---
project: anomaly-atlas
document: README
author: Simon-Pierre Boucher
contact: contact@spboucher.ai
data_source: hfmarketdata.io
created: 2026-08-12
modified: 2026-08-12
status: reviewed
---

<p align="center">
  <a href="https://www.anomaly-atlas.io"><img src="web/public/logo.svg" alt="anomaly-atlas logo — atlas graticule with a circled outlier" width="96" height="96"></a>
</p>

<h1 align="center">anomaly-atlas</h1>

<p align="center"><em>An honest atlas of what is real, what is artifact, and what is merely wishful<br>in open high-frequency market data.</em></p>

<p align="center">
  <a href="https://www.anomaly-atlas.io"><img alt="Live atlas" src="https://img.shields.io/badge/atlas-anomaly--atlas.io-2456c4"></a>
  <img alt="Experiments" src="https://img.shields.io/badge/micro--experiments-8%2F8%20complete-1e7a4d">
  <img alt="Synthetic gate" src="https://img.shields.io/badge/synthetic%20gate-29%20tests%20passing-1e7a4d">
  <img alt="Findings" src="https://img.shields.io/badge/atlas%20findings-2%20%C3%97%20Level%202-2456c4">
  <img alt="Hypothesis budget" src="https://img.shields.io/badge/hypothesis%20budget-22%20(declared)-8a5a1e">
  <img alt="Data" src="https://img.shields.io/badge/data-hfmarketdata.io%20(sole%20source)-4a3aa7">
  <img alt="Rows analyzed" src="https://img.shields.io/badge/bars%20analyzed-~68M-5d6167">
  <img alt="Platform" src="https://img.shields.io/badge/platform-macOS%20%2F%20Apple%20Silicon-1a1c20">
  <img alt="License" src="https://img.shields.io/badge/license-all%20rights%20reserved-8b1e1e">
</p>

**Which statistical regularities in open high-frequency market data are real —
and which are artifacts?** A systematic, pre-registered, fully reproducible
research project that scans 1-minute-to-daily bars (equities, ETFs, futures,
indices, FX, crypto, options chains) for mean-reversion, lead-lag, and
calendar anomalies, then pushes every candidate through a three-layer
validation ladder: **measured artifact nulls → multiple-testing correction →
transaction costs → out-of-sample confirmation**.

> **Honesty doctrine.** Every candidate anomaly is an artifact until proven
> otherwise. In-sample results are never findings. Negative results are
> first-class. Nothing here is investment advice or a trading system.

## Headline results (train 2000–2016 → validation 2016–2021)

| Validation layer | Survivors |
|---|---|
| Searched rule universe (2 signs × every scanned cell/pair/class) | 372 |
| Naive \|t\| > 1.96 | 232 (62 %) |
| Benjamini–Hochberg FDR 5 % | 226 (61 %) |
| Hansen SPA (data-snooping correction) | 68 (18 %) — *gross, artifact-laden* |
| EDGE cost model, full half-spread per trade | **0** |
| Out-of-sample (validation split, opened once) | **0** — the negative replicates |

The SPA survivors carried paper Sharpes of 10–31 — bounce harvesting, not
economics — and a deliberately included known artifact (the SPX→SPY "lead")
passed statistical correction unharmed: **statistical correction corrects
for search, not for mechanism.** Median breakeven cost: the surviving rules
capture ~1 % of one half-spread per trade. Meanwhile the artifacts
themselves replicate out-of-sample perfectly.

**First atlas entries (Level 2 — corrected, OOS-confirmed, robust):**

- **F001** — *Nothing in the searched universe survives the full ladder* (negative finding, the project's headline).
- **F002** — *The SPX→SPY minute-scale "lead" is index content staleness* (Fisher 1966, measured live; survives print synchronization AND SPA).

Full write-up: [**P001 — The Artifact Frontier, Part I**](https://www.anomaly-atlas.io/publications/P001_artifact_frontier_part1)
(every figure regenerates live from committed `results.json`).

## What's in the box

| Path | Contents |
|---|---|
| `research/` | Pre-registered charter artifacts: [data-source profile](research/data_source_profile.md), [artifact taxonomy T1–T7 with measured magnitudes](research/artifact_taxonomy.md), [52-source verified bibliography](research/bibliography.md), [22-hypothesis budget](research/research_gaps.md), append-only [LOG](research/LOG.md), [publications](research/publications/) |
| `src/anomaly_atlas/` | The library: single cache-first API client (never silently refetches; committed data manifest), gated statistics (VR, AC1, lead-lag, block/stationary bootstrap, BH-FDR, White RC, Hansen SPA, DSR), artifact detectors (Roll, EDGE, staleness, LOCF) |
| `benchmarks/synthetic/` | **The §8.1 gate** — 29 tests on series with known properties; no detector touches real data before passing (it caught 2 real bugs) |
| `experiments/micro/` | expA–expH, each with pre-registered `hypothesis.md` (falsification criterion + artifact nulls) and `analysis.md` |
| `atlas/` | Confidence-labeled findings (Level 0–3) with full provenance, created only via `tools/new_finding.py` |
| `web/` | The public platform (Node/Express, server-rendered SVG figures from results JSON, mobile-first, comments) |

## Methodology in one paragraph

Universes, time splits (train / validation / **sealed holdout 2022→**), and
the 22-hypothesis budget were frozen in writing before any scan. Every
detector passes a synthetic gate first (random walk → nothing; planted
effects → recovered; pure bounce → flagged artifact). Scans report effects
*net of measured artifact nulls* (variance-consistent bounce null, both-fresh
synchronization, permuted calendar). Survivors face White RC / Hansen SPA
over the full searched universe, then an EDGE-spread cost sweep, then the
validation split — opened exactly once. All data flows through one frozen
cache indexed by a committed manifest; two experiments ran with **zero
network requests**.

## Reproduce

```bash
make setup      # venv + deps (macOS / Apple Silicon)
make test       # 29 synthetic-gate + unit tests
make headers    # author-header compliance
python experiments/micro/expA_data_reality/benchmark.py   # then B..H in order
```

Every result JSON embeds the hardware manifest, client instrumentation, and
attribution; every finding cites its commits and the SHA-256 of the data
manifest.

## Author

**Simon-Pierre Boucher** — <contact@spboucher.ai>
Data source: [hfmarketdata.io](https://www.hfmarketdata.io) (sole source) ·
Live atlas: [www.anomaly-atlas.io](https://www.anomaly-atlas.io)

*Research on statistical properties of market data. Not investment advice,
not a trading system; past statistical regularity does not imply future
returns. All rights reserved.*