Split View: 개발자를 위한 ETF 투자 전략 — 자산배분부터 퀀트 백테스팅까지

| Feature         | Individual Stocks | Active Funds     | ETF (Passive)     |
|----------------|-------------------|------------------|-------------------|
| Diversification | No (manual)       | Yes (fund mgr)   | Yes (index track) |
| Fees (cost)     | None              | 1-2%/year        | 0.03-0.5%/year    |
| Trading         | Real-time         | Once per day      | Real-time         |
| Transparency    | High              | Low               | High              |
| Time required   | A lot             | Little            | Very little       |

# Core ETFs for global asset allocation
CORE_ETFS = {
    # Equities
    "VTI": "US Total Stock Market",
    "VXUS": "Non-US Developed + Emerging Markets",
    "VWO": "Emerging Markets",
    "QQQ": "NASDAQ 100 (Tech Stocks)",

    # Bonds
    "BND": "US Aggregate Bonds",
    "TLT": "US Long-Term Treasury (20+ years)",
    "IEF": "US Intermediate Treasury (7-10 years)",
    "TIP": "Treasury Inflation-Protected Securities (TIPS)",

    # Alternatives
    "GLD": "Gold",
    "VNQ": "US REITs (Real Estate)",
    "DBC": "Commodities",

    # Korea
    "KODEX200": "KOSPI 200",
    "TIGER US S&P500": "S&P 500 (KRW denominated)",
    "ACE US NASDAQ100": "NASDAQ 100 (KRW denominated)",
}

ALL_WEATHER = {
    "VTI": 0.30,   # US Stocks 30%
    "TLT": 0.40,   # Long-Term Treasury 40%
    "IEF": 0.15,   # Intermediate Treasury 15%
    "GLD": 0.075,  # Gold 7.5%
    "DBC": 0.075,  # Commodities 7.5%
}
# Feature: Prepared for all 4 economic seasons (Growth/Recession x Inflation/Deflation)
# Average annual return: ~7% (2005-2025)
# Maximum drawdown: ~12%

PERMANENT = {
    "VTI": 0.25,   # Stocks 25% (Growth)
    "TLT": 0.25,   # Long-Term Treasury 25% (Recession)
    "GLD": 0.25,   # Gold 25% (Inflation)
    "BIL": 0.25,   # Short-Term Treasury 25% (Safe Asset)
}
# Feature: Extremely simple, 25% always shines in any economic condition
# Average annual return: ~6%
# Maximum drawdown: ~10%

CLASSIC_60_40 = {
    "VTI": 0.60,   # Stocks 60%
    "BND": 0.40,   # Bonds 40%
}
# Feature: The most traditional and simple
# Average annual return: ~8%
# Maximum drawdown: ~20%

CORE_SATELLITE = {
    # Core (80%) — Passive, low-cost
    "VTI": 0.50,    # US Total Market
    "VXUS": 0.20,   # International Stocks
    "BND": 0.10,    # Bonds

    # Satellite (20%) — Alpha seeking
    "QQQ": 0.10,    # Tech Growth
    "VNQ": 0.05,    # REITs
    "GLD": 0.05,    # Gold
}
# Feature: Capture market returns (beta) + seek some alpha

import yfinance as yf
import pandas as pd
import numpy as np

def get_etf_data(tickers: list, start: str = "2010-01-01") -> pd.DataFrame:
    """Download ETF price data"""
    data = yf.download(tickers, start=start, auto_adjust=True)
    prices = data["Close"]
    returns = prices.pct_change().dropna()
    return prices, returns


# Download data
tickers = ["VTI", "TLT", "IEF", "GLD", "DBC", "BND"]
prices, returns = get_etf_data(tickers)

def backtest_portfolio(
    returns: pd.DataFrame,
    weights: dict,
    rebalance_freq: str = "Q",  # Q=Quarterly, M=Monthly, Y=Yearly
    initial_capital: float = 10000
) -> pd.DataFrame:
    """Portfolio backtest"""
    tickers = list(weights.keys())
    w = np.array([weights[t] for t in tickers])

    # Daily portfolio returns
    port_returns = (returns[tickers] * w).sum(axis=1)

    # Rebalancing simulation
    if rebalance_freq:
        rebalance_dates = returns.resample(rebalance_freq).last().index

    # Cumulative returns
    cumulative = (1 + port_returns).cumprod() * initial_capital

    # Performance metrics
    total_return = cumulative.iloc[-1] / initial_capital - 1
    annual_return = (1 + total_return) ** (252 / len(port_returns)) - 1
    annual_vol = port_returns.std() * np.sqrt(252)
    sharpe = annual_return / annual_vol
    max_dd = (cumulative / cumulative.cummax() - 1).min()

    stats = {
        "Total Return": f"{total_return:.1%}",
        "CAGR": f"{annual_return:.1%}",
        "Annual Volatility": f"{annual_vol:.1%}",
        "Sharpe Ratio": f"{sharpe:.2f}",
        "Maximum Drawdown (MDD)": f"{max_dd:.1%}",
    }

    return cumulative, stats


# Backtest each strategy
strategies = {
    "All Weather": ALL_WEATHER,
    "Permanent Portfolio": PERMANENT,
    "60/40": CLASSIC_60_40,
}

for name, weights in strategies.items():
    cumulative, stats = backtest_portfolio(returns, weights)
    print(f"\n{'='*40}")
    print(f"  {name}")
    print(f"{'='*40}")
    for k, v in stats.items():
        print(f"  {k}: {v}")

def simulate_rebalancing(
    prices: pd.DataFrame,
    weights: dict,
    initial_capital: float = 10000,
    rebalance_freq: str = "Q"
) -> pd.DataFrame:
    """Actual rebalancing trade simulation"""
    tickers = list(weights.keys())
    target_weights = np.array([weights[t] for t in tickers])

