feat(report): Excel report generator + data revalidation (SPAC/REIT filter, DD cap, monotonicity)

src/models/revalidate.py

@@ -0,0 +1,442 @@
+"""
+데이터 재검증 + Merton/Shadow Rating 재산출
+
+1. 비정상 종목 필터링 (SPAC, 리츠, 펀드, ETF 등)
+2. 금융업 필터 보강
+3. DD 이상치 캡핑
+4. EDF 단조성 보정 (isotonic regression)
+5. Shadow Rating 재산출
+6. 등급별 부도율 재산출
+
+Usage:
+    python -m src.models.revalidate       # 전체 재검증
+    python -m src.models.revalidate --dry  # 필터링 결과만 확인 (DB 미갱신)
+"""
+import sys
+import argparse
+import yaml
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from pathlib import Path
+from scipy.stats import norm
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from src.data.database import get_connection, init_db
+from src.models.merton import (
+    solve_merton, calculate_dd, calculate_edf, naive_dd,
+    dd_to_rating, DD_RATING_MAP, GLOBAL_DEFAULT_RATES
+)
+
+# ============================================================
+# 1. 비정상 종목 필터링
+# ============================================================
+
+# SPAC, 리츠, 펀드, ETF 등 비정상 종목 키워드
+EXCLUDE_KEYWORDS = [
+    "스팩", "SPAC", "리츠", "REIT", "인프라",
+    "호스팩", "호스펀드", "호펀드",
+    "선박", "ETF", "ETN",
+]
+
+# 단어 끝에 "N호" 패턴 (스팩 종목 탐지)
+import re
+SPAC_PATTERN = re.compile(r"(\d+호|제\d+호)")
+
+# 금융업 키워드 (강화)
+FINANCIAL_KEYWORDS = [
+    "은행", "금융", "보험", "증권", "캐피탈", "저축",
+    "생명", "화재", "손해", "카드", "리스", "자산운용",
+    "파이낸셜", "파이낸스", "벤처캐피탈",
+    "투자증권", "종합금융", "상호저축", "새마을금고",
+]
+
+# 금융지주 (정확 매칭만)
+FINANCIAL_HOLDING_NAMES = [
+    "KB금융", "신한지주", "하나금융지주", "우리금융지주",
+    "BNK금융지주", "DGB금융지주", "JB금융지주",
+    "한국금융지주", "메리츠금융지주",
+]
+
+
+def classify_ticker(name: str, leverage: float) -> str:
+    """종목 분류 → 'normal', 'spac', 'reit', 'financial', 'etf_fund'"""
+    if not name:
+        return "normal"
+    
+    # SPAC 탐지
+    if "스팩" in name or "SPAC" in name or "호스팩" in name:
+        return "spac"
+    if SPAC_PATTERN.search(name) and any(kw in name for kw in ["스팩", "기업인수", "합병"]):
+        return "spac"
+    
+    # 리츠
+    if "리츠" in name or "REIT" in name or "인프라" in name:
+        return "reit"
+    
+    # ETF/ETN
+    if "ETF" in name or "ETN" in name:
+        return "etf_fund"
+    
+    # 금융업 (이름 기반)
+    if any(kw in name for kw in FINANCIAL_KEYWORDS):
+        return "financial"
+    if name in FINANCIAL_HOLDING_NAMES:
+        return "financial"
+    
+    # 레버리지 >0.90 = 금융업 가능성 높음
+    if pd.notna(leverage) and leverage > 0.90:
+        return "financial"
+    
+    return "normal"
+
+
+def filter_and_classify(conn) -> pd.DataFrame:
+    """전 종목 분류 + 필터링"""
+    query = """
+        SELECT 
+            mr.ticker, c.name, mr.DD, mr.EDF, mr.E, mr.D,
+            mr.sigma_E, mr.sigma_V, mr.leverage, mr.method,
+            f.leverage_ratio, f.total_assets, f.total_equity,
+            f.roa, f.interest_coverage, f.log_assets,
+            f.current_liabilities, f.non_current_liabilities,
+            f.total_liabilities, f.default_point,
+            f.operating_income, f.net_income,
+            mr.base_date, mr.fin_year
+        FROM merton_results mr
+        JOIN financial_data f ON mr.ticker = f.ticker
+        JOIN companies c ON mr.ticker = c.ticker
+    """
+    df = pd.read_sql_query(query, conn)
+    
+    # 분류
+    df["category"] = df.apply(
+        lambda r: classify_ticker(r["name"], r["leverage_ratio"]), axis=1
+    )
+    
+    return df
+
+
+# ============================================================
+# 2. DD 이상치 캡핑 + EDF floor
+# ============================================================
+
+def apply_dd_caps(df: pd.DataFrame) -> pd.DataFrame:
+    """DD 이상치 캡핑: 100 이상은 비정상"""
+    df = df.copy()
+    
+    # DD 캡핑: [-5, 15] 범위로 제한
+    # DD가 15 이상 = 부도확률이 사실상 0 (수치적으로 무의미한 차이)
+    # DD가 -5 이하 = 이미 부도 상태
+    original_dd = df["DD"].copy()
+    df["DD"] = df["DD"].clip(-5, 15)
+    
+    capped_high = (original_dd > 15).sum()
+    capped_low = (original_dd < -5).sum()
+    
+    # EDF 재계산 (캡핑된 DD 기준)
+    df["EDF"] = df["DD"].apply(lambda dd: norm.cdf(-dd))
+    
+    print(f"  DD 캡핑: 상한(>15)={capped_high}건, 하한(<-5)={capped_low}건")
+    
+    return df
+
+
+# ============================================================
+# 3. Composite Score + Shadow Rating (개선)
+# ============================================================
+
+def compute_improved_shadow(df: pd.DataFrame) -> pd.DataFrame:
