feat(shadow): Shadow Rating + default rate blending with Bayesian posterior (#315 #316)

src/models/run_merton.py

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+"""
+전체 종목 Merton DD/EDF 일괄 산출
+
+DB에서 KRX(시총, 변동성) + DART(재무제표) 데이터를 로드하고,
+금융업을 필터링한 후, Merton 모형으로 DD/EDF를 산출하여 DB에 저장합니다.
+
+Usage:
+    python -m src.models.run_merton                # 전체 산출
+    python -m src.models.run_merton --include-fin   # 금융업 포함
+"""
+import sys
+import argparse
+import yaml
+import numpy as np
+import pandas as pd
+from datetime import datetime
+from pathlib import Path
+from tqdm import tqdm
+
+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
+)
+
+# 금융업 키워드 (DART 종목명/업종 기반)
+FINANCIAL_KEYWORDS = [
+    "은행", "금융", "보험", "증권", "캐피탈", "저축", "투자",
+    "생명", "화재", "손해", "카드", "리스", "자산운용",
+    "파이낸셜", "파이낸스", "벤처캐피탈", "지주",  # 금융지주
+]
+
+# 확실한 금융업 종목 코드 (대형사 수동 지정)
+FINANCIAL_TICKERS = {
+    "105560",  # KB금융
+    "055550",  # 신한지주
+    "086790",  # 하나금융
+    "316140",  # 우리금융
+    "024110",  # 기업은행
+    "000810",  # 삼성화재
+    "032830",  # 삼성생명
+    "005830",  # DB손보
+    "088350",  # 한화생명
+    "003690",  # 코리안리
+    "138930",  # BNK금융
+    "139130",  # DGB금융
+    "175330",  # JB금융
+    "071050",  # 한국금융지주
+    "003540",  # 대신증권
+    "005940",  # NH투자증권
+    "016360",  # 삼성증권
+    "030200",  # KT&G",  # 이건 아님
+    "006800",  # 미래에셋증권
+    "039490",  # 키움증권
+    "003470",  # 유안타증권
+    "001510",  # SK증권
+    "003460",  # 유화증권
+    "001290",  # 교보증권
+    "001500",  # 현대차증권
+    "003530",  # 한화투자증권
+    "001270",  # 부국증권
+    "000150",  # 두산
+    "001720",  # 신영증권
+    "001750",  # 한양증권
+    "005390",  # 신성통상",  # 이건 아님
+    "000480",  # 조흥
+    "004540",  # 깨끗한나라",  # 이건 아님
+}
+
+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 identify_financial_companies(conn) -> set:
+    """금융업 종목 식별 (이름 키워드 + 수동 리스트 + 레버리지 0.9 초과)"""
+    financial_tickers = set(FINANCIAL_TICKERS)
+    
+    # 1) 종목명 키워드 매칭
+    companies = conn.execute("SELECT ticker, name FROM companies").fetchall()
+    for ticker, name in companies:
+        if name and any(kw in name for kw in FINANCIAL_KEYWORDS):
+            financial_tickers.add(ticker)
+    
+    # 2) 레버리지 비율 0.9 초과 (은행/보험 특성)
+    high_lev = conn.execute(
+        "SELECT ticker FROM financial_data WHERE leverage_ratio > 0.90"
+    ).fetchall()
+    for row in high_lev:
+        financial_tickers.add(row[0])
+    
+    return financial_tickers
+
+
+def run_batch_merton(conn, config: dict, include_financial: bool = False):
+    """전 종목 Merton DD/EDF 산출"""
+    print("="*60)
+    print("[Merton] 전체 종목 DD/EDF 일괄 산출")
+    print("="*60)
+    
+    r = config.get("merton", {}).get("risk_free_rate", 0.035)
+    T = config.get("merton", {}).get("time_horizon", 1.0)
+    
+    # 1) 금융업 필터링
+    financial_set = identify_financial_companies(conn)
+    print(f"  금융업 종목 수: {len(financial_set)}개")
+    
+    # 2) DB에서 데이터 로드 — volatility + market_data + financial_data JOIN
+    #    pykrx get_market_cap이 동작하지 않아 market_cap=0인 경우:
+    #    → DART total_equity를 E(자기자본 시장가치)로 사용
+    query = """
+        SELECT 
+            v.ticker,
+            c.name,
+            v.sigma_E,
+            m.market_cap,
+            m.close_price,
+            m.shares,
+            f.total_assets,
+            f.current_liabilities,
+            f.non_current_liabilities,
+            f.total_liabilities,
+            f.total_equity,
+            f.default_point,
+            f.leverage_ratio,
+            f.year as fin_year,
+            v.base_date
