feat: 리밸런싱 엔진 V1 + GAS 버그 수정 (2026-06-13)
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>
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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from typing import Any
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ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_V1 = ROOT / "Temp" / "strategy_hardening_harness_v1.json"
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DEFAULT_OUTCOME_LOCK = ROOT / "Temp" / "operational_outcome_lock_v1.json"
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DEFAULT_DQ_LOCK = ROOT / "Temp" / "data_integrity_100_lock_v2.json"
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DEFAULT_SCR_V4 = ROOT / "Temp" / "smart_cash_recovery_v4.json"
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DEFAULT_SCR_V5 = ROOT / "Temp" / "smart_cash_recovery_v5.json"
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DEFAULT_ENGINE_GATE = ROOT / "Temp" / "engine_harness_gate_result.json"
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DEFAULT_PRED = ROOT / "Temp" / "prediction_accuracy_harness_v2.json"
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DEFAULT_OAC_V2 = ROOT / "Temp" / "operational_alpha_calibration_v2.json"
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DEFAULT_FIR_V1 = ROOT / "Temp" / "formula_runtime_registry_v1.json"
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DEFAULT_DQR_V1 = ROOT / "Temp" / "data_quality_reconciliation_v1.json"
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DEFAULT_OUT = ROOT / "Temp" / "strategy_hardening_harness_v2.json"
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def _load(path: Path) -> dict[str, Any]:
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if not path.exists():
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return {}
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try:
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obj = json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return {}
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return obj if isinstance(obj, dict) else {}
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def _f(v: Any, default: float = 0.0) -> float:
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try:
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return float(v)
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except Exception:
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return default
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--v1", default=str(DEFAULT_V1))
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ap.add_argument("--outcome-lock", default=str(DEFAULT_OUTCOME_LOCK))
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ap.add_argument("--dq-lock", default=str(DEFAULT_DQ_LOCK))
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ap.add_argument("--scr-v4", default=str(DEFAULT_SCR_V4))
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ap.add_argument("--scr-v5", default=str(DEFAULT_SCR_V5))
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ap.add_argument("--engine-gate", default=str(DEFAULT_ENGINE_GATE))
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ap.add_argument("--prediction", default=str(DEFAULT_PRED))
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ap.add_argument("--alpha-calibration", default=str(DEFAULT_OAC_V2))
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ap.add_argument("--formula-runtime", default=str(DEFAULT_FIR_V1))
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ap.add_argument("--data-quality-recon", default=str(DEFAULT_DQR_V1))
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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v1 = Path(args.v1)
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ol = Path(args.outcome_lock)
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dl = Path(args.dq_lock)
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sv = Path(args.scr_v4)
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sv5 = Path(args.scr_v5)
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eg = Path(args.engine_gate)
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pi = Path(args.prediction)
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op = Path(args.out)
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for p in (v1, ol, dl, sv, sv5, eg, pi, op):
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if not p.is_absolute():
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p = ROOT / p
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base = _load(ROOT / Path(args.v1) if not Path(args.v1).is_absolute() else Path(args.v1))
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outcome_lock = _load(ROOT / Path(args.outcome_lock) if not Path(args.outcome_lock).is_absolute() else Path(args.outcome_lock))
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dq_lock = _load(ROOT / Path(args.dq_lock) if not Path(args.dq_lock).is_absolute() else Path(args.dq_lock))
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scr_v4 = _load(ROOT / Path(args.scr_v4) if not Path(args.scr_v4).is_absolute() else Path(args.scr_v4))
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scr_v5 = _load(ROOT / Path(args.scr_v5) if not Path(args.scr_v5).is_absolute() else Path(args.scr_v5))
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engine = _load(ROOT / Path(args.engine_gate) if not Path(args.engine_gate).is_absolute() else Path(args.engine_gate))
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pred = _load(ROOT / Path(args.prediction) if not Path(args.prediction).is_absolute() else Path(args.prediction))
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oac = _load(ROOT / Path(args.alpha_calibration) if not Path(args.alpha_calibration).is_absolute() else Path(args.alpha_calibration))
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fir = _load(ROOT / Path(args.formula_runtime) if not Path(args.formula_runtime).is_absolute() else Path(args.formula_runtime))
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dqr = _load(ROOT / Path(args.data_quality_recon) if not Path(args.data_quality_recon).is_absolute() else Path(args.data_quality_recon))
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scr_current = scr_v5 if scr_v5 else scr_v4
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ds = base.get("domain_scores") if isinstance(base.get("domain_scores"), dict) else {}
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ms = base.get("meta_scores") if isinstance(base.get("meta_scores"), dict) else {}
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data_integrity = _f(ds.get("data_integrity"))
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outcome_quality = _f(ds.get("outcome_quality"))
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t20_pass = _f(ds.get("t20_pass_rate"))
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algo_proof = _f(ds.get("algorithm_guidance_proof"))
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pred_summary = pred.get("summary") if isinstance(pred.get("summary"), dict) else {}
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pred_match = _f(
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pred_summary.get("match_rate_pct")
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if pred_summary
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else pred.get("t5_ap_combined")
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if pred.get("t5_ap_combined") is not None
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else pred.get("t20_replay_rate")
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)
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if pred_match <= 0.0:
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pred_match = _f(pred.get("t5_ap_combined"), _f(pred.get("t20_replay_rate")))
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value_damage = _f(scr_current.get("value_damage_pct_avg"))
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expect = _f((outcome_lock.get("metrics") or {}).get("execution_expectancy_pct"))
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win_rate = _f((outcome_lock.get("metrics") or {}).get("execution_win_rate_pct"))
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t20_oper_count = _f((outcome_lock.get("metrics") or {}).get("operational_t20_count"))
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t20_oper_pass = _f((outcome_lock.get("metrics") or {}).get("operational_t20_pass_rate"))
