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 / pyproject.toml

pyproject.toml 50 lines
# =============================================================================
#  Project   : anomaly-atlas
#  File      : pyproject.toml
#  Purpose   : Python project configuration (deps, ruff, pytest)
#  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)
# =============================================================================

[project]
name = "anomaly-atlas"
version = "0.0.1"
description = "Research: systematic discovery & rigorous validation of statistical anomalies in open HF market data (hfmarketdata.io)"
authors = [{ name = "Simon-Pierre Boucher", email = "contact@spboucher.ai" }]
requires-python = ">=3.11"
dependencies = [
    "numpy",
    "pandas",
    "polars",
    "pyarrow",
    "duckdb",
    "requests",
    "scipy",
    "statsmodels",
    "arch",
    "pyyaml",
    "bidask",
]

[project.optional-dependencies]
dev = ["ruff", "pytest"]

[tool.setuptools.packages.find]
where = ["src"]

[tool.ruff]
line-length = 100
target-version = "py311"

[tool.ruff.lint]
select = ["E", "F", "W", "I", "UP", "B"]

[tool.pytest.ini_options]
testpaths = ["src", "experiments", "benchmarks"]
python_files = ["test_*.py"]