"""Advanced Market Intelligence System."""

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

import pandas as pd

from ..quantum.intelligence.quantum_intelligence import QuantumIntelligence


@dataclass
class MarketConfig:
    """Configuration for market intelligence"""

    # Analysis parameters
    time_window: int = 100  # Days of historical data
    update_frequency: int = 1  # Hours between updates
    confidence_threshold: float = 0.8

    # Data sources
    use_price_data: bool = True
    use_volume_data: bool = True
    use_order_book: bool = True
    use_sentiment: bool = True
    use_news: bool = True

    # Advanced features
    use_quantum_analysis: bool = True
    use_ml_models: bool = True
    use_correlation_analysis: bool = True

    # Performance
    batch_size: int = 1000
    use_gpu: bool = True
    n_workers: int = 8


class MarketIntelligence:
    """Advanced Market Intelligence System"""

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

        # Initialize analysis components
        self.price_analyzer = self._initialize_price_analyzer()
        self.volume_analyzer = self._initialize_volume_analyzer()
        self.sentiment_analyzer = self._initialize_sentiment_analyzer()
        self.correlation_analyzer = self._initialize_correlation_analyzer()

        # Market state
        self.market_state = {}
        self.historical_analysis = []
        self.current_patterns = []
        self.risk_metrics = {}

    async def analyze_market(
        self, market_data: Dict, context: Optional[Dict] = None
    ) -> Dict:
        """Perform comprehensive market analysis"""
        try:
            # Process market data
            processed_data = await self._process_market_data(market_data)

            # Analyze different aspects
            analysis = {}

            if self.config.use_price_data:
                analysis["price"] = await self._analyze_price_patterns(
                    processed_data["price"]
                )

            if self.config.use_volume_data:
                analysis["volume"] = await self._analyze_volume_patterns(
                    processed_data["volume"]
                )

            if self.config.use_sentiment:
                analysis["sentiment"] = await self._analyze_sentiment(
                    processed_data["news"]
                )

            if self.config.use_correlation_analysis:
                analysis["correlations"] = await self._analyze_correlations(
                    processed_data
                )

            # Apply quantum analysis
            if self.config.use_quantum_analysis:
                quantum_analysis = await self._apply_quantum_analysis(
                    processed_data, analysis
                )
                analysis["quantum"] = quantum_analysis

            # Update market state
            self._update_market_state(analysis)

            return {
                "analysis": analysis,
                "market_state": self.market_state,
                "risk_metrics": self._calculate_risk_metrics(analysis),
            }

        except Exception as e:
            print(f"Error analyzing market: {str(e)}")
            raise

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

            # Process data for prediction
            processed_data = await self._process_market_data(market_data)

            # Generate quantum features
            quantum_features = await self.quantum_intelligence.enhance_intelligence(
                processed_data["features"]
            )

            # Predict trends
            predictions, confidence = (
                await self.quantum_intelligence.predict_optimal_actions(
                    quantum_features
                )
            )

            # Analyze prediction confidence
            confidence_analysis = self._analyze_prediction_confidence(
                predictions, confidence
            )

            return {
                "predictions": predictions,
                "confidence": confidence,
                "analysis": confidence_analysis,
            }

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

    async def detect_patterns(
        self, market_data: Dict, pattern_types: Optional[List[str]] = None
    ) -> Dict:
        """Detect market patterns using quantum analysis"""
        try:
            # Process data for pattern detection
            processed_data = await self._process_market_data(market_data)

            # Apply quantum pattern detection
            patterns = await self._detect_quantum_patterns(
                processed_data, pattern_types
            )

            # Analyze pattern significance
            significance = self._analyze_pattern_significance(patterns)

            # Update current patterns
            self.current_patterns = patterns

            return {
                "patterns": patterns,
                "significance": significance,
                "recommendations": self._generate_pattern_recommendations(patterns),
            }

        except Exception as e:
            print(f"Error detecting patterns: {str(e)}")
            raise

    async def _process_market_data(self, market_data: Dict) -> Dict:
        """Process raw market data"""
        processed = {}

        # Process price data
        if "price" in market_data:
            processed["price"] = pd.DataFrame(market_data["price"])
            processed["price"] = self._preprocess_price_data(processed["price"])

        # Process volume data
        if "volume" in market_data:
            processed["volume"] = pd.DataFrame(market_data["volume"])
            processed["volume"] = self._preprocess_volume_data(processed["volume"])

        # Process news data
        if "news" in market_data:
            processed["news"] = self._preprocess_news_data(market_data["news"])

        # Extract features
        processed["features"] = self._extract_features(processed)

        return processed

    async def _analyze_price_patterns(self, price_data: pd.DataFrame) -> Dict:
        """Analyze price patterns"""
        patterns = {
            "trends": self._detect_trends(price_data),
            "support_resistance": self._detect_support_resistance(price_data),
            "volatility": self._calculate_volatility(price_data),
        }
        return patterns

    async def _analyze_volume_patterns(self, volume_data: pd.DataFrame) -> Dict:
        """Analyze volume patterns"""
        patterns = {
            "trends": self._detect_volume_trends(volume_data),
            "anomalies": self._detect_volume_anomalies(volume_data),
            "correlations": self._analyze_price_volume_correlation(volume_data),
        }
        return patterns

    async def _analyze_sentiment(self, news_data: List[Dict]) -> Dict:
        """Analyze market sentiment"""
        sentiment = {
            "overall_score": self._calculate_sentiment_score(news_data),
            "trends": self._detect_sentiment_trends(news_data),
            "topics": self._analyze_sentiment_topics(news_data),
        }
        return sentiment

    async def _analyze_correlations(self, processed_data: Dict) -> Dict:
        """Analyze market correlations"""
        correlations = {
            "price_volume": self._calculate_price_volume_correlation(processed_data),
            "cross_asset": self._calculate_cross_asset_correlation(processed_data),
            "temporal": self._calculate_temporal_correlation(processed_data),
        }
        return correlations

    async def _apply_quantum_analysis(
        self, processed_data: Dict, classical_analysis: Dict
    ) -> Dict:
        """Apply quantum analysis techniques"""
        # Convert data to quantum features
        quantum_features = await self.quantum_intelligence.enhance_intelligence(
            processed_data["features"]
        )

        # Perform quantum analysis
        quantum_analysis = {
            "enhanced_patterns": self._analyze_quantum_patterns(quantum_features),
            "quantum_correlations": self._analyze_quantum_correlations(
                quantum_features
            ),
            "entanglement_metrics": self._calculate_entanglement_metrics(
                quantum_features
            ),
        }

        return quantum_analysis

    def _initialize_price_analyzer(self):
        """Initialize price analysis component"""
        return None

    def _initialize_volume_analyzer(self):
        """Initialize volume analysis component"""
        return None

    def _initialize_sentiment_analyzer(self):
        """Initialize sentiment analysis component"""
        return None

    def _initialize_correlation_analyzer(self):
        """Initialize correlation analysis component"""
        return None

    def _update_market_state(self, analysis: Dict) -> None:
        """Update market state with new analysis"""
        self.market_state = {
            "timestamp": pd.Timestamp.now(),
            "analysis": analysis,
            "patterns": self.current_patterns,
            "risk_metrics": self.risk_metrics,
        }

    def _calculate_risk_metrics(self, analysis: Dict) -> Dict:
        """Calculate comprehensive risk metrics"""
        return {
            "volatility": self._calculate_volatility_risk(analysis),
            "var": self._calculate_value_at_risk(analysis),
            "tail_risk": self._calculate_tail_risk(analysis),
            "liquidity_risk": self._calculate_liquidity_risk(analysis),
        }
