"""
Moving Average Convergence Divergence (MACD) indicator implementation.
"""

import pandas as pd

from ..base import BaseIndicator


class MACDIndicator(BaseIndicator):
    """
    Moving Average Convergence Divergence (MACD) technical indicator.
    
    Calculates trend-following momentum indicator that shows the relationship
    between two moving averages of a security's price.
    Handles sparse data appropriately without interpolation.
    """
    
    def calculate(self, df: pd.DataFrame, fast_period: int = 12,
                 slow_period: int = 26, signal_period: int = 9,
                 price_column: str = 'close') -> pd.DataFrame:
        """
        Calculate Moving Average Convergence Divergence (MACD).
        
        Args:
            df: DataFrame with OHLCV data
            fast_period: Fast EMA period (default 12)
            slow_period: Slow EMA period (default 26)
            signal_period: Signal line EMA period (default 9)
            price_column: Price column to use ('open', 'high', 'low', 'close')
            
        Returns:
            DataFrame with MACD values and metadata, indexed by timestamp
        """
        # Validate input data
        if not self.validate_dataframe(df, slow_period):
            return pd.DataFrame()
        try:
            df = df.copy()
            df['macd'] = df[price_column].ewm(span=fast_period, adjust=False).mean() - \
                         df[price_column].ewm(span=slow_period, adjust=False).mean()
            df['signal'] = df['macd'].ewm(span=signal_period, adjust=False).mean()
            df['histogram'] = df['macd'] - df['signal']
            # Only keep rows with valid MACD, and only 'timestamp', 'macd', 'signal', 'histogram' columns
            result_df = df.loc[df['macd'].notna() & df['signal'].notna() & df['histogram'].notna(), ['timestamp', 'macd', 'signal', 'histogram']].copy()
            # Only keep rows after enough data for MACD and signal
            min_required = max(slow_period, signal_period)
            result_df = result_df.iloc[min_required-1:]
            result_df.set_index('timestamp', inplace=True)
            return result_df
        except Exception:
            return pd.DataFrame()