2025년 다우의 개 전략은 2026년에도 통할까? 수익률 충격 분석

import yfinance as yf
import pandas as pd

# ✅ 다우 30 종목 (정확)
dow30 = [
    "AAPL","MSFT","JPM","V","UNH","HD","PG","KO","MRK","JNJ",
    "CRM","CVX","XOM","IBM","CSCO","INTC","VZ","WMT","DIS","NKE",
    "MCD","AMGN","HON","BA","GS","CAT","TRV","AXP","DOW","MMM"
]

data = []

# =========================
# 1️⃣ 2025 배당 + 주가 분석
# =========================
for ticker in dow30:
    stock = yf.Ticker(ticker)
    
    hist = stock.history(start="2024-01-01", end="2026-01-05")
    
    if hist.empty:
        continue
    
    # ✅ 연도 기준 필터 (KeyError 방지)
    hist_2024 = hist[hist.index.year == 2024]
    hist_2025 = hist[hist.index.year == 2025]
    
    if hist_2024.empty or hist_2025.empty:
        continue
    
    price_2024 = hist_2024.iloc[-1]["Close"]
    price_2025 = hist_2025.iloc[-1]["Close"]
    
    # 📌 2025년 주가 변화율
    price_change_pct = (price_2025 - price_2024) / price_2024 * 100
    
    # ✅ 배당 (안전 처리)
    dividends = stock.dividends
    
    if not dividends.empty:
        div_2025 = dividends[
            (dividends.index >= "2025-01-01") & 
            (dividends.index <= "2025-12-31")
        ].sum()
    else:
        div_2025 = 0
    
    # 📌 배당수익률
    yield_pct = (div_2025 / price_2025 * 100) if price_2025 > 0 else 0
    
    # 📌 섹터
    try:
        sector = stock.info.get("sector", "N/A")
    except:
        sector = "N/A"
    
    data.append([
        ticker,
        sector,
        round(price_2024, 2),
        round(price_2025, 2),
        round(price_change_pct, 2),
        round(div_2025, 2),
        round(yield_pct, 2)
    ])

df_all = pd.DataFrame(data, columns=[
    "Ticker",
    "Sector",
    "2024 Price",
    "2025 Price",
    "2025 Price Change (%)",
    "TTM Dividend (2025)",
    "Dividend Yield (%)"
])

# =========================
# 2️⃣ 다우의 개 TOP10
# =========================
df_sorted = df_all.sort_values(by="Dividend Yield (%)", ascending=False)
dogs = df_sorted.head(10)

# =========================
# 3️⃣ 2026년 성과 (전체 30개)
# =========================
results = []

for ticker in dow30:
    stock = yf.Ticker(ticker)
    
    hist = stock.history(start="2026-01-01", end="2026-04-25")
    
    if hist.empty:
        continue
    
    start_price = hist.iloc[0]["Close"]
    end_price = hist.iloc[-1]["Close"]
    
    price_return = (end_price - start_price) / start_price * 100
    
    dividends = stock.dividends
    
    if not dividends.empty:
        div_2026 = dividends[
            (dividends.index >= "2026-01-01") & 
            (dividends.index <= "2026-04-24")
        ].sum()
    else:
        div_2026 = 0
    
    dividend_yield = (div_2026 / start_price * 100) if start_price > 0 else 0
    
    total_return = price_return + dividend_yield
    
    results.append([
        ticker,
        round(start_price, 2),
        round(end_price, 2),
        round(price_return, 2),
        round(dividend_yield, 2),
        round(total_return, 2)
    ])

df_2026 = pd.DataFrame(results, columns=[
    "Ticker",
    "2026 Start Price",
    "2026 End Price",
    "Price Return (%)",
    "Dividend Yield (%)",
    "Total Return (%)"
])

# =========================
# 4️⃣ 병합 (전체 30개)
# =========================
df_final = pd.merge(df_all, df_2026, on="Ticker", how="left")

# =========================
# 5️⃣ 다우의 개 성과만
# =========================
dogs_performance = df_final[df_final["Ticker"].isin(dogs["Ticker"])]

# =========================
# 6️⃣ 엑셀 저장
# =========================
file_name = "dow_dogs_2025_2026_analysis.xlsx"

with pd.ExcelWriter(file_name) as writer:
    df_final.to_excel(writer, sheet_name="Dow30_All", index=False)
    dogs.to_excel(writer, sheet_name="Dogs_of_Dow_2025", index=False)
    dogs_performance.to_excel(writer, sheet_name="Dogs_Performance_2026", index=False)

print(f"\n✅ 엑셀 파일 저장 완료: {file_name}")