anomaly-atlas
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.

The atlas

Confidence-labeled statistical regularities (and non-regularities) in hfmarketdata.io data. Every entry carries provenance — commit, config, data-manifest hash, hardware — and the exact command that regenerates it. Negative results are first-class entries.

No findings yet — and that is the point

The atlas publishes only Level ≥ 1 findings: effects that survive multiple-testing correction and a clean out-of-sample split with the artifact null subtracted. Nothing has earned that yet — the framework is being built so that nothing can enter without earning it. The artifact taxonomy and the validation methodology are primary deliverables in their own right.

Foundations

Artifact taxonomy

The catalogue of artifacts in this dataset that masquerade as anomalies — often the most useful output.

Validation methodology

The pre-registered protocol that turns a hypothesis into a confidence-labeled entry.