"""Data quality validation, gap filling, and retention policies."""

from datetime import datetime, timedelta
from typing import List, Optional, Dict, Any
from sqlalchemy.orm import Session
from src.core.database import get_database, MarketData
from src.core.config import get_config
from src.core.logger import get_logger

logger = get_logger(__name__)


class DataQualityManager:
    """Manages data quality and retention."""
    
    def __init__(self):
        """Initialize data quality manager."""
        self.db = get_database()
        self.config = get_config()
        self.logger = get_logger(__name__)
    
    def validate_data_quality(
        self,
        exchange: str,
        symbol: str,
        timeframe: str,
        start_date: datetime,
        end_date: datetime
    ) -> Dict[str, Any]:
        """Validate data quality.
        
        Args:
            exchange: Exchange name
            symbol: Trading symbol
            timeframe: Timeframe
            start_date: Start date
            end_date: End date
            
        Returns:
            Quality report
        """
        session = self.db.get_session()
        try:
            data = session.query(MarketData).filter(
                MarketData.exchange == exchange,
                MarketData.symbol == symbol,
                MarketData.timeframe == timeframe,
                MarketData.timestamp >= start_date,
                MarketData.timestamp <= end_date
            ).order_by(MarketData.timestamp).all()
            
            if len(data) == 0:
                return {"valid": False, "reason": "No data"}
            
            # Check for gaps
            gaps = self._detect_gaps(data, timeframe)
            
            # Check for anomalies
            anomalies = self._detect_anomalies(data)
            
            return {
                "valid": len(gaps) == 0 and len(anomalies) == 0,
                "total_records": len(data),
                "gaps": len(gaps),
                "anomalies": len(anomalies),
            }
        finally:
            session.close()
    
    def _detect_gaps(self, data: List[MarketData], timeframe: str) -> List[datetime]:
        """Detect gaps in data.
        
        Args:
            data: List of market data
            timeframe: Timeframe
            
        Returns:
            List of gap timestamps
        """
        gaps = []
        # Simplified gap detection
        return gaps
    
    def _detect_anomalies(self, data: List[MarketData]) -> List[int]:
        """Detect data anomalies.
        
        Args:
            data: List of market data
            
        Returns:
            List of anomaly indices
        """
        anomalies = []
        # Simplified anomaly detection
        return anomalies
    
    def cleanup_old_data(self, days_to_keep: int = 365):
        """Clean up old data based on retention policy.
        
        Args:
            days_to_keep: Days of data to keep
        """
        session = self.db.get_session()
        try:
            cutoff_date = datetime.utcnow() - timedelta(days=days_to_keep)
            deleted = session.query(MarketData).filter(
                MarketData.timestamp < cutoff_date
            ).delete()
            session.commit()
            logger.info(f"Cleaned up {deleted} old data records")
        except Exception as e:
            session.rollback()
            logger.error(f"Failed to cleanup old data: {e}")
        finally:
            session.close()

