feat(data): implement market-implied PD floor and 7x7 transition matrix parsing #task-290

.agents/references/architecture.md

@@ -76,6 +76,6 @@ C:\ProgramData\miniforge3\envs\quant\python.exe main.py --estimate-rho
 
 ## 미완료 사항 (다음 작업자 참고)
 
-1. **#290 실제 3사 전이행렬 CSV 변환** — `doc/`의 PDF에서 전이행렬을 추출하여 `data/raw/` CSV로 변환 필요. 현재는 builtin synthetic 데이터 사용 중
-2. **#293 거시 시나리오 고도화** — IMF WEO/KDI 전망치를 직접 입력하여 시나리오 정밀화
-3. **GDP 성장률 코드** — 현재 `902Y015/KOR` (국제 비교 통계) 사용. 한은 국민계정 직접 통계표코드 발견 시 교체 권장
+1. **#293 거시 시나리오 고도화** — IMF WEO/KDI 전망치를 직접 입력하여 시나리오 정밀화
+2. **GDP 성장률 코드** — 현재 `902Y015/KOR` (국제 비교 통계) 사용. 한은 국민계정 직접 통계표코드 발견 시 교체 권장
+3. **전처리 파이프라인 개선** — PD 플로어 적용, CCC B↔D 보간 방식 개선 (docs/methodology.md §2.0 참조)

.agents/references/known-issues.md

@@ -69,3 +69,9 @@
 - **원인**: 한국 경제 특수성(유가의존, 수출주도, 부동산 영향 등) 미반영
 - **해결**: 31개 변수로 확장 (KOSPI, OIL_PRICE, USDKRW, DISHONOR_RATE, HOUSING_PRICE, BSI_MANUF 등). `data/ecos_fetcher.py` + `data/cache/macro_ecos.csv` 캐싱 구조
 - **주의**: fallback 데이터는 대략적 수치. 정밀 분석 시 ECOS API 실제 호출 필요 (`--force` 옵션)
+
+### [2026-03-11] Windows Python — 소스 코드 특수문자 SyntaxError
+- **증상**: 파이썬 코드 내 문자열(예: docstring)에 `×` (U+00D7), `→` (U+2192) 등 특수문자나 한글 이외의 유니코드가 포함될 때, Windows CP949 환경에서 `SyntaxError: invalid character` 또는 `unterminated triple-quoted string literal` 로 파싱 오류가 발생함.
+- **원인**: Windows Python 3.12 런타임이 파일 맨 앞 `# -*- coding: utf-8 -*-` 이나 `-X utf8` 런타임 플래그도 소스 구문 분석 레벨에서 완벽하게 해석해내지 못하는 Windows 인코딩 고질병 문제 발생.
+- **해결**: 소스 코드 내에선 (특히 docstring 등 Python 인터프리터가 읽을 영역에서는) `×` 대신 `x` 또는 `by`를 사용하고, `→` 대신 `->` 로 ASCII 코드로 치환하여 작성함 (정규식 치환 등 활용). 한글 자체는 정상적으로 파싱됨.
+- **주의**: 모델링, 모듈 코드 작성 시 문자열이나 주석 내에 특수 유니코드 기호(×, →, —, §)를 직접 삽입하지 말고 안전한 ASCII 문자로 대체하여 코딩할 것.

.agents/workflows/services.md

@@ -125,4 +125,4 @@ python .agents\workflows\helpers\vikunja_helper.py list todo
 
 - `curl` → PowerShell에서 `Invoke-WebRequest`의 별칭. **반드시 `curl.exe`** 사용
 - `npm` → PowerShell에서 실행 정책 문제 시 `cmd /c npm` 사용
-- JSON 파이프 파싱 시 PowerShell 이스케이핑 문제 → `.py` 스크립트 파일로 만들어 실행 권장
+- JSON 파이프 파싱 시 PowerShell 이스케이핑 문제 → `.py` 스크립트 파일로 만들어 실행 권장

data/parse_pdf_matrices.py

@@ -29,7 +29,8 @@ if sys.stdout.encoding != 'utf-8':
     sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')
     sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')
 
-MODEL_GRADES = ["AAA", "AA", "A", "BBB", "BB", "B", "CCC", "D"]
+MODEL_GRADES_7 = ["AAA", "AA", "A", "BBB", "BB", "B", "D"]
+MODEL_GRADES_8 = ["AAA", "AA", "A", "BBB", "BB", "B", "CCC", "D"]
 GRADE_LABELS = ["AAA", "AA", "A", "BBB", "BB"]  # B이하 is separate
 
