feat: fix rebalance plan for MID cap 75% violation and implement validate_factor_lifecycle_completeness_v1.py

tools/build_horizon_rebalance_plan_v1.py

@@ -19,7 +19,7 @@ DEFAULT_JSON = ROOT / "GatherTradingData.json"
 DEFAULT_OUT = TEMP / "horizon_rebalance_plan_v1.json"
 FORMULA_ID = "HORIZON_REBALANCE_PLAN_V1"
 
-SHORT_CAP_PCT = 40.0
+HORIZON_CAPS = {"SHORT": 40.0, "MID": 50.0, "LONG": 80.0}
 
 
 def _load(path: Path) -> Any:
@@ -69,17 +69,10 @@ def main() -> int:
     routing = _load(TEMP / "strategy_routing_audit_v1.json")
 
     alloc = hz.get("allocation_pct") or {}
-    short_pct = _f(alloc.get("SHORT", 0))
-    excess_pct = max(0.0, short_pct - SHORT_CAP_PCT)
-
-    # SHORT 종목 목록 (horizon_classification)
     hz_rows = hz.get("rows") or []
-    short_tickers = [r for r in hz_rows if isinstance(r, dict) and r.get("horizon") == "SHORT"]
-
-    # final_judgment_gate의 verdict와 confidence 병합
     fj_map = {r.get("ticker"): r for r in (fj.get("rows") or []) if isinstance(r, dict)}
 
-    # 총 포트폴리오 자산
+    # 총 포트폴리오 자산 및 주식 자산 산출
     total_asset = _f(harness.get("total_asset_krw", 0))
     portfolio_equity = total_asset - _f(harness.get("settlement_cash_d2_krw", 0))
 
@@ -93,78 +86,128 @@ def main() -> int:
         if isinstance(item, dict):
             weight_map[str(item.get("ticker", ""))] = _f(item.get("weight_pct", 0))
 
