"""
Ecosystem KPI Tracker for AITBC
Tracks key performance indicators for ecosystem health and strategy reviews
"""

import asyncio
import json
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional
from dataclasses import dataclass, asdict
from decimal import Decimal
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from sqlalchemy import create_engine, select, func, and_, or_
from sqlalchemy.orm import sessionmaker
from enum import Enum

from ..config import settings
from ..database import get_db


class KPICategory(Enum):
    """Categories of KPIs"""
    MARKETPLACE = "marketplace"
    CROSS_CHAIN = "cross_chain"
    DEVELOPER = "developer"
    USER = "user"
    FINANCIAL = "financial"
    TECHNICAL = "technical"


@dataclass
class KPIDefinition:
    """Definition of a KPI"""
    name: str
    category: KPICategory
    description: str
    unit: str
    target: Optional[float]
    calculation_method: str
    data_sources: List[str]
    frequency: str  # daily, weekly, monthly
    importance: str  # high, medium, low


@dataclass
class KPIValue:
    """A single KPI measurement"""
    timestamp: datetime
    kpi_name: str
    value: float
    unit: str
    category: str
    metadata: Dict[str, Any]


class EcosystemKPITracker:
    """Main KPI tracking system"""
    
    def __init__(self):
        self.engine = create_engine(settings.DATABASE_URL)
        self.Session = sessionmaker(bind=self.engine)
        self.logger = __import__('logging').getLogger(__name__)
        
        # Define all tracked KPIs
        self.kpi_definitions = self._initialize_kpi_definitions()
        
    def _initialize_kpi_definitions(self) -> Dict[str, KPIDefinition]:
        """Initialize all KPI definitions"""
        return {
            # Marketplace KPIs
            "active_marketplaces": KPIDefinition(
                name="active_marketplaces",
                category=KPICategory.MARKETPLACE,
                description="Number of active marketplaces on the platform",
                unit="count",
                target=50.0,
                calculation_method="count_active_marketplaces",
                data_sources=["marketplace_service", "tenant_db"],
                frequency="daily",
                importance="high"
            ),
            "total_volume_usd": KPIDefinition(
                name="total_volume_usd",
                category=KPICategory.MARKETPLACE,
                description="Total transaction volume in USD",
                unit="USD",
                target=10000000.0,
                calculation_method="sum_transaction_volume",
                data_sources=["transaction_db", "price_oracle"],
                frequency="daily",
                importance="high"
            ),
            "marketplace_utilization": KPIDefinition(
                name="marketplace_utilization",
                category=KPICategory.MARKETPLACE,
                description="Percentage of utilized marketplace capacity",
                unit="percent",
                target=75.0,
                calculation_method="calculate_utilization",
                data_sources=["marketplace_service", "usage_metrics"],
                frequency="hourly",
                importance="medium"
            ),
            
            # Cross-Chain KPIs
            "cross_chain_volume": KPIDefinition(
                name="cross_chain_volume",
                category=KPICategory.CROSS_CHAIN,
                description="Total cross-chain transaction volume",
                unit="USD",
                target=5000000.0,
                calculation_method="sum_cross_chain_volume",
                data_sources=["bridge_service", "transaction_db"],
                frequency="daily",
                importance="high"
            ),
            "active_bridges": KPIDefinition(
                name="active_bridges",
                category=KPICategory.CROSS_CHAIN,
                description="Number of active cross-chain bridges",
                unit="count",
                target=10.0,
                calculation_method="count_active_bridges",
                data_sources=["bridge_service"],
                frequency="daily",
                importance="medium"
            ),
            "bridge_success_rate": KPIDefinition(
                name="bridge_success_rate",
                category=KPICategory.CROSS_CHAIN,
                description="Success rate of cross-chain transactions",
                unit="percent",
                target=95.0,
                calculation_method="calculate_bridge_success_rate",
                data_sources=["bridge_service", "transaction_db"],
                frequency="hourly",
                importance="high"
            ),
            
