"""
Data Source Implementations for Calendar Generation Framework

Concrete implementations of data sources for content strategy, gap analysis,
keywords, content pillars, performance data, and AI analysis.
"""

import logging
from typing import Dict, Any, List, Optional
from datetime import datetime

from .interfaces import (
    DataSourceInterface, 
    DataSourceType, 
    DataSourcePriority, 
    DataSourceValidationResult
)

logger = logging.getLogger(__name__)


class ContentStrategyDataSource(DataSourceInterface):
    """
    Enhanced content strategy data source with 30+ fields.
    
    Provides comprehensive content strategy data including business objectives,
    target audience, content pillars, brand voice, and editorial guidelines.
    """
    
    def __init__(self):
        super().__init__(
            source_id="content_strategy",
            source_type=DataSourceType.STRATEGY,
            priority=DataSourcePriority.CRITICAL
        )
        self.version = "2.0.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get comprehensive content strategy data.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing comprehensive strategy data
        """
        try:
            # Get strategy data from database directly
            from services.content_planning_db import ContentPlanningDBService
            
            db_service = ContentPlanningDBService()
            strategy_data = await db_service.get_strategy_data(strategy_id)
            
            self.mark_updated()
            logger.info(f"Retrieved content strategy data for strategy {strategy_id}")
            
            return strategy_data
            
        except Exception as e:
            logger.error(f"Error getting content strategy data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate content strategy data quality.
        
        Args:
            data: Strategy data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for content strategy
        required_fields = [
            "strategy_id", "strategy_name", "industry", "target_audience",
            "content_pillars", "business_objectives", "content_preferences"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "brand_voice", "editorial_guidelines", "content_frequency",
            "preferred_formats", "content_mix", "ai_recommendations"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (70% required, 30% enhanced)
        result.quality_score = (required_score * 0.7) + (enhanced_score * 0.3)
        
        # Add recommendations
        if result.quality_score < 0.8:
            result.add_recommendation("Consider adding more enhanced strategy fields for better calendar generation")
        
        if not data.get("brand_voice"):
            result.add_recommendation("Add brand voice guidelines for consistent content tone")
        
        if not data.get("editorial_guidelines"):
            result.add_recommendation("Add editorial guidelines for content standards")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance strategy data with AI insights.
        
        Args:
            data: Original strategy data
            
        Returns:
            Enhanced strategy data
        """
        enhanced_data = data.copy()
        
        # Add AI-generated insights if not present
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "strategy_analysis" not in enhanced_data:
            enhanced_data["strategy_analysis"] = await self._analyze_strategy(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "ContentStrategyDataSource"
        }
        
        logger.info(f"Enhanced content strategy data with AI insights")
        return enhanced_data
    
    async def _generate_ai_recommendations(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for content strategy."""
        # Implementation for AI recommendations
        return {
            "content_opportunities": [],
            "optimization_suggestions": [],
            "trend_recommendations": [],
            "performance_insights": []
        }
    
    async def _analyze_strategy(self, strategy_data: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze strategy completeness and quality."""
        # Implementation for strategy analysis
        return {
            "completeness_score": 0.0,
            "coherence_analysis": {},
            "gap_identification": [],
            "optimization_opportunities": []
        }


class GapAnalysisDataSource(DataSourceInterface):
    """
    Enhanced gap analysis data source with AI-powered insights.
    
    Provides comprehensive gap analysis including content gaps, keyword opportunities,
    competitor analysis, and market positioning insights.
    """
    
    def __init__(self):
        super().__init__(
            source_id="gap_analysis",
            source_type=DataSourceType.ANALYSIS,
            priority=DataSourcePriority.HIGH
        )
        self.version = "1.5.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get enhanced gap analysis data.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing gap analysis data
        """
        try:
            gap_data = await self._get_enhanced_gap_analysis(user_id, strategy_id)
            self.mark_updated()
            logger.info(f"Retrieved gap analysis data for strategy {strategy_id}")
            return gap_data
            
        except Exception as e:
            logger.error(f"Error getting gap analysis data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate gap analysis data quality.
        
        Args:
            data: Gap analysis data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for gap analysis
        required_fields = [
            "content_gaps", "keyword_opportunities", "competitor_insights"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "market_trends", "content_opportunities", "performance_insights",
            "ai_recommendations", "gap_prioritization"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (60% required, 40% enhanced)
        result.quality_score = (required_score * 0.6) + (enhanced_score * 0.4)
        
        # Add recommendations
        if result.quality_score < 0.7:
            result.add_recommendation("Enhance gap analysis with AI-powered insights")
        
        if not data.get("market_trends"):
            result.add_recommendation("Add market trend analysis for better content opportunities")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance gap analysis data with AI insights.
        
