"""
Relative Strength Index (RSI) indicator implementation.
"""

from typing import List
import pandas as pd

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


class RSIIndicator(BaseIndicator):
    """
    Relative Strength Index (RSI) technical indicator.
    
    Measures momentum by comparing the magnitude of recent gains to recent losses.
    Handles sparse data appropriately without interpolation.
    """
    
    def calculate(self, df: pd.DataFrame, period: int = 14,
                 price_column: str = 'close') -> List[IndicatorResult]:
        """
        Calculate Relative Strength Index (RSI).
        
        Args:
            df: DataFrame with OHLCV data
            period: Number of periods for RSI calculation (default: 14)
            price_column: Price column to use ('open', 'high', 'low', 'close')
            
        Returns:
            List of indicator results with RSI values
        """
        # Validate input data
        if not self.validate_dataframe(df, period + 1):  # Need extra period for diff
            return []
        
        try:
            # Calculate price changes
            df['price_change'] = df[price_column].diff()
            
            # Separate gains and losses
            df['gain'] = df['price_change'].where(df['price_change'] > 0, 0)
            df['loss'] = (-df['price_change']).where(df['price_change'] < 0, 0)
            
            # Calculate average gain and loss using EMA
            df['avg_gain'] = df['gain'].ewm(span=period, adjust=False).mean()
            df['avg_loss'] = df['loss'].ewm(span=period, adjust=False).mean()
            
            # Calculate RS and RSI
            df['rs'] = df['avg_gain'] / df['avg_loss']
            df['rsi'] = 100 - (100 / (1 + df['rs']))
            
            # Handle division by zero
            df['rsi'] = df['rsi'].fillna(50)  # Neutral RSI when no losses
            
            # Convert results to IndicatorResult objects
            results = []
            for i, (timestamp, row) in enumerate(df.iterrows()):
                # Only return results after minimum period
                if i >= period and not pd.isna(row['rsi']):
                    result = IndicatorResult(
                        timestamp=timestamp,
                        symbol=row['symbol'],
                        timeframe=row['timeframe'],
                        values={'rsi': row['rsi']},
                        metadata={'period': period, 'price_column': price_column}
                    )
                    results.append(result)
            
            return results
            
        except Exception as e:
            if self.logger:
                self.logger.error(f"Error calculating RSI: {e}")
            return []