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

from typing import List
import pandas as pd

from ..base import BaseIndicator
from ..result import IndicatorResult


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') -> List[IndicatorResult]:
        """
        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:
            List of indicator results with MACD, signal, and histogram values
        """
        # Validate input data
        if not self.validate_dataframe(df, slow_period):
            return []
        
        try:
            # Calculate fast and slow EMAs
            df['ema_fast'] = df[price_column].ewm(span=fast_period, adjust=False).mean()
            df['ema_slow'] = df[price_column].ewm(span=slow_period, adjust=False).mean()
            
            # Calculate MACD line
            df['macd'] = df['ema_fast'] - df['ema_slow']
            
            # Calculate signal line (EMA of MACD)
            df['signal'] = df['macd'].ewm(span=signal_period, adjust=False).mean()
            
            # Calculate histogram
            df['histogram'] = df['macd'] - df['signal']
            
            # Convert results to IndicatorResult objects
            results = []
            for i, (timestamp, row) in enumerate(df.iterrows()):
                # Only return results after minimum period
                if i >= slow_period - 1:
                    if not (pd.isna(row['macd']) or pd.isna(row['signal']) or pd.isna(row['histogram'])):
                        result = IndicatorResult(
                            timestamp=timestamp,
                            symbol=row['symbol'],
                            timeframe=row['timeframe'],
                            values={
                                'macd': row['macd'],
                                'signal': row['signal'],
                                'histogram': row['histogram']
                            },
                            metadata={
                                'fast_period': fast_period,
                                'slow_period': slow_period,
                                'signal_period': signal_period,
                                'price_column': price_column
                            }
                        )
                        results.append(result)
            
            return results
            
        except Exception as e:
            if self.logger:
                self.logger.error(f"Error calculating MACD: {e}")
            return []