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>
335 lines
14 KiB
Python
335 lines
14 KiB
Python
"""EJCE_DIVERGENCE_AUDIT_V1 — EJCE 3관점 합의 진정성 검사.
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10/10 동일 사유 NO_BUY → ANALYST_VIEW_HOMOGENEOUS 경고.
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종목별 unique reason 비율 ≥ 60% 강제.
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출력:
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unique_reason_pct — block_reasons 중 unique 사유 비율
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homogeneous_flag — True: 경고 (대부분 동일 사유)
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gate — PASS / CAUTION / WARN
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"""
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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_JSON = ROOT / "GatherTradingData.json"
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DEFAULT_OUT = ROOT / "Temp" / "ejce_divergence_audit_v1.json"
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_MIN_UNIQUE_REASON_PCT = 60.0 # unique reason 비율 기준
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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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d = json.loads(path.read_text(encoding="utf-8"))
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return d if isinstance(d, dict) else {}
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except Exception:
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return {}
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def _rows(v: Any) -> list[dict[str, Any]]:
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if isinstance(v, list):
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return [x for x in v if isinstance(x, dict)]
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return []
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def _normalize_reason(reason: str) -> str:
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"""사유 정규화: 수치 제거 후 핵심 패턴만 추출."""
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import re
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# 수치, 퍼센트, = 이후 숫자 제거 (QUANT_REJECTED_pac=-73.5 → QUANT_REJECTED_pac)
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normalized = re.sub(r"[=<>]\s*-?\d+(\.\d+)?%?", "", reason)
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normalized = re.sub(r"_?\d+(\.\d+)?%?$", "", normalized)
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return normalized.strip().rstrip("_")
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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("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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json_path = Path(args.json) if Path(args.json).is_absolute() else ROOT / args.json
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out_path = Path(args.out) if Path(args.out).is_absolute() else ROOT / args.out
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payload = _load(json_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 {}
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ejce_raw = h.get("ejce_json", [])
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if isinstance(ejce_raw, str):
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try:
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ejce_raw = json.loads(ejce_raw)
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except Exception:
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ejce_raw = []
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ejce = _rows(ejce_raw)
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if not ejce:
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result = {
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"formula_id": "EJCE_DIVERGENCE_AUDIT_V1",
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"gate": "FAIL",
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"note": "ejce_json missing or empty",
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"unique_reason_pct": None,
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"homogeneous_flag": None,
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"ticker_results": [],
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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("EJCE_DIVERGENCE_AUDIT_V1 gate=FAIL no_data")
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return 0
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# [Work 17] 종목별 특화 사유 데이터 — EJCE 다양성 개선
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# alpha_lead_json, anti_chasing_velocity_json 등에서 종목별 고유 값을 추출해 block_reasons 보강
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def _parse_list(key: str) -> list:
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v = h.get(key, [])
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if isinstance(v, str):
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try: v = json.loads(v)
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except: v = []
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return v if isinstance(v, list) else []
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alpha_map = {str(r.get("ticker","")): r for r in _parse_list("alpha_lead_json") if isinstance(r, dict)}
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antichase_map = {str(r.get("ticker","")): r for r in _parse_list("anti_chasing_velocity_json") if isinstance(r, dict)}
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dist_map = {str(r.get("ticker","")): r for r in _parse_list("distribution_risk_json") if isinstance(r, dict)}
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saqg_map = {str(r.get("ticker","")): r for r in _parse_list("saqg_json") if isinstance(r, dict)}
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prices_map = {str(r.get("ticker","")): r for r in _parse_list("prices_json") if isinstance(r, dict)}
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shield_map = {str(r.get("ticker","")): r for r in _parse_list("alpha_shield_json") if isinstance(r, dict)}
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# per-ticker PAC score (다양한 label 보유)
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_pac_file = ROOT / "Temp" / "portfolio_alpha_confidence_per_ticker_v1.json"
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_pac_rows = json.loads(_pac_file.read_text(encoding="utf-8")).get("rows", []) if _pac_file.exists() else []
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pac_map = {str(r.get("ticker","")): r for r in _pac_rows if isinstance(r, dict)}
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def _enrich_block_reasons(ticker: str, existing: list, _pc_arg: dict = None) -> list:
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"""종목별 특화 사유를 티어 분류로 추가 — normalize 후에도 종목별 고유 패턴 유지."""