-    # SHORT 종목별 리밸런싱 우선순위 산출
-    # 우선순위: SELL verdict > 낮은 confidence > 높은 weight
-    candidates = []
-    for r in short_tickers:
-        ticker = r.get("ticker", "")
-        fj_row = fj_map.get(ticker, {})
-        verdict = str(fj_row.get("action_verdict", "UNKNOWN"))
-        conf = _f(fj_row.get("effective_confidence", 50))
-        weight_pct = weight_map.get(ticker, 0)
-        market_value = portfolio_equity * weight_pct / 100 if portfolio_equity > 0 else 0
-        disparity = _f(r.get("disparity_pct", 0))
-        rsi14 = _f(r.get("rsi14", 50))
-
-        # 우선순위 점수 (높을수록 먼저 줄임)
-        priority = 0
-        if verdict in ("SELL",): priority += 40
-        elif verdict in ("TRIM",): priority += 20
-        priority += max(0, 60 - conf)          # confidence 낮을수록 +
-        priority += max(0, disparity - 5) * 2  # 이격도 높을수록 +
-        priority += max(0, rsi14 - 60) * 0.5   # RSI 과매수일수록 +
-
-        candidates.append({
-            "ticker": ticker,
-            "name": r.get("name", ""),
-            "horizon": "SHORT",
-            "verdict": verdict,
-            "effective_confidence": conf,
-            "weight_pct": weight_pct,
-            "market_value_krw": round(market_value),
-            "disparity_pct": disparity,
-            "rsi14": rsi14,
-            "priority_score": round(priority, 1),
-        })
-
-    candidates.sort(key=lambda x: x["priority_score"], reverse=True)
-
-    # 목표: SHORT 비중을 40%로 줄이기 위한 최소 감축량
-    target_short_pct = SHORT_CAP_PCT
-    # 단순 비례: 현재 71.4% → 40% = 31.4%p 감축 필요
-    # 각 종목의 비중을 합산해 필요 감축 시뮬레이션
-    required_reduction_pct = excess_pct  # 31.4%p (SHORT 내 비중)
-    # 절대 금액 환산 (portfolio_equity 기준)
-    required_reduction_krw = portfolio_equity * required_reduction_pct / 100 if portfolio_equity > 0 else 0
-
-    # 누적 시뮬레이션
-    cum_reduction = 0.0
+    # 호라이즌별 산출 데이터 저장소
+    horizon_results = {}
     plan_rows = []
-    for c in candidates:
-        if cum_reduction >= required_reduction_pct:
-            break
-        # 해당 종목 전량 매도 시 감축 pct (portfolio_equity 기준)
-        trim_pct = c["weight_pct"]  # 포트폴리오 비중 = 감축 효과
-        action = "FULL_TRIM" if verdict == "SELL" else "PARTIAL_TRIM"
-        plan_rows.append({
-            **c,
-            "recommended_action": action,
-            "trim_weight_pct": round(trim_pct, 2),
-            "cum_short_reduction_pct": round(cum_reduction + trim_pct, 2),
-        })
-        cum_reduction += trim_pct
+
+    for H, cap_pct in HORIZON_CAPS.items():
+        current_pct = _f(alloc.get(H, 0))
+        excess_pct = max(0.0, current_pct - cap_pct)
+        required_reduction_pct = excess_pct
+        required_reduction_krw = portfolio_equity * required_reduction_pct / 100 if portfolio_equity > 0 else 0
+
+        # 해당 호라이즌 종목 목록 추출
+        h_tickers = [r for r in hz_rows if isinstance(r, dict) and r.get("horizon") == H]
+
+        candidates = []
+        for r in h_tickers:
+            ticker = r.get("ticker", "")
+            fj_row = fj_map.get(ticker, {})
+            verdict = str(fj_row.get("action_verdict", "UNKNOWN"))
+            conf = _f(fj_row.get("effective_confidence", 50))
+            weight_pct = weight_map.get(ticker, 0)
+            market_value = portfolio_equity * weight_pct / 100 if portfolio_equity > 0 else 0
+            disparity = _f(r.get("disparity_pct", 0))
+            rsi14 = _f(r.get("rsi14", 50))
+
+            # 우선순위 점수 산출 (기존 로직 유지)
+            priority = 0
+            if verdict in ("SELL",): priority += 40
+            elif verdict in ("TRIM",): priority += 20
+            priority += max(0, 60 - conf)
+            priority += max(0, disparity - 5) * 2
+            priority += max(0, rsi14 - 60) * 0.5
+
+            candidates.append({
+                "ticker": ticker,
+                "name": r.get("name", ""),
+                "horizon": H,
+                "verdict": verdict,
+                "effective_confidence": conf,
+                "weight_pct": weight_pct,
+                "market_value_krw": round(market_value),
+                "disparity_pct": disparity,
+                "rsi14": rsi14,
+                "priority_score": round(priority, 1),
+            })
+
+        candidates.sort(key=lambda x: x["priority_score"], reverse=True)
+
+        # 누적 감축 계획 시뮬레이션
+        cum_reduction = 0.0
+        h_plan_rows = []
+        if excess_pct > 0:
+            for c in candidates:
+                if cum_reduction >= required_reduction_pct:
+                    break
+                trim_pct = c["weight_pct"]
+                action = "FULL_TRIM" if c["verdict"] == "SELL" else "PARTIAL_TRIM"
+                plan_row = {
+                    **c,
+                    "recommended_action": action,
+                    "trim_weight_pct": round(trim_pct, 2),
+                }
+                if H == "SHORT":
+                    plan_row["cum_short_reduction_pct"] = round(cum_reduction + trim_pct, 2)
+                elif H == "MID":
+                    plan_row["cum_mid_reduction_pct"] = round(cum_reduction + trim_pct, 2)
+                elif H == "LONG":
+                    plan_row["cum_long_reduction_pct"] = round(cum_reduction + trim_pct, 2)
+                
+                h_plan_rows.append(plan_row)
+                cum_reduction += trim_pct
+
+        estimated_after_plan = max(0.0, current_pct - cum_reduction)
+        gate_status = "PASS" if estimated_after_plan <= cap_pct else "FAIL"
+
+        horizon_results[H] = {
+            "current_pct": current_pct,
+            "cap_pct": cap_pct,
+            "excess_pct": round(excess_pct, 1),
+            "required_reduction_pct": round(required_reduction_pct, 1),
+            "required_reduction_krw": round(required_reduction_krw),
+            "estimated_after_plan": round(estimated_after_plan, 1),
+            "gate_status": gate_status,
+            "candidates": candidates,
+            "plan_rows": h_plan_rows,
+        }
+        plan_rows.extend(h_plan_rows)
+
+    # 전체 게이트 판정
+    all_gate_status = "PASS" if all(res["gate_status"] == "PASS" for res in horizon_results.values()) else "FAIL"
 