            # Developer KPIs
            "active_developers": KPIDefinition(
                name="active_developers",
                category=KPICategory.DEVELOPER,
                description="Number of active developers in ecosystem",
                unit="count",
                target=1000.0,
                calculation_method="count_active_developers",
                data_sources=["github_api", "developer_db"],
                frequency="weekly",
                importance="high"
            ),
            "new_extensions": KPIDefinition(
                name="new_extensions",
                category=KPICategory.DEVELOPER,
                description="Number of new marketplace extensions created",
                unit="count",
                target=25.0,
                calculation_method="count_new_extensions",
                data_sources=["extension_registry", "github_api"],
                frequency="weekly",
                importance="medium"
            ),
            "developer_satisfaction": KPIDefinition(
                name="developer_satisfaction",
                category=KPICategory.DEVELOPER,
                description="Developer satisfaction score (1-5)",
                unit="score",
                target=4.5,
                calculation_method="calculate_satisfaction_score",
                data_sources=["surveys", "github_issues", "discord_sentiment"],
                frequency="monthly",
                importance="medium"
            ),
            
            # User KPIs
            "active_users": KPIDefinition(
                name="active_users",
                category=KPICategory.USER,
                description="Number of active users (30-day)",
                unit="count",
                target=10000.0,
                calculation_method="count_active_users",
                data_sources=["user_db", "auth_service"],
                frequency="daily",
                importance="high"
            ),
            "user_retention": KPIDefinition(
                name="user_retention",
                category=KPICategory.USER,
                description="30-day user retention rate",
                unit="percent",
                target=80.0,
                calculation_method="calculate_retention_rate",
                data_sources=["user_db", "analytics_service"],
                frequency="weekly",
                importance="high"
            ),
            "net_promoter_score": KPIDefinition(
                name="net_promoter_score",
                category=KPICategory.USER,
                description="Net Promoter Score",
                unit="score",
                target=50.0,
                calculation_method="calculate_nps",
                data_sources=["surveys", "feedback_service"],
                frequency="monthly",
                importance="medium"
            ),
            
            # Financial KPIs
            "revenue": KPIDefinition(
                name="revenue",
                category=KPICategory.FINANCIAL,
                description="Total platform revenue",
                unit="USD",
                target=1000000.0,
                calculation_method="calculate_revenue",
                data_sources=["billing_service", "payment_processor"],
                frequency="monthly",
                importance="high"
            ),
            "cost_per_transaction": KPIDefinition(
                name="cost_per_transaction",
                category=KPICategory.FINANCIAL,
                description="Average cost per transaction",
                unit="USD",
                target=0.10,
                calculation_method="calculate_cost_per_tx",
                data_sources=["billing_service", "metrics_service"],
                frequency="monthly",
                importance="medium"
            ),
            "profit_margin": KPIDefinition(
                name="profit_margin",
                category=KPICategory.FINANCIAL,
                description="Platform profit margin",
                unit="percent",
                target=20.0,
                calculation_method="calculate_profit_margin",
                data_sources=["billing_service", "financial_db"],
                frequency="quarterly",
                importance="high"
            ),
            
            # Technical KPIs
            "network_hash_rate": KPIDefinition(
                name="network_hash_rate",
                category=KPICategory.TECHNICAL,
                description="Network hash rate",
                unit="H/s",
                target=1000000000.0,
                calculation_method="get_hash_rate",
                data_sources=["blockchain_node", "metrics_service"],
                frequency="hourly",
                importance="high"
            ),
            "block_time": KPIDefinition(
                name="block_time",
                category=KPICategory.TECHNICAL,
                description="Average block time",
                unit="seconds",
                target=12.0,
                calculation_method="calculate_average_block_time",
                data_sources=["blockchain_node", "block_db"],
                frequency="hourly",
                importance="high"
            ),
            "uptime": KPIDefinition(
                name="uptime",
                category=KPICategory.TECHNICAL,
                description="Platform uptime percentage",
                unit="percent",
                target=99.9,
                calculation_method="calculate_uptime",
                data_sources=["monitoring_service", "health_checks"],
                frequency="daily",
                importance="high"
            ),
        }
    
    async def collect_all_kpis(self, period: str = "daily") -> List[KPIValue]:
        """Collect all KPIs for a given period"""
        kpi_values = []
        
        for kpi_name, kpi_def in self.kpi_definitions.items():
            if kpi_def.frequency == period or period == "all":
                try:
                    value = await self._calculate_kpi(kpi_name, kpi_def)
                    kpi_value = KPIValue(
                        timestamp=datetime.utcnow(),
                        kpi_name=kpi_name,
                        value=value,
                        unit=kpi_def.unit,
                        category=kpi_def.category.value,
                        metadata={
                            "target": kpi_def.target,
                            "importance": kpi_def.importance,
                        }
                    )
                    kpi_values.append(kpi_value)
                except Exception as e:
                    self.logger.error(f"Failed to calculate KPI {kpi_name}: {e}")
        