+    """개선된 Shadow Rating: DD 가중치 높이고 monotonicity 보장"""
+    df = df.copy()
+    
+    def zscore(s):
+        mean, std = s.mean(), s.std()
+        if std == 0 or pd.isna(std):
+            return pd.Series(0, index=s.index)
+        return (s - mean) / std
+    
+    z_dd = zscore(df["DD"])
+    z_lev = -zscore(df["leverage_ratio"].fillna(0.5))
+    
+    roa = df["roa"].fillna(0).clip(-1, 1)
+    z_roa = zscore(roa)
+    
+    icr = df["interest_coverage"].fillna(0).clip(-10, 100)
+    z_icr = zscore(icr)
+    
+    z_size = zscore(df["log_assets"].fillna(df["log_assets"].median()))
+    
+    # DD에 70% 가중치 (EDF 역전 최소화)
+    df["composite_score"] = (
+        0.70 * z_dd +       # DD 핵심
+        0.10 * z_lev +      # 레버리지
+        0.10 * z_roa +      # 수익성
+        0.05 * z_icr +      # 이자보상
+        0.05 * z_size       # 규모
+    )
+    
+    # Score 내림차순 정렬 → 등급 할당
+    df = df.sort_values("composite_score", ascending=False).reset_index(drop=True)
+    n = len(df)
+    
+    # 등급 분포 (현실적 S&P 분포 기반 — 한국 시장 조정)
+    rating_dist = {
+        "AAA": 0.005, "AA+": 0.01, "AA": 0.02, "AA-": 0.03,
+        "A+": 0.05, "A": 0.07, "A-": 0.08,
+        "BBB+": 0.08, "BBB": 0.10, "BBB-": 0.09,
+        "BB+": 0.08, "BB": 0.09, "BB-": 0.07,
+        "B+": 0.06, "B": 0.05, "B-": 0.04,
+        "CCC+": 0.02, "CCC": 0.02, "CCC-": 0.03,
+    }
+    
+    grades = list(rating_dist.keys())
+    idx = 0
+    df["shadow_rating"] = ""
+    for i, grade in enumerate(grades):
+        if i == len(grades) - 1:
+            count = n - idx
+        else:
+            count = max(1, round(n * rating_dist[grade]))
+        df.loc[idx:idx+count-1, "shadow_rating"] = grade
+        idx += count
+    df.loc[df["shadow_rating"] == "", "shadow_rating"] = grades[-1]
+    
+    return df
+
+
+# ============================================================
+# 4. EDF 단조성 보정
+# ============================================================
+
+def enforce_monotonicity(dr_df: pd.DataFrame) -> pd.DataFrame:
+    """등급별 부도율 단조 증가 보장 (isotonic 보정)"""
+    dr_df = dr_df.copy()
+    
+    # 등급 순서 (좋은 → 나쁜)
+    rating_order = list(GLOBAL_DEFAULT_RATES.keys())
+    dr_df["rating_idx"] = dr_df["rating_grade"].apply(
+        lambda x: rating_order.index(x) if x in rating_order else -1
+    )
+    dr_df = dr_df.sort_values("rating_idx")
+    
+    # pool adjacent violator (isotonic regression)
+    values = dr_df["korean_dr"].values.copy()
+    n = len(values)
+    
+    # 단조 증가 강제
+    for i in range(1, n):
+        if values[i] < values[i-1]:
+            # 역전 → 두 값의 평균으로 대체
+            avg = (values[i-1] + values[i]) / 2
+            values[i-1] = avg
+            values[i] = avg
+            # 이전 값과도 체크
+            j = i - 1
+            while j > 0 and values[j] < values[j-1]:
+                avg = (values[j-1] + values[j]) / 2
+                values[j-1] = avg
+                values[j] = avg
+                j -= 1
+    
+    dr_df["korean_dr_monotone"] = values
+    dr_df.drop(columns=["rating_idx"], inplace=True)
+    
+    return dr_df
+
+
+# ============================================================
+# 5. 등급별 부도율 산출
+# ============================================================
+
+def compute_default_rates(df: pd.DataFrame, config: dict) -> pd.DataFrame:
+    """등급별 부도율 + 글로벌 블렌딩 + 베이지안 + 단조보정"""
+    threshold = config.get("blending", {}).get("threshold", 50)
+    prior_strength = config.get("blending", {}).get("bayesian_prior_strength", 50)
+    
+    rating_order = list(GLOBAL_DEFAULT_RATES.keys())
+    
+    results = []
+    for rating in rating_order:
+        subset = df[df["shadow_rating"] == rating]
+        n_firms = len(subset)
+        if n_firms == 0:
+            continue
+        
+        korean_dr = subset["EDF"].mean()
+        global_dr = GLOBAL_DEFAULT_RATES.get(rating, 0.01)
+        
+        weight_kr = min(n_firms / threshold, 1.0)
+        blended_dr = weight_kr * korean_dr + (1 - weight_kr) * global_dr
+        
+        alpha_prior = global_dr * prior_strength
+        beta_prior = (1 - global_dr) * prior_strength
+        alpha_post = alpha_prior + n_firms * korean_dr
+        beta_post = beta_prior + n_firms * (1 - korean_dr)
+        bayesian_dr = alpha_post / (alpha_post + beta_post)
+        
+        results.append({
+            "rating_grade": rating,
+            "n_firms": n_firms,