 BASE_DIR = Path(__file__).parent.parent
@@ -460,87 +461,72 @@ def _parse_kr_numbers(s: str) -> Optional[List[float]]:
 
 
 # ============================================================
-# 후처리: 6×8 → 8×8
+# 후처리: 6x8 -> 7x7 (WR->D 보정 + CCC 제거)
 # ============================================================
-def postprocess_matrix(raw_6x8: np.ndarray) -> np.ndarray:
-    """6×8 (AAA~B이하 × AAA~WR) → 8×8 (AAA~D × AAA~D)"""
+
+_BROAD_GRADE_MAP_6 = {0: "AAA", 1: "AA", 2: "A", 3: "BBB", 4: "BB", 5: "B"}
+
+
+def postprocess_matrix(raw_6x8, pd_floors=None):
+    """6x8 (AAA~B이하 x AAA~WR+D) -> 7x7 (AAA~B+D)
+
+    Steps:
+    1. PD floor correction: if observed PD < floor, transfer from WR to D
+    2. Remaining WR -> proportional redistribution
+    3. B이하 -> B mapping
+    4. Add D row (absorbing state)
+    5. Normalize rows to sum=1
+    """
     assert raw_6x8.shape == (6, 8), f"Expected (6,8), got {raw_6x8.shape}"
 
-    # WR 열(7) 제거 → 비례 재배분
-    mat_6x7 = raw_6x8[:, :7].copy()
-    for i in range(6):
-        row_sum = mat_6x7[i].sum()
-        if row_sum > 0:
-            mat_6x7[i] = mat_6x7[i] / row_sum * 100.0
+    mat = raw_6x8.copy()
+    COL_D = 6
+    COL_WR = 7
 
-    # 8×8 구성: B이하(5) → B(5), D:col6→col7
-    mat = np.zeros((8, 8))
+    # Step 1: PD floor correction (WR -> D transfer)
+    if pd_floors is not None:
+        for i in range(6):
+            broad = _BROAD_GRADE_MAP_6[i]
+            if broad not in pd_floors:
+                continue
+            floor_pct = pd_floors[broad] * 100  # decimal -> %
+            observed_pd = mat[i, COL_D]
+            wr_available = mat[i, COL_WR]
+            if observed_pd < floor_pct and wr_available > 0:
+                deficit = floor_pct - observed_pd
+                transfer = min(deficit, wr_available)
+                mat[i, COL_D] += transfer
+                mat[i, COL_WR] -= transfer
+
+    # Step 2: Remaining WR -> proportional redistribution
+    for i in range(6):
+        wr_remaining = mat[i, COL_WR]
+        if wr_remaining > 0:
+            non_wr_cols = mat[i, :7]
+            non_wr_sum = non_wr_cols.sum()
+            if non_wr_sum > 0:
+                mat[i, :7] = non_wr_cols * (non_wr_sum + wr_remaining) / non_wr_sum
+            mat[i, COL_WR] = 0.0
+
+    # Step 3: B이하 -> B mapping + build 7x7
+    mat_7x7 = np.zeros((7, 7))
     for i in range(6):
         for j in range(6):
-            mat[i, j] = mat_6x7[i, j]
-        mat[i, 7] = mat_6x7[i, 6]  # D
+            mat_7x7[i, j] = mat[i, j]
+        mat_7x7[i, 6] = mat[i, COL_D]
 
-    # CCC 행/열 extrapolation
-    mat = _extrapolate_ccc(mat)
+    # Step 4: D row (absorbing state)
+    mat_7x7[6, :] = 0.0
+    mat_7x7[6, 6] = 100.0
 
-    # D 행
-    mat[7, :] = 0.0
-    mat[7, 7] = 100.0
-
-    # → 확률, 행합 정규화
-    mat /= 100.0
-    for i in range(8):
-        s = mat[i].sum()
+    # Step 5: Convert to probability and normalize
+    mat_7x7 /= 100.0
+    for i in range(7):
+        s = mat_7x7[i].sum()
         if s > 0:
-            mat[i] /= s
+            mat_7x7[i] /= s
 
