"""
Exponential Moving Average (EMA) indicator implementation.
"""

import pandas as pd

from ..base import BaseIndicator


class EMAIndicator(BaseIndicator):
    """
    Exponential Moving Average (EMA) technical indicator.
    
    Calculates weighted moving average giving more weight to recent prices.
    Handles sparse data appropriately without interpolation.
    """
    
    def calculate(self, df: pd.DataFrame, period: int = 20,
                 price_column: str = 'close') -> pd.DataFrame:
        """
        Calculate Exponential Moving Average (EMA).
        
        Args:
            df: DataFrame with OHLCV data
            period: Number of periods for moving average (default: 20)
            price_column: Price column to use ('open', 'high', 'low', 'close')
            
        Returns:
            DataFrame with EMA values and metadata, indexed by timestamp
        """
        # Validate input data
        if not self.validate_dataframe(df, period):
            return pd.DataFrame()
        try:
            df = df.copy()
            df['ema'] = df[price_column].ewm(span=period, adjust=False).mean()
            # Only keep rows with valid EMA, and only 'timestamp' and 'ema' columns
            result_df = df.loc[df['ema'].notna(), ['timestamp', 'ema']].copy()
            # Only keep rows after enough data for EMA
            result_df = result_df.iloc[period-1:]
            result_df.set_index('timestamp', inplace=True)
            return result_df
        except Exception:
            return pd.DataFrame()