        # Store KPIs
        await self._store_kpis(kpi_values)
        
        return kpi_values
    
    async def _calculate_kpi(self, kpi_name: str, kpi_def: KPIDefinition) -> float:
        """Calculate a specific KPI"""
        method_name = kpi_def.calculation_method
        method = getattr(self, method_name, None)
        
        if method is None:
            raise ValueError(f"Unknown calculation method: {method_name}")
        
        return await method()
    
    async def _store_kpis(self, kpi_values: List[KPIValue]):
        """Store KPI values in database"""
        with self.Session() as db:
            for kpi in kpi_values:
                # Implementation would store in KPI table
                pass
    
    # KPI Calculation Methods
    
    async def count_active_marketplaces(self) -> float:
        """Count active marketplaces"""
        with self.Session() as db:
            # Query active tenants with marketplace enabled
            count = db.execute(
                select(func.count(Tenant.id))
                .where(
                    and_(
                        Tenant.status == "active",
                        Tenant.features.contains(["marketplace"])
                    )
                )
            ).scalar()
            return float(count)
    
    async def sum_transaction_volume(self) -> float:
        """Sum total transaction volume in USD"""
        with self.Session() as db:
            # Get transactions in last 24 hours
            total = db.execute(
                select(func.sum(Transaction.amount_usd))
                .where(
                    Transaction.timestamp >= datetime.utcnow() - timedelta(days=1)
                )
            ).scalar()
            return float(total or 0)
    
    async def calculate_utilization(self) -> float:
        """Calculate marketplace utilization percentage"""
        # Get total capacity and used capacity
        total_capacity = await self._get_total_capacity()
        used_capacity = await self._get_used_capacity()
        
        if total_capacity == 0:
            return 0.0
        
        return (used_capacity / total_capacity) * 100
    
    async def sum_cross_chain_volume(self) -> float:
        """Sum cross-chain transaction volume"""
        with self.Session() as db:
            total = db.execute(
                select(func.sum(CrossChainTransaction.amount_usd))
                .where(
                    CrossChainTransaction.timestamp >= datetime.utcnow() - timedelta(days=1)
                )
            ).scalar()
            return float(total or 0)
    
    async def count_active_bridges(self) -> float:
        """Count active cross-chain bridges"""
        # Query bridge service
        bridges = await self._query_bridge_service("/bridges?status=active")
        return float(len(bridges))
    
    async def calculate_bridge_success_rate(self) -> float:
        """Calculate bridge transaction success rate"""
        with self.Session() as db:
            total = db.execute(
                select(func.count(CrossChainTransaction.id))
                .where(
                    CrossChainTransaction.timestamp >= datetime.utcnow() - timedelta(hours=24)
                )
            ).scalar()
            
            successful = db.execute(
                select(func.count(CrossChainTransaction.id))
                .where(
                    and_(
                        CrossChainTransaction.timestamp >= datetime.utcnow() - timedelta(hours=24),
                        CrossChainTransaction.status == "completed"
                    )
                )
            ).scalar()
            
            if total == 0:
                return 100.0
            
            return (successful / total) * 100
    
    async def count_active_developers(self) -> float:
        """Count active developers (last 30 days)"""
        # Query GitHub API and local records
        github_contributors = await self._query_github_api("/contributors")
        local_developers = await self._count_local_developers()
        
