"""
Base classes and types for quantum trading strategies.
"""

import abc
import pandas as pd
from typing import Dict, Any
import logging
from enum import Enum, auto

logger = logging.getLogger(__name__)

class StrategyType(Enum):
    """Types of quantum trading strategies."""
    TREND_FOLLOWING = auto()
    MEAN_REVERSION = auto()
    VOLATILITY_BASED = auto()
    MULTI_ASSET = auto()
    HYBRID = auto()
    CUSTOM = auto()

# Placeholder for the factory class
class QuantumTradingStrategyFactory:
    @staticmethod
    def create_strategy(strategy_type: StrategyType, **kwargs) -> 'BaseQuantumStrategy':
        # This is a placeholder implementation.
        # In a real scenario, this would return actual strategy instances.
        logger.warning(f"Using placeholder QuantumTradingStrategyFactory for type {strategy_type}")
        # Return a dummy instance or raise NotImplementedError if preferred
        # For now, let's raise an error to indicate it's not functional
        raise NotImplementedError("QuantumTradingStrategyFactory placeholder is not implemented.")

class SignalType(Enum):
    """Types of trading signals."""
    HOLD = 0
    BUY = 1
    STRONG_BUY = 2
    SELL = 3
    STRONG_SELL = 4

class BaseQuantumStrategy(abc.ABC):
    """
    Abstract base class for all quantum trading strategies.
    """

    def __init__(self, name: str, description: str, max_position: float = 1.0):
        self.name = name
        self.description = description
        self.max_position = max_position
        self.current_position = 0.0
        self.positions_history = []
        self.signals_history = []
        logger.info(f"Initialized strategy: {self.name}")

    @abc.abstractmethod
    def process_data(self, data: pd.DataFrame) -> Dict[str, Any]:
        """
        Process market data and generate trading signals.
        Must be implemented by subclasses.

        Args:
            data: Market data DataFrame.

        Returns:
            Dictionary with trading decision details (e.g., signal, position_size, confidence).
        """
        pass

    def update(self, data: pd.DataFrame) -> Dict[str, Any]:
        """
        Update the strategy with new market data.
        Default implementation simply calls process_data.

        Args:
            data: New market data.

        Returns:
            Dictionary with updated trading decision.
        """
        return self.process_data(data)

    def evaluate_performance(self, price_data: pd.DataFrame) -> Dict[str, float]:
        """
        Evaluate the strategy's performance based on its history.

        Args:
            price_data: DataFrame with historical price data (index must match history timestamps).

        Returns:
            Dictionary of performance metrics.
        """
        # Basic performance evaluation (can be expanded)
        if not self.positions_history:
            return {"total_return": 0.0, "sharpe_ratio": 0.0, "max_drawdown": 0.0}

        history_df = pd.DataFrame(self.positions_history)
        history_df['timestamp'] = pd.to_datetime(history_df['timestamp'])
        history_df.set_index('timestamp', inplace=True)

        # Align history with price data
        aligned_data = price_data.join(history_df['position'], how='left')
        aligned_data['position'] = aligned_data['position'].ffill().fillna(0)
        
        returns = aligned_data['close'].pct_change().fillna(0)
        strategy_returns = aligned_data['position'].shift(1) * returns
        strategy_returns = strategy_returns.fillna(0)
        # Ensure numeric type before calculation
        strategy_returns_numeric = pd.to_numeric(strategy_returns, errors='coerce').fillna(0)

        total_return = (1 + strategy_returns_numeric).prod() - 1
        
        # Placeholder for more metrics
        metrics = {
            "total_return": total_return,
            "sharpe_ratio": 0.0,  # Placeholder
            "max_drawdown": 0.0   # Placeholder
        }
        logger.info(f"Performance Evaluation ({self.name}): {metrics}")
        return metrics

    def calculate_pnl(self, price_series: pd.Series, position_series: pd.Series) -> pd.Series:
        """
        Calculate the Profit and Loss (PnL) series.

        Args:
            price_series: Series of prices.
            position_series: Series of strategy positions.

        Returns:
            Series representing the cumulative PnL.
        """
        returns = price_series.pct_change().fillna(0)
        strategy_returns = position_series.shift(1).fillna(0) * returns
        cumulative_pnl = strategy_returns.cumsum()
        return cumulative_pnl

    def get_name(self) -> str:
        return self.name

    def get_description(self) -> str:
        return self.description

    def get_current_position(self) -> float:
        return self.current_position

    def get_positions_history(self) -> list:
        return self.positions_history

    def get_signals_history(self) -> list:
        return self.signals_history 

# Placeholder for specific strategy classes needed by tests
class TrendFollowingQuantumStrategy(BaseQuantumStrategy):
    def __init__(self, **kwargs):
        super().__init__(name="Placeholder TrendFollowing", description="Placeholder")
    def process_data(self, data: pd.DataFrame) -> Dict[str, Any]:
        logger.warning("Using placeholder TrendFollowingQuantumStrategy.process_data")
        return {'signal': SignalType.HOLD, 'position_size': 0.0}

class MeanReversionQuantumStrategy(BaseQuantumStrategy):
    def __init__(self, **kwargs):
        super().__init__(name="Placeholder MeanReversion", description="Placeholder")
    def process_data(self, data: pd.DataFrame) -> Dict[str, Any]:
        logger.warning("Using placeholder MeanReversionQuantumStrategy.process_data")
        return {'signal': SignalType.HOLD, 'position_size': 0.0} 