Honesty doctrine. Every candidate anomaly is an artifact until proven otherwise; in-sample results are never findings; past statistical regularity does not imply future returns. This is research on statistical properties of market data — not investment advice, not a trading system.

Code / experiments/candidate_03/hypothesis.md

experiments/candidate_03/hypothesis.md 39 lines
---
project: anomaly-atlas
document: candidate_03/hypothesis
author: Simon-Pierre Boucher
contact: contact@spboucher.ai
data_source: hfmarketdata.io
created: 2026-08-12
status: draft
---

# Hypothesis — candidate_03

```text
Hypothesis
  <what we believe and whypre-specified BEFORE looking at results>

Falsification criterion
  <the concrete measurable outcome that would prove this wrong>

Artifact null(s)
  <the fake-signal baseline(s) this must beat: bounce / staleness /
   non-synchronous timestamps / permuted calendar / random walk>

Method
  <exact procedure, universe, split (train/validation/holdout), seeds,
   number of hypotheses tested, correction applied>

Result
  <filled after the run: effect size, bootstrap CIs, corrected p-values,
   OOS status, cost-adjusted effect, credits used>

Interpretation
  <what the numbers mean, WITH confidence level (0-3); alternative
   explanations consideredartifact first>

Next experiment
  <the most informative follow-up given this result>
```