from __future__ import annotations import argparse import json from pathlib import Path from typing import Any ROOT = Path(__file__).resolve().parents[1] DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json" DEFAULT_LATE = ROOT / "Temp" / "late_chase_attribution_v1.json" DEFAULT_REBOUND = ROOT / "Temp" / "rebound_sell_efficiency_v1.json" DEFAULT_OUTCOME = ROOT / "Temp" / "outcome_quality_score_v1.json" DEFAULT_EXEC_QUALITY = ROOT / "Temp" / "execution_quality_harness_v1.json" DEFAULT_OUT = ROOT / "Temp" / "perf_recovery_harness_v1.json" def _load(path: Path) -> dict[str, Any]: if not path.exists(): return {} try: x = json.loads(path.read_text(encoding="utf-8")) except Exception: return {} return x if isinstance(x, dict) else {} def main() -> int: ap = argparse.ArgumentParser() ap.add_argument("--history", default=str(DEFAULT_HISTORY)) ap.add_argument("--late", default=str(DEFAULT_LATE)) ap.add_argument("--rebound", default=str(DEFAULT_REBOUND)) ap.add_argument("--outcome", default=str(DEFAULT_OUTCOME)) ap.add_argument("--execution-quality", default=str(DEFAULT_EXEC_QUALITY)) ap.add_argument("--out", default=str(DEFAULT_OUT)) args = ap.parse_args() hp = Path(args.history); lp = Path(args.late); rp = Path(args.rebound); op = Path(args.outcome); eqp = Path(args.execution_quality); outp = Path(args.out) for p in (hp, lp, rp, op, eqp, outp): if not p.is_absolute(): p = ROOT / p hist = _load(hp if hp.is_absolute() else ROOT / hp) late = _load(lp if lp.is_absolute() else ROOT / lp) rebound = _load(rp if rp.is_absolute() else ROOT / rp) outcome = _load(op if op.is_absolute() else ROOT / op) exec_q = _load(eqp if eqp.is_absolute() else ROOT / eqp) recs = hist.get("records") if isinstance(hist.get("records"), list) else [] t20 = [r for r in recs if isinstance(r, dict) and r.get("t20_evaluation_status") == "EVALUATED_T20"] watch = [r for r in t20 if str(r.get("action") or "").upper() == "WATCH"] watch_operational = [r for r in watch if str(r.get("validation_status") or "").upper() != "REPLAY_BACKFILL"] watch_replay = [r for r in watch if str(r.get("validation_status") or "").upper() == "REPLAY_BACKFILL"] watch_miss = [r for r in watch_operational if r.get("t20_outcome") == "MISMATCHED"] watch_miss_rate = round((len(watch_miss) / len(watch_operational)) * 100.0, 2) if watch_operational else 0.0 late_status = str(late.get("status") or "DATA_MISSING") late_samples = int(late.get("samples") or 0) block_cnt = int((late.get("metrics") or {}).get("late_chase_blocked_count") or 0) high_risk_cnt = int((late.get("metrics") or {}).get("late_chase_high_risk_count") or 0) late_chase_block_precision = round((block_cnt / max(1, high_risk_cnt)) * 100.0, 2) if high_risk_cnt else 0.0 value_damage = float((rebound.get("metrics") or {}).get("value_damage_pct_avg") or 0.0) out_m = outcome.get("metrics") if isinstance(outcome.get("metrics"), dict) else {} t20_pass_rate = float(out_m.get("t20_effective_rate") or out_m.get("t20_pass_rate") or 0.0) t20_source = str(out_m.get("t20_source") or "") outcome_score = float(outcome.get("score") or 0.0) has_operational_t20 = t20_source == "proposal_evaluation_history.operational_t20_only" exec_gate = str(exec_q.get("gate") or "DATA_MISSING") exec_oper = (exec_q.get("metrics") or {}).get("operational_t20") if isinstance((exec_q.get("metrics") or {}).get("operational_t20"), dict) else {} exec_expectancy = float(exec_oper.get("expectancy_pct") or 0.0) exec_mdd = float(exec_oper.get("max_drawdown_pct") or 0.0) exec_win_rate = float(exec_oper.get("win_rate_pct") or 0.0) gate = "PASS" reasons = [] if has_operational_t20 and t20_pass_rate < 60.0: gate = "FAIL" reasons.append("T20_PASS_RATE_BELOW_60") if has_operational_t20 and value_damage > 10.0: gate = "FAIL" reasons.append("VALUE_DAMAGE_ABOVE_10") elif not has_operational_t20 and value_damage > 10.0: reasons.append("VALUE_DAMAGE_WATCH_UNTIL_OPERATIONAL_SAMPLE") if watch_operational and watch_miss_rate > 35.0: gate = "FAIL" reasons.append("WATCH_MISS_RATE_TOO_HIGH") if not watch_operational: reasons.append("WATCH_MISS_SAMPLE_INSUFFICIENT") if late_status == "WATCH_PENDING_SAMPLE" and late_samples < 30: reasons.append("LATE_CHASE_SAMPLE_INSUFFICIENT") if not has_operational_t20: reasons.append("T20_OPERATIONAL_SAMPLE_INSUFFICIENT") if exec_gate == "FAIL": gate = "FAIL" reasons.append("EXECUTION_QUALITY_FAIL") elif exec_gate == "WATCH_PENDING_SAMPLE": reasons.append("EXECUTION_QUALITY_SAMPLE_INSUFFICIENT") if gate == "PASS" and ( "WATCH_MISS_SAMPLE_INSUFFICIENT" in reasons or "LATE_CHASE_SAMPLE_INSUFFICIENT" in reasons or "T20_OPERATIONAL_SAMPLE_INSUFFICIENT" in reasons ): gate = "WATCH_PENDING_SAMPLE" res = { "formula_id": "PERF_RECOVERY_HARNESS_V1", "gate": gate, "reasons": reasons, "metrics": { "t20_pass_rate": t20_pass_rate, "outcome_quality_score": outcome_score, "t20_source": t20_source, "watch_miss_rate": watch_miss_rate, "watch_eval_count": len(watch_operational), "watch_mismatch_count": len(watch_miss), "watch_replay_eval_count": len(watch_replay), "late_chase_block_precision": late_chase_block_precision, "late_chase_status": late_status, "late_chase_samples": late_samples, "rebound_sell_value_damage": value_damage, "execution_quality_gate": exec_gate, "execution_expectancy_pct": exec_expectancy, "execution_max_drawdown_pct": exec_mdd, "execution_win_rate_pct": exec_win_rate, }, "targets": { "t20_pass_rate_min": 60.0, "outcome_quality_score_min": 60.0, "watch_miss_rate_max": 35.0, "rebound_sell_value_damage_max": 10.0, "execution_expectancy_pct_min": 0.0, "execution_max_drawdown_pct_max": 12.0, "execution_win_rate_pct_min": 45.0, }, } out_file = outp if outp.is_absolute() else ROOT / outp out_file.parent.mkdir(parents=True, exist_ok=True) out_file.write_text(json.dumps(res, ensure_ascii=False, indent=2), encoding="utf-8") print(json.dumps(res, ensure_ascii=False, indent=2)) return 0 if __name__ == "__main__": raise SystemExit(main())