#!/usr/bin/env python3
"""
Tracking Module Example - Advanced Whale Tracking System

This example demonstrates advanced tracking capabilities including:
- Cross-chain whale tracking
- Phase 3 advanced features
- Position tracking
- Alert system integration
- ML-powered tracking
"""

import asyncio
import json
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional

# Import tracking components
from hyperliquid_monitor_plus.tracking import (
    AdvancedWhaleBot, CrossChainTracker, AdvancedAlertSystem, MLModelManager
)

# Set up logging
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)


class AdvancedTrackingDemo:
    """Demonstration of advanced whale tracking system"""
    
    def __init__(self):
        self.cross_chain_tracker = CrossChainTracker()
        self.advanced_alerts = AdvancedAlertSystem()
        self.ml_models = MLModelManager()
    
    async def cross_chain_tracking_demo(self):
        """Demonstrate cross-chain whale tracking"""
        print("⛓️  Cross-Chain Whale Tracking Demo")
        print("=" * 50)
        
        # Sample cross-chain whale activity
        cross_chain_activity = [
            {
                "address": "0x010461C14e146ac35Fe42271BDC1134EE31C703a",
                "chains": ["Ethereum", "Arbitrum", "Polygon"],
                "tokens": ["ETH", "USDC", "WBTC"],
                "total_value_usd": 15000000,
                "activity_score": 0.89
            },
            {
                "address": "0x8eb8a3b98659Cce290402893d0123abb75E3ab28",
                "chains": ["Solana", "Ethereum"],
                "tokens": ["SOL", "USDC"],
                "total_value_usd": 8500000,
                "activity_score": 0.72
            },
            {
                "address": "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
                "chains": ["BSC", "Polygon", "Avalanche"],
                "tokens": ["BNB", "MATIC", "AVAX"],
                "total_value_usd": 4200000,
                "activity_score": 0.65
            }
        ]
        
        print("🔍 Cross-Chain Analysis:")
        
        for activity in cross_chain_activity:
            tracking_result = self.cross_chain_tracker.track_whale_across_chains(
                address=activity["address"],
                chains=activity["chains"],
                tokens=activity["tokens"],
                total_value=activity["total_value_usd"]
            )
            
            print(f"\n   🐋 Address: {activity['address'][:16]}...")
            print(f"      💰 Total Value: ${activity['total_value_usd']:,.2f}")
            print(f"      ⛓️  Chains: {', '.join(activity['chains'])}")
            print(f"      🪙 Tokens: {', '.join(activity['tokens'])}")
            print(f"      📊 Activity Score: {activity['activity_score']:.2f}")
            print(f"      🎯 Cross-Chain Status: {tracking_result.status}")
            
            # Risk assessment
            if activity['total_value_usd'] > 10000000:
                risk_level = "🔴 HIGH RISK"
            elif activity['total_value_usd'] > 5000000:
                risk_level = "🟡 MEDIUM RISK"
            else:
                risk_level = "🟢 LOW RISK"
            
            print(f"      ⚠️  Risk Level: {risk_level}")
        
        print("✅ Cross-chain tracking completed!")
    
    async def ml_tracking_demo(self):
        """Demonstrate ML-powered tracking"""
        print("\n🤖 ML-Powered Whale Tracking")
        print("=" * 50)
        
        # Sample ML predictions
        ml_predictions = [
            {
                "address": "0x010461C14e146ac35Fe42271BDC1134EE31C703a",
                "next_move_prediction": "Large BUY order",
                "confidence": 0.85,
                "time_horizon": "2-4 hours",
                "expected_coins": ["ETH", "BTC"],
                "estimated_size": 2500000
            },
            {
                "address": "0x8eb8a3b98659Cce290402893d0123abb75E3ab28",
                "next_move_prediction": "Profit taking",
                "confidence": 0.72,
                "time_horizon": "1-2 hours", 
                "expected_coins": ["SOL", "AVAX"],
                "estimated_size": 1800000
            },
            {
                "address": "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
                "next_move_prediction": "Position rebalancing",
                "confidence": 0.68,
                "time_horizon": "6-12 hours",
                "expected_coins": ["BNB", "MATIC"],
                "estimated_size": 950000
            }
        ]
        
        print("🧠 ML Model Predictions:")
        
        for prediction in ml_predictions:
            ml_result = self.ml_models.predict_whale_behavior(
                address=prediction["address"],
                historical_data=self._generate_sample_history(prediction["address"]),
                market_context=self._generate_market_context()
            )
            
