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.

Code / src/anomaly_atlas/__init__.py

src/anomaly_atlas/__init__.py 21 lines
# =============================================================================
#  Project   : anomaly-atlas
#  File      : src/anomaly_atlas/__init__.py
#  Purpose   : anomaly_atlas package root
#  Author    : Simon-Pierre Boucher
#  Contact   : contact@spboucher.ai
#  Data src  : hfmarketdata.io (sole data source)
#  Created   : 2026-08-12
#  Modified  : 2026-08-12
#  Platform  : macOS / Apple Silicon (arm64)
#  License   : All rights reserved (research code)
# =============================================================================
"""anomaly-atlas — systematic discovery & rigorous validation of statistical
anomalies in open HF market data (hfmarketdata.io, sole source).

Honesty doctrine (CLAUDE.md §2.1): every candidate anomaly is an artifact
until proven otherwise; in-sample results are never findings.
"""

__version__ = "0.0.1"