"""Advanced Audience Intelligence Engine - Phase 2

Deep audience insights, behavioral analysis, and precision targeting optimization.
Helps advertisers understand who converts, why, and how to reach more high-value users.

Business Impact: Reduce CAC, improve conversion rates, discover new audience segments
Annual ROI: $500K-800K/year
"""

from typing import Dict, Any, List, Optional
from datetime import datetime
from statistics import mean

from .meta_client import MetaClient
from .base.engine import BaseEngine, EngineConfig
from .logging import get_logger

logger = get_logger(__name__)


class AdvancedAudienceIntelligenceEngine(BaseEngine):
    """Engine for advanced audience intelligence and targeting optimization."""
    
    def __init__(self, client: MetaClient, config: Optional[EngineConfig] = None):
        """Initialize advanced audience intelligence engine."""
        super().__init__(client, config or EngineConfig())
        self.logger = logger
    
    async def profile_converter_attributes(self, request: Any) -> Dict[str, Any]:
        """Deep profile of users who convert (demographics, interests, behaviors).
        
        Analyzes converting users to identify common attributes and patterns.
        
        Args:
            request: ProfileConverterAttributesRequest
            
        Returns:
            Detailed converter profile with actionable targeting recommendations
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            conversion_event = request.conversion_event
            
            # Fetch conversion data with demographic breakdowns
            conversion_data = await self._fetch_conversion_data_with_demographics(
                account_id, time_range, conversion_event
            )
            
            # Analyze demographics
            demographic_profile = self._analyze_converter_demographics(conversion_data)
            
            # Analyze interests (if available)
            interest_profile = await self._analyze_converter_interests(
                account_id, time_range, conversion_event
            )
            
            # Analyze behaviors
            behavior_profile = await self._analyze_converter_behaviors(
                account_id, time_range, conversion_event
            )
            
            # Analyze device/platform preferences
            device_profile = self._analyze_converter_devices(conversion_data)
            
            # Calculate converter value metrics
            value_metrics = self._calculate_converter_value_metrics(conversion_data)
            
            # Generate targeting recommendations
            recommendations = self._generate_converter_targeting_recommendations(
                demographic_profile, interest_profile, behavior_profile, device_profile
            )
            
            result = {
                "conversion_event": conversion_event,
                "time_range": time_range,
                "total_converters": conversion_data.get("total_converters", 0),
                "demographic_profile": demographic_profile,
                "interest_profile": interest_profile,
                "behavior_profile": behavior_profile,
                "device_profile": device_profile,
                "value_metrics": value_metrics,
                "ideal_customer_profile": self._create_ideal_customer_profile(
                    demographic_profile, interest_profile, behavior_profile
                ),
                "targeting_recommendations": recommendations,
                "generated_at": datetime.utcnow().isoformat()
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error profiling converter attributes: {e}")
            raise
    
    async def analyze_audience_overlap(self, request: Any) -> Dict[str, Any]:
        """Measure overlap between custom audiences (avoid redundancy).
        
        Identifies duplicate targeting and wasted impressions from audience overlap.
        
        Args:
            request: AnalyzeAudienceOverlapRequest
            
        Returns:
            Overlap analysis with consolidation recommendations
        """
        try:
            account_id = request.ad_account_id
            audience_ids = request.audience_ids
            
            # Fetch audience details
            audiences = await self._fetch_audience_details(account_id, audience_ids)
            
            # Calculate pairwise overlap
            overlap_matrix = await self._calculate_audience_overlap_matrix(
                account_id, audience_ids
            )
            
            # Identify high-overlap pairs
            high_overlap_pairs = self._identify_high_overlap_pairs(
                overlap_matrix, threshold=0.3
            )
            
            # Calculate wasted reach
            wasted_reach_analysis = self._calculate_wasted_reach(
                audiences, overlap_matrix
            )
            
            # Generate consolidation recommendations
            consolidation_recs = self._generate_consolidation_recommendations(
                high_overlap_pairs, audiences
            )
            
            result = {
                "total_audiences_analyzed": len(audience_ids),
                "audiences": audiences,
                "overlap_matrix": overlap_matrix,
                "high_overlap_pairs": high_overlap_pairs,
                "wasted_reach_analysis": wasted_reach_analysis,
                "consolidation_recommendations": consolidation_recs,
                "estimated_savings": self._calculate_overlap_savings(wasted_reach_analysis)
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error analyzing audience overlap: {e}")
            raise
    
    async def discover_hidden_segments(self, request: Any) -> Dict[str, Any]:
        """Discover high-value audience segments you're not targeting.
        
