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

# Artifact taxonomy (living document)

The catalogue of mechanisms **in this specific dataset** that manufacture
fake anomalies — a primary deliverable (Q4). Every entry: mechanism,
detection, measured magnitude on this data, neutralization. Magnitudes from
`results/expA_data_reality/20260812T054515Z/` and
`results/expB_artifact_baselines/20260812T055602Z/` (Q1 2024, RTH 1min,
pre-specified universe). Detectors validated on synthetic ground truth first
(`benchmarks/synthetic/test_synthetic_gate.py`, 11 tests).

## T1 — Bid-ask bounce (Roll)

* **Mechanism.** Trades alternate bid/ask; observed returns gain a negative
  lag-1 autocovariance (−s²/4) with no economics. Masquerades as
  mean-reversion (Q1).
* **Detection.** Roll implied relative spread `2·√(−autocov1)`;
  bounce-implied AC1; `excess_reversion()` (validation/artifacts.py).
* **Measured.** Median AC1 by staleness tercile: −0.009 (fresh) / −0.051
  (mid) / **−0.232 (stale)**; Roll spread 1.4 / 3.8 / 17.0 bp. Mega-caps:
  no measurable bounce (AC1 CI covers 0; Roll undefined ~half the time —
  positive autocov).
* **Neutralize.** Report reversion net of the liquidity-bucket bounce null;
  never average AC1 across liquidity buckets; treat Roll-undefined as
  "no bounce measurable", not zero spread.
* **expC amendment (measured).** A daily *effective* spread (EDGE) combined
  with pure Roll alternation predicts impossible intraday AC1 (−3 to −27):
  consecutive intraday closes do not flip sides each bar. Spread-based
  bounce nulls MUST be variance-consistent with the target series; for
  intraday scans use the MA(1)-consistent VR null
  (VR_null(q) = 1 + 2·AC1·(1−1/q)) — conservative, absorbs all lag-1
  effects.

## T2 — Stale prices / missing minutes

* **Mechanism.** Bars exist only where trades occurred (expA: zero
  zero-volume bars; an illiquid name printed 38 bars/day). LOCF joins add a
  large mass of zero returns and depress variance-ratio statistics.
* **Detection.** `staleness_ratio` on the 390-min RTH grid.
* **Measured.** Staleness up to 0.90 (RITM.B); **VR(30) = 0.55 median for
  the stale tercile — 0.35 extreme — with zero planted economics.** 11/42
  pre-specified tickers had too little data to analyze at all.
* **Neutralize.** Explicit grids with observed-masks (never silent LOCF);
  liquidity filters pre-specified; VR/AC1 claims benchmarked against the
  staleness-matched null, not against 1.0.

## T3 — Non-synchronous lead-lag (LOCF cross-correlation)

* **Mechanism.** A fresh series LOCF-joined to a stale one appears to LEAD
  it: the stale print reflects old common information (classic
  non-synchronous trading bias).
* **Detection.** Lagged cross-correlation vs SPY on the LOCF grid; synthetic
  ground truth: rho=0.7 pair with 30 % observation → spurious +1 lag corr.
* **Measured.** SPY "leads" mid-staleness tickers by +0.047 at +1 min
  (Spearman vs staleness +0.43). **SPX-vs-SPY: +0.065 with SPY leading
  1 min** at 0.965 contemporaneous corr — index prints lag the ETF.
* **Neutralize.** Any Q2 lead-lag claim must exceed the staleness-predicted
  cross-correlation; test on synchronized (both-fresh) subsamples; index
  series are stale by construction.
* **expD amendments (measured).** (i) The artifact is NON-MONOTONE in
  staleness: LOCF joins inflate leads for mid-stale names (+0.047, expB) but
  DILUTE them for ultra-stale names (HTD raw +0.048 vs both-fresh +0.073) —
  naive joins can hide lead-lag as well as manufacture it. (ii) For computed
  indices, synchronizing on PRINT times removes nothing: SPY leads SPX
  +0.132 both-fresh (fresh_frac 0.996) — content staleness must be modeled,
  not print gaps.

## T4 — Auction close vs last bar

* **Mechanism.** Daily bars carry the official closing-auction print; 1min
  bars do not (expA: AAPL 312.41 daily close vs 312.49 last RTH 1min close).
  Mixing conventions manufactures phantom overnight/close-to-close returns.
* **Measured.** 8 bp discrepancy on a calm day for the most liquid stock.
* **Neutralize.** Pick ONE close convention per experiment and state it;
  never compute overnight returns across mixed conventions.

## T5 — Rolling adjustment anchor

* **Mechanism.** `adj_splitdiv` re-bases the whole history to the dataset
  build date (expA: AAPL 2020-08-31 close = 125.17 adjusted vs 129.04
  traded). Adjusted series are not point-in-time stable → silent look-ahead
  and irreproducibility if the cache is refreshed mid-study.
* **Neutralize.** Frozen local cache (hf_client never silently refetches);
  data-manifest hash in every provenance; intraday work uses within-day
  returns (adjustment-invariant) or UNADJUSTED plus explicit factors.

## T6 — Daily vs intraday volume conventions

* **Mechanism.** Daily volume includes auction/consolidated prints absent
  from 1min bars (expA: 46.1 M daily vs 34.7 M extended-1min sum vs 25.7 M
  RTH-1min sum for AAPL on one day — a 1.8× spread across conventions).
* **Neutralize.** Volume-based signals pick one convention; never mix daily
  and intraday volume in one feature.

## T7 — Vendor session / timezone semantics

* **Mechanism.** All timestamps are US/Eastern wall-clock without a marker;
  sessions differ per class (equities 04:00–19:59, SPX prints to 16:20,
  futures ≈24 h, fx ET-week, crypto 24/7). Cross-asset joins on naive
  timestamps silently compare different market states.
* **Neutralize.** One canonical calendar module (`data/calendars.py`),
  explicit session filters per asset class, DST-aware conversions.

---

**T1/T2 intraday profile (measured, expE/H20 — liquid 12, 1min, 2014-2015).**
Volatility is U-shaped: median |1min return| 6.6 bp at 09:30 → 2.4 bp midday
→ 2.9 bp at 15:30. The EDGE spread declines monotonically 2.8 → 1.2 bp (an
L, not a U). Consequence: intraday return claims concentrated at the open
face 2–3× the midday artifact level and double the closing spread — expE/expG
must bucket-match their nulls and costs by time of day.

*Open items: continuous-futures splice choice (3 variants exposed by the
API) as a testable artifact for futures-based hypotheses — expD found the
ES→SPY cross-serial effect splice-INVARIANT, so the residual exposure is
futures-only level/trend work.*