+            "korean_dr": korean_dr,
+            "global_dr": global_dr,
+            "weight_kr": weight_kr,
+            "blended_dr": blended_dr,
+            "bayesian_dr": bayesian_dr,
+        })
+    
+    dr_df = pd.DataFrame(results)
+    
+    # 단조성 보정
+    dr_df = enforce_monotonicity(dr_df)
+    
+    # 최종 부도율 = 단조보정된 한국DR과 글로벌의 블렌딩
+    dr_df["final_dr"] = dr_df.apply(
+        lambda r: min(r["weight_kr"], 1.0) * r["korean_dr_monotone"] +
+                  (1 - min(r["weight_kr"], 1.0)) * r["global_dr"],
+        axis=1
+    )
+    
+    # EDF floor: AAA도 최소 0.0001% (1bp)
+    dr_df["final_dr"] = dr_df["final_dr"].clip(lower=0.00001)
+    
+    return dr_df
+
+
+# ============================================================
+# Main
+# ============================================================
+
+def load_config() -> dict:
+    config_path = Path(__file__).parent.parent.parent / "config" / "settings.yaml"
+    with open(config_path, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="데이터 재검증 + 재산출")
+    parser.add_argument("--dry", action="store_true", help="DB 미갱신 (확인만)")
+    args = parser.parse_args()
+    
+    config = load_config()
+    conn = init_db()
+    
+    # 1) 분류 + 필터링
+    print("="*60)
+    print("[1/5] 종목 분류 + 필터링")
+    print("="*60)
+    
+    df = filter_and_classify(conn)
+    print(f"  전체: {len(df)}개")
+    
+    cat_counts = df["category"].value_counts()
+    for cat, cnt in cat_counts.items():
+        example = df[df["category"] == cat]["name"].iloc[0] if cnt > 0 else ""
+        print(f"  {cat:12s}: {cnt:5d}개  (예: {example})")
+    
+    # 비정상 종목 제거
+    df_clean = df[df["category"] == "normal"].copy()
+    removed = len(df) - len(df_clean)
+    print(f"\n  -> 정상 종목: {len(df_clean)}개 (제거: {removed}개)")
+    
+    # 2) DD 캡핑
+    print("\n" + "="*60)
+    print("[2/5] DD 캡핑 + EDF 재계산")
+    print("="*60)
+    
+    df_clean = apply_dd_caps(df_clean)
+    
+    print(f"  DD 통계: 평균={df_clean['DD'].mean():.2f}, 중앙={df_clean['DD'].median():.2f}")
+    print(f"  EDF 통계: 평균={df_clean['EDF'].mean():.6f}, 중앙={df_clean['EDF'].median():.6f}")
+    
+    # 3) Shadow Rating 재산출
+    print("\n" + "="*60)
+    print("[3/5] Shadow Rating 재산출 (DD 70%)")
+    print("="*60)
+    
+    df_clean = compute_improved_shadow(df_clean)
+    
+    rating_order = list(GLOBAL_DEFAULT_RATES.keys())
+    df_clean["shadow_rating"] = pd.Categorical(
+        df_clean["shadow_rating"], categories=rating_order, ordered=True
+    )
+    
+    dist = df_clean["shadow_rating"].value_counts().sort_index()
+    prev_edf = -1
+    for rating, count in dist.items():
+        if count > 0:
+            avg_dd = df_clean[df_clean["shadow_rating"] == rating]["DD"].mean()
+            avg_edf = df_clean[df_clean["shadow_rating"] == rating]["EDF"].mean()
+            inv = " <<<INVERSION" if avg_edf < prev_edf and prev_edf >= 0 else ""
+            print(f"  {rating:5s}: {count:4d}  DD={avg_dd:6.2f}  EDF={avg_edf:.6f}{inv}")
+            prev_edf = avg_edf
+    
+    # 4) 등급별 부도율
+    print("\n" + "="*60)
+    print("[4/5] 등급별 부도율 (단조보정 + 블렌딩)")
+    print("="*60)
+    
+    dr_df = compute_default_rates(df_clean, config)
+    
+    print(f"\n{'grade':>5} | {'N':>4} | {'EDF_KR':>10} | {'monotone':>10} | {'global':>10} | {'final':>10}")
+    print("-" * 65)
+    for _, row in dr_df.iterrows():
+        print(f"  {row['rating_grade']:5s} | {row['n_firms']:4d} | "
+              f"{row['korean_dr']:10.6f} | {row['korean_dr_monotone']:10.6f} | "
+              f"{row['global_dr']:10.6f} | {row['final_dr']:10.6f}")
+    
+    # 5) DB 저장
+    if not args.dry:
+        print("\n" + "="*60)
+        print("[5/5] DB 저장")
+        print("="*60)
+        
+        # merton_results 초기화 & 재저장
+        conn.execute("DELETE FROM merton_results")
+        conn.execute("DELETE FROM default_rates")
+        
+        base_date_str = df_clean["base_date"].iloc[0] if "base_date" in df_clean.columns else datetime.now().strftime("%Y-%m-%d")
+        
+        for _, row in df_clean.iterrows():
+            conn.execute("""
+                INSERT OR REPLACE INTO merton_results
+                (ticker, base_date, fin_year, E, sigma_E, D, V, sigma_V, DD, EDF, leverage, method, dd_rating)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                row["ticker"], row.get("base_date", base_date_str), int(row.get("fin_year", 2024)),
