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 statistics import mean, quantiles
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from typing import Any
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ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_JSON = ROOT / "GatherTradingData.json"
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DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json"
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DEFAULT_OUT = ROOT / "Temp" / "late_chase_attribution_v1.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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data = json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return {}
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return data if isinstance(data, dict) else {}
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def _parse_rows(value: Any) -> list[dict[str, Any]]:
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if isinstance(value, list):
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return [x for x in value if isinstance(x, dict)]
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if isinstance(value, str):
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try:
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parsed = json.loads(value)
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return _parse_rows(parsed)
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except Exception:
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return []
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return []
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def _to_float(value: Any) -> float | None:
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try:
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if value is None or value == "":
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return None
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return float(value)
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except Exception:
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return None
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--json", default=str(DEFAULT_JSON))
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ap.add_argument("--history", default=str(DEFAULT_HISTORY))
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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json_path = Path(args.json)
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hist_path = Path(args.history)
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out_path = Path(args.out)
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if not json_path.is_absolute():
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json_path = ROOT / json_path
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if not hist_path.is_absolute():
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hist_path = ROOT / hist_path
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if not out_path.is_absolute():
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out_path = ROOT / out_path
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payload = _load(json_path)
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history = _load(hist_path)
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data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
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h = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else (payload.get("hApex") or {})
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entry_rows = _parse_rows(h.get("entry_freshness_json"))
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alpha_fb = h.get("alpha_feedback_json") if isinstance(h.get("alpha_feedback_json"), dict) else {}
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# Operational samples are drawn from the candidate ledger when a T+5 outcome exists.
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# The history does not carry explicit velocity_1d for those rows, so we use
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# buy_timing_score as the entry-timing proxy from the same operational record.
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recs = history.get("records") if isinstance(history.get("records"), list) else []
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op_candidates = [
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r for r in recs
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if isinstance(r, dict)
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and str(r.get("validation_status") or "").upper() != "REPLAY_BACKFILL"
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and str(r.get("t5_evaluation_status") or "") == "EVALUATED_T5"
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and _to_float(r.get("buy_timing_score")) is not None
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]
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proxy_field = "buy_timing_score"
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proxy_values = [float(r.get(proxy_field)) for r in op_candidates if _to_float(r.get(proxy_field)) is not None]
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# Current watchlist remains sourced from the live entry freshness gate.
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high_risk = [r for r in entry_rows if float(r.get("late_chase_risk_score") or 0) >= 70]
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blocked = [r for r in entry_rows if str(r.get("freshness_state") or "").upper() == "BLOCK_LATE_CHASE"]
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pullback_wait = [r for r in entry_rows if str(r.get("freshness_state") or "").upper() == "PULLBACK_WAIT"]
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watchlist = []
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for r in high_risk:
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watchlist.append(
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{
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"ticker": r.get("ticker"),
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"name": r.get("name"),
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"late_chase_risk_score": r.get("late_chase_risk_score"),
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"freshness_state": r.get("freshness_state"),
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"follow_through_state": r.get("follow_through_state"),
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"action_hint": "NO_BUY_UNTIL_PULLBACK" if str(r.get("freshness_state")) == "BLOCK_LATE_CHASE" else "WATCH_PULLBACK_ONLY",
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}
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)
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threshold_grid = [20, 30, 40, 50, 60, 70, 80]
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threshold_ledger: list[dict[str, Any]] = []
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chosen: dict[str, Any] | None = None
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for threshold in threshold_grid:
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blocked_rows = [r for r in op_candidates if float(r.get(proxy_field)) < threshold]
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if not blocked_rows:
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continue
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matched = sum(1 for r in blocked_rows if r.get("t5_outcome") == "MATCHED")
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mismatched = sum(1 for r in blocked_rows if r.get("t5_outcome") == "MISMATCHED")
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decisive = matched + mismatched
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match_rate = round((matched / decisive) * 100.0, 2) if decisive else None
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false_positive_rate = round((matched / decisive) * 100.0, 2) if decisive else None
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avg_t5_return = None
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t5_returns = [float(r.get("t5_return_pct")) for r in blocked_rows if _to_float(r.get("t5_return_pct")) is not None]
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if t5_returns:
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avg_t5_return = round(mean(t5_returns), 2)
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row = {
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"threshold": threshold,
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"proxy_field": proxy_field,
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"blocked_count": len(blocked_rows),
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"matched_count": matched,
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"mismatched_count": mismatched,
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"decisive_count": decisive,
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"match_rate_pct": match_rate,
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"false_positive_rate_pct": false_positive_rate,
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"avg_t5_return_pct": avg_t5_return,
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}
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threshold_ledger.append(row)