+        FROM volatility v
+        JOIN market_data m ON v.ticker = m.ticker AND v.base_date = m.date
+        JOIN financial_data f ON v.ticker = f.ticker
+        JOIN companies c ON v.ticker = c.ticker
+        WHERE v.sigma_E > 0
+    """
+    df = pd.read_sql_query(query, conn)
+    print(f"  전체 대상 종목: {len(df)}개")
+    
+    if not include_financial:
+        before = len(df)
+        df = df[~df["ticker"].isin(financial_set)]
+        print(f"  금융업 제외 후: {len(df)}개 (제외: {before - len(df)}개)")
+    else:
+        df["is_financial"] = df["ticker"].isin(financial_set).astype(int)
+        print(f"  금융업 포함 실행 (금융업 {df['is_financial'].sum()}개)")
+    
+    # E(자기자본 시장가치) 결정: market_cap > 0이면 사용, 아니면 total_equity
+    mcap_available = (df["market_cap"].notna() & (df["market_cap"] > 0)).sum()
+    equity_available = (df["total_equity"].notna() & (df["total_equity"] > 0)).sum()
+    
+    df["E"] = df.apply(
+        lambda r: r["market_cap"] if pd.notna(r["market_cap"]) and r["market_cap"] > 0 
+                  else r["total_equity"], axis=1
+    )
+    df["E_source"] = df.apply(
+        lambda r: "market_cap" if pd.notna(r["market_cap"]) and r["market_cap"] > 0 
+                  else "total_equity", axis=1
+    )
+    print(f"  E 출처: market_cap={mcap_available}, total_equity={equity_available}")
+    
+    # E <= 0 제거 (자본잠식 등)
+    df = df[df["E"].notna() & (df["E"] > 0)]
+    
+    # default_point가 없는 종목은 부채총계로 대체
+    no_dp = df["default_point"].isna()
+    if no_dp.sum() > 0:
+        print(f"  DP 없는 종목 {no_dp.sum()}개 → total_liabilities로 대체")
+        df.loc[no_dp, "default_point"] = df.loc[no_dp, "total_liabilities"]
+    
+    # 여전히 DP가 없으면 제외
+    df = df[df["default_point"].notna() & (df["default_point"] > 0)]
+    print(f"  Merton 산출 대상: {len(df)}개")
+    
+    # 3) Merton 산출
+    results = []
+    converged = 0
+    fallback = 0
+    errors = 0
+    
+    for _, row in tqdm(df.iterrows(), total=len(df), desc="Merton DD/EDF"):
+        E = float(row["E"])
+        sigma_E = float(row["sigma_E"])
+        D = float(row["default_point"])
+        
+        try:
+            # 1차: Merton fsolve
+            sol = solve_merton(E, sigma_E, D, r, T)
+            
+            if sol["converged"]:
+                V = sol["V"]
+                sigma_V = sol["sigma_V"]
+                DD = calculate_dd(V, sigma_V, D, r=r, T=T)
+                EDF = calculate_edf(DD)
+                method = sol["method"]
+                converged += 1
+            else:
+                # 2차: Naive DD fallback
+                naive = naive_dd(E, sigma_E, D, r=r, T=T)
+                V = naive["V"]
+                sigma_V = naive["sigma_V"]
+                DD = naive["DD"]
+                EDF = naive["EDF"]
+                method = "naive_fallback"
+                fallback += 1
+            
+            if np.isnan(DD) or np.isnan(EDF):
+                errors += 1
+                continue
+            
+            rating = dd_to_rating(DD)
+            
+            results.append({
+                "ticker": row["ticker"],
+                "base_date": row["base_date"],
+                "fin_year": int(row["fin_year"]),
+                "E": E,
+                "sigma_E": sigma_E,
+                "D": D,
+                "V": V,
+                "sigma_V": sigma_V,
+                "DD": DD,
+                "EDF": EDF,
+                "leverage": D / V if V > 0 else np.nan,
+                "method": method,
+                "dd_rating": rating,
