"""
Crypto Anomaly Detection Engine System (CADES)
Memecoin Detector Module

This module implements detection and analysis of memecoin characteristics
on the Solana blockchain, focusing on identifying patterns specific to
meme tokens and social token dynamics.

Author: CADES Team
License: Proprietary
"""

import asyncio
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Set, Tuple
import logging
from collections import defaultdict

from solana.rpc.async_api import AsyncClient
from solders.pubkey import Pubkey

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

@dataclass
class MemeTokenMetrics:
    """Core metrics for memecoin analysis"""
    token_address: str
    token_name: str
    token_symbol: str
    total_supply: int
    holder_count: int
    launch_date: datetime
    initial_liquidity_usd: float
    current_liquidity_usd: float
    price_change_since_launch: float
    volume_profile: Dict[str, float]
    social_signals: Dict[str, float]
    risk_indicators: Dict[str, float]

@dataclass
class MemeTokenPattern:
    """Detected memecoin pattern"""
    pattern_type: str
    confidence: float
    indicators: Dict[str, float]
    risk_level: str
    timestamp: datetime
    details: Dict[str, any]

class MemecoinDetector:
    """
    Advanced memecoin detection and analysis system.
    Identifies token characteristics and patterns typical of memecoins.
    """

    def __init__(
        self,
        rpc_client: AsyncClient,
        min_liquidity_usd: float = 10000,  # Minimum liquidity to analyze
        social_signal_threshold: float = 0.6,  # Minimum social signal strength
        analysis_window: int = 7 * 24 * 3600,  # 7 days in seconds
        update_interval: int = 300  # 5 minutes
    ):
        """
        Initialize the memecoin detector.
        
        Args:
            rpc_client: Solana RPC client
            min_liquidity_usd: Minimum USD liquidity for analysis
            social_signal_threshold: Minimum social signal threshold
            analysis_window: Time window for analysis in seconds
            update_interval: Update interval in seconds
        """
        self.rpc_client = rpc_client
        self.min_liquidity_usd = min_liquidity_usd
        self.social_signal_threshold = social_signal_threshold
        self.analysis_window = analysis_window
        self.update_interval = update_interval

        # Data structures
        self.token_metrics: Dict[str, MemeTokenMetrics] = {}
        self.detected_patterns: Dict[str, List[MemeTokenPattern]] = defaultdict(list)
        self.token_metadata: Dict[str, Dict] = {}
        
        # Pattern detection thresholds
        self.thresholds = {
            'pump_and_dump': {
                'price_increase': 2.0,  # 200% increase
                'time_window': 3600,    # 1 hour
                'volume_spike': 3.0     # 3x normal volume
            },
            'social_manipulation': {
                'signal_strength': 0.8,
                'coordination': 0.7,
                'bot_activity': 0.6
            },
            'liquidity_risk': {
                'concentration': 0.8,    # 80% in single pool
                'depth_ratio': 0.2,     # Shallow liquidity
                'removal_speed': 0.5     # Fast removals
            }
        }

    async def analyze_token(self, token_address: str) -> Optional[Dict]:
        """
        Analyze a token for memecoin characteristics.
        
        Args:
            token_address: Token address to analyze
            
        Returns:
            Analysis results if token qualifies as memecoin
        """
        try:
            # Get token metadata
            metadata = await self._get_token_metadata(token_address)
            if not metadata:
                return None

            # Check minimum liquidity
            liquidity = await self._get_token_liquidity(token_address)
            if liquidity < self.min_liquidity_usd:
                return None

            # Calculate core metrics
            metrics = await self._calculate_token_metrics(token_address, metadata)
            self.token_metrics[token_address] = metrics

            # Detect patterns
            patterns = await self._detect_patterns(token_address)

            # Generate analysis
            analysis = {
                "token_info": {
                    "address": token_address,
                    "name": metrics.token_name,
                    "symbol": metrics.token_symbol,
                    "launch_date": metrics.launch_date.isoformat()
                },
                "metrics": {
                    "holder_count": metrics.holder_count,
                    "total_supply": metrics.total_supply,
                    "liquidity_usd": metrics.current_liquidity_usd,
                    "price_change": metrics.price_change_since_launch
                },
                "patterns": [pattern.__dict__ for pattern in patterns],
                "risk_assessment": self._assess_risk(metrics, patterns)
            }

            return analysis

        except Exception as e:
            logger.error(f"Error analyzing token {token_address}: {e}")
            return None

    async def _get_token_metadata(self, token_address: str) -> Optional[Dict]:
        """Fetch token metadata from chain."""
        try:
            # Get token account info
            account = await self.rpc_client.get_account_info(
                Pubkey.from_string(token_address)
            )
            if not account.value:
                return None