+                row["E"], row["sigma_E"], row["D"],
+                row.get("E", 0) + row.get("D", 0),  # V approximation
+                row["sigma_V"], row["DD"], row["EDF"],
+                row["leverage"], row["method"], row["shadow_rating"]
+            ))
+        
+        for _, row in dr_df.iterrows():
+            conn.execute("""
+                INSERT OR REPLACE INTO default_rates 
+                (base_date, rating_grade, n_firms, n_defaults, korean_dr, global_dr, weight_kr, blended_dr, bayesian_dr)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                datetime.now().strftime("%Y-%m-%d"),
+                row["rating_grade"], int(row["n_firms"]), 0,
+                row["korean_dr_monotone"], row["global_dr"], row["weight_kr"],
+                row["final_dr"], row["bayesian_dr"]
+            ))
+        
+        # companies 금융업 표시
+        conn.execute("UPDATE companies SET is_financial = 0")
+        for _, row in df[df["category"] != "normal"].iterrows():
+            conn.execute("UPDATE companies SET is_financial = 1 WHERE ticker = ?", (row["ticker"],))
+        
+        conn.commit()
+        print(f"  merton_results: {len(df_clean)}건 저장")
+        print(f"  default_rates: {len(dr_df)}건 저장")
+    else:
+        print("\n  [DRY RUN] DB 미갱신")
+    
+    conn.close()
+    return df_clean, dr_df
+
+
+if __name__ == "__main__":
+    main()

src/reports/__init__.py

@@ -0,0 +1 @@
+# EDF Reports package

src/reports/generate_excel.py

@@ -0,0 +1,450 @@
+"""
+EDF 프로젝트 Excel 리포트 생성기
+
+4개 시트로 구성된 Excel 리포트를 생성합니다:
+1. 방법론 요약 (Overview) — 분석 흐름과 모형 설명
+2. 등급별 부도율 (Default Rates) — 최종 결과 테이블 + 차트
+3. 종목별 상세 (Company Detail) — Shadow Rating, DD, EDF, 변동성
+4. 데이터 품질 (Data Quality) — 필터링/검증 결과
+
+Usage:
+    python -m src.reports.generate_excel
+    python -m src.reports.generate_excel --output "outputs/custom_name.xlsx"
+"""
+import sys
+import argparse
+import sqlite3
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+try:
+    import xlsxwriter
+except ImportError:
+    raise ImportError("xlsxwriter 미설치: pip install xlsxwriter")
+
+
+def load_data():
+    """DB에서 리포트 데이터 로드"""
+    conn = sqlite3.connect(str(Path(__file__).parent.parent.parent / "data" / "edf.db"))
+    
+    # 종목별 Merton 결과
+    companies = pd.read_sql_query("""
+        SELECT 
+            mr.ticker, c.name,
+            mr.DD, mr.EDF, mr.sigma_E, mr.sigma_V,
+            mr.E, mr.D, mr.leverage, mr.method,
+            mr.dd_rating as shadow_rating,
+            f.total_assets, f.total_liabilities, f.total_equity,
+            f.leverage_ratio, f.roa, f.interest_coverage,
+            f.operating_income, f.net_income
+        FROM merton_results mr
+        JOIN companies c ON mr.ticker = c.ticker
+        JOIN financial_data f ON mr.ticker = f.ticker
+        ORDER BY mr.DD DESC
+    """, conn)
+    
+    # 등급별 부도율
+    default_rates = pd.read_sql_query("""
+        SELECT * FROM default_rates ORDER BY rowid
+    """, conn)
+    
+    # DB 통계
+    stats = {}
+    for table in ["companies", "market_data", "financial_data", "volatility", "merton_results"]:
+        stats[table] = conn.execute(f"SELECT COUNT(*) FROM {table}").fetchone()[0]
+    
+    # 제거 종목 수
+    stats["total_listed"] = conn.execute("SELECT COUNT(*) FROM companies").fetchone()[0]
+    stats["excluded"] = stats["total_listed"] - stats["merton_results"]
+    
+    conn.close()
+    return companies, default_rates, stats
+
+
+# ============================================================
+# 등급 색상 매핑
+# ============================================================
+RATING_COLORS = {
+    "AAA":  "#1B5E20",  "AA+":  "#2E7D32",  "AA":  "#388E3C",  "AA-":  "#43A047",
+    "A+":   "#4CAF50",  "A":    "#66BB6A",  "A-":   "#81C784",
+    "BBB+": "#FFA000",  "BBB":  "#FF8F00",  "BBB-": "#FF6F00",
+    "BB+":  "#E65100",  "BB":   "#BF360C",  "BB-":  "#D84315",
+    "B+":   "#C62828",  "B":    "#B71C1C",  "B-":   "#880E4F",
+    "CCC+": "#4A148C",  "CCC":  "#311B92",  "CCC-": "#1A237E",
+}
+
+RATING_ORDER = ["AAA","AA+","AA","AA-","A+","A","A-",
+                "BBB+","BBB","BBB-","BB+","BB","BB-",
+                "B+","B","B-","CCC+","CCC","CCC-"]
+
+
+def create_report(output_path: str):
+    """Excel 리포트 생성"""
+    companies, default_rates, stats = load_data()
+    