-    return mat
-
-
-def _extrapolate_ccc(mat: np.ndarray) -> np.ndarray:
-    """CCC 행/열 extrapolation from B이하 PD 패턴"""
-    pd_bb = mat[4, 7]
-    pd_b = mat[5, 7]
-
-    # CCC PD
-    if pd_bb > 0 and pd_b > pd_bb:
-        ratio = pd_b / pd_bb
-    else:
-        ratio = 2.5
-    pd_ccc = min(pd_b * ratio, 60.0)
-    pd_ccc = max(pd_ccc, pd_b * 1.5)
-
-    # Stay rates
-    stay_bb = mat[4, 4]
-    stay_b = mat[5, 5]
-    stay_ratio = (stay_b / stay_bb) if (stay_bb > 0 and stay_b < stay_bb) else 0.7
-    stay_ccc = max(stay_b * stay_ratio, 5.0)
-
-    upgrade_to_b = mat[5, 4] * 0.8 if mat[5, 4] > 0 else 2.0
-
-    # CCC 행
-    mat[6, :] = [0, 0, 0.1, 0.2, 0.3, upgrade_to_b, stay_ccc, pd_ccc]
-
-    ccc_sum = mat[6].sum()
-    if ccc_sum > 100:
-        mat[6, 6] = max(mat[6, 6] - (ccc_sum - 100), 1.0)
-    elif ccc_sum < 100:
-        mat[6, 6] += (100 - ccc_sum)
-
-    # CCC 열: B→CCC, BB→CCC, BBB→CCC 전이 분리
-    b_to_ccc = mat[5, 5] * 0.15
-    mat[5, 6] = b_to_ccc
-    mat[5, 5] -= b_to_ccc
-
-    bb_to_ccc = mat[4, 5] * 0.1 if mat[4, 5] > 0 else 0.5
-    mat[4, 6] = bb_to_ccc
-    mat[4, 5] = max(mat[4, 5] - bb_to_ccc, 0)
-
-    mat[3, 6] = 0.3
-    mat[3, 5] = max(mat[3, 5] - 0.15, 0)
-    mat[3, 3] = max(mat[3, 3] - 0.15, 0)
-
-    return mat
+    return mat_7x7
 
 
 # ============================================================
@@ -549,6 +535,17 @@ def _extrapolate_ccc(mat: np.ndarray) -> np.ndarray:
 def main():
     OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
 
+    # Load PD floors
+    broad_floors = None
+    try:
+        import sys as _sys
+        _sys.path.insert(0, str(BASE_DIR))
+        from data.pd_floor import build_complete_pd_floor_table
+        broad_floors, _, _ = build_complete_pd_floor_table()
+        print(f"  PD floor loaded: {', '.join(f'{g}={v*10000:.1f}bp' for g, v in broad_floors.items())}")
+    except Exception as e:
+        print(f"  PD floor load failed ({e}), proceeding without floor")
+
     all_matrices = {}
 
     for agency, pdf_path in PDF_FILES.items():
@@ -582,7 +579,7 @@ def main():
         processed = {}
         for year, raw_mat in sorted(raw.items()):
             try:
-                processed[year] = postprocess_matrix(raw_mat)
+                processed[year] = postprocess_matrix(raw_mat, pd_floors=broad_floors)
             except Exception as e:
                 print(f"  ERROR {year}: {e}")
 
@@ -590,7 +587,7 @@ def main():
         print(f"  Processed {len(processed)} matrices")
 
         for year, mat in processed.items():
-            df = pd.DataFrame(mat, index=MODEL_GRADES, columns=MODEL_GRADES)
+            df = pd.DataFrame(mat, index=MODEL_GRADES_7, columns=MODEL_GRADES_7)
             df.to_csv(OUTPUT_DIR / f"{agency}_{year}.csv", float_format="%.6f")
 
     # 3사 평균
@@ -609,11 +606,11 @@ def main():
 
     for year in common_years:
         avg = np.mean([all_matrices[a][year] for a in agency_names], axis=0)
-        for i in range(8):
+        for i in range(7):
             s = avg[i].sum()
             if s > 0:
                 avg[i] /= s
-        df = pd.DataFrame(avg, index=MODEL_GRADES, columns=MODEL_GRADES)
+        df = pd.DataFrame(avg, index=MODEL_GRADES_7, columns=MODEL_GRADES_7)
         df.to_csv(OUTPUT_DIR / f"AVG_{year}.csv", float_format="%.6f")
 
