"""
Relative Strength Index (RSI) Strategy Implementation

This module implements an RSI-based momentum trading strategy.
It extends the BaseStrategy and generates buy/sell signals based on
RSI crossing overbought/oversold thresholds.
"""

import pandas as pd
from typing import List, Dict, Any

from ..base import BaseStrategy
from ..data_types import StrategyResult, StrategySignal, SignalType
from ..utils import create_indicator_key, detect_threshold_signals_vectorized


class RSIStrategy(BaseStrategy):
    """
    RSI Strategy.

    Generates buy/sell signals when RSI crosses overbought/oversold thresholds.
    """

    def __init__(self, strategy_name: str, logger=None):
        super().__init__(strategy_name, logger)

    def get_required_indicators(self) -> List[Dict[str, Any]]:
        """
        Defines the indicators required by the RSI strategy.
        It needs one RSI indicator.
        """
        # Default period for RSI, can be overridden by strategy config
        return [
            {'type': 'rsi', 'period': 14, 'price_column': 'close'}
        ]

    def calculate(self, df: pd.DataFrame, indicators_data: Dict[str, pd.DataFrame], **kwargs) -> List[StrategyResult]:
        """
        Calculate RSI strategy signals.

        Args:
            df: DataFrame with OHLCV data.
            indicators_data: Dictionary of pre-calculated indicator DataFrames.
                             Expected key: 'rsi_period_14'.
            **kwargs: Additional strategy parameters (e.g., period, overbought, oversold, price_column).

        Returns:
            List of StrategyResult objects, each containing generated signals.
        """
        # Extract parameters from kwargs or use defaults
        period = kwargs.get('period', 14)
        overbought = kwargs.get('overbought', 70)
        oversold = kwargs.get('oversold', 30)
        price_column = kwargs.get('price_column', 'close')

        # Generate indicator key using shared utility function
        rsi_key = create_indicator_key({'type': 'rsi', 'period': period})

        # Validate that the main DataFrame has enough data for strategy calculation
        if not self.validate_dataframe(df, period):
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Insufficient main DataFrame for calculation.")
            return []

        # Validate that the required RSI indicator data is present and sufficient
        required_indicators = [
            {'type': 'rsi', 'period': period}
        ]
        if not self.validate_indicators_data(indicators_data, required_indicators):
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Missing or insufficient RSI indicator data.")
            return []

        rsi_df = indicators_data.get(rsi_key)

        if rsi_df is None or rsi_df.empty:
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: RSI indicator DataFrame is not found or empty.")
            return []

        # Merge all necessary data into a single DataFrame for easier processing
        merged_df = pd.merge(df[[price_column, 'symbol', 'timeframe']], 
                             rsi_df[['rsi']], 
                             left_index=True, right_index=True, how='inner')

        if merged_df.empty:
            if self.logger:
                self.logger.warning(f"{self.strategy_name}: Merged DataFrame is empty after indicator alignment. Check data ranges.")
            return []

        # Use vectorized signal detection for better performance
        buy_signals, sell_signals = detect_threshold_signals_vectorized(
            merged_df, 'rsi', overbought, oversold
        )
        
        results: List[StrategyResult] = []
        strategy_metadata = {
            'period': period,
            'overbought': overbought,
            'oversold': oversold
        }

        # Process buy signals (RSI crosses above oversold threshold)
        buy_indices = merged_df[buy_signals].index
        for timestamp in buy_indices:
            row = merged_df.loc[timestamp]
            
            # Skip if RSI value is NaN
            if pd.isna(row['rsi']):
                continue
                
            signal = StrategySignal(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                signal_type=SignalType.BUY,
                price=float(row[price_column]),
                confidence=0.7,
                metadata={'rsi_cross': 'oversold_to_buy', **strategy_metadata}
            )
            
            results.append(StrategyResult(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                strategy_name=self.strategy_name,
                signals=[signal],
                indicators_used={'rsi': float(row['rsi'])},
                metadata=strategy_metadata
            ))
            
            if self.logger:
                self.logger.info(f"{self.strategy_name}: BUY signal at {timestamp} for {row['symbol']} (RSI: {row['rsi']:.2f})")

        # Process sell signals (RSI crosses below overbought threshold) 
        sell_indices = merged_df[sell_signals].index
        for timestamp in sell_indices:
            row = merged_df.loc[timestamp]
            
            # Skip if RSI value is NaN
            if pd.isna(row['rsi']):
                continue
                
            signal = StrategySignal(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                signal_type=SignalType.SELL,
                price=float(row[price_column]),
                confidence=0.7,
                metadata={'rsi_cross': 'overbought_to_sell', **strategy_metadata}
            )
            
            results.append(StrategyResult(
                timestamp=timestamp,
                symbol=row['symbol'],
                timeframe=row['timeframe'],
                strategy_name=self.strategy_name,
                signals=[signal],
                indicators_used={'rsi': float(row['rsi'])},
                metadata=strategy_metadata
            ))
            
            if self.logger:
                self.logger.info(f"{self.strategy_name}: SELL signal at {timestamp} for {row['symbol']} (RSI: {row['rsi']:.2f})")

        return results