        # Combine and deduplicate
        all_developers = set(github_contributors + local_developers)
        return float(len(all_developers))
    
    async def count_new_extensions(self) -> float:
        """Count new extensions this week"""
        with self.Session() as db:
            count = db.execute(
                select(func.count(Extension.id))
                .where(
                    Extension.created_at >= datetime.utcnow() - timedelta(weeks=1)
                )
            ).scalar()
            return float(count)
    
    async def calculate_satisfaction_score(self) -> float:
        """Calculate developer satisfaction score"""
        # Aggregate from multiple sources
        survey_scores = await self._get_survey_scores()
        issue_sentiment = await self._analyze_issue_sentiment()
        discord_sentiment = await self._analyze_discord_sentiment()
        
        # Weighted average
        weights = {"survey": 0.5, "issues": 0.25, "discord": 0.25}
        
        score = (
            survey_scores * weights["survey"] +
            issue_sentiment * weights["issues"] +
            discord_sentiment * weights["discord"]
        )
        
        return score
    
    async def count_active_users(self) -> float:
        """Count active users (last 30 days)"""
        with self.Session() as db:
            count = db.execute(
                select(func.count(User.id))
                .where(
                    User.last_active >= datetime.utcnow() - timedelta(days=30)
                )
            ).scalar()
            return float(count)
    
    async def calculate_retention_rate(self) -> float:
        """Calculate 30-day user retention rate"""
        # Cohort analysis
        cohort_users = await self._get_cohort_users(30)  # Users from 30 days ago
        retained_users = await self._count_retained_users(cohort_users)
        
        if not cohort_users:
            return 0.0
        
        return (retained_users / len(cohort_users)) * 100
    
    async def calculate_nps(self) -> float:
        """Calculate Net Promoter Score"""
        responses = await self._get_nps_responses()
        
        if not responses:
            return 0.0
        
        promoters = sum(1 for r in responses if r >= 9)
        detractors = sum(1 for r in responses if r <= 6)
        
        nps = ((promoters - detractors) / len(responses)) * 100
        return nps
    
    async def calculate_revenue(self) -> float:
        """Calculate total platform revenue"""
        with self.Session() as db:
            total = db.execute(
                select(func.sum(Revenue.amount))
                .where(
                    Revenue.period == "monthly"
                )
            ).scalar()
            return float(total or 0)
    
    async def calculate_cost_per_tx(self) -> float:
        """Calculate cost per transaction"""
        total_cost = await self._get_monthly_costs()
        tx_count = await self._get_monthly_tx_count()
        
        if tx_count == 0:
            return 0.0
        
        return total_cost / tx_count
    
    async def calculate_profit_margin(self) -> float:
        """Calculate profit margin percentage"""
        revenue = await self.calculate_revenue()
        costs = await self._get_monthly_costs()
        
        if revenue == 0:
            return 0.0
        
        profit = revenue - costs
        return (profit / revenue) * 100
    
    async def get_hash_rate(self) -> float:
        """Get current network hash rate"""
        # Query blockchain node metrics
        metrics = await self._query_blockchain_metrics()
        return float(metrics.get("hash_rate", 0))
    
    async def calculate_average_block_time(self) -> float:
        """Calculate average block time"""
        with self.Session() as db:
            avg_time = db.execute(
                select(func.avg(Block.timestamp_diff))
                .where(
                    Block.timestamp >= datetime.utcnow() - timedelta(hours=1)
                )
            ).scalar()
            return float(avg_time or 0)
    
    async def calculate_uptime(self) -> float:
        """Calculate platform uptime percentage"""
        # Get uptime from monitoring service
        uptime_data = await self._query_monitoring_service("/uptime")
        return float(uptime_data.get("uptime_percentage", 0))
    
    # Helper methods for data collection
    
    async def _get_total_capacity(self) -> float:
        """Get total marketplace capacity"""
        # Implementation would query marketplace service
        return 10000.0  # Sample
    
    async def _get_used_capacity(self) -> float:
        """Get used marketplace capacity"""
        # Implementation would query usage metrics
        return 7500.0  # Sample
    
    async def _query_bridge_service(self, endpoint: str) -> List[Dict]:
        """Query bridge service API"""
        # Implementation would make HTTP request
        return []  # Sample
    
    async def _query_github_api(self, endpoint: str) -> List[str]:
        """Query GitHub API"""
        # Implementation would use GitHub API
        return []  # Sample
    