        Args:
            data: Original gap analysis data
            
        Returns:
            Enhanced gap analysis data
        """
        enhanced_data = data.copy()
        
        # Add AI enhancements
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "gap_prioritization" not in enhanced_data:
            enhanced_data["gap_prioritization"] = await self._prioritize_gaps(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "GapAnalysisDataSource"
        }
        
        logger.info(f"Enhanced gap analysis data with AI insights")
        return enhanced_data
    
    async def _get_enhanced_gap_analysis(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """Get enhanced gap analysis with AI insights."""
        # Implementation for enhanced gap analysis
        return {
            "content_gaps": [],
            "keyword_opportunities": [],
            "competitor_insights": [],
            "market_trends": [],
            "content_opportunities": [],
            "performance_insights": []
        }
    
    async def _generate_ai_recommendations(self, gap_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for gap analysis."""
        return {
            "gap_prioritization": [],
            "content_opportunities": [],
            "optimization_suggestions": []
        }
    
    async def _prioritize_gaps(self, gap_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Prioritize content gaps based on impact and effort."""
        return []


class KeywordsDataSource(DataSourceInterface):
    """
    Enhanced keywords data source with dynamic research capabilities.
    
    Provides comprehensive keyword data including research, trending keywords,
    competitor analysis, and difficulty scoring.
    """
    
    def __init__(self):
        super().__init__(
            source_id="keywords",
            source_type=DataSourceType.RESEARCH,
            priority=DataSourcePriority.HIGH
        )
        self.version = "1.5.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get enhanced keywords data with dynamic research.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing keywords data
        """
        try:
            keywords_data = await self._get_enhanced_keywords(user_id, strategy_id)
            self.mark_updated()
            logger.info(f"Retrieved keywords data for strategy {strategy_id}")
            return keywords_data
            
        except Exception as e:
            logger.error(f"Error getting keywords data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate keywords data quality.
        
        Args:
            data: Keywords data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for keywords
        required_fields = [
            "primary_keywords", "secondary_keywords", "keyword_research"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "trending_keywords", "competitor_keywords", "keyword_difficulty",
            "search_volume", "keyword_opportunities", "ai_recommendations"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (50% required, 50% enhanced)
        result.quality_score = (required_score * 0.5) + (enhanced_score * 0.5)
        
        # Add recommendations
        if result.quality_score < 0.7:
            result.add_recommendation("Enhance keyword research with trending and competitor analysis")
        
        if not data.get("keyword_difficulty"):
            result.add_recommendation("Add keyword difficulty scoring for better content planning")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance keywords data with AI insights.
        
        Args:
            data: Original keywords data
            
        Returns:
            Enhanced keywords data
        """
        enhanced_data = data.copy()
        
        # Add AI enhancements
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "keyword_optimization" not in enhanced_data:
            enhanced_data["keyword_optimization"] = await self._optimize_keywords(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "KeywordsDataSource"
        }
        
        logger.info(f"Enhanced keywords data with AI insights")
        return enhanced_data
    
    async def _get_enhanced_keywords(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """Get enhanced keywords with dynamic research."""
        # Implementation for enhanced keywords
        return {
            "primary_keywords": [],
            "secondary_keywords": [],
            "keyword_research": {},
            "trending_keywords": [],
            "competitor_keywords": [],
            "keyword_difficulty": {},
            "search_volume": {},
            "keyword_opportunities": []
        }
    
    async def _generate_ai_recommendations(self, keywords_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for keywords."""
        return {
            "keyword_opportunities": [],
            "optimization_suggestions": [],
            "trend_recommendations": []
        }
    
    async def _optimize_keywords(self, keywords_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize keywords based on performance and trends."""
        return {
            "optimized_keywords": [],
            "performance_insights": {},
            "optimization_recommendations": []
        }


class ContentPillarsDataSource(DataSourceInterface):
    """
    Enhanced content pillars data source with AI-generated dynamic pillars.
    
    Provides comprehensive content pillar data including AI-generated pillars,
    market-based optimization, and performance-based adjustment.
    """
    
    def __init__(self):
        super().__init__(
            source_id="content_pillars",
            source_type=DataSourceType.STRATEGY,
            priority=DataSourcePriority.MEDIUM
        )
        self.version = "1.5.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get enhanced content pillars data.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing content pillars data
        """
        try:
            pillars_data = await self._get_enhanced_pillars(user_id, strategy_id)
            self.mark_updated()
            logger.info(f"Retrieved content pillars data for strategy {strategy_id}")
            return pillars_data
            
        except Exception as e:
            logger.error(f"Error getting content pillars data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate content pillars data quality.
        