            # Action recommendation
            if prediction["confidence"] >= 0.8:
                action = "🟢 FOLLOW AGGRESSIVELY"
            elif prediction["confidence"] >= 0.6:
                action = "🟡 FOLLOW CAUTIOUSLY"
            else:
                action = "⚪ MONITOR ONLY"
            
            print(f"\n   🤖 Whale: {prediction['address'][:16]}...")
            print(f"      🔮 Prediction: {prediction['next_move_prediction']}")
            print(f"      🎯 Confidence: {prediction['confidence']:.2f} | Action: {action}")
            print(f"      ⏰ Time Horizon: {prediction['time_horizon']}")
            print(f"      🪙 Expected Coins: {', '.join(prediction['expected_coins'])}")
            print(f"      💰 Estimated Size: ${prediction['estimated_size']:,.2f}")
            print(f"      📊 Model Accuracy: {ml_result.model_accuracy:.2f}")
        
        print("✅ ML tracking completed!")
    
    async def advanced_alerts_demo(self):
        """Demonstrate advanced alert system"""
        print("\n🚨 Advanced Alert System Demo")
        print("=" * 50)
        
        # Sample alert scenarios
        alert_scenarios = [
            {
                "alert_type": "Large Position Entry",
                "priority": "HIGH",
                "whale_address": "0x010461C14e146ac35Fe42271BDC1134EE31C703a",
                "details": {"coin": "ETH", "size": 1500, "price": 2450},
                "channels": ["Telegram", "Discord", "Dashboard"]
            },
            {
                "alert_type": "Cross-Chain Movement",
                "priority": "MEDIUM",
                "whale_address": "0x8eb8a3b98659Cce290402893d0123abb75E3ab28",
                "details": {"from_chain": "Solana", "to_chain": "Ethereum", "value": 2500000},
                "channels": ["Telegram", "Email"]
            },
            {
                "alert_type": "ML Pattern Detected",
                "priority": "HIGH",
                "whale_address": "0xd8dA6BF26964aF9D7eEd9e03E53415D37aA96045",
                "details": {"pattern": "Accumulation", "confidence": 0.87, "coins": ["BNB", "MATIC"]},
                "channels": ["Discord", "Dashboard"]
            },
            {
                "alert_type": "Unusual Activity",
                "priority": "CRITICAL",
                "whale_address": "0x00000000219ab540356cbb839cbe05303d7705fa",
                "details": {"activity": "30x normal volume", "coins": ["BTC", "ETH"]},
                "channels": ["All"]
            }
        ]
        
        print("📱 Alert Scenarios:")
        
        for scenario in alert_scenarios:
            alert = self.advanced_alerts.create_advanced_alert(
                alert_type=scenario["alert_type"],
                priority=scenario["priority"],
                whale_address=scenario["whale_address"],
                details=scenario["details"],
                channels=scenario["channels"]
            )
            
            # Priority styling
            priority_icons = {"CRITICAL": "🔴", "HIGH": "🟠", "MEDIUM": "🟡", "LOW": "🟢"}
            icon = priority_icons.get(scenario["priority"], "⚪")
            
            print(f"\n   {icon} {scenario['alert_type']} | Priority: {scenario['priority']}")
            print(f"      🐋 Whale: {scenario['whale_address'][:16]}...")
            print(f"      📊 Details: {json.dumps(scenario['details'], indent=10)}")
            print(f"      📱 Channels: {', '.join(scenario['channels'])}")
            print(f"      ⏰ Alert ID: {alert.id}")
            print(f"      📧 Status: {alert.status}")
        
        print("✅ Advanced alerts completed!")
    
    async def position_tracking_demo(self):
        """Demonstrate real-time position tracking"""
        print("\n💼 Real-time Position Tracking")
        print("=" * 50)
        
        # Sample position data
        positions = [
            {
                "address": "0x010461C14e146ac35Fe42271BDC1134EE31C703a",
                "positions": [
                    {"coin": "ETH", "size": 2500.5, "entry_price": 2200, "current_price": 2450, "pnl": 625125},
                    {"coin": "BTC", "size": 50.2, "entry_price": 42000, "current_price": 45200, "pnl": 160640},
                    {"coin": "USDC", "size": 1500000, "entry_price": 1.0, "current_price": 1.0, "pnl": 0}
                ],
                "total_pnl": 785765,
                "success_rate": 0.78
            },
            {
                "address": "0x8eb8a3b98659Cce290402893d0123abb75E3ab28",
                "positions": [
                    {"coin": "SOL", "size": 50000, "entry_price": 85, "current_price": 95, "pnl": 500000},
                    {"coin": "AVAX", "size": 25000, "entry_price": 35, "current_price": 42, "pnl": 175000}
                ],
                "total_pnl": 675000,
                "success_rate": 0.65
            }
        ]
        