     # PD 요약
@@ -632,7 +629,7 @@ def main():
         if sample_year not in common_years and not any(sample_year in all_matrices[a] for a in agency_names):
             continue
         print(f"\n  Year {sample_year}:")
-        for gi, grade in enumerate(MODEL_GRADES[:-1]):
+        for gi, grade in enumerate(MODEL_GRADES_7[:-1]):
             print(f"  {grade:>5}:", end='')
             for a in agency_names:
                 if sample_year in all_matrices[a]:

data/pd_floor.py

@@ -0,0 +1,325 @@
+"""
+시장 YTM 기반 PD 플로어 산출 모듈
+
+KAP(한국자산평가) 등급별 회사채 수익률에서 신용스프레드를 추출하고,
+시장 내재 부도율(PD)을 산출한 뒤, AAA=5bp 앵커링 + 로그 스케일링으로
+각 등급의 PD 플로어를 결정합니다.
+
+핵심 공식:
+    PD_raw = spread / (1 - recovery_rate)        [LGD = 1 - RR]
+    scale = log(anchor_AAA) / log(PD_raw_AAA)
+    PD_floor(g) = exp(log(PD_raw(g)) × scale)
+
+참고:
+    - Basel III CRE30.4: 기업 PD 플로어 5bp (0.05%)
+    - 한국 시장 회수율: 약 40% (LGD = 60%)
+    - docs/pd_floor_reference.md 참조
+"""
+
+import numpy as np
+import logging
+from typing import Dict, Optional, Tuple, List
+
+logger = logging.getLogger(__name__)
+
+# 기본 파라미터
+DEFAULT_RECOVERY_RATE = 0.40   # 회수율 40% → LGD 60%
+DEFAULT_AAA_ANCHOR = 0.0005    # 5bp = 0.05% = 0.0005
+DEFAULT_LGD = 1 - DEFAULT_RECOVERY_RATE  # 0.60
+
+# 대등급 순서 (인덱스 기반 외삽에 사용)
+BROAD_GRADE_ORDER = ["AAA", "AA", "A", "BBB", "BB", "B"]
+BROAD_GRADE_INDEX = {g: i for i, g in enumerate(BROAD_GRADE_ORDER)}
+
+
+def compute_market_implied_pd(
+    spread_bp: float,
+    lgd: float = DEFAULT_LGD
+) -> float:
+    """
+    신용스프레드에서 시장 내재 PD 산출
+
+    PD ≈ spread / LGD
+
+    Parameters
+    ----------
+    spread_bp : float
+        신용스프레드 (bp, 1bp = 0.01%)
+    lgd : float
+        Loss Given Default (기본: 0.60)
+
+    Returns
+    -------
+    float
+        시장 내재 PD (소수, 예: 0.000727 = 7.27bp)
+    """
+    spread_decimal = spread_bp / 10000  # bp → 소수
+    return spread_decimal / lgd
+
+
+def compute_pd_floors(
+    spreads_bp: Dict[str, float],
+    anchor_aaa: float = DEFAULT_AAA_ANCHOR,
+    lgd: float = DEFAULT_LGD
+) -> Dict[str, float]:
+    """
+    AAA=5bp 앵커링 + 로그 스케일링으로 등급별 PD 플로어 산출
+
+    Parameters
+    ----------
+    spreads_bp : Dict[str, float]
+        등급별 신용스프레드 (bp). 대등급(AAA, AA, A, BBB) 기준.
+    anchor_aaa : float
+        AAA 앵커 PD (기본: 5bp = 0.0005)
+    lgd : float
+        Loss Given Default (기본: 0.60)
+
+    Returns
+    -------
+    Dict[str, float]
+        등급별 PD 플로어 (소수). 예: {'AAA': 0.0005, 'AA': 0.00057, ...}
+    """
+    # Step 1: 시장 내재 PD 산출
+    pd_raw = {}
+    for grade, spread in spreads_bp.items():
+        pd_raw[grade] = compute_market_implied_pd(spread, lgd)
+
+    if "AAA" not in pd_raw or pd_raw["AAA"] <= 0:
+        raise ValueError("AAA 스프레드가 0 이하입니다. 데이터를 확인하세요.")
+
+    # Step 2: 로그 스케일링
+    # scale = log(anchor) / log(pd_raw_AAA)
+    log_anchor = np.log(anchor_aaa)
+    log_aaa_raw = np.log(pd_raw["AAA"])
+    scale = log_anchor / log_aaa_raw
+
+    pd_floors = {}
+    for grade, pd_val in pd_raw.items():
+        pd_floors[grade] = np.exp(np.log(pd_val) * scale)
+
+    logger.info(f"PD 플로어 스케일링: scale={scale:.4f}, "
+                f"AAA raw={pd_raw['AAA']*10000:.1f}bp → floor={pd_floors['AAA']*10000:.1f}bp")
+
+    return pd_floors
+
+