     result = {
         "formula_id": FORMULA_ID,
-        "current_short_pct": short_pct,
-        "short_cap_pct": SHORT_CAP_PCT,
-        "excess_pct": round(excess_pct, 1),
-        "required_reduction_pct": round(required_reduction_pct, 1),
-        "required_reduction_krw": round(required_reduction_krw),
-        "estimated_short_after_plan": round(max(0, short_pct - cum_reduction), 1),
-        "gate_after_plan": "PASS" if max(0, short_pct - cum_reduction) <= SHORT_CAP_PCT else "FAIL",
+        
+        # 하위 호환성 필드 (SHORT 기준)
+        "current_short_pct": horizon_results["SHORT"]["current_pct"],
+        "short_cap_pct": horizon_results["SHORT"]["cap_pct"],
+        "excess_pct": horizon_results["SHORT"]["excess_pct"],
+        "required_reduction_pct": horizon_results["SHORT"]["required_reduction_pct"],
+        "required_reduction_krw": horizon_results["SHORT"]["required_reduction_krw"],
+        "estimated_short_after_plan": horizon_results["SHORT"]["estimated_after_plan"],
+        "gate_after_plan": all_gate_status,
+        
+        # 신규 확장 필드 (MID 기준)
+        "current_mid_pct": horizon_results["MID"]["current_pct"],
+        "mid_cap_pct": horizon_results["MID"]["cap_pct"],
+        "mid_excess_pct": horizon_results["MID"]["excess_pct"],
+        "required_mid_reduction_pct": horizon_results["MID"]["required_reduction_pct"],
+        "required_mid_reduction_krw": horizon_results["MID"]["required_reduction_krw"],
+        "estimated_mid_after_plan": horizon_results["MID"]["estimated_after_plan"],
+        
+        # 신규 확장 필드 (LONG 기준)
+        "current_long_pct": horizon_results["LONG"]["current_pct"],
+        "long_cap_pct": horizon_results["LONG"]["cap_pct"],
+        "long_excess_pct": horizon_results["LONG"]["excess_pct"],
+        "required_long_reduction_pct": horizon_results["LONG"]["required_reduction_pct"],
+        "required_long_reduction_krw": horizon_results["LONG"]["required_reduction_krw"],
+        "estimated_long_after_plan": horizon_results["LONG"]["estimated_after_plan"],
+
         "plan_rows": plan_rows,
-        "all_short_candidates": candidates,
+        "all_short_candidates": horizon_results["SHORT"]["candidates"],
+        "all_mid_candidates": horizon_results["MID"]["candidates"],
+        "all_long_candidates": horizon_results["LONG"]["candidates"],
         "note": (
             "포트폴리오 total_asset 기준 시뮬레이션. "
             "실제 weight_pct는 prices_json 기준이며 "
@@ -174,9 +217,9 @@ def main() -> int:
 
     out_path.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
     print(
-        f"[{FORMULA_ID}] SHORT={short_pct}% excess={excess_pct}%p "
+        f"[{FORMULA_ID}] SHORT={result['current_short_pct']}%(excess={result['excess_pct']}%p) "
+        f"MID={result['current_mid_pct']}%(excess={result['mid_excess_pct']}%p) "
         f"plan_tickers={[r['ticker'] for r in plan_rows]} "
-        f"after_plan={result['estimated_short_after_plan']}% "
         f"gate={result['gate_after_plan']} -> {out_path}"
     )
     return 0