        Args:
            data: Content pillars data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for content pillars
        required_fields = [
            "content_pillars", "pillar_topics", "pillar_keywords"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "ai_generated_pillars", "market_optimization", "performance_adjustment",
            "audience_preferences", "pillar_prioritization", "ai_recommendations"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (60% required, 40% enhanced)
        result.quality_score = (required_score * 0.6) + (enhanced_score * 0.4)
        
        # Add recommendations
        if result.quality_score < 0.7:
            result.add_recommendation("Enhance content pillars with AI-generated insights")
        
        if not data.get("pillar_prioritization"):
            result.add_recommendation("Add pillar prioritization for better content planning")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance content pillars data with AI insights.
        
        Args:
            data: Original content pillars data
            
        Returns:
            Enhanced content pillars data
        """
        enhanced_data = data.copy()
        
        # Add AI enhancements
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "pillar_optimization" not in enhanced_data:
            enhanced_data["pillar_optimization"] = await self._optimize_pillars(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "ContentPillarsDataSource"
        }
        
        logger.info(f"Enhanced content pillars data with AI insights")
        return enhanced_data
    
    async def _get_enhanced_pillars(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """Get enhanced content pillars with AI generation."""
        # Implementation for enhanced content pillars
        return {
            "content_pillars": [],
            "pillar_topics": {},
            "pillar_keywords": {},
            "ai_generated_pillars": [],
            "market_optimization": {},
            "performance_adjustment": {},
            "audience_preferences": {},
            "pillar_prioritization": []
        }
    
    async def _generate_ai_recommendations(self, pillars_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for content pillars."""
        return {
            "pillar_opportunities": [],
            "optimization_suggestions": [],
            "trend_recommendations": []
        }
    
    async def _optimize_pillars(self, pillars_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize content pillars based on performance and market trends."""
        return {
            "optimized_pillars": [],
            "performance_insights": {},
            "optimization_recommendations": []
        }


class PerformanceDataSource(DataSourceInterface):
    """
    Enhanced performance data source with real-time tracking capabilities.
    
    Provides comprehensive performance data including conversion rates,
    engagement metrics, ROI calculations, and optimization insights.
    """
    
    def __init__(self):
        super().__init__(
            source_id="performance_data",
            source_type=DataSourceType.PERFORMANCE,
            priority=DataSourcePriority.MEDIUM
        )
        self.version = "1.0.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get enhanced performance data.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing performance data
        """
        try:
            performance_data = await self._get_enhanced_performance(user_id, strategy_id)
            self.mark_updated()
            logger.info(f"Retrieved performance data for strategy {strategy_id}")
            return performance_data
            
        except Exception as e:
            logger.error(f"Error getting performance data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate performance data quality.
        
        Args:
            data: Performance data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for performance data
        required_fields = [
            "engagement_metrics", "conversion_rates", "performance_insights"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "roi_calculations", "optimization_insights", "trend_analysis",
            "predictive_analytics", "ai_recommendations", "performance_forecasting"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (50% required, 50% enhanced)
        result.quality_score = (required_score * 0.5) + (enhanced_score * 0.5)
        
        # Add recommendations
        if result.quality_score < 0.6:
            result.add_recommendation("Enhance performance tracking with real-time metrics")
        
        if not data.get("roi_calculations"):
            result.add_recommendation("Add ROI calculations for better performance measurement")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance performance data with AI insights.
        
        Args:
            data: Original performance data
            
        Returns:
            Enhanced performance data
        """
        enhanced_data = data.copy()
        
        # Add AI enhancements
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "performance_optimization" not in enhanced_data:
            enhanced_data["performance_optimization"] = await self._optimize_performance(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "PerformanceDataSource"
        }
        
        logger.info(f"Enhanced performance data with AI insights")
        return enhanced_data
    
    async def _get_enhanced_performance(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """Get enhanced performance data with real-time tracking."""
        # Implementation for enhanced performance data
        return {
            "engagement_metrics": {},
            "conversion_rates": {},
            "performance_insights": {},
            "roi_calculations": {},
            "optimization_insights": {},
            "trend_analysis": {},
            "predictive_analytics": {},
            "performance_forecasting": {}
        }
    
    async def _generate_ai_recommendations(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for performance optimization."""
        return {
            "optimization_opportunities": [],
            "performance_suggestions": [],
            "trend_recommendations": []
        }
    
    async def _optimize_performance(self, performance_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize performance based on analytics and trends."""
        return {
            "optimization_strategies": [],
            "performance_insights": {},
            "optimization_recommendations": []
        }


class AIAnalysisDataSource(DataSourceInterface):
    """
    Enhanced AI analysis data source with strategic intelligence generation.
    
