108 lines
4.1 KiB
Python
108 lines
4.1 KiB
Python
from __future__ import annotations
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import json
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import multiprocessing as mp
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import sys
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from concurrent.futures import ProcessPoolExecutor, as_completed
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from dataclasses import asdict
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from pathlib import Path
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import pandas as pd
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PACKAGE_PARENT = Path(__file__).resolve().parents[2]
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if str(PACKAGE_PARENT) not in sys.path:
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sys.path.insert(0, str(PACKAGE_PARENT))
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from strategy32.research.soft_router import load_component_bundle, score_candidate
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from strategy32.scripts.run_current_cash_learned_blocker import LearnedBlockerCandidate
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from strategy32.scripts.run_current_cash_learned_blocker_exact import _exact_period_worker
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from strategy32.scripts.run_current_relaxed_hybrid_experiment import WINDOWS, YEAR_PERIODS, YTD_START
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OUT_JSON = Path("/tmp/strategy32_current_cash_blocked_scale_exact_sweep.json")
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BASELINE_JSON = Path("/tmp/strategy32_live_combo_backtest.json")
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CACHE_PATH = "/tmp/strategy32_fixed66_bundle.pkl"
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def _candidate_space() -> list[LearnedBlockerCandidate]:
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scales = (0.25, 0.40, 0.50, 0.60, 0.70, 0.75, 0.80, 0.90)
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return [
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LearnedBlockerCandidate(
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block_bars=42,
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train_min_blocks=12,
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lookback_blocks=24,
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ridge_alpha=1.0,
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prediction_threshold=-0.0025,
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blocked_scale=scale,
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)
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for scale in scales
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]
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def main() -> None:
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_, latest_bar = load_component_bundle(CACHE_PATH)
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period_specs: list[tuple[str, str, pd.Timestamp, pd.Timestamp]] = []
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for days, label in WINDOWS:
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period_specs.append(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar))
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for label, start, end_exclusive in YEAR_PERIODS:
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period_specs.append(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))))
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period_specs.append(("year", "2026_YTD", YTD_START, latest_bar))
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ctx = mp.get_context("fork")
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results: list[dict[str, object]] = []
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candidates = _candidate_space()
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for idx, candidate in enumerate(candidates, start=1):
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print(f"[candidate] {idx}/{len(candidates)} {candidate.name}", flush=True)
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window_results: dict[str, dict[str, float]] = {}
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year_results: dict[str, dict[str, float]] = {}
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with ProcessPoolExecutor(max_workers=min(6, len(period_specs)), mp_context=ctx) as executor:
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future_map = {
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executor.submit(
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_exact_period_worker,
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CACHE_PATH,
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candidate_payload=asdict(candidate),
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kind=kind,
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label=label,
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start_text=str(start),
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end_text=str(end),
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): (kind, label)
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for kind, label, start, end in period_specs
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}
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for future in as_completed(future_map):
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kind, label, metrics = future.result()
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if kind == "window":
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window_results[label] = metrics
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else:
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year_results[label] = metrics
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score, negative_years, mdd_violations = score_candidate(
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{label: window_results[label] for _, label in WINDOWS},
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{k: year_results[k] for k, _, _ in YEAR_PERIODS},
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)
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results.append(
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{
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"candidate": asdict(candidate),
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"name": candidate.name,
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"score": score,
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"negative_years": negative_years,
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"mdd_violations": mdd_violations,
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"windows": {label: window_results[label] for _, label in WINDOWS},
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"years": year_results,
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}
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)
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results.sort(key=lambda item: float(item["score"]), reverse=True)
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output = {
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"analysis": "current_cash_blocked_scale_exact_sweep",
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"latest_bar": str(latest_bar),
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"results": results,
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"baseline_exact": json.loads(BASELINE_JSON.read_text(encoding="utf-8")) if BASELINE_JSON.exists() else None,
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}
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OUT_JSON.write_text(json.dumps(output, indent=2), encoding="utf-8")
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print(json.dumps(output["results"], indent=2))
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print(f"[saved] {OUT_JSON}", flush=True)
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if __name__ == "__main__":
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main()
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