ProEA Lab · Research

Studies you can re-run, not screenshots you have to trust.

Every study on this page ships its dataset frozen in the repository, its analysis code committed with fixed seeds, and one command that reproduces every published number. Two of the three audit our own products — including the parts that failed.

An independent adversarial reviewer re-executes each study before publication; corrections are credited in the articles themselves. Cite anything here with a link — and if you find an error, email support@pulltrade.app and we'll log the correction publicly.

Study 1 · Volume profile vs its folklore

Does yesterday's POC attract price more than a random level the same distance away? Does the “80% rule” hold?

Data:
GC=F (COMEX gold futures) 5m bars with real exchange volume, 50 full CME sessions (2026-04-29 → 2026-07-10), frozen in-repo
Sample:
50 session pairs; per-pair distance-band random-level controls (K=1,000)
Control:
Distance-band random levels [0.8d, 1.2d], real-level ±$2 excluded — redesigned after adversarial review broke the first null
Limitation:
One symbol, one 50-session window; bar-resolution volume approximation

Finding: POC touch rate 69.4% vs control 71.5% — the magnet is folklore; the “80% rule” failed all three principled readings; one narrow POC-respect effect graded “suggestive, unproven”

For technical readers — the one-command re-run

node --experimental-strip-types scripts/research/volume-profile-study.ts

Study 2 · Our own SMC zone score, shuffle-tested

Do zones our toolkit scores higher actually resolve “respected” more often — or does a shuffled score column do as well?

Data:
Same frozen gold tape, re-exported at bar level (13,640 bars), in-repo
Sample:
1,729 zones detected by a line-annotated port of the shipped Pine at defaults; 1,693 resolved
Control:
Scores shuffled across resolved zones ×2,000 (fixed seed) + 12-bar thinning for zone clustering + shipped age-prune robustness
Limitation:
One market and timeframe; a port, not the TradingView runtime; respect definition is mechanical, not a win rate

Finding: Composite score: no better than shuffled (+2.6 pts, p=0.21; thinned −1.9, p=0.72). Two ingredients real: displacement +18.0 pts (p < 0.0005), volume +10.5 (p=0.003)

For technical readers — the one-command re-run

node --experimental-strip-types scripts/research/smc-zone-study.ts

Study 3 · Multi-timeframe agreement vs the next hour

When five timeframes agree, does the next hour behave differently? Is “full stack” alignment rare enough to mean anything?

Data:
Frozen gold tape (observation clock + 15m stack) + frozen 60m/730d and 1d/5y series for honest higher-TF warmup, all in-repo
Sample:
1,131 hourly stack readings (non-overlapping forward windows); 1,124 directional
Control:
Confluence column shuffled ×2,000 + session-cluster bootstrap for serial dependence
Limitation:
One market and window; UTC-bucketed 4h/weekly resamples; hourly readings remain serially dependent after guards

Finding: Full alignment held 28.6% of all hours (median run 3h) — common, not special; alignment level separated nothing (+0.4 pts, p=0.43; CI [−5.2, +10.0]); the 100% alert hit 44.8% vs 48.2% baseline

For technical readers — the one-command re-run

node --experimental-strip-types scripts/research/mtf-confluence-study.ts

Method notes: nulls are permutation-based with fixed seeds; serial dependence is guarded with thinning or session-cluster bootstraps; sensitivity readings that flip under guards are reported and then dismissed as artifacts, in the open. Nothing on this page claims a win rate, an edge, or a prediction — these are audits of claims, including our own.

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