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oac_conf = _f(oac.get("confidence_score"))
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oac_gate = str(oac.get("gate") or "MISSING")
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runtime_coverage = _f(fir.get("runtime_adjusted_coverage_pct"))
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dq_conflict = bool(dqr.get("quality_conflict_flag"))
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dq_invest = _f(dqr.get("investment_quality_score"))
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dq_cap_basis = _f(dqr.get("confidence_cap_basis_score"), dq_invest)
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readiness_reasons: list[str] = []
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if str(dq_lock.get("gate") or "") != "PASS_100":
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readiness_reasons.append("DATA_INTEGRITY_LOCK_NOT_PASS_100")
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if outcome_quality < 60.0:
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readiness_reasons.append("OUTCOME_QUALITY_LT_60")
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if t20_oper_count < 30:
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readiness_reasons.append("OPERATIONAL_T20_SAMPLE_LT_30")
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if t20_oper_pass < 60.0:
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readiness_reasons.append("OPERATIONAL_T20_PASS_LT_60")
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if expect <= 0.1:
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readiness_reasons.append("EXPECTANCY_LE_0_1")
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if win_rate < 45.0:
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readiness_reasons.append("WIN_RATE_LT_45")
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if pred_match < 60.0:
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readiness_reasons.append("PREDICTION_MATCH_LT_60")
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if value_damage > 10.0:
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readiness_reasons.append("VALUE_DAMAGE_GT_10")
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if str(engine.get("status") or "") != "OK":
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readiness_reasons.append("ENGINE_GATE_NOT_OK")
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if oac_gate not in {"PERFORMANCE_READY", "NOT_READY"}:
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readiness_reasons.append("ALPHA_CALIBRATION_MISSING")
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if runtime_coverage < 100.0:
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readiness_reasons.append("RUNTIME_COVERAGE_LT_100")
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if dq_conflict:
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readiness_reasons.append("DATA_QUALITY_CONFLICT")
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if dq_cap_basis < 50.0:
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readiness_reasons.append("DATA_QUALITY_CAP_BASIS_LT_50")
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readiness_gate = "PERFORMANCE_READY" if not readiness_reasons else "NOT_PERFORMANCE_READY"
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if "OPERATIONAL_T20_SAMPLE_LT_30" in readiness_reasons:
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readiness_gate = "WATCH_PENDING_SAMPLE"
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control = _f(ms.get("control_score"))
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perf_v1 = _f(ms.get("performance_score"))
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lock_boost = 100.0 if str(outcome_lock.get("unlock_state") or "") == "PERFORMANCE_READY" else 50.0
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perf_v2 = round((perf_v1 * 0.5) + (t20_oper_pass * 0.2) + (pred_match * 0.15) + (max(0.0, 100.0 - value_damage * 5.0) * 0.15), 2)
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overall = round(control * 0.55 + perf_v2 * 0.45, 2)
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truth_hardening_score = round(min(overall, max(0.0, dq_cap_basis), max(0.0, 100.0 - max(0.0, value_damage - 10.0) * 10.0)), 2)
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result = {
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"formula_id": "STRATEGY_HARDENING_HARNESS_V2",
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"domain_scores": {
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**ds,
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"prediction_match_rate_pct": pred_match,
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"cash_recovery_value_damage_pct": value_damage,
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"operational_t20_count": t20_oper_count,
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"operational_t20_pass_rate": t20_oper_pass,
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"execution_expectancy_pct_operational": expect,
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"execution_win_rate_pct_operational": win_rate,
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"alpha_calibration_confidence_score": oac_conf,
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"formula_runtime_coverage_pct": runtime_coverage,
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"data_quality_investment_score": dq_invest,
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"data_quality_cap_basis_score": dq_cap_basis,
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"data_quality_conflict_flag": dq_conflict,
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"algorithm_guidance_proof": algo_proof,
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"t20_pass_rate": t20_pass,
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"data_integrity": data_integrity,
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"outcome_quality": outcome_quality,
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},
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"meta_scores": {
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"control_score": control,
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"performance_score_v1": perf_v1,
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"performance_score_v2": perf_v2,
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"lock_score": lock_boost,
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"overall_hardening_score": overall,
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"truth_hardening_score": truth_hardening_score,
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"readiness_gate": readiness_gate,
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"readiness_reasons": readiness_reasons,
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"alpha_calibration_gate": oac_gate,
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},
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"targets": {
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"data_integrity_score": 100.0,
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"outcome_quality_min": 60.0,
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"operational_t20_sample_min": 30,
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"operational_t20_pass_min": 60.0,
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"execution_expectancy_pct_min": 0.1,
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"execution_win_rate_pct_min": 45.0,
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"prediction_match_rate_pct_min": 60.0,
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"value_damage_pct_avg_max": 10.0,
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"engine_gate_status": "OK",
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"formula_runtime_coverage_pct": 100.0,
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"data_quality_conflict_flag": False,
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},
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}
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out_path = ROOT / Path(args.out) if not Path(args.out).is_absolute() else Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
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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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