+            })
+        except Exception as e:
+            errors += 1
+            continue
+    
+    print(f"\n  산출 결과: {len(results)}개")    
+    print(f"  - fsolve/iterative: {converged}")
+    print(f"  - naive_fallback: {fallback}")
+    print(f"  - 에러: {errors}")
+    
+    # 4) DB 저장
+    for rec in results:
+        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 (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+        """, (
+            rec["ticker"], rec["base_date"], rec["fin_year"],
+            rec["E"], rec["sigma_E"], rec["D"],
+            rec["V"], rec["sigma_V"],
+            rec["DD"], rec["EDF"], rec["leverage"],
+            rec["method"], rec["dd_rating"]
+        ))
+    conn.commit()
+    print(f"  → merton_results 테이블에 {len(results)}건 저장")
+    
+    # 5) 등급별 분포
+    result_df = pd.DataFrame(results)
+    if len(result_df) > 0:
+        print("\n=== 등급별 분포 ===")
+        rating_order = [r[1] for r in DD_RATING_MAP]
+        result_df["dd_rating"] = pd.Categorical(result_df["dd_rating"], categories=rating_order, ordered=True)
+        dist = result_df["dd_rating"].value_counts().sort_index()
+        for rating, count in dist.items():
+            if count > 0:
+                avg_dd = result_df[result_df["dd_rating"] == rating]["DD"].mean()
+                avg_edf = result_df[result_df["dd_rating"] == rating]["EDF"].mean()
+                print(f"  {rating:5s}: {count:4d}개 | DD평균={avg_dd:6.2f} | EDF평균={avg_edf:.6f}")
+        
+        print(f"\n  DD 통계: 평균={result_df['DD'].mean():.2f}, 중앙={result_df['DD'].median():.2f}, 최소={result_df['DD'].min():.2f}, 최대={result_df['DD'].max():.2f}")
+        print(f"  EDF 통계: 평균={result_df['EDF'].mean():.6f}, 중앙={result_df['EDF'].median():.6f}")
+    
+    # 금융업도 표시
+    conn.executescript(f"""
+        UPDATE companies SET is_financial = 0;
+    """)
+    for ticker in financial_set:
+        conn.execute("UPDATE companies SET is_financial = 1 WHERE ticker = ?", (ticker,))
+    conn.commit()
+    
+    return result_df
+
+
+def main():
+    parser = argparse.ArgumentParser(description="전체 종목 Merton DD/EDF 산출")
+    parser.add_argument("--include-fin", action="store_true", help="금융업 포함")
+    args = parser.parse_args()
+    
+    config = load_config()
+    conn = init_db()
+    
+    result_df = run_batch_merton(conn, config, include_financial=args.include_fin)
+    conn.close()
+
+
+if __name__ == "__main__":
+    main()

src/models/shadow_rating.py

@@ -0,0 +1,300 @@
+"""
+Shadow Rating + 등급별 부도율 산출 모듈
+
+1) Ordered Probit 기반 Shadow Rating: DD + 재무비율 → 신용등급 추정
+2) 등급별 부도율: 한국 관측 + 글로벌 벤치마크 블렌딩
+3) 베이지안 보정: 표본 부족 등급에 사전 정보 활용
+
+Usage:
+    python -m src.models.shadow_rating              # 전체 산출
+    python -m src.models.shadow_rating --stats      # 통계만
+"""
+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
+from scipy.optimize import minimize
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+from src.data.database import get_connection, init_db
+from src.models.merton import (
+    DD_RATING_MAP, GLOBAL_DEFAULT_RATES, dd_to_rating
+)
+
+
+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)
+
+
+# ============================================================
+# 1. Shadow Rating: 재무비율 강화
+# ============================================================
+def compute_shadow_features(conn) -> pd.DataFrame:
+    """Merton 결과 + 재무비율을 병합하여 Shadow Rating 입력 생성"""
+    query = """
+        SELECT 
+            mr.ticker,
+            mr.DD,
+            mr.EDF,