+def extrapolate_speculative_grades(
+    pd_floors: Dict[str, float],
+    grades_to_extrapolate: List[str] = ["BB", "B"]
+) -> Dict[str, float]:
+    """
+    투자적격등급(AAA~BBB)의 로그 트렌드를 외삽하여 투기등급 PD 산출
+
+    log(PD) vs grade_index의 선형 트렌드를 BBB 이후로 연장합니다.
+
+    Parameters
+    ----------
+    pd_floors : Dict[str, float]
+        투자적격등급 PD 플로어 (AAA~BBB)
+    grades_to_extrapolate : List[str]
+        외삽할 등급 (기본: ["BB", "B"])
+
+    Returns
+    -------
+    Dict[str, float]
+        투기등급이 추가된 PD 플로어
+    """
+    # 기존 투자적격 등급의 log(PD) vs index 관계
+    available = []
+    for g in BROAD_GRADE_ORDER:
+        if g in pd_floors:
+            available.append((BROAD_GRADE_INDEX[g], np.log(pd_floors[g])))
+
+    if len(available) < 2:
+        raise ValueError("외삽을 위해 최소 2개 등급의 PD가 필요합니다.")
+
+    # 선형 회귀: log(PD) = a × index + b
+    x = np.array([a[0] for a in available])
+    y = np.array([a[1] for a in available])
+    coeffs = np.polyfit(x, y, 1)  # [기울기, 절편]
+
+    result = pd_floors.copy()
+    for grade in grades_to_extrapolate:
+        if grade in BROAD_GRADE_INDEX:
+            idx = BROAD_GRADE_INDEX[grade]
+            log_pd = coeffs[0] * idx + coeffs[1]
+            result[grade] = np.exp(log_pd)
+            logger.info(f"외삽 PD [{grade}]: {result[grade]*10000:.1f}bp "
+                       f"(index={idx}, log_pd={log_pd:.4f})")
+
+    return result
+
+
+def interpolate_ccc_pd(
+    pd_b: float,
+    pd_d: float = 1.0,
+    method: str = "geometric"
+) -> Dict[str, float]:
+    """
+    B와 D(=100%) 사이를 보간하여 CCC/CC/C 등급 PD 산출
+
+    Parameters
+    ----------
+    pd_b : float
+        B등급 PD (소수)
+    pd_d : float
+        D등급 PD (기본: 1.0 = 100%)
+    method : str
+        'geometric': 기하평균
+        'log': 로그 등간격 보간
+
+    Returns
+    -------
+    Dict[str, float]
+        {'CCC': ..., 'CC': ..., 'C': ...}
+    """
+    if method == "geometric":
+        # B → CCC → CC → C → D 순서로 기하적 등간격
+        # 4개 구간 (B→CCC, CCC→CC, CC→C, C→D)
+        log_b = np.log(pd_b)
+        log_d = np.log(pd_d)
+        step = (log_d - log_b) / 4
+
+        ccc_pd = np.exp(log_b + step * 1)
+        cc_pd = np.exp(log_b + step * 2)
+        c_pd = np.exp(log_b + step * 3)
+    else:  # log-linear
+        log_b = np.log(pd_b)
+        log_d = np.log(pd_d)
+        step = (log_d - log_b) / 4
+
+        ccc_pd = np.exp(log_b + step * 1)
+        cc_pd = np.exp(log_b + step * 2)
+        c_pd = np.exp(log_b + step * 3)
+
+    return {"CCC": ccc_pd, "CC": cc_pd, "C": c_pd}
+
+
+def compute_notch_pd_ratios(
+    notch_spreads: Dict[str, float],
+    lgd: float = DEFAULT_LGD
+) -> Dict[str, Dict[str, float]]:
+    """
+    노치등급 간 PD 비율 산출 (나중에 등급 쪼개기에 사용)
+
+    예: AA+ : AA : AA- = 0.91 : 1.00 : 1.07 (AA 대비 비율)
+
+    Parameters
+    ----------
+    notch_spreads : Dict[str, float]
+        노치등급별 스프레드 (bp)
+
+    Returns
+    -------
+    Dict[str, Dict[str, float]]
+        대등급별 {노치등급: 비율} 딕셔너리
+        예: {'AA': {'AA+': 0.91, 'AA': 1.00, 'AA-': 1.07}}
+    """
+    from data.ytm_fetcher import NOTCH_TO_BROAD
+    from collections import defaultdict
+
+    # 대등급별 노치 그룹핑
+    groups = defaultdict(dict)
+    for notch, spread in notch_spreads.items():
+        broad = NOTCH_TO_BROAD.get(notch, notch)