    # Initial positions
    shares = {}
    for ticker, w in weights.items():
        price = prices[ticker].iloc[0]
        shares[ticker] = (initial_capital * w) / price

    portfolio_value = []
    rebalance_log = []

    rebalance_dates = prices.resample(rebalance_freq).last().index

    for date in prices.index:
        # Current portfolio value
        current_value = sum(
            shares[t] * prices[t].loc[date]
            for t in tickers
        )
        portfolio_value.append(current_value)

        # Is it a rebalancing date?
        if date in rebalance_dates:
            current_weights = np.array([
                shares[t] * prices[t].loc[date] / current_value
                for t in tickers
            ])

            drift = np.abs(current_weights - target_weights).max()

            if drift > 0.05:  # Rebalance only when drift exceeds 5%
                for ticker, tw in weights.items():
                    target_value = current_value * tw
                    shares[ticker] = target_value / prices[ticker].loc[date]

                rebalance_log.append({
                    "date": date,
                    "value": current_value,
                    "drift": drift,
                })

    return pd.Series(portfolio_value, index=prices.index), rebalance_log

def dollar_cost_averaging(
    prices: pd.DataFrame,
    ticker: str,
    monthly_amount: float = 500000,  # 500,000 KRW per month
    start_date: str = "2015-01-01"
) -> dict:
    """Dollar cost averaging simulation"""
    monthly_prices = prices[ticker].resample("MS").first()
    monthly_prices = monthly_prices[start_date:]

    total_invested = 0
    total_shares = 0

    for date, price in monthly_prices.items():
        shares_bought = monthly_amount / price
        total_shares += shares_bought
        total_invested += monthly_amount

    final_value = total_shares * prices[ticker].iloc[-1]
    total_return = (final_value / total_invested - 1) * 100
    avg_cost = total_invested / total_shares

    return {
        "Total Invested": f"{total_invested:,.0f} KRW",
        "Current Value": f"{final_value:,.0f} KRW",
        "Return": f"{total_return:.1f}%",
        "Average Cost Basis": f"{avg_cost:,.0f} KRW",
        "Current Price": f"{prices[ticker].iloc[-1]:,.0f} KRW",
        "Investment Period": f"{len(monthly_prices)} months",
    }

# Korean ETF tax structure
TAX_RULES = {
    "Domestic Stock ETF": {
        "Capital Gains": "Tax-free",
        "Distributions": "15.4% dividend income tax",
        "Examples": "KODEX200, TIGER KRX300",
    },
    "Domestic Other ETF": {
        "Capital Gains": "15.4% dividend income tax (holding period basis)",
        "Distributions": "15.4% dividend income tax",
        "Examples": "KODEX Gold, TIGER US S&P500",
    },
    "Direct Overseas Investment": {
        "Capital Gains": "22% capital gains tax (after 2.5 million KRW exemption)",
        "Distributions": "15% withholding tax",
        "Examples": "VTI, QQQ, TLT (direct US purchase)",
    },
}

def calculate_tax(profit: float, invest_type: str) -> float:
    """After-tax profit calculation"""
    if invest_type == "domestic_stock":
        return profit  # Tax-free
    elif invest_type == "domestic_other":
        return profit * (1 - 0.154)
    elif invest_type == "overseas_direct":
        taxable = max(0, profit - 2500000)  # 2.5 million KRW exemption
        tax = taxable * 0.22
        return profit - tax

import yfinance as yf
from datetime import datetime

def check_rebalance_needed(
    portfolio: dict,
    target_weights: dict,
    threshold: float = 0.05
) -> list:
    """Check if rebalancing is needed"""
    # Get current prices
    tickers = list(portfolio.keys())
    current_prices = {}
    for t in tickers:
        data = yf.Ticker(t)
        current_prices[t] = data.info.get("regularMarketPrice", 0)

    # Current portfolio value
    total_value = sum(
        portfolio[t]["shares"] * current_prices[t]
        for t in tickers
    )

    # Drift check
    alerts = []
    for ticker in tickers:
        current_value = portfolio[ticker]["shares"] * current_prices[ticker]
        current_weight = current_value / total_value
        target_weight = target_weights[ticker]
        drift = abs(current_weight - target_weight)

        if drift > threshold:
            action = "Buy" if current_weight < target_weight else "Sell"
            amount = abs(current_weight - target_weight) * total_value

            alerts.append({
                "ticker": ticker,
                "Current Weight": f"{current_weight:.1%}",
                "Target Weight": f"{target_weight:.1%}",
                "Drift": f"{drift:.1%}",
                "Action": f"{action} {amount:,.0f} KRW",
            })

    return alerts


# Quarterly check (run as cron job)
alerts = check_rebalance_needed(my_portfolio, ALL_WEATHER)
if alerts:
    print("Warning: Rebalancing needed!")
    for a in alerts:
        print(f"  {a['ticker']}: {a['Current Weight']} -> {a['Target Weight']} ({a['Action']})")

1. Automate
   - Monthly auto-transfer -> auto-purchase
   - Automate rebalancing alerts
   - Leave no room for emotions to interfere

2. Decide with data
   - Never use a strategy without backtesting
   - Past returns are not equal to future returns, but useful for risk measurement

3. Save time
   - Portfolio check: once per quarter is enough
   - Time spent on news/charts -> redirect to coding/learning

4. Reduce costs
   - Choose ETFs with expense ratios under 0.1%
   - Frequent trading = more taxes + more fees

5. Invest long-term
   - Minimum 10+ year investment horizon
   - Do not try to time the market
   - "Time in the market > Timing the market"