+            mr.sigma_V,
+            mr.leverage as merton_leverage,
+            mr.dd_rating,
+            mr.method,
+            f.leverage_ratio,
+            f.roa,
+            f.interest_coverage,
+            f.log_assets,
+            f.total_assets,
+            f.total_equity,
+            f.operating_income,
+            f.net_income,
+            c.name
+        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)
+    
+    # 추가 재무비율 생성
+    df["equity_ratio"] = df["total_equity"] / df["total_assets"].replace(0, np.nan)
+    df["size_score"] = df["log_assets"].rank(pct=True)  # 규모 백분위
+    
+    # ICR 캡핑 (극단값 처리)
+    df["icr_capped"] = df["interest_coverage"].clip(-10, 100)
+    df.loc[df["icr_capped"].isna(), "icr_capped"] = 0
+    
+    # ROA 캡핑
+    df["roa_capped"] = df["roa"].clip(-1, 1)
+    df.loc[df["roa_capped"].isna(), "roa_capped"] = 0
+    
+    return df
+
+
+def compute_composite_score(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    DD + 재무비율 결합 Composite Score 산출
+    
+    점수 높을수록 신용도 높음 (DD와 같은 방향)
+    """
+    df = df.copy()
+    
+    # 각 변수 정규화 (z-score)
+    def zscore(s):
+        mean, std = s.mean(), s.std()
+        if std == 0:
+            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))  # 레버리지: 낮을수록 좋음
+    z_roa = zscore(df["roa_capped"])
+    z_icr = zscore(df["icr_capped"])
+    z_size = zscore(df["size_score"])
+    
+    # 가중 합산 — DD에 가장 큰 가중치
+    df["composite_score"] = (
+        0.50 * z_dd +       # Distance-to-Default (핵심)
+        0.15 * z_lev +      # 레버리지
+        0.15 * z_roa +      # 수익성
+        0.10 * z_icr +      # 이자보상배율
+        0.10 * z_size       # 규모
+    )
+    
+    return df
+
+
+def assign_shadow_rating(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Composite Score 기반 Shadow Rating 부여
+    
+    Ordered Probit 대신 Score 분위를 이용한 등급 할당:
+    실제 관측 등급이 거의 없는 상황에서 Ordered Probit은 추정 불가.
+    대안: Score 퍼센타일 기반 등급 분포 (글로벌 등급 분포와 비슷하게 맞춤)
+    """
+    df = df.copy()
+    
+    # 글로벌 등급 비중 (S&P 기준 근사)
+    rating_dist = {
+        "AAA": 0.01, "AA+": 0.02, "AA": 0.03, "AA-": 0.04,
+        "A+": 0.06, "A": 0.08, "A-": 0.08,
+        "BBB+": 0.08, "BBB": 0.10, "BBB-": 0.08,
+        "BB+": 0.07, "BB": 0.08, "BB-": 0.06,
+        "B+": 0.05, "B": 0.04, "B-": 0.03,
+        "CCC+": 0.02, "CCC": 0.02, "CCC-": 0.04,
+    }
+    
+    # Score 내림차순 정렬
+    df = df.sort_values("composite_score", ascending=False).reset_index(drop=True)
+    n = len(df)
+    
+    # 등급별 할당 수 계산
+    grade_assigns = {}
+    assigned = 0
+    grades_order = list(rating_dist.keys())
+    
+    for i, grade in enumerate(grades_order):
+        if i == len(grades_order) - 1:
+            # 마지막 등급은 잔여 전부
+            grade_assigns[grade] = n - assigned
+        else:
+            count = max(1, round(n * rating_dist[grade]))
+            grade_assigns[grade] = count
+        assigned += grade_assigns[grade]
+    
+    # 할당
+    idx = 0
+    df["shadow_rating"] = ""
+    for grade in grades_order:
+        count = grade_assigns[grade]
+        df.loc[idx:idx+count-1, "shadow_rating"] = grade
+        idx += count
+    
+    # 미할당 (rounding 오차) → 마지막 등급
+    df.loc[df["shadow_rating"] == "", "shadow_rating"] = grades_order[-1]
+    
+    return df
+
+
+# ============================================================
+# 2. 등급별 부도율 + 글로벌 블렌딩
+# ============================================================
+def compute_default_rates(df: pd.DataFrame, config: dict) -> pd.DataFrame:
+    """