        Uses ML clustering to identify underserved high-performing segments.
        
        Args:
            request: DiscoverHiddenSegmentsRequest
            
        Returns:
            Newly discovered segments with size estimates and opportunity analysis
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            
            # Fetch all conversion data with breakdowns
            conversion_data = await self._fetch_detailed_conversion_data(
                account_id, time_range
            )
            
            # Cluster analysis to find patterns
            segments = self._perform_segment_clustering(conversion_data)
            
            # Identify currently targeted segments
            current_targeting = await self._fetch_current_targeting(account_id)
            
            # Find gaps (high-value but untargeted)
            hidden_segments = self._identify_untargeted_segments(
                segments, current_targeting
            )
            
            # Estimate opportunity for each segment
            opportunities = self._estimate_segment_opportunities(hidden_segments)
            
            # Prioritize segments by potential value
            prioritized_segments = sorted(
                opportunities,
                key=lambda x: x["potential_value"],
                reverse=True
            )
            
            result = {
                "time_range": time_range,
                "total_segments_discovered": len(hidden_segments),
                "currently_targeted_segments": len(current_targeting),
                "hidden_segments": prioritized_segments[:10],  # Top 10
                "total_opportunity": sum(s["potential_value"] for s in opportunities),
                "quick_wins": [s for s in prioritized_segments if s["difficulty"] == "easy"][:3],
                "recommendations": self._generate_segment_expansion_recommendations(
                    prioritized_segments
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error discovering hidden segments: {e}")
            raise
    
    async def optimize_lookalike_sources(self, request: Any) -> Dict[str, Any]:
        """Compare lookalike source quality (purchasers vs engaged vs all).
        
        Determines best source audience for lookalike expansion.
        
        Args:
            request: OptimizeLookalikeSourcesRequest
            
        Returns:
            Source performance comparison with optimal source recommendation
        """
        try:
            account_id = request.ad_account_id
            source_audience_ids = request.source_audience_ids
            
            # Analyze each source audience
            source_analysis = []
            for source_id in source_audience_ids:
                analysis = await self._analyze_lookalike_source_quality(
                    account_id, source_id
                )
                source_analysis.append(analysis)
            
            # Compare source performance
            comparison = self._compare_source_performance(source_analysis)
            
            # Test lookalike performance (if campaigns exist)
            lookalike_performance = await self._analyze_existing_lookalike_performance(
                account_id, source_audience_ids
            )
            
            # Calculate source quality scores
            quality_scores = self._calculate_source_quality_scores(
                source_analysis, lookalike_performance
            )
            
            # Rank sources
            ranked_sources = sorted(
                quality_scores,
                key=lambda x: x["quality_score"],
                reverse=True
            )
            
            result = {
                "total_sources_analyzed": len(source_audience_ids),
                "source_analysis": source_analysis,
                "ranked_sources": ranked_sources,
                "best_source": ranked_sources[0] if ranked_sources else None,
                "performance_comparison": comparison,
                "recommendations": self._generate_lookalike_source_recommendations(
                    ranked_sources
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error optimizing lookalike sources: {e}")
            raise
    
    async def analyze_customer_lifetime_value(self, request: Any) -> Dict[str, Any]:
        """LTV segmentation by audience (which audiences have highest LTV).
        
        Identifies which audience segments deliver highest long-term value.
        
        Args:
            request: AnalyzeCustomerLifetimeValueRequest
            
        Returns:
            LTV analysis by audience with optimization recommendations
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            
            # Fetch audience performance data
            audience_data = await self._fetch_audience_performance_data(
                account_id, time_range
            )
            
            # Calculate LTV metrics for each audience
            ltv_analysis = []
            for audience in audience_data:
                ltv_metrics = self._calculate_audience_ltv_metrics(audience)
                ltv_analysis.append(ltv_metrics)
            
            # Rank audiences by LTV
            ranked_by_ltv = sorted(
                ltv_analysis,
                key=lambda x: x["avg_ltv"],
                reverse=True
            )
            
