"""Strategic Intelligence System for Optimal Decision Making."""

from dataclasses import dataclass
from typing import Dict, List, Optional

import numpy as np

from ..quantum.intelligence.quantum_intelligence import QuantumIntelligence


@dataclass
class StrategyConfig:
    """Configuration for strategic intelligence"""

    # Strategy parameters
    time_horizon: int = 10  # Steps to look ahead
    risk_tolerance: float = 0.7  # 0-1, higher means more risk-taking
    exploration_rate: float = 0.2  # 0-1, higher means more exploration

    # Optimization parameters
    optimization_horizon: int = 100
    update_frequency: int = 10
    confidence_threshold: float = 0.8

    # Advanced features
    use_market_data: bool = True
    use_sentiment_analysis: bool = True
    use_trend_prediction: bool = True

    # Performance
    parallel_simulations: int = 16
    use_gpu: bool = True


class StrategicIntelligence:
    """Strategic Intelligence System"""

    def __init__(
        self,
        config: Optional[StrategyConfig] = None,
        quantum_intelligence: Optional[QuantumIntelligence] = None,
    ):
        self.config = config or StrategyConfig()
        self.quantum_intelligence = quantum_intelligence or QuantumIntelligence()

        # Initialize strategy components
        self.market_analyzer = self._initialize_market_analyzer()
        self.sentiment_analyzer = self._initialize_sentiment_analyzer()
        self.trend_predictor = self._initialize_trend_predictor()

        # Strategy state
        self.current_strategy = None
        self.strategy_performance = []
        self.market_state = {}
        self.risk_profile = self._initialize_risk_profile()

    async def optimize_strategy(
        self,
        market_data: Dict,
        constraints: Optional[Dict] = None,
        context: Optional[Dict] = None,
    ) -> Dict:
        """Optimize strategy using quantum intelligence"""
        try:
            # Analyze market conditions
            market_analysis = await self._analyze_market(market_data)

            # Process market data through quantum intelligence
            quantum_features = await self.quantum_intelligence.enhance_intelligence(
                market_analysis["features"], context=context
            )

            # Generate strategic options
            strategy_options = await self._generate_strategy_options(
                quantum_features, constraints
            )

            # Evaluate strategies through simulation
            evaluation_results = await self._evaluate_strategies(
                strategy_options, market_analysis
            )

            # Select optimal strategy
            optimal_strategy = await self._select_optimal_strategy(
                evaluation_results, context
            )

            # Update strategy state
            self.current_strategy = optimal_strategy
            self.strategy_performance.append(evaluation_results["performance"])

            return {
                "strategy": optimal_strategy,
                "performance_metrics": evaluation_results["metrics"],
                "confidence_score": evaluation_results["confidence"],
            }

        except Exception as e:
            print(f"Error optimizing strategy: {str(e)}")
            raise

    async def predict_outcomes(
        self, strategy: Dict, market_data: Dict, time_horizon: Optional[int] = None
    ) -> Dict:
        """Predict strategy outcomes using quantum intelligence"""
        try:
            horizon = time_horizon or self.config.time_horizon

            # Process market data
            market_features = await self._process_market_data(market_data)

            # Generate predictions using quantum intelligence
            predictions, confidence = (
                await self.quantum_intelligence.predict_optimal_actions(market_features)
            )

            # Simulate strategy execution
            simulation_results = await self._simulate_strategy(
                strategy, predictions, horizon
            )

            # Calculate risk metrics
            risk_metrics = self._calculate_risk_metrics(simulation_results)

            return {
                "predictions": predictions,
                "confidence": confidence,
                "simulation_results": simulation_results,
                "risk_metrics": risk_metrics,
            }

        except Exception as e:
            print(f"Error predicting outcomes: {str(e)}")
            raise

    async def adapt_strategy(
        self, performance_metrics: Dict, market_conditions: Dict
    ) -> Dict:
        """Adapt strategy based on performance and conditions"""
        try:
            # Analyze adaptation needs
            adaptation_needs = self._analyze_adaptation_needs(
                performance_metrics, market_conditions
            )

            # Generate adaptation options
            adaptation_options = await self._generate_adaptation_options(
                adaptation_needs
            )

            # Evaluate adaptations
            evaluation_results = await self._evaluate_adaptations(
                adaptation_options, market_conditions
            )

