feat(quant-engine): v8.9 제안서 P0-P3 로드맵 채택 — 15개 의사결정 엔진 신규 구현

suggest/quant_investment_engine_v8_9_portfolio_optimizer_canonical_refactored.yaml의
implementation_todo_v8_9(P0~P4) 전체를 spec/tool/golden case 레벨로 구현.

- P0: PORTFOLIO_TRANSITION_UTILITY_V1, SELL_LOT_PARETO_SELECTOR_V1, FORECAST_SIMULATION_ENGINE_V1
- P1: SECTOR_EXPOSURE_GRAPH_V1/LEADER_LIFECYCLE_GATE_V1, EXECUTION_CAPACITY_LADDER_V1, MODEL_GOVERNANCE_KILL_SWITCH_V1
- P2: SCENARIO_SHOCK_MATRIX_V1, TRANSITION_SET_ENUMERATOR_V1, IMMUTABLE_DECISION_LEDGER_V1, EXECUTION_PLAN_COMPILER_V1
- P3: STATE_VECTOR_CONSTRUCTOR_V1, WALK_FORWARD_BOOTSTRAP_V1, TRANSITION_SET_ENUMERATOR_V1(MRC/CVaR 확장),
      REBALANCE_CADENCE_GATE_V1, WEEKLY_LEGACY_TRANSFER_PLAN_V1

기존 regime/cluster 연동 정책 수치(현금방어선, 반도체 cap)는 그대로 유지하고 신규 cap 필드만 추가.
spec/09_decision_flow.yaml과 runtime/active_artifact_manifest.yaml에 전 엔진 배선 완료.
governance/todo/v8_9_p{0,1,2,3}_adoption_plan.yaml에 각 단계 작업 추적 기록.

검증: validate_specs/validate_golden_coverage_100(100%)/validate_calibration_registry_v1/
validate_schema_model_generation_v1/validate_agents_shrink_v1 전부 PASS. golden test 53/53 PASS.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

tools/build_sector_exposure_graph_v1.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""SECTOR_EXPOSURE_GRAPH_V1 + LEADER_LIFECYCLE_GATE_V1 — spec/formulas/domains/sector.yaml.
+
+ETF lookthrough exposure + factor beta residualization + leader role promotion/demotion.
+governance/todo/v8_9_p1_adoption_plan.yaml P1-A.3.
+
+Hard rule (AGENTS.md): missing ETF constituents or peer betas are never assumed zero.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_POSITIONS = ROOT / "Temp" / "sector_exposure_positions_v1.json"
+DEFAULT_OUT = ROOT / "Temp" / "sector_exposure_graph_v1.json"
+
+PROMOTION_PATH = ["LAGGARD", "CYCLICAL_BETA", "ENABLER", "CORE_LEADER", "CAPTAIN"]
+
+
+def _load(path: Path) -> dict:
+    if not path.exists():
+        return {}
+    try:
+        data = json.loads(path.read_text(encoding="utf-8"))
+        return data if isinstance(data, dict) else {}
+    except Exception:
+        return {}
+
+
+def sector_exposure(position: dict) -> dict:
+    direct_weight_pct = float(position.get("direct_weight_pct") or 0.0)
+    etf_constituents = position.get("etf_constituents_json")
+    etf_weight_pct = position.get("etf_weight_pct")
+    sector_id = position.get("sector_id")
+
+    if etf_constituents is None or etf_weight_pct is None:
+        return {
+            "sector_id": sector_id,
+            "direct_weight_pct": direct_weight_pct,
+            "lookthrough_etf_weight_pct": None,
+            "sector_family_total_pct": None,
+            "gate": "ETF_BUY_BLOCKED",
+            "reason_code": "constituents_missing",
+        }
+
+    lookthrough = sum(
+        float(c.get("weight_pct", 0.0)) * float(etf_weight_pct) / 100.0
+        for c in etf_constituents
+        if isinstance(c, dict) and c.get("sector_id") == sector_id
+    )
+    sector_family_total_pct = direct_weight_pct + lookthrough
+    return {
+        "sector_id": sector_id,
+        "direct_weight_pct": direct_weight_pct,
+        "lookthrough_etf_weight_pct": lookthrough,
+        "sector_family_total_pct": sector_family_total_pct,
+        "gate": "PASS",
+    }
+
+
+def residualize_factor_beta(factor_beta_raw: float, peer_sector_betas: list | None) -> tuple[float | None, str]:
+    if peer_sector_betas is None:
+        return factor_beta_raw, "PARTIAL_raw_beta_peer_data_missing"
+    shared_variance = sum(float(p.get("shared_variance", 0.0)) for p in peer_sector_betas if isinstance(p, dict))
+    return factor_beta_raw - shared_variance, "PASS"
+
+
+def evaluate_leader_role(position: dict) -> dict:
+    required_fields = [
+        "relative_strength_leads_sector",
+        "volume_quality_confirmed",
+        "above_ma60_or_reclaim_confirmed",
+        "earnings_revision_status",
+        "institutional_flow_status",
+    ]
+    current_role = position.get("current_role") or "LAGGARD"
+    if any(position.get(f) is None for f in required_fields):
+        return {"leader_role": current_role, "role_transition_reason": "DATA_MISSING", "role_changed": False}
+
+    demotion = (
+        (position["above_ma60_or_reclaim_confirmed"] is False and position["institutional_flow_status"] == "distribution")
+        or position["earnings_revision_status"] == "negative"
+        or (
+            position["institutional_flow_status"] == "distribution"
+            and current_role in ("CAPTAIN", "CORE_LEADER")
+        )
+    )
+    if demotion:
+        return {
+            "leader_role": "DISTRIBUTION_RISK",
+            "role_transition_reason": "demotion_trigger",
+            "role_changed": current_role != "DISTRIBUTION_RISK",
+        }
+
+    promotion_ok = (
+        position["relative_strength_leads_sector"] is True
+        and position["volume_quality_confirmed"] is True
+        and position["above_ma60_or_reclaim_confirmed"] is True
+        and position["earnings_revision_status"] != "negative"
+        and position["institutional_flow_status"] != "distribution"
+    )
+    if promotion_ok and current_role in PROMOTION_PATH:
+        idx = PROMOTION_PATH.index(current_role)
+        next_role = PROMOTION_PATH[min(idx + 1, len(PROMOTION_PATH) - 1)]
+        return {
+            "leader_role": next_role,
+            "role_transition_reason": "promotion_requires_all_satisfied",
+            "role_changed": next_role != current_role,
+        }
+
+    return {"leader_role": current_role, "role_transition_reason": "no_change", "role_changed": False}
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--positions", default=str(DEFAULT_POSITIONS))
+    ap.add_argument("--out", default=str(DEFAULT_OUT))
+    args = ap.parse_args()
+
+    doc = _load(Path(args.positions))
+    positions = doc.get("positions") if isinstance(doc.get("positions"), list) else []
+
+    rows = []
+    for position in positions:
+        if not isinstance(position, dict):
+            continue
+        exposure = sector_exposure(position)
+        leader = evaluate_leader_role(position)
+        beta_residualized, beta_status = residualize_factor_beta(
+            float(position.get("factor_beta_raw") or 0.0), position.get("peer_sector_betas")
+        )
+        rows.append({**exposure, **leader, "factor_beta_residualized": beta_residualized, "beta_status": beta_status})
+
+    result = {
+        "formula_id": "SECTOR_EXPOSURE_GRAPH_V1",
+        "gate": "PASS" if rows else "DATA_MISSING",
+        "rows": rows,
+        "source_paths": [str(Path(args.positions))],
+    }
+    out = Path(args.out)
+    out.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())