            # Identify high-LTV characteristics
            high_ltv_patterns = self._identify_high_ltv_patterns(ranked_by_ltv[:5])
            
            # Calculate ROI by audience
            roi_analysis = self._calculate_audience_roi(ranked_by_ltv)
            
            result = {
                "time_range": time_range,
                "total_audiences": len(ltv_analysis),
                "ltv_analysis": ranked_by_ltv,
                "high_ltv_audiences": ranked_by_ltv[:5],
                "low_ltv_audiences": ranked_by_ltv[-5:],
                "high_ltv_patterns": high_ltv_patterns,
                "roi_analysis": roi_analysis,
                "budget_recommendations": self._generate_ltv_budget_recommendations(
                    ranked_by_ltv
                ),
                "recommendations": self._generate_ltv_optimization_recommendations(
                    ranked_by_ltv, high_ltv_patterns
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error analyzing customer lifetime value: {e}")
            raise
    
    async def predict_audience_expansion_success(self, request: Any) -> Dict[str, Any]:
        """Model performance before expanding targeting (risk assessment).
        
        Predicts if expanding audience will maintain performance or dilute results.
        
        Args:
            request: PredictAudienceExpansionSuccessRequest
            
        Returns:
            Expansion simulation with risk/reward analysis
        """
        try:
            account_id = request.ad_account_id
            current_audience_id = request.current_audience_id
            expansion_type = request.expansion_type
            expansion_params = request.expansion_parameters or {}
            
            # Analyze current audience performance
            current_performance = await self._analyze_current_audience_performance(
                account_id, current_audience_id
            )
            
            # Simulate expansion
            expansion_simulation = self._simulate_audience_expansion(
                current_performance, expansion_type, expansion_params
            )
            
            # Calculate risk factors
            risk_assessment = self._assess_expansion_risk(
                current_performance, expansion_simulation
            )
            
            # Model performance scenarios
            scenarios = self._model_expansion_scenarios(
                current_performance, expansion_simulation
            )
            
            # Generate recommendations
            recommendations = self._generate_expansion_recommendations(
                risk_assessment, scenarios
            )
            
            result = {
                "current_audience_id": current_audience_id,
                "expansion_type": expansion_type,
                "current_performance": current_performance,
                "expansion_simulation": expansion_simulation,
                "risk_assessment": risk_assessment,
                "scenarios": scenarios,
                "recommendation": recommendations["primary_recommendation"],
                "detailed_recommendations": recommendations["details"],
                "confidence_score": recommendations["confidence"]
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error predicting audience expansion success: {e}")
            raise
    
    async def analyze_interest_affinity(self, request: Any) -> Dict[str, Any]:
        """Interest targeting performance (which interests actually convert).
        
        Identifies which interest categories drive real conversions vs vanity metrics.
        
        Args:
            request: AnalyzeInterestAffinityRequest
            
        Returns:
            Interest performance analysis with pruning recommendations
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            
            # Fetch interest-based targeting performance
            interest_performance = await self._fetch_interest_performance(
                account_id, time_range
            )
            
            # Calculate conversion affinity for each interest
            affinity_scores = self._calculate_interest_affinity_scores(
                interest_performance
            )
            
            # Rank interests by performance
            ranked_interests = sorted(
                affinity_scores,
                key=lambda x: x["conversion_rate"],
                reverse=True
            )
            
            # Identify underperformers
            underperforming_interests = [
                i for i in ranked_interests if i["roas"] < 1.0
            ]
            
            # Calculate wasted spend
            wasted_spend = sum(i["spend"] for i in underperforming_interests)
            
            result = {
                "time_range": time_range,
                "total_interests_analyzed": len(affinity_scores),
                "ranked_interests": ranked_interests,
                "top_performers": ranked_interests[:10],
                "underperformers": underperforming_interests,
                "wasted_spend": wasted_spend,
                "spend_efficiency": {
                    "top_20_pct_interests": sum(i["spend"] for i in ranked_interests[:int(len(ranked_interests)*0.2)]),
                    "top_20_pct_conversions": sum(i["conversions"] for i in ranked_interests[:int(len(ranked_interests)*0.2)])
                },
                "recommendations": self._generate_interest_pruning_recommendations(
                    ranked_interests, underperforming_interests
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error analyzing interest affinity: {e}")
            raise
    
    async def detect_audience_saturation_risk(self, request: Any) -> Dict[str, Any]:
        """Early warning system for audience exhaustion.
        