    Provides comprehensive AI analysis including strategic insights,
    market intelligence, competitive analysis, and predictive analytics.
    """
    
    def __init__(self):
        super().__init__(
            source_id="ai_analysis",
            source_type=DataSourceType.AI,
            priority=DataSourcePriority.HIGH
        )
        self.version = "2.0.0"
    
    async def get_data(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """
        Get enhanced AI analysis data.
        
        Args:
            user_id: User identifier
            strategy_id: Strategy identifier
            
        Returns:
            Dictionary containing AI analysis data
        """
        try:
            ai_data = await self._get_enhanced_ai_analysis(user_id, strategy_id)
            self.mark_updated()
            logger.info(f"Retrieved AI analysis data for strategy {strategy_id}")
            return ai_data
            
        except Exception as e:
            logger.error(f"Error getting AI analysis data: {e}")
            return {}
    
    async def validate_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Validate AI analysis data quality.
        
        Args:
            data: AI analysis data to validate
            
        Returns:
            Validation result dictionary
        """
        result = DataSourceValidationResult()
        
        # Required fields for AI analysis
        required_fields = [
            "strategic_insights", "market_intelligence", "competitive_analysis"
        ]
        
        # Check for missing fields
        for field in required_fields:
            if not data.get(field):
                result.add_missing_field(field)
        
        # Enhanced fields validation
        enhanced_fields = [
            "predictive_analytics", "trend_forecasting", "opportunity_identification",
            "risk_assessment", "ai_recommendations", "strategic_recommendations"
        ]
        
        enhanced_count = sum(1 for field in enhanced_fields if data.get(field))
        enhanced_score = enhanced_count / len(enhanced_fields)
        
        # Calculate overall quality score
        required_count = len(required_fields) - len(result.missing_fields)
        required_score = required_count / len(required_fields)
        
        # Weighted quality score (40% required, 60% enhanced)
        result.quality_score = (required_score * 0.4) + (enhanced_score * 0.6)
        
        # Add recommendations
        if result.quality_score < 0.8:
            result.add_recommendation("Enhance AI analysis with predictive analytics and trend forecasting")
        
        if not data.get("opportunity_identification"):
            result.add_recommendation("Add opportunity identification for better strategic planning")
        
        self.update_quality_score(result.quality_score)
        return result.to_dict()
    
    async def enhance_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Enhance AI analysis data with additional insights.
        
        Args:
            data: Original AI analysis data
            
        Returns:
            Enhanced AI analysis data
        """
        enhanced_data = data.copy()
        
        # Add AI enhancements
        if "ai_recommendations" not in enhanced_data:
            enhanced_data["ai_recommendations"] = await self._generate_ai_recommendations(data)
        
        if "strategic_optimization" not in enhanced_data:
            enhanced_data["strategic_optimization"] = await self._optimize_strategy(data)
        
        # Add enhancement metadata
        enhanced_data["enhancement_metadata"] = {
            "enhanced_at": datetime.utcnow().isoformat(),
            "enhancement_version": self.version,
            "enhancement_source": "AIAnalysisDataSource"
        }
        
        logger.info(f"Enhanced AI analysis data with additional insights")
        return enhanced_data
    
    async def _get_enhanced_ai_analysis(self, user_id: int, strategy_id: int) -> Dict[str, Any]:
        """Get enhanced AI analysis with strategic intelligence."""
        # Implementation for enhanced AI analysis
        return {
            "strategic_insights": {},
            "market_intelligence": {},
            "competitive_analysis": {},
            "predictive_analytics": {},
            "trend_forecasting": {},
            "opportunity_identification": [],
            "risk_assessment": {},
            "strategic_recommendations": []
        }
    
    async def _generate_ai_recommendations(self, ai_data: Dict[str, Any]) -> Dict[str, Any]:
        """Generate AI recommendations for strategic optimization."""
        return {
            "strategic_opportunities": [],
            "optimization_suggestions": [],
            "trend_recommendations": []
        }
    
    async def _optimize_strategy(self, ai_data: Dict[str, Any]) -> Dict[str, Any]:
        """Optimize strategy based on AI analysis and insights."""
        return {
            "optimization_strategies": [],
            "strategic_insights": {},
            "optimization_recommendations": []
        }