            # Select optimal adaptation
            optimal_adaptation = await self._select_optimal_adaptation(
                evaluation_results
            )

            # Apply adaptation
            await self._apply_adaptation(optimal_adaptation)

            return {
                "adaptation": optimal_adaptation,
                "evaluation": evaluation_results,
                "updated_strategy": self.current_strategy,
            }

        except Exception as e:
            print(f"Error adapting strategy: {str(e)}")
            raise

    async def _analyze_market(self, market_data: Dict) -> Dict:
        """Analyze market conditions"""
        analysis = {}

        # Analyze market data if enabled
        if self.config.use_market_data:
            analysis["market"] = await self.market_analyzer.analyze(market_data)

        # Analyze sentiment if enabled
        if self.config.use_sentiment_analysis:
            analysis["sentiment"] = await self.sentiment_analyzer.analyze(
                market_data.get("news", [])
            )

        # Predict trends if enabled
        if self.config.use_trend_prediction:
            analysis["trends"] = await self.trend_predictor.predict(
                market_data.get("historical", [])
            )

        # Extract features for quantum processing
        analysis["features"] = self._extract_features(analysis)

        return analysis

    async def _generate_strategy_options(
        self, quantum_features: np.ndarray, constraints: Optional[Dict]
    ) -> List[Dict]:
        """Generate strategic options"""
        # Generate base options
        base_options = self._generate_base_options(constraints)

        # Enhance options with quantum intelligence
        enhanced_options = []
        for option in base_options:
            enhanced_option = await self._enhance_option(option, quantum_features)
            enhanced_options.append(enhanced_option)

        return enhanced_options

    async def _evaluate_strategies(
        self, strategies: List[Dict], market_analysis: Dict
    ) -> Dict:
        """Evaluate strategy options"""
        results = {"performance": [], "metrics": {}, "confidence": 0.0}

        # Evaluate each strategy
        for strategy in strategies:
            # Simulate strategy
            simulation = await self._simulate_strategy(
                strategy, market_analysis, self.config.optimization_horizon
            )

            # Calculate performance metrics
            metrics = self._calculate_performance_metrics(simulation)

            results["performance"].append(metrics)

        # Aggregate results
        results["metrics"] = self._aggregate_metrics(results["performance"])
        results["confidence"] = self._calculate_confidence_score(results["metrics"])

        return results

    async def _select_optimal_strategy(
        self, evaluation_results: Dict, context: Optional[Dict]
    ) -> Dict:
        """Select the optimal strategy"""
        # Filter strategies by confidence threshold
        valid_strategies = self._filter_by_confidence(
            evaluation_results, self.config.confidence_threshold
        )

        # Rank strategies by performance and risk
        ranked_strategies = self._rank_strategies(
            valid_strategies, self.config.risk_tolerance
        )

        # Select best strategy
        optimal_strategy = ranked_strategies[0]

        return optimal_strategy

    def _initialize_market_analyzer(self):
        """Initialize market analysis component"""
        # Placeholder for market analyzer initialization
        return None

    def _initialize_sentiment_analyzer(self):
        """Initialize sentiment analysis component"""
        # Placeholder for sentiment analyzer initialization
        return None

    def _initialize_trend_predictor(self):
        """Initialize trend prediction component"""
        # Placeholder for trend predictor initialization
        return None

    def _initialize_risk_profile(self) -> Dict:
        """Initialize risk profile"""
        return {
            "risk_tolerance": self.config.risk_tolerance,
            "max_drawdown": 0.2,
            "volatility_target": 0.15,
            "position_limits": {"max_position": 1.0, "min_position": -1.0},
        }

    def _extract_features(self, analysis: Dict) -> np.ndarray:
        """Extract features from analysis results"""
        # Placeholder for feature extraction
        return np.array([])

    def _calculate_risk_metrics(self, simulation_results: Dict) -> Dict:
        """Calculate risk metrics"""
        return {
            "volatility": 0.0,
            "var": 0.0,
            "expected_shortfall": 0.0,
            "max_drawdown": 0.0,
        }

    def _calculate_performance_metrics(self, simulation: Dict) -> Dict:
        """Calculate performance metrics"""
        return {
            "returns": 0.0,
            "sharpe_ratio": 0.0,
            "information_ratio": 0.0,
            "max_drawdown": 0.0,
        }