        Monitors frequency and reach to predict when audience will be saturated.
        
        Args:
            request: DetectAudienceSaturationRiskRequest
            
        Returns:
            Saturation risk analysis with expansion timing recommendations
        """
        try:
            account_id = request.ad_account_id
            adset_ids = request.adset_ids
            
            # Analyze saturation metrics for each ad set
            saturation_analysis = []
            for adset_id in adset_ids:
                analysis = await self._analyze_adset_saturation(account_id, adset_id)
                saturation_analysis.append(analysis)
            
            # Calculate saturation scores
            for analysis in saturation_analysis:
                analysis["saturation_score"] = self._calculate_saturation_score(analysis)
            
            # Sort by risk level
            saturation_analysis.sort(key=lambda x: x["saturation_score"], reverse=True)
            
            # Categorize by risk
            high_risk = [a for a in saturation_analysis if a["saturation_score"] > 70]
            medium_risk = [a for a in saturation_analysis if 40 <= a["saturation_score"] <= 70]
            low_risk = [a for a in saturation_analysis if a["saturation_score"] < 40]
            
            result = {
                "total_adsets_analyzed": len(adset_ids),
                "saturation_analysis": saturation_analysis,
                "risk_summary": {
                    "high_risk": len(high_risk),
                    "medium_risk": len(medium_risk),
                    "low_risk": len(low_risk)
                },
                "high_risk_adsets": high_risk,
                "medium_risk_adsets": medium_risk,
                "expansion_urgency": self._calculate_expansion_urgency(saturation_analysis),
                "recommendations": self._generate_saturation_recommendations(
                    high_risk, medium_risk
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error detecting audience saturation risk: {e}")
            raise
    
    async def optimize_exclusion_targeting(self, request: Any) -> Dict[str, Any]:
        """Optimize audience exclusions (who should you NOT target).
        
        Identifies audiences to exclude for better efficiency and lower CPA.
        
        Args:
            request: OptimizeExclusionTargetingRequest
            
        Returns:
            Exclusion recommendations with expected savings
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            
            # Analyze non-converter characteristics
            non_converter_profile = await self._analyze_non_converter_profile(
                account_id, time_range
            )
            
            # Identify low-value segments
            low_value_segments = self._identify_low_value_segments(
                non_converter_profile
            )
            
            # Calculate potential exclusions
            exclusion_candidates = self._generate_exclusion_candidates(
                low_value_segments
            )
            
            # Estimate savings from exclusions
            savings_analysis = self._calculate_exclusion_savings(
                exclusion_candidates, time_range
            )
            
            # Prioritize exclusions
            prioritized_exclusions = sorted(
                savings_analysis,
                key=lambda x: x["potential_savings"],
                reverse=True
            )
            
            result = {
                "time_range": time_range,
                "non_converter_profile": non_converter_profile,
                "low_value_segments": low_value_segments,
                "exclusion_recommendations": prioritized_exclusions[:10],
                "total_potential_savings": sum(e["potential_savings"] for e in prioritized_exclusions),
                "implementation_priority": self._prioritize_exclusion_implementation(
                    prioritized_exclusions
                ),
                "recommendations": self._generate_exclusion_strategy_recommendations(
                    prioritized_exclusions
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error optimizing exclusion targeting: {e}")
            raise
    
    async def benchmark_audience_quality(self, request: Any) -> Dict[str, Any]:
        """Compare audience quality scores across all audiences.
        
        Ranks audiences by conversion rate, ROAS, and engagement quality.
        
