spb/anomaly-atlas Public License
Systematic discovery & rigorous validation of statistical anomalies in open HF market data (hfmarketdata.io) — pre-registered, artifact-null-driven, fully reproducible. Live atlas: www.anomaly-atlas.io
Python 61.4%
JavaScript 28.7%
CSS 8.6%
Shell 0.7%
Makefile 0.5%
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expG: cost frontier — the double-filtered pool dies at a fraction of the spread
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- net = gross - kappa*(EDGE/2)*turnover swept over kappa in {0,.1,.25,.5,1,2} - median breakeven kappa* = 0.0114 (max 0.28): the median rule captures ~1% of one half-spread per trade; ES->SPY kappa*=0.0028 splice-invariant - survivors: 3 at kappa=0.1 (all sparse names), 1 at 0.25 (CKX 1day, skeptical prior), 0 at 0.5+; zero intraday at kappa=1 — pre-registered falsification did not trigger; three-layer doctrine closes - results sanitized (NaN -> null: Python json emits bare NaN, invalid for every other consumer); kappa* log-scale figure with cost reference lines Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> -
Phase 0.5: Experiment A (data reality check) + hf_client implementation
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- hf_client.py: cache-first single API client (throttle, 429/5xx back-off, 50k-row pagination, committed data-manifest index) + 5 offline unit tests - expA benchmark: 8 probe families, results with embedded hardware manifest - research/data_source_profile.md (reviewed): coverage, timestamps ET, session structure, daily-vs-1min semantics, adjustment re-basing, limits - LOG entry; expA hypothesis/analysis finalized Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Bootstrap: full research skeleton (CLAUDE.md §3, localvm-research layout) + web platform
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Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>