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if chosen is None and false_positive_rate is not None and false_positive_rate <= 20.0:
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chosen = row
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if len(op_candidates) < 30:
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status = "WATCH_PENDING_SAMPLE"
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elif chosen is not None:
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status = "PASS"
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else:
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status = "DEGRADE_BUY_PERMISSION"
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if chosen is None and threshold_ledger:
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chosen = max(threshold_ledger, key=lambda r: float(r.get("match_rate_pct") or 0.0))
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# [LC1/NF3] velocity_decile_thresholds — buy_timing_score 실측 분포 10분위 계산
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# samples >= 30 이면 실측 분위를 BUY 차단 커트오프 후보로 제공
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velocity_decile_thresholds: dict[str, object] = {}
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if len(proxy_values) >= 30:
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# 10분위 경계값 계산 (1~9 분위점)
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decile_cuts = quantiles(proxy_values, n=10)
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# T+5 승률 최저 분위 → 차단 임계값 권고
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recommended_cut = chosen.get("threshold") if chosen else None
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velocity_decile_thresholds = {
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"source": "실측 분포 (buy_timing_score 10분위)",
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"proxy_field": proxy_field,
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"sample_n": len(proxy_values),
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"decile_1_pct": round(decile_cuts[0], 2),
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"decile_2_pct": round(decile_cuts[1], 2),
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"decile_3_pct": round(decile_cuts[2], 2),
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"decile_5_pct": round(decile_cuts[4], 2),
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"decile_7_pct": round(decile_cuts[6], 2),
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"decile_9_pct": round(decile_cuts[8], 2),
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"recommended_block_threshold": recommended_cut,
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"calibration_status": "CALIBRATED_FROM_LEDGER",
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"note": "velocity_1d 실측값 미확보 → buy_timing_score 분위 사용. T+5 최저승률 분위를 BUY 차단 기준으로 권고.",
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}
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else:
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# [LC1] samples < 30 → 프록시값 사용 금지, WATCH_PENDING_SAMPLE 명시
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velocity_decile_thresholds = {
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"source": "WATCH_PENDING_SAMPLE",
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"proxy_field": proxy_field,
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"sample_n": len(proxy_values),
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"recommended_block_threshold": None,
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"calibration_status": "WATCH_PENDING_SAMPLE",
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"note": (
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f"[LC1] samples={len(proxy_values)}<30 — 실측 분위 캘리브레이션 불가. "
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"현재 임계값은 EXPERT_PRIOR(3%/10%). 30건 누적 후 자동 교체."
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),
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}
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# [LC1] late_chase_block_precision — 프록시 100.0 금지, 실측값만
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precision_val = chosen.get("match_rate_pct") if chosen else None
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if precision_val is not None and len(op_candidates) < 30:
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# 표본 부족 시 precision 노출 자체를 WATCH_PENDING_SAMPLE으로 표기
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precision_label = "WATCH_PENDING_SAMPLE"
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else:
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precision_label = f"{precision_val}%" if precision_val is not None else "DATA_MISSING"
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result = {
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"formula_id": "LATE_CHASE_ATTRIBUTION_V1",
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"status": status,
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"samples": len(op_candidates) if op_candidates else int(alpha_fb.get("total_samples") or 0),
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"operational_samples": len(op_candidates),
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"gate_hit_miss_rate_published": True,
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# [LC1] velocity_decile_thresholds — 실측 분위 임계값
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"velocity_decile_thresholds": velocity_decile_thresholds,
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"metrics": {
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"late_chase_high_risk_count": len(high_risk),
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"late_chase_blocked_count": len(blocked),
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"pullback_wait_count": len(pullback_wait),
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"chase_entry_rate": float(alpha_fb.get("chase_entry_rate") or 0.0),
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"distribution_entry_rate": float(alpha_fb.get("distribution_entry_rate") or 0.0),
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"late_chase_proxy_field": proxy_field,
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"late_chase_proxy_mean": round(mean(proxy_values), 2) if proxy_values else None,
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"late_chase_proxy_min": round(min(proxy_values), 2) if proxy_values else None,
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"late_chase_proxy_max": round(max(proxy_values), 2) if proxy_values else None,
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# [LC1] 실측 precision — 프록시 100.0 금지
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"late_chase_block_precision_label": precision_label,
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"late_chase_proxy_match_rate_pct": chosen.get("match_rate_pct") if chosen else None,
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"late_chase_proxy_false_positive_rate_pct": chosen.get("false_positive_rate_pct") if chosen else None,
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},
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"policy": {
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"pilot_only_threshold": 0.25,
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"no_buy_days_threshold": 0.35,
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"applied_mode": (
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"NO_BUY_DAYS_3" if float(alpha_fb.get("chase_entry_rate") or 0.0) >= 0.35
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else "PILOT_ONLY" if float(alpha_fb.get("chase_entry_rate") or 0.0) >= 0.25
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else "NORMAL"
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),
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# [LC1] 현재 임계값 하드코딩 여부 명시
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"velocity_threshold_source": (
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"CALIBRATED_FROM_LEDGER" if len(proxy_values) >= 30 else "EXPERT_PRIOR_PENDING_CALIBRATION"
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),
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},
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"threshold_ledger": threshold_ledger,
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"watchlist": watchlist,
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"supporting_artifacts": [
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"Temp/proposal_evaluation_history.json",
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"Temp/entry_freshness_json",
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],
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"note": (
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"operational_samples는 proposal_evaluation_history의 비-REPLAY T+5 평가행이며, "
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"explicit velocity_1d가 없어 buy_timing_score를 entry-timing proxy로 사용. "
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"[LC1] samples<30 구간에서 precision/precision_label=WATCH_PENDING_SAMPLE."
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),
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}
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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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