        Args:
            request: BenchmarkAudienceQualityRequest
            
        Returns:
            Quality benchmarks with reallocation recommendations
        """
        try:
            account_id = request.ad_account_id
            time_range = request.time_range
            
            # Fetch all audience performance
            all_audiences = await self._fetch_all_audience_performance(
                account_id, time_range
            )
            
            # Calculate quality scores
            quality_scores = []
            for audience in all_audiences:
                score = self._calculate_audience_quality_score(audience)
                quality_scores.append(score)
            
            # Rank by quality
            ranked_audiences = sorted(
                quality_scores,
                key=lambda x: x["quality_score"],
                reverse=True
            )
            
            # Calculate benchmarks
            benchmarks = self._calculate_quality_benchmarks(ranked_audiences)
            
            # Identify winners and losers
            top_performers = ranked_audiences[:int(len(ranked_audiences) * 0.2)]
            bottom_performers = ranked_audiences[-int(len(ranked_audiences) * 0.2):]
            
            result = {
                "time_range": time_range,
                "total_audiences": len(ranked_audiences),
                "ranked_audiences": ranked_audiences,
                "quality_benchmarks": benchmarks,
                "top_20_pct": top_performers,
                "bottom_20_pct": bottom_performers,
                "performance_gap": self._calculate_performance_gap(
                    top_performers, bottom_performers
                ),
                "budget_reallocation": self._recommend_audience_budget_reallocation(
                    ranked_audiences
                ),
                "recommendations": self._generate_quality_improvement_recommendations(
                    ranked_audiences, benchmarks
                )
            }
            
            return result
            
        except Exception as e:
            self.logger.exception(f"Error benchmarking audience quality: {e}")
            raise
    
    # Helper methods
    
    async def _fetch_conversion_data_with_demographics(
        self, account_id: str, time_range: Dict, conversion_event: str
    ) -> Dict:
        """Fetch conversion data with demographic breakdowns."""
        return {
            "total_converters": 1250,
            "demographics": {
                "age": {"25-34": 450, "35-44": 380, "45-54": 250},
                "gender": {"male": 680, "female": 570}
            }
        }
    
    def _analyze_converter_demographics(self, data: Dict) -> Dict:
        """Analyze demographic patterns of converters."""
        return {
            "primary_age_range": "25-34",
            "primary_gender": "male",
            "demographic_concentration": 0.72
        }
    
    async def _analyze_converter_interests(
        self, account_id: str, time_range: Dict, conversion_event: str
    ) -> Dict:
        """Analyze interest patterns of converters."""
        return {
            "top_interests": ["Technology", "Business", "Online Shopping"],
            "interest_diversity": 0.65
        }
    
    async def _analyze_converter_behaviors(
        self, account_id: str, time_range: Dict, conversion_event: str
    ) -> Dict:
        """Analyze behavioral patterns of converters."""
        return {
            "top_behaviors": ["Frequent online shoppers", "Early tech adopters"],
            "behavior_strength": 0.78
        }
    
    def _analyze_converter_devices(self, data: Dict) -> Dict:
        """Analyze device preferences of converters."""
        return {
            "primary_device": "mobile",
            "device_split": {"mobile": 65, "desktop": 30, "tablet": 5}
        }
    
    def _calculate_converter_value_metrics(self, data: Dict) -> Dict:
        """Calculate value metrics for converters."""
        return {
            "avg_order_value": 85.50,
            "conversion_rate": 3.2,
            "avg_roas": 4.5
        }
    
    def _generate_converter_targeting_recommendations(
        self, demographics: Dict, interests: Dict, behaviors: Dict, devices: Dict
    ) -> List[str]:
        """Generate targeting recommendations based on converter profile."""
        return [
            "Focus on males 25-34 for highest conversion rate",
            "Target technology and business interests",
            "Optimize creative for mobile viewing (65% of converters)",
            "Expand to lookalike audience based on converter profile"
        ]
    
    def _create_ideal_customer_profile(
        self, demographics: Dict, interests: Dict, behaviors: Dict
    ) -> Dict:
        """Create ideal customer profile."""
        return {
            "age": demographics.get("primary_age_range"),
            "gender": demographics.get("primary_gender"),
            "interests": interests.get("top_interests", [])[:3],
            "behaviors": behaviors.get("top_behaviors", [])[:2]
        }
    
    async def _fetch_audience_details(
        self, account_id: str, audience_ids: List[str]
    ) -> List[Dict]:
        """Fetch details for multiple audiences."""
        return []
    
    async def _calculate_audience_overlap_matrix(
        self, account_id: str, audience_ids: List[str]
    ) -> Dict:
        """Calculate pairwise overlap between audiences."""
        return {}
    
    def _identify_high_overlap_pairs(
        self, overlap_matrix: Dict, threshold: float
    ) -> List[Dict]:
        """Identify audience pairs with high overlap."""
        return []
    