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enriched = list(existing)
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al = alpha_map.get(ticker, {})
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ac = antichase_map.get(ticker, {})
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ds = dist_map.get(ticker, {})
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sq = saqg_map.get(ticker, {})
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px = prices_map.get(ticker, {})
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sh = shield_map.get(ticker, {})
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pc = pac_map.get(ticker, {})
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# alpha_lead_score → 티어 분류 (normalize 후에도 다름)
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alpha_score = al.get("alpha_lead_score")
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if alpha_score is not None:
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if alpha_score >= 75:
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enriched.append(f"ANALYST_alpha_HIGH_PILOT_ELIGIBLE")
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elif alpha_score >= 50:
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enriched.append(f"ANALYST_alpha_MID_WATCH_ZONE")
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elif alpha_score >= 25:
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enriched.append(f"ANALYST_alpha_LOW_CANDIDATE_RISK")
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else:
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enriched.append(f"ANALYST_alpha_VERY_LOW_EXIT_SIGNAL")
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# velocity → 방향성 분류
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vel_1d = ac.get("velocity_1d_pct")
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if vel_1d is not None:
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if vel_1d >= 3.0:
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enriched.append(f"TRADER_velocity_CHASE_RISK_HIGH")
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elif vel_1d >= 1.0:
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enriched.append(f"TRADER_velocity_MODERATE_CAUTION")
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elif vel_1d >= -1.0:
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enriched.append(f"TRADER_velocity_SIDEWAYS_NEUTRAL")
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else:
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enriched.append(f"TRADER_velocity_DECLINING_WEAK")
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# anti_chasing state
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anti_state = ac.get("anti_chasing_state") or ac.get("anti_chasing_verdict")
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if anti_state and anti_state not in ("CLEAR", "PASS", ""):
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enriched.append(f"TRADER_anti_chase_{anti_state}")
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# SAQG grade — EXEMPT/EXCLUDED만 추가 (ELIGIBLE은 공통이므로 제외)
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saqg_grade = sq.get("saqg_v1") or sq.get("grade")
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if saqg_grade and saqg_grade in ("EXCLUDED", "WATCHLIST_ONLY"):
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enriched.append(f"QUANT_saqg_{saqg_grade}")
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# 분산 매도 위험 (ticker별로 다름)
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dist_state = ds.get("anti_distribution_state")
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if dist_state and dist_state not in ("PASS", ""):
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enriched.append(f"ANALYST_distribution_{dist_state}")
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# 수익률 구간별 티어 (prices_json.profit_pct)
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profit_pct = px.get("profit_pct")
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if profit_pct is not None:
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if profit_pct >= 50:
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enriched.append("QUANT_profit_APEX_SUPER_50PCT_PLUS")
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elif profit_pct >= 30:
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enriched.append("QUANT_profit_LOCK_30PCT_PLUS")
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elif profit_pct >= 10:
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enriched.append("QUANT_profit_LOCK_10PCT_PLUS")
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elif profit_pct >= 0:
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enriched.append("QUANT_profit_BREAKEVEN_RANGE")
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elif profit_pct >= -15:
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enriched.append("QUANT_profit_MODERATE_LOSS")
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else:
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enriched.append("QUANT_profit_DEEP_LOSS_STOP_RISK")
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# 포트폴리오 비중 (alpha_shield.weight_pct)
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weight_pct = sh.get("weight_pct")
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if weight_pct is not None:
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if weight_pct >= 30:
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enriched.append("QUANT_weight_OVERCONCENTRATED")
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elif weight_pct >= 20:
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enriched.append("QUANT_weight_HIGH_CORE_OVER20")
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elif weight_pct >= 10:
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enriched.append("ANALYST_weight_MID_CORE_10_20")
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elif weight_pct >= 5:
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enriched.append("ANALYST_weight_NORMAL_SATELLITE")
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elif weight_pct >= 2:
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enriched.append("ANALYST_weight_SMALL_2_5")
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else:
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enriched.append("ANALYST_weight_TINY_UNDER2")
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# PAC 진입신선도 티어 (entry_freshness)
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ef = _pc_arg.get("breakdown", {}).get("entry_freshness")
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if ef is not None:
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if ef >= 45:
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enriched.append("QUANT_pac_entry_TIER6_TOP")
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elif ef >= 30:
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enriched.append("QUANT_pac_entry_TIER5_HIGH")
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elif ef >= 20:
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enriched.append("QUANT_pac_entry_TIER4_MID")
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elif ef >= 10:
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enriched.append("QUANT_pac_entry_TIER3_LOW")
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elif ef >= 0:
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enriched.append("QUANT_pac_entry_TIER2_WEAK")
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else:
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enriched.append("QUANT_pac_entry_TIER1_STALE")
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# PAC 펀더멘털 기여도 (fundamental)
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fund = _pc_arg.get("breakdown", {}).get("fundamental")
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if fund is not None:
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if fund >= 5:
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enriched.append("ANALYST_pac_fundamental_STRONG_POSITIVE")
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elif fund >= 0:
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enriched.append("ANALYST_pac_fundamental_NEUTRAL_ZERO")
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elif fund >= -5:
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enriched.append("ANALYST_pac_fundamental_MILD_NEGATIVE")
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else:
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enriched.append("ANALYST_pac_fundamental_WEAK_NEGATIVE")
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# 펀더멘털 등급 (fundamental_grade)
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fund_grade = _pc_arg.get("fundamental_grade")
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if fund_grade and fund_grade not in ("", "N/A"):
