index_ratio.py

def calculate_portfolio_performance(
    stock_data: pd.DataFrame, 
    tickers: List[str], 
    allocations: Dict[str, float], 
    risk_free_rate: float = 0.02,
    target_return: float = 0.0,
    annualization_factor: float = 252
) -> Dict[str, float]:
    """
    Calculate comprehensive portfolio performance metrics.
    
    Args:
        stock_data: DataFrame with price data for each ticker
        tickers: List of stock tickers
        allocations: Dictionary mapping tickers to their weight allocations
        risk_free_rate: Risk-free rate (annualized)
        target_return: Minimum acceptable return for Sortino ratio (default: 0)
        annualization_factor: Number of trading days in a year
        
    Returns:
        Dictionary with portfolio performance metrics
    """
    # Ensure allocations are provided for all tickers
    weights_dict = {ticker: allocations.get(ticker, 0) for ticker in tickers}
    
    # Convert weights to numpy array
    weights_array = weights_to_array(tickers, weights_dict)
    
    # Calculate expected returns and covariance matrix
    mu, S = calculate_returns_and_covariance(stock_data)
    
    # Calculate basic portfolio metrics (expected return, std dev, Sharpe)
    basic_metrics = calculate_portfolio_metrics(weights_array, mu, S, risk_free_rate)
    
    # Calculate Sortino ratio
    sortino_ratio = calculate_sortino_ratio(
        weights_array, 
        stock_data, 
        risk_free_rate,
        target_return,
        annualization_factor
    )
    
    # Calculate max drawdown
    daily_returns = stock_data.pct_change().dropna()
    portfolio_returns = pd.Series(0, index=daily_returns.index)
    
    for ticker, weight in weights_dict.items():
        if ticker in daily_returns.columns:
            portfolio_returns += daily_returns[ticker] * weight
    
    # Calculate cumulative returns
    cumulative_returns = (1 + portfolio_returns).cumprod()
    max_drawdown = (cumulative_returns / cumulative_returns.cummax() - 1).min()
    
    # Combine all metrics
    return {
        "expected_return": basic_metrics["expected_return"],
        "standard_deviation": basic_metrics["standard_deviation"],
        "sharpe_ratio": basic_metrics["sharpe_ratio"],
        "sortino_ratio": sortino_ratio,
        "max_drawdown": float(max_drawdown),
        "risk_free_rate": basic_metrics["risk_free_rate"]
    }
  
  
  ##########################################################################################
  
  # Example usage
tickers = ["AAPL", "MSFT", "GOOG"]
allocations = {"AAPL": 0.4, "MSFT": 0.3, "GOOG": 0.3}

# Fetch data
stock_data, _, _ = fetch_stock_data(tickers, "2020-01-01", "2023-01-01")

# Calculate metrics
performance = calculate_portfolio_performance(stock_data, tickers, allocations, risk_free_rate=0.02)

print(f"Expected Return: {performance['expected_return']:.2%}")
print(f"Standard Deviation: {performance['standard_deviation']:.2%}")
print(f"Sharpe Ratio: {performance['sharpe_ratio']:.2f}")
print(f"Sortino Ratio: {performance['sortino_ratio']:.2f}")
print(f"Maximum Drawdown: {performance['max_drawdown']:.2%}")