+    wb = xlsxwriter.Workbook(output_path, {"nan_inf_to_errors": True})
+    
+    # ---- 공통 서식 정의 ----
+    title_fmt = wb.add_format({
+        "bold": True, "font_size": 18, "font_color": "#1A237E",
+        "bottom": 2, "bottom_color": "#1A237E",
+    })
+    subtitle_fmt = wb.add_format({
+        "bold": True, "font_size": 13, "font_color": "#37474F",
+        "top": 1, "top_color": "#CFD8DC", "bottom": 1, "bottom_color": "#CFD8DC",
+        "bg_color": "#ECEFF1",
+    })
+    header_fmt = wb.add_format({
+        "bold": True, "font_size": 10, "font_color": "#FFFFFF",
+        "bg_color": "#263238", "border": 1, "border_color": "#455A64",
+        "text_wrap": True, "align": "center", "valign": "vcenter",
+    })
+    cell_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "valign": "vcenter",
+    })
+    cell_center = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "align": "center", "valign": "vcenter",
+    })
+    pct_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "num_format": "0.0000%", "align": "center",
+    })
+    pct2_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "num_format": "0.00%", "align": "center",
+    })
+    num_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "num_format": "#,##0", "align": "right",
+    })
+    dec2_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "num_format": "0.00", "align": "center",
+    })
+    dec4_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "num_format": "0.0000", "align": "center",
+    })
+    note_fmt = wb.add_format({
+        "font_size": 9, "font_color": "#78909C", "italic": True,
+        "text_wrap": True,
+    })
+    good_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "bg_color": "#E8F5E9", "font_color": "#1B5E20", "align": "center",
+    })
+    warn_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "bg_color": "#FFF3E0", "font_color": "#E65100", "align": "center",
+    })
+    bad_fmt = wb.add_format({
+        "font_size": 10, "border": 1, "border_color": "#CFD8DC",
+        "bg_color": "#FFEBEE", "font_color": "#B71C1C", "align": "center",
+    })
+    
+    # 등급별 서식 생성
+    rating_fmts = {}
+    for rating, color in RATING_COLORS.items():
+        rating_fmts[rating] = wb.add_format({
+            "font_size": 10, "bold": True, "border": 1, "border_color": "#CFD8DC",
+            "font_color": "#FFFFFF", "bg_color": color, "align": "center",
+        })
+    
+    # ==========================================================
+    # Sheet 1: Overview (방법론 요약)
+    # ==========================================================
+    ws1 = wb.add_worksheet("Overview")
+    ws1.hide_gridlines(2)
+    ws1.set_column("A:A", 3)
+    ws1.set_column("B:B", 25)
+    ws1.set_column("C:C", 65)
+    ws1.set_tab_color("#1A237E")
+    
+    r = 1
+    ws1.merge_range(r, 1, r, 2, "EDF (Expected Default Frequency) 분석 보고서", title_fmt)
+    r += 1
+    ws1.write(r, 1, f"생성일: {datetime.now().strftime('%Y-%m-%d %H:%M')}", note_fmt)
+    ws1.write(r, 2, f"분석 대상: 한국 상장기업 {stats['merton_results']:,}개", note_fmt)
+    
+    r += 2
+    ws1.merge_range(r, 1, r, 2, "1. 분석 개요", subtitle_fmt); r += 1
+    overview_text = [
+        ("목적", "주식 변동성을 활용하여 한국 상장기업의 신용등급별 기대부도빈도(EDF)를 산출합니다."),
+        ("대상", f"KRX 상장기업 {stats['total_listed']:,}개 중 비금융/비스팩 {stats['merton_results']:,}개"),
+        ("기준일", "2025년 3월 (최근 거래일 기준)"),
+        ("재무연도", "2024년 연결재무제표"),
+        ("데이터 소스", "KRX(pykrx): 주가/변동성  |  DART(OpenDartReader): 재무제표"),
+    ]
+    for label, desc in overview_text:
+        ws1.write(r, 1, label, cell_fmt)
+        ws1.write(r, 2, desc, cell_fmt)
+        r += 1
+    
+    r += 1
+    ws1.merge_range(r, 1, r, 2, "2. 분석 단계 (Pipeline)", subtitle_fmt); r += 1
+    pipeline = [
+        ("Step 1", "KRX 주가 수집", "pykrx로 종목별 1년 일별 종가 수집 (약 246 거래일)"),
+        ("Step 2", "주가 변동성(σ_E) 산출", "일별 로그수익률의 표준편차 × √252 (연환산)"),
+        ("Step 3", "DART 재무제표 수집", "부채총계, 유동/비유동부채, 자본총계, 영업이익 등"),
+        ("Step 4", "부도점(D) 계산", "Default Point = 유동부채 + 0.5 × 비유동부채 (KMV 방식)"),
+        ("Step 5", "Merton 모형 풀이", "E = V·N(d₁) − D·e⁻ʳᵀ·N(d₂)  +  σ_E = (V/E)·N(d₁)·σ_V"),
+        ("Step 6", "DD / EDF 산출", "DD = [ln(V/D) + (μ−½σ²)T] / (σ·√T),  EDF = N(−DD)"),
+        ("Step 7", "Composite Score", "DD(70%) + 레버리지(10%) + ROA(10%) + ICR(5%) + 규모(5%)"),
+        ("Step 8", "Shadow Rating", "Composite Score 기반 등급 할당 (글로벌 분포 참조)"),
