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zhaoli c0eb65efa7 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)

2026-08-19 15:30:46 +00:00

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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

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)).
Trace + run + record as a standalone study (dataset-study status, not necessarily a traced model experiment).

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