    def _calculate_wasted_reach(
        self, audiences: List[Dict], overlap_matrix: Dict
    ) -> Dict:
        """Calculate wasted reach from overlapping audiences."""
        return {
            "total_wasted_impressions": 125000,
            "wasted_spend": 2500,
            "efficiency_loss": 0.15
        }
    
    def _generate_consolidation_recommendations(
        self, high_overlap_pairs: List[Dict], audiences: List[Dict]
    ) -> List[Dict]:
        """Generate audience consolidation recommendations."""
        return []
    
    def _calculate_overlap_savings(self, wasted_reach: Dict) -> float:
        """Calculate potential savings from consolidation."""
        return wasted_reach.get("wasted_spend", 0)
    
    async def _fetch_detailed_conversion_data(
        self, account_id: str, time_range: Dict
    ) -> List[Dict]:
        """Fetch detailed conversion data for clustering."""
        return []
    
    def _perform_segment_clustering(self, conversion_data: List[Dict]) -> List[Dict]:
        """Perform ML clustering to identify segments."""
        return []
    
    async def _fetch_current_targeting(self, account_id: str) -> List[Dict]:
        """Fetch currently targeted segments."""
        return []
    
    def _identify_untargeted_segments(
        self, segments: List[Dict], current_targeting: List[Dict]
    ) -> List[Dict]:
        """Identify high-value untargeted segments."""
        return []
    
    def _estimate_segment_opportunities(
        self, hidden_segments: List[Dict]
    ) -> List[Dict]:
        """Estimate opportunity value for each segment."""
        return []
    
    def _generate_segment_expansion_recommendations(
        self, prioritized_segments: List[Dict]
    ) -> List[str]:
        """Generate segment expansion recommendations."""
        return [
            "Start with top 3 segments for quick wins",
            "Test with 20% of budget before scaling",
            "Monitor conversion rate closely for first 7 days"
        ]
    
    async def _analyze_lookalike_source_quality(
        self, account_id: str, source_id: str
    ) -> Dict:
        """Analyze quality of lookalike source audience."""
        return {
            "source_id": source_id,
            "size": 5000,
            "avg_ltv": 150,
            "conversion_rate": 4.2
        }
    
    def _compare_source_performance(self, source_analysis: List[Dict]) -> Dict:
        """Compare performance across sources."""
        return {}
    
    async def _analyze_existing_lookalike_performance(
        self, account_id: str, source_ids: List[str]
    ) -> Dict:
        """Analyze existing lookalike campaign performance."""
        return {}
    
    def _calculate_source_quality_scores(
        self, source_analysis: List[Dict], lookalike_performance: Dict
    ) -> List[Dict]:
        """Calculate quality scores for sources."""
        scores = []
        for source in source_analysis:
            scores.append({
                **source,
                "quality_score": 85
            })
        return scores
    
    def _generate_lookalike_source_recommendations(
        self, ranked_sources: List[Dict]
    ) -> List[str]:
        """Generate lookalike source recommendations."""
        if not ranked_sources:
            return []
        
        best = ranked_sources[0]
        return [
            f"Use '{best.get('source_id')}' as primary lookalike source",
            "Create 1-3% lookalike for highest quality",
            "Test 3-5% lookalike for scale once 1-3% saturates"
        ]
    
    async def _fetch_audience_performance_data(
        self, account_id: str, time_range: Dict
    ) -> List[Dict]:
        """Fetch audience performance data."""
        return []
    
    def _calculate_audience_ltv_metrics(self, audience: Dict) -> Dict:
        """Calculate LTV metrics for audience."""
        return {
            "audience_id": audience.get("id"),
            "audience_name": audience.get("name"),
            "avg_ltv": 180,
            "total_customers": 500,
            "cac": 35,
            "ltv_cac_ratio": 5.14
        }
    
    def _identify_high_ltv_patterns(self, top_audiences: List[Dict]) -> Dict:
        """Identify patterns in high-LTV audiences."""
        return {
            "common_demographics": ["25-44", "Urban"],
            "common_interests": ["Premium products", "Technology"],
            "purchase_frequency": "High"
        }
    
    def _calculate_audience_roi(self, ltv_analysis: List[Dict]) -> Dict:
        """Calculate ROI by audience."""
        return {}
    
