d385456867
인과 GT leg 엔진·drawdown tier·train 캘리브레이션, Phase 2 Go/No-Go 및 시뮬 리포트를 반영한다. Co-authored-by: Cursor <cursoragent@cursor.com>
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
#!/usr/bin/env python3
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"""Hybrid DD tier 임계값 train 캘리브레이션 (Option C 2차)."""
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import runpy
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from pathlib import Path
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runpy.run_path(str(Path(__file__).resolve().parent / "_bootstrap.py"))
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import pandas as pd
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from config import CHART_LOOKBACK_DAYS, MATCH_PRIMARY_INTERVAL, SYMBOL
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from deepcoin.data.mtf_bb import load_frames_from_db
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from deepcoin.ground_truth.ground_truth import load_ground_truth
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from deepcoin.ground_truth.hybrid_dd_calibrate import run_and_save_calibration
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from deepcoin.matching.load_rules import load_matched_rules
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from deepcoin.ops.monitor import Monitor
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from deepcoin.paths import MATCHING_FIRE_OUTCOMES, MATCHING_HYBRID_DD_CALIBRATION_JSON
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def main() -> None:
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"""monitor 발화 + OHLC로 DD tier 캘리브레이션."""
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outcomes = pd.read_csv(MATCHING_FIRE_OUTCOMES)
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matched = load_matched_rules()
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monitor_ids = {r["rule_id"] for r in matched.get("monitor_rules", [])}
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fires = outcomes[outcomes["rule_id"].isin(monitor_ids)]
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mon = Monitor(cooldown_file=None)
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ohlc = load_frames_from_db(mon, SYMBOL, lookback_days=CHART_LOOKBACK_DAYS)[
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MATCH_PRIMARY_INTERVAL
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]
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gt = load_ground_truth() or {}
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mark = (gt.get("summary") or {}).get("mark_price")
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result = run_and_save_calibration(
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fires,
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ohlc,
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outcomes=outcomes,
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last_price=float(mark) if mark else None,
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)
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best = result.get("best_params") or {}
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metrics = result.get("best_metrics") or {}
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print(f"[hybrid DD] 저장: {MATCHING_HYBRID_DD_CALIBRATION_JSON}")
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print(
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f"[hybrid DD] best dd_large={best.get('dd_large_pct')} "
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f"dd_medium={best.get('dd_medium_pct')} "
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f"train={metrics.get('train_pnl_pct')}% "
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f"holdout={metrics.get('holdout_pnl_pct')}% "
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f"full={metrics.get('full_pnl_pct')}%"
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)
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if __name__ == "__main__":
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main()
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