import pandas as pd

class Portfolio:
    def __init__(self, price_data, currencies=['btc', 'eth']):
        self.price_data = price_data  # Initialize price data
        self.currencies = currencies  # Initialize list of currencies
        
        # Ensure price_data is provided and not empty
        if not isinstance(self.price_data, pd.DataFrame) or self.price_data.empty:
            raise ValueError("price_data must be a non-empty DataFrame")
        
        self.returns_data = self._calculate_returns()  # Initialize returns data by calling _calculate_returns()

    def _calculate_returns(self):
        # Check if 'date' column exists in price_data
        if 'date' not in self.price_data.columns:
            raise ValueError("price_data must contain a 'date' column")
        
        # Convert 'date' column to datetime and set it as index
        self.price_data.index = pd.to_datetime(self.price_data['date'])
        # Sort price_data by index (date)
        self.price_data = self.price_data.sort_index()

        # Resample to monthly data, calculate percentage change
        monthly_data = self.price_data.drop('date', axis=1)[self.currencies].resample('M').last()
        monthly_returns = monthly_data.pct_change()

        return monthly_returns  # Return monthly returns DataFrame

    def rebalance_portfolio(self):
        # Ensure returns_data is initialized
        if not hasattr(self, 'returns_data') or self.returns_data.empty:
            raise ValueError("returns_data is not initialized or empty. Call _calculate_returns() first.")
        
        # Calculate portfolio return as mean of currency returns
        self.returns_data['portfolio_return'] = self.returns_data[self.currencies].mean(axis=1)
        # Calculate cumulative return
        self.returns_data['cumulative_return'] = (1 + self.returns_data['portfolio_return']).cumprod()

        return self.returns_data  # Return updated returns data DataFrame

# Example usage with error handling
try:
    portfolio = Portfolio(price_data=my_price_data)
    portfolio.rebalance_portfolio()
except ValueError as e:
    print(f"Error occurred: {str(e)}")