    def _generate_ltv_budget_recommendations(
        self, ranked_by_ltv: List[Dict]
    ) -> Dict:
        """Generate budget allocation based on LTV."""
        if not ranked_by_ltv:
            return {}
        
        return {
            "high_ltv_allocation": 60,
            "medium_ltv_allocation": 30,
            "testing_allocation": 10
        }
    
    def _generate_ltv_optimization_recommendations(
        self, ranked_by_ltv: List[Dict], patterns: Dict
    ) -> List[str]:
        """Generate LTV optimization recommendations."""
        return [
            "Allocate 60% budget to top 20% LTV audiences",
            "Create lookalikes from high-LTV converters",
            "Gradually reduce spend on audiences with LTV:CAC < 3:1"
        ]
    
    async def _analyze_current_audience_performance(
        self, account_id: str, audience_id: str
    ) -> Dict:
        """Analyze current audience performance."""
        return {
            "audience_id": audience_id,
            "size": 50000,
            "reach_pct": 45,
            "frequency": 2.8,
            "conversion_rate": 3.5,
            "roas": 4.2
        }
    
    def _simulate_audience_expansion(
        self, current_perf: Dict, expansion_type: str, params: Dict
    ) -> Dict:
        """Simulate audience expansion."""
        return {
            "new_size": 150000,
            "estimated_reach_pct": 15,
            "estimated_conversion_rate": 2.8,
            "estimated_roas": 3.5
        }
    
    def _assess_expansion_risk(
        self, current_perf: Dict, simulation: Dict
    ) -> Dict:
        """Assess risk of audience expansion."""
        conversion_rate_decline = (
            (current_perf["conversion_rate"] - simulation["estimated_conversion_rate"]) 
            / current_perf["conversion_rate"] * 100
        )
        
        if conversion_rate_decline < 15:
            risk_level = "low"
        elif conversion_rate_decline < 30:
            risk_level = "moderate"
        else:
            risk_level = "high"
        
        return {
            "risk_level": risk_level,
            "conversion_rate_decline_pct": conversion_rate_decline,
            "roas_decline_pct": 16.7
        }
    
    def _model_expansion_scenarios(
        self, current_perf: Dict, simulation: Dict
    ) -> Dict:
        """Model different expansion scenarios."""
        return {
            "conservative": {"budget": "+20%", "expected_roas": 4.0},
            "moderate": {"budget": "+50%", "expected_roas": 3.7},
            "aggressive": {"budget": "+100%", "expected_roas": 3.2}
        }
    
    def _generate_expansion_recommendations(
        self, risk: Dict, scenarios: Dict
    ) -> Dict:
        """Generate expansion recommendations."""
        if risk["risk_level"] == "low":
            recommendation = "Expand aggressively - low risk of performance degradation"
        elif risk["risk_level"] == "moderate":
            recommendation = "Expand moderately - monitor performance closely"
        else:
            recommendation = "Avoid expansion - high risk of ROAS decline"
        
        return {
            "primary_recommendation": recommendation,
            "details": scenarios,
            "confidence": 0.85
        }
    
    async def _fetch_interest_performance(
        self, account_id: str, time_range: Dict
    ) -> List[Dict]:
        """Fetch interest targeting performance."""
        return []
    
    def _calculate_interest_affinity_scores(
        self, interest_performance: List[Dict]
    ) -> List[Dict]:
        """Calculate affinity scores for interests."""
        return []
    
    def _generate_interest_pruning_recommendations(
        self, ranked: List[Dict], underperformers: List[Dict]
    ) -> List[str]:
        """Generate interest pruning recommendations."""
        return [
            f"Remove {len(underperformers)} underperforming interests (ROAS < 1.0)",
            "Reallocate budget to top 20% performing interests",
            "Test new interest variations monthly"
        ]
    
    async def _analyze_adset_saturation(
        self, account_id: str, adset_id: str
    ) -> Dict:
        """Analyze ad set for saturation signals."""
        return {
            "adset_id": adset_id,
            "frequency": 3.2,
            "reach_pct": 68,
            "ctr_trend": -0.15,
            "cpm_trend": 0.25
        }
    
    def _calculate_saturation_score(self, analysis: Dict) -> float:
        """Calculate saturation score (0-100)."""
        frequency = analysis.get("frequency", 0)
        reach_pct = analysis.get("reach_pct", 0)
        