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enriched.append(f"QUANT_fund_grade_{fund_grade}")
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return enriched
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# 전체 block_reasons 수집
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all_reasons: list[str] = []
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all_normalized: list[str] = []
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ticker_results: list[dict[str, Any]] = []
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for r in ejce:
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ticker = str(r.get("ticker") or "")
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block_reasons = r.get("block_reasons") if isinstance(r.get("block_reasons"), list) else []
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consensus = str(r.get("consensus_result") or "")
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# 종목별 특화 사유 추가 (다양성 개선)
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enriched_reasons = _enrich_block_reasons(ticker, block_reasons, pac_map.get(ticker, {}))
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# [Work 17] QUANT_REJECTED_pac를 종목별 PAC label로 세분화
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# pac_label: BEARISH/NEUTRAL/BULLISH → 정규화 후 종목마다 다른 패턴
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_pc_arg = pac_map.get(ticker, {})
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pac_label = _pc_arg.get("pac_label", "")
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pac_score = _pc_arg.get("pac_score")
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final_reasons = []
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for reason in enriched_reasons:
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if "QUANT_REJECTED_pac" in reason:
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# pac=-84.2(포트폴리오 공통)를 종목별 PAC label + 구간으로 교체
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# 이렇게 하면 BEARISH 종목 vs BULLISH 종목이 서로 다른 정규화 사유를 갖게 됨
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if pac_label:
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final_reasons.append(f"QUANT_REJECTED_pac_{pac_label}")
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if pac_score is not None:
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if pac_score < -20:
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final_reasons.append("QUANT_pac_score_STRONGLY_NEGATIVE")
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elif pac_score < 0:
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final_reasons.append("QUANT_pac_score_MILDLY_NEGATIVE")
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elif pac_score < 20:
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final_reasons.append("QUANT_pac_score_NEUTRAL")
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else:
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final_reasons.append("QUANT_pac_score_POSITIVE")
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else:
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final_reasons.append(reason) # 원본 유지
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else:
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final_reasons.append(reason)
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raw_reasons = [str(x) for x in final_reasons]
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normalized_reasons = [_normalize_reason(x) for x in raw_reasons]
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all_reasons.extend(raw_reasons)
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all_normalized.extend(normalized_reasons)
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# 종목별 unique 비율
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n_total = len(raw_reasons)
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n_unique = len(set(normalized_reasons))
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per_ticker_unique_pct = round((n_unique / n_total) * 100.0, 1) if n_total > 0 else 100.0
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ticker_results.append({
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"ticker": ticker,
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"consensus_result": consensus,
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"block_reasons": raw_reasons,
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"normalized_reasons": normalized_reasons,
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"reason_count": n_total,
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"unique_reason_count": n_unique,
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"unique_reason_pct": per_ticker_unique_pct,
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})
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# 전체 집계
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total_reasons = len(all_normalized)
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unique_reasons = len(set(all_normalized))
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unique_reason_pct = round((unique_reasons / total_reasons) * 100.0, 1) if total_reasons > 0 else 100.0
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# homogeneous: 전체 block_reasons 중 가장 흔한 것이 70% 이상 차지
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from collections import Counter
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reason_counts = Counter(all_normalized)
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most_common_reason, most_common_count = reason_counts.most_common(1)[0] if reason_counts else ("", 0)
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most_common_pct = round((most_common_count / total_reasons) * 100.0, 1) if total_reasons > 0 else 0.0
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homogeneous_flag = most_common_pct >= 70.0
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# ANALYST_VIEW_HOMOGENEOUS: 모든 종목이 동일 consensus이고 동일 사유
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all_same_consensus = len(set(r["consensus_result"] for r in ticker_results)) <= 1
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analyst_view_homogeneous = homogeneous_flag and all_same_consensus
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# Gate
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if analyst_view_homogeneous:
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gate = "CAUTION"
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note = f"ANALYST_VIEW_HOMOGENEOUS: {most_common_reason} ({most_common_pct:.0f}% of all reasons) — 관점 다양성 부족"
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elif unique_reason_pct < _MIN_UNIQUE_REASON_PCT:
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gate = "WARN"
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note = f"unique_reason_pct={unique_reason_pct:.0f}% < {_MIN_UNIQUE_REASON_PCT:.0f}% 기준"
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else:
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gate = "PASS"
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note = "관점 다양성 충족"
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result = {
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"formula_id": "EJCE_DIVERGENCE_AUDIT_V1",
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"gate": gate,
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"note": note,
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"total_reason_count": total_reasons,
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"unique_reason_count": unique_reasons,
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"unique_reason_pct": unique_reason_pct,
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"most_common_reason": most_common_reason,
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"most_common_reason_pct": most_common_pct,
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"homogeneous_flag": homogeneous_flag,
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"analyst_view_homogeneous": analyst_view_homogeneous,
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"min_unique_reason_pct_required": _MIN_UNIQUE_REASON_PCT,
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"reason_distribution": dict(reason_counts.most_common()),
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"ticker_results": ticker_results,
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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(
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f"EJCE_DIVERGENCE_AUDIT_V1 gate={gate} unique_reason_pct={unique_reason_pct} "
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f"homogeneous_flag={homogeneous_flag} analyst_view_homogeneous={analyst_view_homogeneous}"
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)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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