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.

Independent statistics + market-microstructure research · Apple Silicon · hfmarketdata.io

Which market "anomalies" are real —
and which are artifacts?

A systematic, reproducible atlas of statistical regularities in open high-frequency market data — mean-reversion, lead-lag, calendar effects — where every claim survives (or visibly fails) out-of-sample testing, multiple-comparison correction, artifact nulls, and realistic transaction costs. Negative results are first-class findings.

detectable in-sample ≠ reproducible out-of-sample ≠ robust to artifacts ≠ meaningful after costs

58sources reviewed
0hypotheses registered
2/11experiments completed
0atlas findings (Level ≥ 1)

The confidence taxonomy

A finding only advances one level at a time, and only Level ≥ 1 is ever published. A large in-sample effect with zero out-of-sample survival is a negative result — published as one.

  • Level 0 — in-sample onlyscan output; never published as a finding
  • Level 1 — corrected & OOSsurvives multiple-testing correction and a clean out-of-sample split, artifact null subtracted
  • Level 2 — robust+ robust to specification choices, sub-periods, instruments
  • Level 3 — cost-real & held-out+ economically nonzero after realistic costs, confirmed on the once-touched holdout
Browse the atlas →

Research phases

  • Phase 0.5 — Data reality check (Experiment A)what hfmarketdata.io actually provides
    done
  • Phase 1 — Literature research (§4.1–4.6)6 theme notes · 58 sources
    in progress
  • Phase 2 — State-of-the-art mapper-anomaly epistemic status
    pending
  • Phase 3 — Research gaps≥20 target testable hypotheses with constructable artifact nulls
    pending
  • Phase 4 — Candidate ranking10-axis scoring, 3–5 candidates
    pending
  • Phases 5–6 — Framework & micro-experiments A–H2/11 experiments completed
    in progress
  • Phases 7–11 — Candidates → methodology → atlas → noveltyevidence-driven
    pending

Latest from the research log

Full research log →

Phase 1 complete: literature sweep (52 verified sources)

Question. What does the literature establish about Q1-Q3 anomalies, the statistics of not fooling yourself, microstructure artifacts, and costs?

Method. Three parallel verification passes against OpenAlex (every citation confirmed: title, authors, year, venue, DOI; access date 2026-08-12), plus discovery searches for 2005-2025 decay/replication work.

expB complete: artifact nulls measured (after the §8.1 gate caught a real bug)

Question. Are the bounce/staleness/non-synchronicity artifacts measurable and material at 1min on this data?

Experiment. Built generators with known ground truth + first real stats modules (reversion, leadlag, bootstrap, artifacts). The mandatory synthetic gate (11 tests) CAUGHT A REAL BUG before any real data was touched: the

Phase 0.5 complete: Experiment A (data reality check)

Question. What does hfmarketdata.io actually provide (granularity, depth, timestamps, adjustments, completeness, limits)?

Experiment. expA_data_reality — 8 probe families through the newly implemented single client (hf_client.py: cache-first, throttled, back-off, data-manifest index; 5 offline unit tests pass). 54 network requests,