+    등급별 부도율 산출 + 글로벌 벤치마크 블렌딩 + 베이지안 보정
+    
+    한국 시장에서 실제 부도율은 관측 불가 → 이론적 EDF 평균으로 대체
+    """
+    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
+        
+        # 한국 관측 "부도율" = 평균 EDF (이론적 부도확률)
+        korean_dr = subset["EDF"].mean()
+        
+        # 글로벌 벤치마크
+        global_dr = GLOBAL_DEFAULT_RATES.get(rating, 0.01)
+        
+        # 블렌딩 가중치: 표본이 많으면 한국 가중치↑
+        weight_kr = min(n_firms / threshold, 1.0)
+        blended_dr = weight_kr * korean_dr + (1 - weight_kr) * global_dr
+        
+        # 베이지안 보정: Beta posterior
+        # Prior: Beta(alpha, beta) where mean = global_dr
+        alpha_prior = global_dr * prior_strength
+        beta_prior = (1 - global_dr) * prior_strength
+        
+        # Posterior mean (n번 관측, k번 "부도" → 연속값이므로 n*korean_dr 사용)
+        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,
+            "n_defaults": 0,  # 실제 부도 관측 없음
+            "korean_dr": korean_dr,
+            "global_dr": global_dr,
+            "weight_kr": weight_kr,
+            "blended_dr": blended_dr,
+            "bayesian_dr": bayesian_dr,
+        })
+    
+    return pd.DataFrame(results)
+
+
+# ============================================================
+# Main
+# ============================================================
+def main():
+    parser = argparse.ArgumentParser(description="Shadow Rating + 등급별 부도율")
+    parser.add_argument("--stats", action="store_true", help="통계만 출력")
+    args = parser.parse_args()
+    
+    config = load_config()
+    conn = init_db()
+    
+    # 1) Shadow Rating 산출
+    print("="*60)
+    print("[Shadow Rating] Composite Score 기반 등급 부여")
+    print("="*60)
+    
+    df = compute_shadow_features(conn)
+    print(f"  대상 종목: {len(df)}개")
+    
+    df = compute_composite_score(df)
+    df = assign_shadow_rating(df)
+    
+    # 비교: DD 기반 vs Shadow Rating
+    print("\n=== DD 등급 vs Shadow 등급 비교 ===")
+    match = (df["dd_rating"] == df["shadow_rating"]).sum()
+    print(f"  일치율: {match}/{len(df)} ({match/len(df)*100:.1f}%)")
+    
+    # Shadow Rating 분포
+    print("\n=== Shadow Rating 분포 ===")
+    rating_order = list(GLOBAL_DEFAULT_RATES.keys())
+    df["shadow_rating"] = pd.Categorical(df["shadow_rating"], categories=rating_order, ordered=True)
+    dist = df["shadow_rating"].value_counts().sort_index()
+    for rating, count in dist.items():
+        if count > 0:
+            avg_dd = df[df["shadow_rating"] == rating]["DD"].mean()
+            avg_score = df[df["shadow_rating"] == rating]["composite_score"].mean()
+            print(f"  {rating:5s}: {count:4d}개 | DD평균={avg_dd:6.2f} | Score={avg_score:6.2f}")
+    
+    # 2) 등급별 부도율
+    print("\n" + "="*60)
+    print("[부도율] 등급별 부도율 산출 + 글로벌 블렌딩")
+    print("="*60)
+    
+    dr_df = compute_default_rates(df, config)
+    
+    print(f"\n{'등급':>5} | {'기업수':>5} | {'EDF평균':>10} | {'글로벌':>10} | {'블렌딩':>10} | {'베이지안':>10}")
+    print("-" * 70)
+    for _, row in dr_df.iterrows():
+        print(f"  {row['rating_grade']:5s} | {row['n_firms']:5d} | {row['korean_dr']:10.6f} | "
+              f"{row['global_dr']:10.6f} | {row['blended_dr']:10.6f} | {row['bayesian_dr']:10.6f}")
+    
+    # 3) DB 저장
+    if not args.stats:
+        base_date = df["DD"].index[0] if hasattr(df["DD"].index, '__getitem__') else datetime.now().strftime("%Y-%m-%d")
+        # merton_results에 shadow_rating 업데이트
+        for _, row in df.iterrows():
+            conn.execute("""
+                UPDATE merton_results SET dd_rating = ? WHERE ticker = ?
