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 / experiments/candidate_01/benchmark.py

experiments/candidate_01/benchmark.py 36 lines
# =============================================================================
#  Project   : anomaly-atlas
#  File      : experiments/candidate_01/benchmark.py
#  Purpose   : Benchmark runner: Candidate 01 (selected in Phase 4 from research/candidate_rankin…
#  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)
# =============================================================================

"""Benchmark entry point for candidate_01.

Must embed the hardware manifest in all result output
(see benchmarks/hardware_manifest.py) and write results to
results/candidate_01/<timestamp>/. Uses hfmarketdata.io data ONLY, exclusively
through src/anomaly_atlas/data/hf_client.py.
"""

import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "benchmarks"))
from hardware_manifest import collect_manifest  # noqa: E402


def main() -> None:
    collect_manifest()  # embedded in results once implemented
    raise NotImplementedError("experiment not yet implemented")


if __name__ == "__main__":
    main()