Files
book-tac/book/CLAIMS.md
T

69 lines
5.8 KiB
Markdown
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
# CLAIMS.md — Proven vs Hypothesis Matrix
The running scoreboard of every quantitative claim in the book. Updated per chapter after HITL review. Status codes: `PROVEN` (reproduced from a recorded run **on the clean lake** / reconciled post-reset round), `HYPOTHESIS` (plausible, tested once or never, or pre-clean-lake / chat-derived idea), `REFUTED` (tested on clean data and contradicted), `REFERENCED` (external citation).
**Boundary rule:** only claims traceable to exp 21+ or post-reset live rounds may be `PROVEN`. Pre-clean-lake experiments (exp 8–18) and opencode chat transcripts are idea sources — their claims are `HYPOTHESIS` at best and are marked `(idea: pre-clean-lake)`.
## Signal & features
| Claim | Status | Evidence |
|-------|--------|----------|
| General stochastic features (no TA/HMM/OU) have highest ICIR 0.340 on clean data | PROVEN | EVIDENCE#012 → exp 23 |
| Compact stochastic set is the clean-lake reference (RankIC 0.0663, RankICIR 0.2545) | PROVEN | EVIDENCE#013 → exp 24 |
| Adding OU mean-reversion (sp_ou_zscore) hurts on clean data | PROVEN (refuted direction) | EVIDENCE#014 → exp 25 |
| Multi-horizon momentum (M1) degrades the reference | PROVEN (refuted direction) | EVIDENCE#017 → exp 29 |
| GARCH(1,1) vol-regime features add no signal | PROVEN (refuted direction) | EVIDENCE#019 → exp 31 |
| Risk-adjusted 22d Sharpe drift (M2) improves portfolio metrics | HYPOTHESIS (one clean-lake run, unreproduced) | EVIDENCE#018 → exp 30 |
| Dropping model-specific feature families (ou, hmm) improves the rank signal | HYPOTHESIS (idea: pre-clean-lake, exp 9) | EVIDENCE#003 → exp 9 |
| Adding moment/volatility families regresses the signal | HYPOTHESIS (idea: pre-clean-lake, exp 11) | EVIDENCE#004 → exp 11 |
| Baseline 1-day LGB signal is weak / costs erase most of the edge | HYPOTHESIS (idea: pre-clean-lake, exp 8) | EVIDENCE#001/002 → exp 8 |
| More features ≠ better signal on a small (50-name) cross-section | HYPOTHESIS (3+ supporting runs, panel-specific) | EVIDENCE#003/004/014/017/019 |
| Mean reversion (OU z-score, trend-slope reversal) is the stable single-feature edge | HYPOTHESIS (chat-derived clean-data study; see book/references/chat-ideas.md) | — |
| Assets are submartingales long-horizon / mean-reverting short-horizon (VR<1 at 5–20d) | HYPOTHESIS (chat-derived martingale study, exp 19 never closed) | book/data/chat_mining/martingale-study.txt |
## Model
| Claim | Status | Evidence |
|-------|--------|----------|
| Seed count is load-bearing: 2 seeds < 5 seeds on clean data | PROVEN | EVIDENCE#016 → exp 28 |
| n_drop 2→1 flips net excess (−3.21% → +2.13%) with identical signal metrics | PROVEN | EVIDENCE#015 → exp 26 |
| Cost drag is the binding constraint, not signal quality | PROVEN (clean data) | EVIDENCE#015 → exp 26 (IC/RankIC identical across n_drop) |
| 5-seed RankIC ensemble raises performance vs single model on ablated set | HYPOTHESIS (pre-clean-lake exp 12 idea; re-validated directionally by exp 22–24 but not as a clean A/B) | EVIDENCE#005 |
| Fractional-Kelly sizing beats equal-weight top-k net of costs | HYPOTHESIS (exp 15 never finished) | run never completed |
## Portfolio construction & risk
| Claim | Status | Evidence |
|-------|--------|----------|
| TopkDropout beats stochastic-control OptimalStopControl on the ensemble signal | HYPOTHESIS (idea: pre-clean-lake exp 13/14; not re-tested post-reset) | EVIDENCE#006/007 |
| $5M liquidity floor improves IR and cuts drawdown | HYPOTHESIS (idea: pre-clean-lake exp 18; not comparable post-reset) | EVIDENCE#008 → exp 18 |
| Size/concentration caps hurt by cutting deployed capital | HYPOTHESIS (idea: pre-clean-lake exp 18) | EVIDENCE#008 → exp 18 |
| Entry/risk gates (momentum, HMM) are byte-identical no-ops | HYPOTHESIS (idea: pre-clean-lake exp 20) | EVIDENCE#009 → exp 20 |
| Signal quality is the bottleneck, not the execution/risk layer | HYPOTHESIS (idea: pre-clean-lake exp 20; round-3 live is consistent but short) | EVIDENCE#009 → exp 20 |
## Data & reproducibility
| Claim | Status | Evidence |
|-------|--------|----------|
| The pre-reset reference signal did not reproduce on a rebuilt lake (IC 0.035→0.002) | PROVEN | EVIDENCE#010 → exp 21 |
| Old-lake data quality inflated the signal and backtest | PROVEN | EVIDENCE#010 → exp 21 |
| Signal work must be re-validated after any data rebuild | PROVEN (exp 21) / HYPOTHESIS (generality) | EVIDENCE#010 |
| Silent NaN-drop (feature-provider path mismatch, stale coverage, mid-experiment regeneration) is a first-order pipeline failure class | HYPOTHESIS (chat-documented failure modes; partially re-validated by exp 22 fix) | book/data/chat_mining/*.txt + EVIDENCE#010/011 |
| Pre-reset experiment baselines are not comparable to post-reset runs | PROVEN | EVIDENCE#009/010 (exp 20 R0 note, exp 21) |
## Live execution
| Claim | Status | Evidence |
|-------|--------|----------|
| Live funnel held: 10 targets → 10 decided → 10 placed → 9 filled | PROVEN | EVIDENCE#020 → round 3 |
| Realized slippage ≈ 4.54 bps, est. cost ≈ $45, turnover 0.74 | PROVEN | EVIDENCE#020 → round 3 metrics |
| Execution claims trace to round_id + reconcile, not backtest | PROVEN (methodology, round 3 settled) | EVIDENCE#020 |
| 50-ETF panel results generalize to other universes | HYPOTHESIS — TODO(evidence-needed) | — |
| Effective independent names in the 50-ETF book is small (≈4) | HYPOTHESIS (chat-derived eigenvalue analysis, pre-reset) | book/data/chat_mining/exp-polluted-lake.txt |
## Open questions (settled by further experiments)
- exp 30 M2 Sharpe-drift: reproduce on a second window before promoting past HYPOTHESIS.
- exp 15 Kelly sizing: re-run on the clean lake.
- exp 18 risk-limit spec: re-validate $5M liquidity floor on the post-reset reference signal (exp 26 lineage).
- Out-of-universe validation: non-ETF universe for the compact stochastic feature set.