+        groups[broad][notch] = compute_market_implied_pd(spread, lgd)
+
+    # 중간 노치 대비 비율 계산
+    ratios = {}
+    for broad, notch_pds in groups.items():
+        if len(notch_pds) == 1:
+            key = list(notch_pds.keys())[0]
+            ratios[broad] = {key: 1.0}
+        else:
+            # 중간 노치(plain) 기준
+            mid_key = broad  # AA+ → AA가 중간
+            if mid_key in notch_pds:
+                mid_pd = notch_pds[mid_key]
+            else:
+                mid_pd = sorted(notch_pds.values())[len(notch_pds) // 2]
+
+            ratios[broad] = {
+                notch: pd / mid_pd for notch, pd in notch_pds.items()
+            }
+
+    return ratios
+
+
+def build_complete_pd_floor_table(
+    date: str = "2025-12-31",
+    anchor_aaa: float = DEFAULT_AAA_ANCHOR,
+    recovery_rate: float = DEFAULT_RECOVERY_RATE
+) -> Tuple[Dict[str, float], Dict[str, float], Dict[str, Dict[str, float]]]:
+    """
+    전체 PD 플로어 테이블 생성 (원스톱)
+
+    Parameters
+    ----------
+    date : str
+        YTM 기준일
+    anchor_aaa : float
+        AAA 앵커 PD (기본: 5bp)
+    recovery_rate : float
+        회수율 (기본: 0.40)
+
+    Returns
+    -------
+    Tuple of:
+        broad_floors : Dict[str, float] - 대등급 PD 플로어 (AAA~B+D)
+        notch_ratios : Dict[str, Dict[str, float]] - 노치 비율
+        full_floors : Dict[str, float] - CCC/CC/C 포함 전체 PD (AAA~C+D)
+    """
+    from data.ytm_fetcher import get_ytm_data, compute_spreads, compute_broad_grade_spreads
+
+    lgd = 1 - recovery_rate
+
+    # 1) YTM 데이터 수집
+    ytm_data = get_ytm_data(date)
+    notch_spreads = compute_spreads(ytm_data)
+    broad_spreads = compute_broad_grade_spreads(notch_spreads)
+
+    logger.info(f"대등급 스프레드: {broad_spreads}")
+
+    # 2) PD 플로어 산출 (투자적격)
+    pd_floors = compute_pd_floors(broad_spreads, anchor_aaa, lgd)
+
+    # 3) 투기등급 외삽
+    pd_floors = extrapolate_speculative_grades(pd_floors, ["BB", "B"])
+
+    # 4) CCC/CC/C 보간
+    ccc_pds = interpolate_ccc_pd(pd_floors["B"], pd_d=1.0, method="geometric")
+    full_floors = {**pd_floors, **ccc_pds, "D": 1.0}
+
+    # 5) 노치 비율
+    notch_ratios = compute_notch_pd_ratios(notch_spreads, lgd)
+
+    return pd_floors, notch_ratios, full_floors
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(message)s")
+
+    print("=" * 60)
+    print("  PD 플로어 산출 (KAP YTM 기반)")
+    print("=" * 60)
+
+    broad_floors, notch_ratios, full_floors = build_complete_pd_floor_table()
+
+    print("\n=== 대등급 PD 플로어 ===")
+    for grade in BROAD_GRADE_ORDER:
+        if grade in broad_floors:
+            print(f"  {grade:>4}: {broad_floors[grade]*10000:8.1f}bp  "
+                  f"({broad_floors[grade]*100:.4f}%)")
+
+    print("\n=== 전체 PD 플로어 (CCC/CC/C 포함) ===")
+    order = ["AAA", "AA", "A", "BBB", "BB", "B", "CCC", "CC", "C", "D"]
+    for grade in order:
+        if grade in full_floors:
+            print(f"  {grade:>4}: {full_floors[grade]*10000:8.1f}bp  "
+                  f"({full_floors[grade]*100:.4f}%)")
+
+    print("\n=== 노치 PD 비율 ===")
+    for broad, ratios in notch_ratios.items():
+        print(f"  {broad}:")
+        for notch, ratio in sorted(ratios.items()):
+            print(f"    {notch:>5}: ×{ratio:.3f}")

