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_forecast_simulation_engine_v1.py

@@ -0,0 +1,155 @@
+#!/usr/bin/env python3
+"""FORECAST_SIMULATION_ENGINE_V1 — spec/formulas/domains/simulation.yaml.
+
+CE70/CE90/CVaR95 from a net-profit distribution, gated by minimum_sample_rules
+per execution_mode (governance/todo/v8_9_p0_adoption_plan.yaml P0-3.2).
+
+Hard rule (AGENTS.md): a missing or undersized sample is never treated as zero
+or filled with an estimate. spec/29_backtest_harness_contract.yaml currently
+reports T+20 realized sample count = 0 (insufficient_data), so this tool is
+expected to emit WATCH_ONLY with null outputs until real samples accumulate.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_BACKTEST_CONTRACT = ROOT / "spec" / "29_backtest_harness_contract.yaml"
+DEFAULT_DISTRIBUTION = ROOT / "Temp" / "net_profit_distribution_v1.json"
+DEFAULT_DECISION_PACKET = ROOT / "Temp" / "final_decision_packet_active.json"
+DEFAULT_OUT = ROOT / "Temp" / "forecast_simulation_engine_v1.json"
+
+MINIMUM_SAMPLE_RULES = {
+    "AUDIT_ONLY": {"sample_count_total_min": 0, "sample_count_same_regime_min": 0},
+    "SHADOW": {"sample_count_total_min": 30, "sample_count_same_regime_min": 10},
+    "PILOT": {"sample_count_total_min": 80, "sample_count_same_regime_min": 20},
+    "LIVE_LIMITED": {"sample_count_total_min": 150, "sample_count_same_regime_min": 30},
+    "LIVE_FULL": {"sample_count_total_min": 300, "sample_count_same_regime_min": 50},
+}
+
+
+def _load_json(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 _load_yaml(path: Path) -> dict:
+    if not path.exists():
+        return {}
+    try:
+        import yaml  # type: ignore
+
+        data = yaml.safe_load(path.read_text(encoding="utf-8"))
+        return data if isinstance(data, dict) else {}
+    except Exception:
+        return {}
+
+
+def _sample_counts_from_backtest_contract(contract: dict) -> tuple[int, int]:
+    metrics = contract.get("current_metrics") or {}
+    direction_accuracy = metrics.get("direction_accuracy") or {}
+    t20 = direction_accuracy.get("t20_op_rate") or {}
+    n_sample = t20.get("n_sample")
+    sample_count_total = n_sample if isinstance(n_sample, int) else 0
+    return sample_count_total, sample_count_total
+
+
+def _quantile(sorted_values: list[float], q: float) -> float:
+    if not sorted_values:
+        raise ValueError("empty distribution")
+    if len(sorted_values) == 1:
+        return sorted_values[0]
+    pos = q * (len(sorted_values) - 1)
+    lower = int(pos)
+    upper = min(lower + 1, len(sorted_values) - 1)
+    frac = pos - lower
+    return sorted_values[lower] + (sorted_values[upper] - sorted_values[lower]) * frac
+
+
+def _cvar95(sorted_values: list[float]) -> float:
+    threshold_idx = max(1, int(len(sorted_values) * 0.05))
+    tail = sorted_values[:threshold_idx]
+    return sum(tail) / len(tail)
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--backtest-contract", default=str(DEFAULT_BACKTEST_CONTRACT))
+    ap.add_argument("--distribution", default=str(DEFAULT_DISTRIBUTION))
+    ap.add_argument("--decision-packet", default=str(DEFAULT_DECISION_PACKET))
+    ap.add_argument("--out", default=str(DEFAULT_OUT))
+    args = ap.parse_args()
+
+    backtest_contract = _load_yaml(Path(args.backtest_contract))
+    distribution_doc = _load_json(Path(args.distribution))
+    decision_packet = _load_json(Path(args.decision_packet))
+
+    execution_mode = (
+        decision_packet.get("execution_mode")
+        or decision_packet.get("global_execution_gate")
+        or "AUDIT_ONLY"
+    )
+    rule = MINIMUM_SAMPLE_RULES.get(execution_mode, MINIMUM_SAMPLE_RULES["AUDIT_ONLY"])
+
+    distribution = distribution_doc.get("net_profit_distribution_after_tax_fee_slippage")
+    if isinstance(distribution, list) and distribution:
+        sample_count_total = len(distribution)
+        sample_count_same_regime = int(
+            distribution_doc.get("sample_count_same_regime") or sample_count_total
+        )
+    else:
+        sample_count_total, sample_count_same_regime = _sample_counts_from_backtest_contract(
+            backtest_contract
+        )
+
+    gate_ok = (
+        sample_count_total >= rule["sample_count_total_min"]
+        and sample_count_same_regime >= rule["sample_count_same_regime_min"]
+    )
+
+    if gate_ok and isinstance(distribution, list) and distribution:
+        sorted_values = sorted(float(v) for v in distribution)
+        result = {
+            "formula_id": "FORECAST_SIMULATION_ENGINE_V1",
+            "execution_mode": execution_mode,
+            "gate": "PASS",
+            "sample_count_total": sample_count_total,
+            "sample_count_same_regime": sample_count_same_regime,
+            "ce70_net_profit_krw": _quantile(sorted_values, 0.30),
+            "ce90_net_profit_krw": _quantile(sorted_values, 0.10),
+            "cvar95_loss_krw": _cvar95(sorted_values),
+        }
+    else:
+        result = {
+            "formula_id": "FORECAST_SIMULATION_ENGINE_V1",
+            "execution_mode": execution_mode,
+            "gate": "WATCH_ONLY",
+            "reason_code": "insufficient_data",
+            "sample_count_total": sample_count_total,
+            "sample_count_same_regime": sample_count_same_regime,
+            "minimum_required": rule,
+            "ce70_net_profit_krw": None,
+            "ce90_net_profit_krw": None,
+            "cvar95_loss_krw": None,
+        }
+
+    result["source_paths"] = [
+        str(Path(args.backtest_contract)),
+        str(Path(args.distribution)),
+        str(Path(args.decision_packet)),
+    ]
+    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())