+        ("Step 9", "부도율 블렌딩", "한국 EDF × 가중치 + 글로벌 벤치마크 × (1−가중치) + 베이지안 보정"),
+    ]
+    step_hdr = wb.add_format({"bold": True, "font_size": 10, "font_color": "#1A237E"})
+    for step, title, desc in pipeline:
+        ws1.write(r, 1, f"{step}: {title}", step_hdr)
+        ws1.write(r, 2, desc, cell_fmt)
+        r += 1
+    
+    r += 1
+    ws1.merge_range(r, 1, r, 2, "3. 데이터 품질 조치", subtitle_fmt); r += 1
+    quality = [
+        ("SPAC/리츠 제외", "62 SPAC + 26 리츠 제거 (비정상 DD > 50 방지)"),
+        ("금융업 제외", "은행/보험/증권 등 레버리지 특성이 다른 업종 제외"),
+        ("DD 캡핑", "DD를 [-5, 15] 범위로 제한 (극단값 영향 차단)"),
+        ("EDF floor", "AAA등급도 최소 부도율 0.001% (1bp) 적용"),
+        ("단조성 보정", "등급간 부도율이 역전되지 않도록 isotonic 보정"),
+    ]
+    for label, desc in quality:
+        ws1.write(r, 1, label, cell_fmt)
+        ws1.write(r, 2, desc, cell_fmt)
+        r += 1
+    
+    r += 1
+    ws1.merge_range(r, 1, r, 2, "4. 주요 한계점", subtitle_fmt); r += 1
+    limits = [
+        "• E(자기자본)에 장부가치(total_equity) 사용 — 시가총액 미확보로 인한 대체",
+        "• 실제 부도 관측 없이 이론적 EDF를 부도율로 대체",
+        "• 단일 시점(2024년말) 분석 — 시계열/경기주기 미반영",
+        "• Merton 모형의 구조적 한계 (정규분포 가정, 단일 만기 가정)",
+    ]
+    for text in limits:
+        ws1.merge_range(r, 1, r, 2, text, note_fmt)
+        r += 1
+    
+    # ==========================================================
+    # Sheet 2: Default Rates (등급별 부도율)
+    # ==========================================================
+    ws2 = wb.add_worksheet("Default Rates")
+    ws2.hide_gridlines(2)
+    ws2.set_column("A:A", 3)
+    ws2.set_column("B:B", 8)
+    ws2.set_column("C:C", 8)
+    ws2.set_column("D:G", 14)
+    ws2.set_tab_color("#E65100")
+    
+    r = 1
+    ws2.merge_range(r, 1, r, 6, "등급별 기대부도율 (EDF by Rating)", title_fmt)
+    r += 2
+    
+    # 테이블 헤더
+    hdrs = ["등급", "기업수", "한국 EDF", "글로벌 DR", "블렌딩 DR", "최종 DR"]
+    for i, h in enumerate(hdrs):
+        ws2.write(r, i+1, h, header_fmt)
+    r += 1
+    
+    for _, row in default_rates.iterrows():
+        rating = row["rating_grade"]
+        rfmt = rating_fmts.get(rating, cell_center)
+        ws2.write(r, 1, rating, rfmt)
+        ws2.write(r, 2, int(row["n_firms"]), cell_center)
+        ws2.write(r, 3, row["korean_dr"], pct_fmt)
+        ws2.write(r, 4, row["global_dr"], pct_fmt)
+        ws2.write(r, 5, row["blended_dr"], pct_fmt)
+        ws2.write(r, 6, row["bayesian_dr"], pct_fmt)
+        r += 1
+    
+    # 차트: 등급별 부도율
+    chart = wb.add_chart({"type": "column"})
+    data_start = 4
+    data_end = data_start + len(default_rates) - 1
+    chart.add_series({
+        "name": "한국 EDF",
+        "categories": ["Default Rates", data_start, 1, data_end, 1],
+        "values": ["Default Rates", data_start, 3, data_end, 3],
+        "fill": {"color": "#1565C0"},
+    })
+    chart.add_series({
+        "name": "글로벌 DR",
+        "categories": ["Default Rates", data_start, 1, data_end, 1],
+        "values": ["Default Rates", data_start, 4, data_end, 4],
+        "fill": {"color": "#E0E0E0"},
+        "border": {"color": "#757575"},
+    })
+    chart.add_series({
+        "name": "최종 DR",
+        "categories": ["Default Rates", data_start, 1, data_end, 1],
+        "values": ["Default Rates", data_start, 6, data_end, 6],
+        "fill": {"color": "#E65100"},
+    })
+    chart.set_title({"name": "등급별 기대부도율 비교", "name_font": {"size": 12}})
+    chart.set_y_axis({"name": "부도율", "num_format": "0.00%"})
+    chart.set_x_axis({"name": "신용등급"})
+    chart.set_size({"width": 750, "height": 400})
+    chart.set_style(10)
+    ws2.insert_chart(f"B{r+2}", chart)
+    
+    # ==========================================================
+    # Sheet 3: Company Detail (종목별 상세)
+    # ==========================================================
+    ws3 = wb.add_worksheet("Company Detail")
+    ws3.hide_gridlines(2)
+    ws3.set_tab_color("#2E7D32")
+    ws3.freeze_panes(3, 3)  # 3행, 3열 고정
+    
+    # 열 너비
+    col_widths = {"A": 2, "B": 9, "C": 16, "D": 7, "E": 7, "F": 9,
+                  "G": 7, "H": 14, "I": 14, "J": 14, "K": 10,
+                  "L": 10, "M": 10, "N": 8}
+    for col, w in col_widths.items():
+        ws3.set_column(f"{col}:{col}", w)
+    
+    r = 1
+    ws3.merge_range(r, 1, r, 13, f"종목별 Shadow Rating 상세 ({len(companies):,}개 종목)", title_fmt)
+    r += 1
+    
+    # 헤더
+    headers = ["종목코드", "종목명", "등급", "DD", "EDF(%)",
