finish experiment 45 (exp/45-q13-weekly-rebalance-construction-q07s-1)
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# QUEUE-19 — Martingale / variance-ratio study close-out (no qrun)
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**Status:** QUEUED · **Priority:** P2 · **Effort:** ad-hoc script under `book/data/`
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## Hypothesis (settle)
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Assets are submartingales long-horizon / mean-reverting short-horizon
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(`VR < 1` at 5–20d). CLAIMS.md marks this HYPOTHESIS (chat-derived martingale
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study; exp 19 was opened but never closed). It is a market-structure claim, not a
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trading claim — settle it with a clean-lake script, then close exp 19 or open a
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scripted EVIDENCE entry.
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## Method (persist everything under `book/data/evidence/q19-vr/`)
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1. Load the 50-ETF panel 1d bars from the lake for 2015-01-01..2026-08-19.
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2. Compute the Lo–MacKinlay variance ratio at horizons 5 / 10 / 20d per symbol,
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with heteroskedasticity-robust z-stats.
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3. Report: per-horizon VR distribution, fraction of symbols with VR < 1 and the
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z-significance, pooled drift vs daily variance (submartingale check).
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4. Cross-check the pooled `sp_trend_slope_5` regression beta claim (β ≈ −0.53,
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t ≈ −24) on the clean lake.
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5. Write `VR_stats.csv` + a one-page summary into the evidence dir.
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## Acceptance
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- VR < 1 at 5–20d for a material fraction of the panel with |z| > 2 → supports
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the mean-reversion HYPOTHESIS; else mark REFUTED or REFERENCED.
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- The result updates CLAIMS.md's "Assets are submartingales…" row and closes the
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exp-19 open thread.
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# QUEUE-20 — Effective independent names in the 50-ETF book (no qrun)
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**Status:** QUEUED · **Priority:** P2 · **Effort:** ad-hoc script under `book/data/`
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## Hypothesis (settle)
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The 50-ETF book has only ~4 effective independent names (CLAIMS.md HYPOTHESIS,
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chat-derived eigenvalue analysis, pre-reset). This is a concentration/diversification
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claim with direct sizing relevance; verify it on the clean lake.
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## Method (persist everything under `book/data/evidence/q20-effective-names/`)
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1. Load the 50-ETF panel 1d returns from the lake for the test window 2026-01-04..2026-08-10.
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2. Standardize returns; compute the correlation matrix and its eigendecomposition.
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3. Count eigenvalues above the Marchenko–Pastur bound (N=50, T≈150) and report the
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cumulative-variance share of the top k components.
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4. Effective-rank measures: participation ratio `(Σλ)² / Σλ²` and cumulative 80%
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variance count.
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5. Write `eigenanalysis.csv` + a one-page summary.
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## Acceptance
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- If effective rank ≈ 4 (top-4 explain ~80%+ variance), the concentration claim is
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PROVEN and feeds chapter 08 sizing guidance (why topk 10→20 adds no breadth).
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- If effective rank is much larger, mark the claim REFUTED.
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