iteration 6 · 2026-07-22 · P2 · laptop-drives-bigblack
The honest-significance instrument: autocorrelated data has fewer independent points than it looks, so count them properly (HAC effective-n), then correct for many tries (Benjamini-Yekutieli FDR). It grounds on a family of 7 real features — and reproduces SEAL's independent measurement to the decimal.
readonly=2. Compute wall ~4 s, RSS ~400 MB. Return is genuinely archetype-d: excess kurtosis 37.7, vol-clustering ACF(r²) 0.66.870,000 bars sounds like a lot of evidence — but consecutive bars move together, so they're worth far fewer independent observations. This instrument counts the real number (HAC), and it finds the real data is worth only about 35% of its face value. Ignoring that (the naive count) would inflate a feature's t-statistic by up to 2.34× — manufacturing confidence out of thin air. It then corrects for testing many features at once using the arbitrary-dependence FDR rule (Benjamini-Yekutieli), which is needed because the features move together.
| Gate | Result | |
|---|---|---|
| Effective-n recovery | N_eff_HAC/N = 0.96 (iid shuffle) → 0.35 (real, deflates) | PASS |
| FDR control (HAC + BY) | complete-null rejection 0.043 / 0.057 / 0.067 (white/block/circular) ≤ q | PASS |
| Necessity (deflation) | real-aligned naive-SE understatement median 1.55; vwap 2.34 (= SEAL) | PASS |
| Power (known-positive) | real look-ahead detected; N_min 3,000 | PASS |
| Known-duplicate | exact clone → identical IC & p-value | PASS |
| Envelope (U-shape) | even dependence marginal IC ~0 → FNR 1 (blind, route to #11/#18) | PASS |
GROUNDED — the second realness-axis instrument.
Next iteration → #6 SFI single-feature OOS (usefulness / the per-feature return object): train on one feature over CPCV, does it beat a coin toss out-of-sample?