data/real/AVG_1998.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,0.576715,0.338615,0.084670,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.000000,0.559352,0.440648,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.017544,0.699909,0.207912,0.071635,0.003000,0.000000
-BB,0.000000,0.000000,0.000000,0.068283,0.515874,0.244982,0.027220,0.143640
-B,0.000000,0.000000,0.000000,0.000000,0.044442,0.476959,0.084169,0.394430
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.048887,0.353468,0.591645
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_1999.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,0.833333,0.166667,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,0.933333,0.066667,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.061773,0.737460,0.117262,0.000000,0.083505,0.000000,0.000000
-BBB,0.000000,0.000000,0.062378,0.797521,0.062053,0.075048,0.003000,0.000000
-BB,0.000000,0.000000,0.000000,0.134711,0.796350,0.062045,0.006894,0.000000
-B,0.000000,0.000000,0.000000,0.023801,0.090450,0.592972,0.104642,0.188136
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.079026,0.452999,0.461975
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2000.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.014493,0.971014,0.014493,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.122075,0.817172,0.047406,0.013347,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.057296,0.883453,0.056255,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.000000,0.075517,0.858165,0.028252,0.004798,0.033268
-B,0.000000,0.000000,0.000000,0.000000,0.115489,0.671687,0.118533,0.094291
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.092391,0.622554,0.279054
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2001.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.061444,0.893772,0.030291,0.014493,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.101044,0.861655,0.037302,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.068367,0.846477,0.060342,0.010752,0.003000,0.011062
-BB,0.000000,0.000000,0.000000,0.062964,0.847031,0.037988,0.004221,0.047796
-B,0.000000,0.000000,0.000000,0.000000,0.160363,0.561351,0.099062,0.179224
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.128291,0.406102,0.459608
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2002.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.076355,0.852232,0.071413,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.114891,0.872030,0.013079,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.047703,0.911130,0.038171,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.000000,0.094675,0.725113,0.119048,0.013228,0.047937
-B,0.000000,0.000000,0.000000,0.000000,0.080869,0.599699,0.105829,0.213603
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.071361,0.374766,0.547872
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2003.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.085849,0.800426,0.113725,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.026047,0.910271,0.049112,0.000000,0.014570,0.000000,0.000000
-BBB,0.000000,0.000000,0.078127,0.898498,0.020379,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.011905,0.073811,0.735706,0.115708,0.012856,0.050014
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.776242,0.136984,0.086774
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.732848,0.241152
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2004.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.081732,0.885698,0.032570,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.105432,0.857125,0.037443,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.118503,0.855562,0.022940,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.000000,0.180688,0.579904,0.029986,0.004990,0.204431
-B,0.000000,0.000000,0.000000,0.067996,0.025653,0.587401,0.103659,0.215291
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.033856,0.478994,0.481151
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2005.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.027063,0.961034,0.011903,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.080727,0.901305,0.017968,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.113735,0.883270,0.000000,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.000000,0.174718,0.805287,0.015011,0.004985,0.000000
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.737199,0.130094,0.132708
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.650564,0.323436
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2006.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.039636,0.949612,0.010752,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.150519,0.849481,0.000000,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.099090,0.862331,0.009213,0.017425,0.002999,0.008942
-BB,0.000000,0.000000,0.052771,0.113733,0.697852,0.102932,0.013095,0.019616
-B,0.000000,0.000000,0.000000,0.000000,0.013320,0.822017,0.145062,0.019601
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.023990,0.921009,0.049001
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2007.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.033387,0.927049,0.039565,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.030441,0.951364,0.018196,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.154455,0.823274,0.019275,0.000000,0.002996,0.000000
-BB,0.000000,0.000000,0.000000,0.000000,0.790519,0.188533,0.020948,0.000000
-B,0.000000,0.000000,0.022231,0.000000,0.037026,0.799631,0.141111,0.000000
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.042954,0.951046,0.000000
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2008.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,0.970045,0.029955,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.031482,0.923305,0.045213,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.100955,0.789007,0.094084,0.000000,0.002996,0.012958
-BB,0.000000,0.000000,0.000000,0.000000,0.838881,0.075012,0.008335,0.077771
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.792549,0.139862,0.067589
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.840113,0.133887
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2009.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.009656,0.990344,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.104447,0.876365,0.019188,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.068251,0.856864,0.000000,0.071886,0.003000,0.000000
-BB,0.000000,0.000000,0.000000,0.000000,0.672167,0.165050,0.018339,0.144445
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.738601,0.130341,0.131057
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.646357,0.327643
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2010.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.016612,0.983388,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.076151,0.916687,0.007162,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.151960,0.747671,0.000000,0.097368,0.003000,0.000000
-BB,0.000000,0.000000,0.000000,0.088045,0.760897,0.033324,0.007019,0.110714
-B,0.000000,0.000000,0.000000,0.000000,0.042016,0.596927,0.105340,0.255717
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.033613,0.432171,0.528216
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2011.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.080891,0.912472,0.000000,0.000000,0.006636,0.000000,0.000000
-BBB,0.000000,0.000000,0.056800,0.900004,0.020100,0.000000,0.002996,0.020100
-BB,0.000000,0.000000,0.000000,0.000000,0.958177,0.000000,0.004975,0.036848
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.722515,0.127503,0.149983
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.640697,0.333303
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2012.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,0.996806,0.003194,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.045306,0.891376,0.057383,0.000000,0.000000,0.000000,0.005935
-BBB,0.000000,0.000000,0.044172,0.854883,0.078990,0.000000,0.002996,0.018959
-BB,0.000000,0.000000,0.000000,0.000000,0.882739,0.042857,0.008079,0.066325
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.527309,0.093055,0.379636
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.371213,0.602787
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2013.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,1.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.006139,0.974049,0.019812,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.061071,0.896095,0.042834,0.000000,0.000000,0.000000,0.000000
-BBB,0.000000,0.000000,0.025036,0.833740,0.000000,0.097981,0.003000,0.040243
-BB,0.000000,0.000000,0.000000,0.019608,0.633854,0.240730,0.026748,0.079061
-B,0.000000,0.000000,0.000000,0.000000,0.000000,0.783775,0.138313,0.077912
-CCC,0.000000,0.000000,0.001000,0.002000,0.003000,0.020000,0.779051,0.194949
-D,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,1.000000