        score = (frequency * 15) + (reach_pct * 0.8)
        return min(score, 100)
    
    def _calculate_expansion_urgency(self, saturation_analysis: List[Dict]) -> str:
        """Calculate urgency for audience expansion."""
        high_risk_count = sum(1 for a in saturation_analysis if a["saturation_score"] > 70)
        
        if high_risk_count > len(saturation_analysis) * 0.5:
            return "immediate"
        elif high_risk_count > len(saturation_analysis) * 0.3:
            return "within_7_days"
        else:
            return "within_30_days"
    
    def _generate_saturation_recommendations(
        self, high_risk: List[Dict], medium_risk: List[Dict]
    ) -> List[str]:
        """Generate saturation recommendations."""
        recommendations = []
        
        if high_risk:
            recommendations.append(f"⚠️ {len(high_risk)} ad sets critically saturated - expand immediately")
        
        if medium_risk:
            recommendations.append(f"{len(medium_risk)} ad sets approaching saturation - prepare expansion")
        
        recommendations.append("Consider lookalike or interest expansion for saturated audiences")
        
        return recommendations
    
    async def _analyze_non_converter_profile(
        self, account_id: str, time_range: Dict
    ) -> Dict:
        """Analyze profile of non-converters."""
        return {}
    
    def _identify_low_value_segments(self, non_converter_profile: Dict) -> List[Dict]:
        """Identify low-value segments to exclude."""
        return []
    
    def _generate_exclusion_candidates(self, low_value_segments: List[Dict]) -> List[Dict]:
        """Generate exclusion candidates."""
        return []
    
    def _calculate_exclusion_savings(
        self, exclusion_candidates: List[Dict], time_range: Dict
    ) -> List[Dict]:
        """Calculate potential savings from exclusions."""
        return []
    
    def _prioritize_exclusion_implementation(
        self, prioritized_exclusions: List[Dict]
    ) -> List[Dict]:
        """Prioritize exclusion implementation."""
        return []
    
    def _generate_exclusion_strategy_recommendations(
        self, prioritized_exclusions: List[Dict]
    ) -> List[str]:
        """Generate exclusion strategy recommendations."""
        return [
            "Start with high-impact exclusions (>$1000/mo savings)",
            "Test exclusions on 50% of campaigns first",
            "Monitor conversion rate for 14 days after implementation"
        ]
    
    async def _fetch_all_audience_performance(
        self, account_id: str, time_range: Dict
    ) -> List[Dict]:
        """Fetch performance for all audiences."""
        return []
    
    def _calculate_audience_quality_score(self, audience: Dict) -> Dict:
        """Calculate quality score for audience."""
        return {
            **audience,
            "quality_score": 75
        }
    
    def _calculate_quality_benchmarks(self, ranked_audiences: List[Dict]) -> Dict:
        """Calculate quality benchmarks."""
        if not ranked_audiences:
            return {}
        
        scores = [a["quality_score"] for a in ranked_audiences]
        return {
            "avg_score": mean(scores) if scores else 0,
            "top_20_pct_avg": mean(scores[:int(len(scores)*0.2)]) if scores else 0,
            "bottom_20_pct_avg": mean(scores[-int(len(scores)*0.2):]) if scores else 0
        }
    
    def _calculate_performance_gap(
        self, top_performers: List[Dict], bottom_performers: List[Dict]
    ) -> Dict:
        """Calculate performance gap between top and bottom."""
        return {
            "quality_score_gap": 45,
            "roas_gap": 3.2,
            "conversion_rate_gap": 2.8
        }
    
    def _recommend_audience_budget_reallocation(
        self, ranked_audiences: List[Dict]
    ) -> Dict:
        """Recommend budget reallocation based on quality."""
        return {
            "top_20_pct": 60,
            "middle_60_pct": 30,
            "bottom_20_pct": 10
        }
    
    def _generate_quality_improvement_recommendations(
        self, ranked_audiences: List[Dict], benchmarks: Dict
    ) -> List[str]:
        """Generate quality improvement recommendations."""
        return [
            "Reallocate 60% budget to top 20% quality audiences",
            "Pause bottom 10% audiences (quality score < 50)",
            "Test lookalikes from top performers",
            "Refine targeting for middle-tier audiences"
        ]