    async def _count_local_developers(self) -> List[str]:
        """Count local developers"""
        with self.Session() as db:
            developers = db.execute(
                select(Developer.github_username)
                .where(
                    Developer.last_active >= datetime.utcnow() - timedelta(days=30)
                )
            ).all()
            return [d[0] for d in developers]
    
    async def _get_survey_scores(self) -> float:
        """Get survey satisfaction scores"""
        # Implementation would query survey service
        return 4.2  # Sample
    
    async def _analyze_issue_sentiment(self) -> float:
        """Analyze GitHub issue sentiment"""
        # Implementation would use sentiment analysis
        return 3.8  # Sample
    
    async def _analyze_discord_sentiment(self) -> float:
        """Analyze Discord message sentiment"""
        # Implementation would use sentiment analysis
        return 4.0  # Sample
    
    async def _get_cohort_users(self, days_ago: int) -> List[str]:
        """Get users from a specific cohort"""
        with self.Session() as db:
            cohort_date = datetime.utcnow() - timedelta(days=days_ago)
            users = db.execute(
                select(User.id)
                .where(
                    and_(
                        User.created_at >= cohort_date,
                        User.created_at < cohort_date + timedelta(days=1)
                    )
                )
            ).all()
            return [u[0] for u in users]
    
    async def _count_retained_users(self, user_ids: List[str]) -> int:
        """Count how many users are still active"""
        with self.Session() as db:
            count = db.execute(
                select(func.count(User.id))
                .where(
                    and_(
                        User.id.in_(user_ids),
                        User.last_active >= datetime.utcnow() - timedelta(days=30)
                    )
                )
            ).scalar()
            return count
    
    async def _get_nps_responses(self) -> List[int]:
        """Get NPS survey responses"""
        # Implementation would query survey service
        return [9, 10, 8, 7, 9, 10, 6, 9]  # Sample
    
    async def _get_monthly_costs(self) -> float:
        """Get monthly operational costs"""
        # Implementation would query financial service
        return 800000.0  # Sample
    
    async def _get_monthly_tx_count(self) -> int:
        """Get monthly transaction count"""
        with self.Session() as db:
            count = db.execute(
                select(func.count(Transaction.id))
                .where(
                    Transaction.timestamp >= datetime.utcnow() - timedelta(days=30)
                )
            ).scalar()
            return count
    
    async def _query_blockchain_metrics(self) -> Dict[str, float]:
        """Query blockchain node metrics"""
        # Implementation would query blockchain node
        return {"hash_rate": 1000000000.0}  # Sample
    
    async def _query_monitoring_service(self, endpoint: str) -> Dict[str, float]:
        """Query monitoring service"""
        # Implementation would query monitoring service
        return {"uptime_percentage": 99.95}  # Sample
    
    async def generate_kpi_dashboard(self, period: str = "monthly") -> Dict[str, Any]:
        """Generate comprehensive KPI dashboard"""
        # Collect all KPIs
        kpis = await self.collect_all_kpis("all")
        
        # Group by category
        by_category = {}
        for kpi in kpis:
            if kpi.category not in by_category:
                by_category[kpi.category] = []
            by_category[kpi.category].append(kpi)
        
        # Calculate health scores
        health_scores = await self._calculate_health_scores(by_category)
        
        # Generate insights
        insights = await self._generate_insights(kpis)
        
        # Create visualizations
        charts = await self._create_charts(kpis)
        
        return {
            "timestamp": datetime.utcnow().isoformat(),
            "period": period,
            "kpis": [asdict(kpi) for kpi in kpis],
            "by_category": {
                cat: [asdict(kpi) for kpi in kpis]
                for cat, kpis in by_category.items()
            },
            "health_scores": health_scores,
            "insights": insights,
            "charts": charts,
        }
    
    async def _calculate_health_scores(self, by_category: Dict[str, List[KPIValue]]) -> Dict[str, float]:
        """Calculate health scores for each category"""
        scores = {}
        
        for category, kpis in by_category.items():
            if not kpis:
                scores[category] = 0.0
                continue
            
            # Weight by importance
            total_score = 0.0
            total_weight = 0.0
            
            for kpi in kpis:
                target = kpi.metadata.get("target", 0)
                if target == 0:
                    continue
                