data/real/AVG_2014.csv

@@ -1,9 +0,0 @@
-,AAA,AA,A,BBB,BB,B,CCC,D
-AAA,0.982722,0.017278,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000
-AA,0.000000,0.935092,0.064908,0.000000,0.000000,0.000000,0.000000,0.000000
-A,0.000000,0.042667,0.911635,0.039382,0.000000,0.000000,0.000000,0.006316
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data/real/AVG_2015.csv

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data/real/AVG_2016.csv

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data/real/AVG_2017.csv

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data/real/AVG_2018.csv

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data/real/AVG_2019.csv

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data/real/AVG_2020.csv

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data/real/AVG_2021.csv

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data/real/AVG_2022.csv

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data/real/AVG_2023.csv

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data/real/AVG_2024.csv

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data/real/AVG_2025.csv

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data/real/KR_1999.csv

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data/real/KR_2010.csv

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data/real/KR_2011.csv

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data/real/KR_2015.csv

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data/real/KR_2016.csv

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data/real/KR_2017.csv

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data/real/KR_2018.csv

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data/real/KR_2019.csv

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data/real/KR_2020.csv

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data/real/KR_2021.csv

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data/real/KR_2022.csv

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data/real/KR_2023.csv

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data/real/KR_2024.csv

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data/real/KR_2025.csv

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data/real/NICE_1998.csv

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data/real/NICE_1999.csv

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data/real/NICE_2000.csv

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data/real/NICE_2001.csv

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data/real/NICE_2002.csv

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data/real/NICE_2003.csv

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data/real/NICE_2022.csv

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data/real/NICE_2025.csv

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data/real/SCI_1998.csv

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data/real/SCI_1999.csv

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data/real/SCI_2000.csv

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data/real/SCI_2001.csv

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data/real/SCI_2002.csv

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data/real/SCI_2003.csv

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data/real/SCI_2004.csv

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data/real/SCI_2005.csv

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data/real/SCI_2006.csv

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data/real/SCI_2007.csv

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data/real/SCI_2008.csv

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data/real/SCI_2009.csv

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