64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
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# -*- coding: utf-8 -*-
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"""Run the walk-forward experiment across all variants and write the research log.
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Usage (from repo root):
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python -m scripts.run_experiment --config configs/default.json --out results/oos_log.csv
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"""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import os
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from src.forecasting.target import ForecastContext
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from src.forecasting.interface import Series
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from src.arima import ArimaConfig
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from src.sax import SaxConfig
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from src.validation import WalkForwardConfig
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from src.pipeline import build_models, run_variants, write_log
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from tests.helpers import make_series
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def load_config(path: str) -> dict:
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with open(path, "r", encoding="utf-8") as fh:
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return json.load(fh)
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def config_hash(cfg: dict) -> str:
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return hashlib.sha256(json.dumps(cfg, sort_keys=True).encode("utf-8")).hexdigest()[:16]
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--config", default="configs/default.json")
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ap.add_argument("--out", default=None)
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ap.add_argument("--bars", type=int, default=400)
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args = ap.parse_args()
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cfg = load_config(args.config)
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h = cfg["horizon"]
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ctx = ForecastContext(symbol=cfg["symbol"], timeframe=cfg["timeframe"],
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horizon=h, atr_period=cfg["atr_period"])
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arima_cfg = ArimaConfig(**cfg["arima"])
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sax_cfg = SaxConfig(**cfg["sax"])
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walk_cfg = WalkForwardConfig(
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min_origin=cfg["data"]["train_length"],
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n_forecast_points=cfg["data"]["train_length"] + 80,
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step=cfg["data"]["step"],
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)
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series = make_series(n=args.bars, seed=cfg["reproducibility"]["seed"])
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c_hash = config_hash(cfg)
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variants = run_variants(series, ctx, arima_cfg, sax_cfg, walk_cfg,
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configuration_hash=c_hash,
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data_snapshot_id=f"synthetic-{args.bars}")
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combined = list(variants["A_naive"]) + list(variants["B_arima"]) + list(variants["C_sax"])
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out = args.out or os.path.join("results", "oos_log.csv")
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os.makedirs(os.path.dirname(out) or ".", exist_ok=True)
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write_log(combined, out)
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print(f"Wrote {len(combined)} records -> {out}")
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if __name__ == "__main__":
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main()
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