                # Calculate score as percentage of target
                score = min((kpi.value / target) * 100, 100)
                
                # Apply importance weight
                weight = {"high": 3, "medium": 2, "low": 1}.get(
                    kpi.metadata.get("importance", "medium"), 2
                )
                
                total_score += score * weight
                total_weight += weight
            
            if total_weight > 0:
                scores[category] = total_score / total_weight
            else:
                scores[category] = 0.0
        
        return scores
    
    async def _generate_insights(self, kpis: List[KPIValue]) -> List[str]:
        """Generate insights from KPI data"""
        insights = []
        
        # Analyze trends
        for kpi in kpis:
            if kpi.value < (kpi.metadata.get("target", 0) * 0.8):
                insights.append(
                    f"⚠️ {kpi.kpi_name} is below target ({kpi.value:.2f} vs {kpi.metadata.get('target')})"
                )
            elif kpi.value > (kpi.metadata.get("target", 0) * 1.2):
                insights.append(
                    f"🎉 {kpi.kpi_name} exceeds target ({kpi.value:.2f} vs {kpi.metadata.get('target')})"
                )
        
        # Cross-category insights
        marketplace_kpis = [k for k in kpis if k.category == "marketplace"]
        if marketplace_kpis:
            volume_kpi = next((k for k in marketplace_kpis if k.kpi_name == "total_volume_usd"), None)
            utilization_kpi = next((k for k in marketplace_kpis if k.kpi_name == "marketplace_utilization"), None)
            
            if volume_kpi and utilization_kpi:
                if volume_kpi.value > 1000000 and utilization_kpi.value < 50:
                    insights.append(
                        "💡 High volume but low utilization - consider increasing capacity"
                    )
        
        return insights[:10]  # Limit to top 10 insights
    
    async def _create_charts(self, kpis: List[KPIValue]) -> Dict[str, str]:
        """Create chart visualizations"""
        charts = {}
        
        # KPI gauge charts
        for kpi in kpis[:5]:  # Limit to top 5
            fig = go.Figure(go.Indicator(
                mode = "gauge+number+delta",
                value = kpi.value,
                domain = {'x': [0, 1], 'y': [0, 1]},
                title = {'text': kpi.kpi_name},
                delta = {'reference': kpi.metadata.get('target', 0)},
                gauge = {
                    'axis': {'range': [None, kpi.metadata.get('target', 100) * 1.5]},
                    'bar': {'color': "darkblue"},
                    'steps': [
                        {'range': [0, kpi.metadata.get('target', 100) * 0.5], 'color': "lightgray"},
                        {'range': [kpi.metadata.get('target', 100) * 0.5, kpi.metadata.get('target', 100)], 'color': "gray"}
                    ],
                    'threshold': {
                        'line': {'color': "red", 'width': 4},
                        'thickness': 0.75,
                        'value': kpi.metadata.get('target', 100) * 0.9
                    }
                }
            ))
            
            charts[f"gauge_{kpi.kpi_name}"] = fig.to_json()
        
        # Category comparison chart
        categories = {}
        for kpi in kpis:
            if kpi.category not in categories:
                categories[kpi.category] = []
            categories[kpi.category].append(kpi.value / (kpi.metadata.get('target', 1) * 100))
        
        fig = px.bar(
            x=list(categories.keys()),
            y=[sum(v)/len(v) for v in categories.values()],
            title="KPI Performance by Category",
            labels={"x": "Category", "y": "Average % of Target"}
        )
        charts["category_comparison"] = fig.to_json()
        
        return charts
    
    async def export_kpis(self, format: str = "csv", period: str = "monthly") -> bytes:
        """Export KPI data"""
        kpis = await self.collect_all_kpis(period)
        
        # Convert to DataFrame
        df = pd.DataFrame([asdict(kpi) for kpi in kpis])
        
        if format == "csv":
            return df.to_csv(index=False).encode('utf-8')
        elif format == "json":
            return df.to_json(orient='records', indent=2).encode('utf-8')
        elif format == "excel":
            return df.to_excel(index=False).encode('utf-8')
        else:
            raise ValueError(f"Unsupported format: {format}")
    
    async def generate_strategy_review(self, quarter: str) -> Dict[str, Any]:
        """Generate quarterly strategy review document"""
        # Get KPI data for the quarter
        kpis = await self.collect_all_kpis("all")
        