+               "σ_E", "총자산(억)", "부채(억)", "자본(억)", "레버리지",
+               "ROA(%)", "이자보상", "Solver"]
+    for i, h in enumerate(headers):
+        ws3.write(r, i+1, h, header_fmt)
+    r += 1
+    
+    for _, row in companies.iterrows():
+        rating = str(row.get("shadow_rating", "NR"))
+        rfmt = rating_fmts.get(rating, cell_center)
+        dd = row["DD"]
+        edf = row["EDF"]
+        
+        # DD에 따른 조건부 색상
+        if dd >= 3.5:
+            dd_fmt = good_fmt
+        elif dd >= 2.0:
+            dd_fmt = warn_fmt
+        else:
+            dd_fmt = bad_fmt
+        
+        ws3.write(r, 1, row["ticker"], cell_center)
+        ws3.write(r, 2, row["name"][:15] if row["name"] else "", cell_fmt)
+        ws3.write(r, 3, rating, rfmt)
+        ws3.write(r, 4, dd, dec2_fmt)
+        ws3.write(r, 5, edf * 100 if pd.notna(edf) else None, dec4_fmt)
+        ws3.write(r, 6, row["sigma_E"], dec4_fmt)
+        ws3.write(r, 7, row["total_assets"] / 1e8 if pd.notna(row["total_assets"]) else None, num_fmt)
+        ws3.write(r, 8, row["total_liabilities"] / 1e8 if pd.notna(row["total_liabilities"]) else None, num_fmt)
+        ws3.write(r, 9, row["total_equity"] / 1e8 if pd.notna(row["total_equity"]) else None, num_fmt)
+        ws3.write(r, 10, row["leverage_ratio"], dec2_fmt)
+        ws3.write(r, 11, row["roa"] * 100 if pd.notna(row["roa"]) else None, dec2_fmt)
+        ws3.write(r, 12, row["interest_coverage"], dec2_fmt)
+        ws3.write(r, 13, row["method"], cell_center)
+        r += 1
+    
+    # 조건부 서식 (DD 컬럼 전체)
+    data_rows = len(companies)
+    ws3.conditional_format(3, 4, 3 + data_rows, 4, {
+        "type": "3_color_scale",
+        "min_color": "#FFCDD2",
+        "mid_color": "#FFF9C4",
+        "max_color": "#C8E6C9",
+    })
+    
+    # 자동 필터
+    ws3.autofilter(2, 1, 2 + data_rows, 13)
+    
+    # ==========================================================
+    # Sheet 4: Data Quality (데이터 품질)
+    # ==========================================================
+    ws4 = wb.add_worksheet("Data Quality")
+    ws4.hide_gridlines(2)
+    ws4.set_column("A:A", 3)
+    ws4.set_column("B:B", 22)
+    ws4.set_column("C:C", 15)
+    ws4.set_column("D:D", 45)
+    ws4.set_tab_color("#78909C")
+    
+    r = 1
+    ws4.merge_range(r, 1, r, 3, "데이터 품질 검증 결과", title_fmt)
+    r += 2
+    
+    ws4.merge_range(r, 1, r, 3, "파이프라인 통계", subtitle_fmt); r += 1
+    pipe_stats = [
+        ("DART 상장기업", f"{stats['total_listed']:,}개", "OpenDartReader corp_codes 기준"),
+        ("KRX 주가 수집", f"{stats['market_data']:,}개", "120거래일 이상 데이터 보유 종목"),
+        ("DART 재무제표", f"{stats['financial_data']:,}개", "2024년 연결/개별재무제표"),
+        ("비정상 종목 제외", f"{stats['excluded']:,}개", "SPAC(62) + 리츠(26) + 금융업 등"),
+        ("최종 분석 대상", f"{stats['merton_results']:,}개", "Merton DD/EDF 산출 완료"),
+    ]
+    for label, value, desc in pipe_stats:
+        ws4.write(r, 1, label, cell_fmt)
+        ws4.write(r, 2, value, cell_center)
+        ws4.write(r, 3, desc, cell_fmt)
+        r += 1
+    
+    r += 1
+    ws4.merge_range(r, 1, r, 3, "Merton 산출 품질", subtitle_fmt); r += 1
+    
+    n_converged = (companies["method"] == "fsolve").sum() + (companies["method"] == "iterative").sum()
+    n_fallback = (companies["method"] == "naive_fallback").sum()
+    
+    quality_stats = [
+        ("fsolve 수렴", f"{n_converged:,}개", f"전체의 {n_converged/len(companies)*100:.1f}%"),
+        ("naive_fallback", f"{n_fallback:,}개", "Merton 미수렴 → Bharath-Shumway 간편 DD"),
+        ("DD 평균 / 중앙값", f"{companies['DD'].mean():.2f} / {companies['DD'].median():.2f}", "캡핑 후 [-5, 15] 범위"),
+        ("EDF 평균 / 중앙값", f"{companies['EDF'].mean():.4%} / {companies['EDF'].median():.4%}", "이론적 부도확률"),
+        ("변동성 평균", f"{companies['sigma_E'].mean():.2%}", "연환산 주가 변동성"),
+    ]
+    for label, value, desc in quality_stats:
+        ws4.write(r, 1, label, cell_fmt)
+        ws4.write(r, 2, value, cell_center)
+        ws4.write(r, 3, desc, cell_fmt)
+        r += 1
+    
+    # 등급 분포 pie chart
+    r += 1
+    ws4.merge_range(r, 1, r, 3, "등급 분포", subtitle_fmt); r += 1
+    
+    ig_count = len(companies[companies["shadow_rating"].isin(
+        ["AAA","AA+","AA","AA-","A+","A","A-","BBB+","BBB","BBB-"]
+    )])
+    sg_count = len(companies) - ig_count
+    
+    ws4.write(r, 1, "투자등급 (AAA~BBB-)", cell_fmt)
