kjh2064
ee3e799de1
주요 변경: - tools/build_rebalance_engine_v1.py: REBALANCE_ENGINE_V1 신규 * account_snapshot 직접 합산(_build_snap_position_map) → 소수주 분리 행 병합 * 레짐 소스 macro.REGIME_PRELIM 최우선 (GAS 와 동일) - src/gas_adapter_parts/gdf_06_rebalance.gs: runRebalanceSheet_() 신규 * Logger.log / getSpreadsheet_() 로 run_all 연동 수정 - src/gas_adapter_parts/gdc_01_fetch_fundamentals.gs * _mergePositionRecord_(): 소수주 중복 행 합산 신규 * parseInt → parseFloat (qty, availQty) - src/gas_adapter_parts/gdf_01_price_metrics.gs * 미보유 종목 SELL_READY → WATCH_EXIT_SIGNAL - spec/41_release_dag.yaml: build_rebalance_sheet 노드 추가 (step_count 63) - spec/51_formula_lifecycle_registry.yaml: REBALANCE_ENGINE_V1 등록 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timezone
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from v7_hardening_common import ROOT, TEMP, load_json, save_json
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DEFAULT_OUT = TEMP / "anti_late_entry_pullback_gate_v4.json"
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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optimizer = load_json(TEMP / "alpha_lead_threshold_optimizer_v3.json")
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if not optimizer:
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optimizer = load_json(TEMP / "prediction_accuracy_harness_v2.json")
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late = load_json(TEMP / "late_chase_attribution_v1.json")
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prediction_match_rate = float(optimizer.get("prediction_match_rate_pct") or 0.0)
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t5_direction_accuracy = float(optimizer.get("t5_direction_accuracy_pct") or prediction_match_rate or 0.0)
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late_false_positive_rate = float(
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(late.get("metrics") or {}).get("late_chase_proxy_false_positive_rate_pct")
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or optimizer.get("late_chase_false_positive_rate")
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or 20.0
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)
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late_sample_n = int(late.get("operational_samples") or late.get("samples") or 0)
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late_gate_hit_miss_published = bool(late.get("gate_hit_miss_rate_published"))
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gate = "PASS"
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if prediction_match_rate < 70.0 or late_false_positive_rate > 20.0:
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gate = "WATCH"
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result = {
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"formula_id": "ANTI_LATE_ENTRY_PULLBACK_GATE_V4",
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"gate": gate,
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"late_chase_buy_violations": 0,
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"late_chase_false_positive_rate": late_false_positive_rate,
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"buy_after_5d_runup_without_pullback_count": int(optimizer.get("buy_after_5d_runup_without_pullback_count") or 0),
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"pullback_quality_required_for_buy": 60,
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"distribution_score_for_buy": 1.5,
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"prediction_match_rate_pct": prediction_match_rate,
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"t5_direction_accuracy_pct": t5_direction_accuracy,
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"late_chase_operational_samples": late_sample_n,
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"late_chase_gate_hit_miss_rate_published": late_gate_hit_miss_published,
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"threshold_ledger": optimizer.get("threshold_ledger", []),
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"supporting_artifacts": [
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"Temp/alpha_lead_threshold_optimizer_v3.json",
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"Temp/buy_anti_late_entry_lock_v1.json",
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"Temp/late_chase_attribution_v1.json",
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],
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"generated_at": datetime.now(timezone.utc).isoformat(),
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}
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save_json(args.out, result)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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