        # Compare with previous quarter
        previous_kpis = await self._get_previous_quarter_kpis(quarter)
        
        # Generate analysis
        analysis = {
            "quarter": quarter,
            "executive_summary": await self._generate_executive_summary(kpis, previous_kpis),
            "key_achievements": await self._identify_achievements(kpis),
            "challenges": await self._identify_challenges(kpis),
            "recommendations": await self._generate_recommendations(kpis, previous_kpis),
            "next_quarter_goals": await self._set_next_quarter_goals(kpis),
        }
        
        return analysis
    
    async def _get_previous_quarter_kpis(self, quarter: str) -> List[KPIValue]:
        """Get KPIs from previous quarter"""
        # Implementation would query historical KPI data
        return []  # Sample
    
    async def _generate_executive_summary(self, kpis: List[KPIValue], previous: List[KPIValue]) -> str:
        """Generate executive summary"""
        # Implementation would analyze KPI trends
        return "Ecosystem shows strong growth with 25% increase in active users and 40% growth in transaction volume."
    
    async def _identify_achievements(self, kpis: List[KPIValue]) -> List[str]:
        """Identify key achievements"""
        achievements = []
        
        for kpi in kpis:
            if kpi.value >= kpi.metadata.get("target", 0):
                achievements.append(
                    f"Exceeded {kpi.kpi_name} target with {kpi.value:.2f} (target: {kpi.metadata.get('target')})"
                )
        
        return achievements
    
    async def _identify_challenges(self, kpis: List[KPIValue]) -> List[str]:
        """Identify challenges and areas for improvement"""
        challenges = []
        
        for kpi in kpis:
            if kpi.value < (kpi.metadata.get("target", 0) * 0.7):
                challenges.append(
                    f"{kpi.kpi_name} below target at {kpi.value:.2f} (target: {kpi.metadata.get('target')})"
                )
        
        return challenges
    
    async def _generate_recommendations(self, kpis: List[KPIValue], previous: List[KPIValue]) -> List[str]:
        """Generate strategic recommendations"""
        recommendations = []
        
        # Analyze trends and generate recommendations
        recommendations.extend([
            "Focus on improving developer onboarding to increase extension creation",
            "Invest in cross-chain infrastructure to support growing volume",
            "Enhance user retention programs to improve 30-day retention rate",
        ])
        
        return recommendations
    
    async def _set_next_quarter_goals(self, kpis: List[KPIValue]) -> Dict[str, float]:
        """Set goals for next quarter"""
        goals = {}
        
        for kpi in kpis:
            # Set goals 10-20% higher than current performance
            current_target = kpi.metadata.get("target", kpi.value)
            next_target = current_target * 1.15
            goals[kpi.kpi_name] = next_target
        
        return goals


# CLI interface
async def main():
    """CLI entry point"""
    import argparse
    
    parser = argparse.ArgumentParser(description="AITBC Ecosystem KPI Tracker")
    parser.add_argument("--collect", action="store_true", help="Collect all KPIs")
    parser.add_argument("--dashboard", action="store_true", help="Generate KPI dashboard")
    parser.add_argument("--export", choices=["csv", "json", "excel"], help="Export KPIs")
    parser.add_argument("--period", default="daily", help="Period for KPI collection")
    parser.add_argument("--strategy-review", help="Generate strategy review for quarter")
    
    args = parser.parse_args()
    
    tracker = EcosystemKPITracker()
    
    if args.collect:
        kpis = await tracker.collect_all_kpis(args.period)
        print(f"Collected {len(kpis)} KPIs")
        for kpi in kpis:
            print(f"{kpi.kpi_name}: {kpi.value:.2f} {kpi.unit}")
    
    elif args.dashboard:
        dashboard = await tracker.generate_kpi_dashboard()
        print(json.dumps(dashboard, indent=2, default=str))
    
    elif args.export:
        data = await tracker.export_kpis(args.export, args.period)
        print(data.decode())
    
    elif args.strategy_review:
        review = await tracker.generate_strategy_review(args.strategy_review)
        print(json.dumps(review, indent=2, default=str))
    
    else:
        parser.print_help()


if __name__ == "__main__":
    asyncio.run(main())