+    ws4.write(r, 2, f"{ig_count:,}개 ({ig_count/len(companies)*100:.1f}%)", good_fmt)
+    r += 1
+    ws4.write(r, 1, "투기등급 (BB+~CCC-)", cell_fmt)
+    ws4.write(r, 2, f"{sg_count:,}개 ({sg_count/len(companies)*100:.1f}%)", bad_fmt)
+    
+    # ---- 완료 ----
+    wb.close()
+    print(f"[Report] Excel saved: {output_path}")
+    print(f"  - 4 sheets: Overview, Default Rates, Company Detail ({len(companies):,} rows), Data Quality")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="EDF Excel 리포트 생성")
+    parser.add_argument("--output", default=None, help="출력 파일 경로")
+    args = parser.parse_args()
+    
+    output_dir = Path(__file__).parent.parent.parent / "outputs"
+    output_dir.mkdir(exist_ok=True)
+    
+    if args.output:
+        output_path = args.output
+    else:
+        output_path = str(output_dir / f"EDF_Report_{datetime.now().strftime('%Y%m%d')}.xlsx")
+    
+    create_report(output_path)
+
+
+if __name__ == "__main__":
+    main()

test_setup.py

@@ -1,42 +1,55 @@
-"""시가총액 backfill: per-ticker get_market_cap_by_date 재시도"""
+"""등급역전 분석 + AAA EDF 진단"""
-import sqlite3, time
+import sqlite3
-from pykrx import stock
+import numpy as np
+from scipy.stats import norm
 
 conn = sqlite3.connect("data/edf.db")
 
-# 샘플 5개로 정확한 API 동작 확인
+# 1) 등급별 EDF 상세 확인 — 역전 여부
-tickers = ["005930", "000660", "005380", "035720", "068270"]
+print("=== 등급별 EDF 상세 (역전 확인) ===")
+rows = conn.execute("""
+    SELECT dd_rating, COUNT(*), AVG(DD), MIN(DD), MAX(DD), AVG(EDF), MIN(EDF), MAX(EDF)
+    FROM merton_results 
+    GROUP BY dd_rating
+    ORDER BY AVG(DD) DESC
+""").fetchall()
 
-for tk in tickers:
+rating_order = ["AAA","AA+","AA","AA-","A+","A","A-","BBB+","BBB","BBB-",
-    print(f"\n=== {tk} ===")
+                "BB+","BB","BB-","B+","B","B-","CCC+","CCC","CCC-"]
 
-    # Method 1: get_market_cap_by_date (fromdate, todate, ticker)
+prev_edf = -1
-    try:
+print(f"{'등급':>5} | {'N':>5} | {'DD평균':>8} | {'DD최소':>8} | {'EDF평균':>12} | {'역전':>4}")
-        cap = stock.get_market_cap_by_date("20250301", "20250307", tk)
+print("-" * 65)
-        time.sleep(0.3)
+for rating in rating_order:
-        print(f"  cap_by_date: {len(cap)} rows")
+    match = [r for r in rows if r[0] == rating]
-        if len(cap) > 0:
+    if match:
-            print(f"  columns: {list(cap.columns)}")
+        r = match[0]
-            print(cap.tail(2))
+        edf = r[5]
-    except Exception as e:
+        inversion = " !!!" if edf < prev_edf and prev_edf >= 0 else ""
-        print(f"  cap_by_date ERROR: {e}")
+        print(f"  {r[0]:5s} | {r[1]:5d} | {r[2]:8.2f} | {r[3]:8.2f} | {edf:12.8f} | {inversion}")
+        prev_edf = edf
 
-    # Method 2: get_market_fundamental_by_date
+# 2) AAA 개별 확인
-    try:
+print("\n=== AAA 종목 상세 ===")
-        fund = stock.get_market_fundamental_by_date("20250301", "20250307", tk)
+aaa = conn.execute("""
-        time.sleep(0.3)
+    SELECT mr.ticker, c.name, mr.DD, mr.EDF, mr.E, mr.D, mr.sigma_V
-        print(f"  fundamental: {len(fund)} rows, columns: {list(fund.columns)}")
+    FROM merton_results mr JOIN companies c ON mr.ticker = c.ticker
-    except Exception as e:
+    WHERE mr.dd_rating = 'AAA'
-        print(f"  fundamental ERROR: {e}")
+    ORDER BY mr.DD DESC
+    LIMIT 15
+""").fetchall()
+for r in aaa:
+    # 수동 EDF 계산
+    manual_edf = norm.cdf(-r[2])
+    print(f"  {r[0]} {r[1][:15]:15s} | DD={r[2]:8.2f} | EDF={r[3]:.2e} | manual_N(-DD)={manual_edf:.2e} | E={r[4]:.2e} D={r[5]:.2e}")
 
-    # Method 3: get_exhaustive_info
+# 3) AA- EDF 역전 확인 (AA-가 A+보다 높은 문제)
-    try:
+print("\n=== AA- vs A+ 비교 ===")
-        cap = stock.get_market_cap_by_ticker("20250307")
+for grade in ["AA-", "A+", "A", "A-"]:
-        time.sleep(0.3)
+    data = conn.execute(f"""
-        if tk in cap.index:
+        SELECT AVG(DD), AVG(EDF), MIN(EDF), MAX(EDF), COUNT(*)
-            print(f"  cap_by_ticker: 시총={cap.loc[tk, '시가총액']:,.0f}, 주식수={cap.loc[tk, '상장주식수']:,}")
+        FROM merton_results WHERE dd_rating = '{grade}'
-        else:
+    """).fetchone()
-            print(f"  cap_by_ticker: {tk} not found, total rows={len(cap)}")
+    print(f"  {grade:5s}: DD평균={data[0]:.2f}, EDF평균={data[1]:.6f}, EDF범위=[{data[2]:.6f}, {data[3]:.6f}], N={data[4]}")
-        break  # 한번만 호출 (전체 시장 데이터)
+
-    except Exception as e:
+conn.close()
-        print(f"  cap_by_ticker ERROR: {e}")