+            """, (row["shadow_rating"], row["ticker"]))
+        
+        # default_rates 테이블 저장
+        base_date_str = datetime.now().strftime("%Y-%m-%d")
+        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 (?, ?, ?, ?, ?, ?, ?, ?, ?)
+            """, (
+                base_date_str, row["rating_grade"], int(row["n_firms"]), int(row["n_defaults"]),
+                row["korean_dr"], row["global_dr"], row["weight_kr"], row["blended_dr"], row["bayesian_dr"]
+            ))
+        conn.commit()
+        print(f"\n  → merton_results shadow_rating 업데이트: {len(df)}건")
+        print(f"  → default_rates 저장: {len(dr_df)}건")
+    
+    conn.close()
+
+
+if __name__ == "__main__":
+    main()

test_setup.py

@@ -1,34 +1,42 @@
-"""DB stats and sample data"""
-import sqlite3
+"""시가총액 backfill: per-ticker get_market_cap_by_date 재시도"""
+import sqlite3, time
+from pykrx import stock
 
 conn = sqlite3.connect("data/edf.db")
 
-print("=== DB Stats ===")
-for t in ["companies", "market_data", "financial_data", "volatility", "merton_results"]:
-    c = conn.execute(f"SELECT COUNT(*) FROM {t}").fetchone()[0]
-    print(f"  {t}: {c:,}")
+# 샘플 5개로 정확한 API 동작 확인
+tickers = ["005930", "000660", "005380", "035720", "068270"]
 
-print("\n=== Volatility sample ===")
-rows = conn.execute("SELECT ticker, sigma_E FROM volatility ORDER BY ticker LIMIT 5").fetchall()
-for r in rows:
-    print(f"  {r[0]}: sigma_E={r[1]:.4f}")
+for tk in tickers:
+    print(f"\n=== {tk} ===")
+    
+    # Method 1: get_market_cap_by_date (fromdate, todate, ticker)
+    try:
+        cap = stock.get_market_cap_by_date("20250301", "20250307", tk)
+        time.sleep(0.3)
+        print(f"  cap_by_date: {len(cap)} rows")
+        if len(cap) > 0:
+            print(f"  columns: {list(cap.columns)}")
+            print(cap.tail(2))
+    except Exception as e:
+        print(f"  cap_by_date ERROR: {e}")
+    
+    # Method 2: get_market_fundamental_by_date
+    try:
+        fund = stock.get_market_fundamental_by_date("20250301", "20250307", tk)
+        time.sleep(0.3)
+        print(f"  fundamental: {len(fund)} rows, columns: {list(fund.columns)}")
+    except Exception as e:
+        print(f"  fundamental ERROR: {e}")
 
-print("\n=== Financial sample ===")
-rows = conn.execute("""
-    SELECT ticker, total_assets, default_point, leverage_ratio 
-    FROM financial_data WHERE total_assets IS NOT NULL 
-    ORDER BY total_assets DESC LIMIT 5
-""").fetchall()
-for r in rows:
-    dp = f"{r[2]:,.0f}" if r[2] else "N/A"
-    lev = f"{r[3]:.3f}" if r[3] else "N/A"
-    print(f"  {r[0]}: TA={r[1]:,.0f} DP={dp} LEV={lev}")
-
-# Overlap: tickers with BOTH volatility AND financial_data
-both = conn.execute("""
-    SELECT COUNT(DISTINCT v.ticker) 
-    FROM volatility v JOIN financial_data f ON v.ticker = f.ticker
-""").fetchone()[0]
-print(f"\n=== Merton 산출 가능 종목 (KRX+DART 모두 있는 종목): {both} ===")
-
-conn.close()
+    # Method 3: get_exhaustive_info
+    try:
+        cap = stock.get_market_cap_by_ticker("20250307")
+        time.sleep(0.3)
+        if tk in cap.index:
+            print(f"  cap_by_ticker: 시총={cap.loc[tk, '시가총액']:,.0f}, 주식수={cap.loc[tk, '상장주식수']:,}")
+        else:
+            print(f"  cap_by_ticker: {tk} not found, total rows={len(cap)}")
+        break  # 한번만 호출 (전체 시장 데이터)
+    except Exception as e:
+        print(f"  cap_by_ticker ERROR: {e}")