per the ch.02 isolation/falsification discipline.

- workflows/: 5 runnable config-only YAMLs (Q01 M2 repro, Q02 seed10, Q03 topk20,
  Q04 label10d, Q05 label22d) byte-derived from the exp-26 reference
- designs/: 6 design docs needing custom strategy modules or tool-only A/B
  (Q06 Kelly, Q07 weekly rebalance, Q08 risk-limit A/B, Q09 long-short,
   Q10 HMM overlay, Q11 standalone reversal)

queue/designs/q11_standalone_reversal.md

@@ -0,0 +1,32 @@
+# QUEUE-11 — Standalone 5-day reversal signal net of costs (unisolated)
+
+**Status:** QUEUED · **Priority:** P2 · **Effort:** dataset study + backtest
+
+## Hypothesis (prove)
+5-day momentum strongly reverses on this panel (pooled regression:
+`sp_trend_slope_5` β = −0.53, t = −24; VR < 1 at 5–20d for ~32/72 assets —
+chat-derived, pre-clean-lake idea material). The reversal has never been tested
+as a **standalone tradable strategy net of costs**. If it clears the 20bp
+round-trip cost, it is an independent alpha source that can be blended with (or
+replace) the model book.
+Source: `book/ch01` "Timeline" + `chat-ideas.md`
+(`TODO(evidence-needed: standalone 5d-reversal strategy net of costs)`).
+
+## Change vs exp-26 reference
+- This is NOT a model-construction variant — it isolates a SINGLE-FEATURE
+  signal: a model trained on `sp_trend_slope_5` (plus raw OHLCV) alone, or a
+  mechanical reversal book (rank by −`sp_trend_slope_5`, buy the most-reverted
+  topk), backtested net of costs over the exp-26 window.
+- Control: exp-26 compact reference on the same window.
+
+## Acceptance
+- Standalone reversal `net_ann_return > 0` (clears 20bp round-trip) — proves
+  the claim "reversal is tradable net of costs". Secondary: excess vs the
+  model book is the blend decision for a future round.
+
+## Execution prerequisites
+1. `rd_train`/workflow with `feature_fields = $open,$high,$low,$close,$vwap,$volume,sp_trend_slope_5`
+   (single feature) OR a mechanical rank backtest via `rd_backtest` on a
+   hand-built pred (pred = −rank(sp_trend_slope_5)).
+2. Trace + run + record as a standalone study (dataset-study status, not
+   necessarily a traced model experiment).