        print("💰 Position Tracking:")
        
        for whale in positions:
            print(f"\n   🐋 Whale: {whale['address'][:16]}...")
            print(f"      💎 Success Rate: {whale['success_rate']:.2f}")
            
            total_pnl = 0
            for position in whale['positions']:
                pnl_percent = (position['pnl'] / (position['size'] * position['entry_price'])) * 100
                total_pnl += position['pnl']
                
                status = "🟢 PROFIT" if position['pnl'] > 0 else "🔴 LOSS"
                
                print(f"      📊 {position['coin']:6}: {position['size']:>10.2f} @ ${position['current_price']:>8,.2f} "
                      f"| P&L: ${position['pnl']:>9,.2f} ({pnl_percent:+.1f}%) {status}")
            
            overall_status = "🟢 WINNING" if total_pnl > 0 else "🔴 LOSING"
            print(f"      🎯 Total P&L: ${total_pnl:,.2f} {overall_status}")
        
        print("✅ Position tracking completed!")
    
    async def tracking_dashboard_demo(self):
        """Demonstrate tracking dashboard"""
        print("\n📊 Advanced Tracking Dashboard")
        print("=" * 50)
        
        # Simulate real-time dashboard
        dashboard_data = {
            "active_whales": 15,
            "total_tracked_value": 250_000_000,
            "alerts_last_hour": 8,
            "ml_predictions": 12,
            "top_performers": [
                {"address": "0x010461C14e146ac35Fe42271BDC1134EE31C703a", "pnl": 785765, "win_rate": 0.78},
                {"address": "0x8eb8a3b98659Cce290402893d0123abb75E3ab28", "pnl": 675000, "win_rate": 0.65}
            ],
            "cross_chain_activity": 7,
            "sentiment_score": 0.72
        }
        
        print("📈 REAL-TIME DASHBOARD")
        print(f"👁️  Active Whales: {dashboard_data['active_whales']}")
        print(f"💰 Total Tracked Value: ${dashboard_data['total_tracked_value']:,.2f}")
        print(f"🚨 Alerts (Last Hour): {dashboard_data['alerts_last_hour']}")
        print(f"🤖 ML Predictions: {dashboard_data['ml_predictions']}")
        print(f"⛓️  Cross-Chain Activity: {dashboard_data['cross_chain_activity']}")
        print(f"😊 Market Sentiment: {dashboard_data['sentiment_score']:.2f}")
        
        print(f"\n🏆 TOP PERFORMERS:")
        for i, performer in enumerate(dashboard_data['top_performers'], 1):
            print(f"   {i}. {performer['address'][:16]}... | "
                  f"${performer['pnl']:,.2f} | {performer['win_rate']:.2f} win rate")
        
        print("✅ Tracking dashboard completed!")
    
    def _generate_sample_history(self, address: str) -> Dict:
        """Generate sample historical data for ML"""
        return {
            "trades_30d": 45,
            "avg_trade_size": 500000,
            "success_rate": 0.72,
            "preferred_coins": ["ETH", "BTC", "SOL"],
            "trading_patterns": ["accumulation", "momentum"]
        }
    
    def _generate_market_context(self) -> Dict:
        """Generate sample market context"""
        return {
            "market_sentiment": 0.68,
            "volatility_index": 0.25,
            "liquidity_score": 0.82,
            "trend_direction": "bullish"
        }
    
    async def run_all_demos(self):
        """Run all tracking demonstrations"""
        print("🎯 HYPERLIQUID MONITOR PLUS - TRACKING MODULE EXAMPLES")
        print("=" * 70)
        
        await self.cross_chain_tracking_demo()
        await self.ml_tracking_demo()
        await self.advanced_alerts_demo()
        await self.position_tracking_demo()
        await self.tracking_dashboard_demo()
        
        print("\n" + "=" * 70)
        print("🎉 All tracking demos completed successfully!")
        print("💡 Advanced tracking capabilities demonstrated:")
        print("   ⛓️  Cross-chain whale tracking")
        print("   🤖 ML-powered behavior prediction")
        print("   🚨 Advanced alert system")
        print("   💼 Real-time position tracking")
        print("   📊 Comprehensive tracking dashboard")
        print("   🎯 Phase 3 advanced features")


async def main():
    """Main demonstration function"""
    
    # Run tracking demos
    tracking_demo = AdvancedTrackingDemo()
    await tracking_demo.run_all_demos()


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