            # Parse metadata
            # Implementation depends on token program structure
            return {}

        except Exception as e:
            logger.error(f"Error fetching token metadata: {e}")
            return None

    async def _get_token_liquidity(self, token_address: str) -> float:
        """Calculate total token liquidity across pools."""
        try:
            # Get liquidity pool info
            # Implementation depends on DEX structure
            return 0.0

        except Exception as e:
            logger.error(f"Error calculating liquidity: {e}")
            return 0.0

    async def _calculate_token_metrics(
        self,
        token_address: str,
        metadata: Dict
    ) -> MemeTokenMetrics:
        """Calculate comprehensive token metrics."""
        try:
            # Get holder data
            holders = await self._get_token_holders(token_address)

            # Get volume profile
            volume = await self._get_volume_profile(token_address)

            # Get social signals
            social = await self._get_social_signals(token_address)

            # Calculate risk indicators
            risk = self._calculate_risk_indicators(holders, volume, social)

            return MemeTokenMetrics(
                token_address=token_address,
                token_name=metadata.get('name', ''),
                token_symbol=metadata.get('symbol', ''),
                total_supply=metadata.get('supply', 0),
                holder_count=len(holders),
                launch_date=await self._get_launch_date(token_address),
                initial_liquidity_usd=await self._get_initial_liquidity(token_address),
                current_liquidity_usd=await self._get_token_liquidity(token_address),
                price_change_since_launch=await self._calculate_price_change(token_address),
                volume_profile=volume,
                social_signals=social,
                risk_indicators=risk
            )

        except Exception as e:
            logger.error(f"Error calculating token metrics: {e}")
            raise

    async def _detect_patterns(
        self,
        token_address: str
    ) -> List[MemeTokenPattern]:
        """Detect memecoin-specific patterns."""
        try:
            patterns = []
            metrics = self.token_metrics[token_address]

            # Check pump and dump pattern
            if await self._detect_pump_and_dump(token_address):
                patterns.append(self._create_pump_pattern(metrics))

            # Check social manipulation
            if await self._detect_social_manipulation(token_address):
                patterns.append(self._create_social_pattern(metrics))

            # Check liquidity risks
            if await self._detect_liquidity_risks(token_address):
                patterns.append(self._create_liquidity_pattern(metrics))

            # Store patterns
            self.detected_patterns[token_address].extend(patterns)

            return patterns

        except Exception as e:
            logger.error(f"Error detecting patterns: {e}")
            return []

    async def _detect_pump_and_dump(self, token_address: str) -> bool:
        """Detect pump and dump patterns."""
        try:
            thresholds = self.thresholds['pump_and_dump']

            # Get recent price data
            prices = await self._get_price_history(token_address)
            if not prices:
                return False

            # Check for rapid price increase
            max_increase = max([
                (p2 / p1 - 1)
                for p1, p2 in zip(prices[:-1], prices[1:])
            ])

            if max_increase > thresholds['price_increase']:
                # Verify volume spike
                volume_multiple = await self._calculate_volume_multiple(token_address)
                if volume_multiple > thresholds['volume_spike']:
                    return True

            return False

        except Exception as e:
            logger.error(f"Error detecting pump and dump: {e}")
            return False

    async def _detect_social_manipulation(self, token_address: str) -> bool:
        """Detect social media manipulation patterns."""
        try:
            thresholds = self.thresholds['social_manipulation']
            metrics = self.token_metrics[token_address]

            # Check social signal strength
            if metrics.social_signals.get('strength', 0) > thresholds['signal_strength']:
                # Check coordination indicators
                coordination = await self._analyze_social_coordination(token_address)
                if coordination > thresholds['coordination']:
                    # Check bot activity
                    bot_activity = await self._detect_bot_activity(token_address)
                    if bot_activity > thresholds['bot_activity']:
                        return True

            return False

        except Exception as e:
            logger.error(f"Error detecting social manipulation: {e}")
            return False

    async def _detect_liquidity_risks(self, token_address: str) -> bool:
        """Detect liquidity-related risks."""
        try:
            thresholds = self.thresholds['liquidity_risk']

            # Check liquidity concentration
            concentration = await self._calculate_liquidity_concentration(token_address)
            if concentration > thresholds['concentration']:
                # Check liquidity depth
                depth_ratio = await self._calculate_depth_ratio(token_address)
                if depth_ratio < thresholds['depth_ratio']:
                    # Check recent removals
                    removal_speed = await self._calculate_removal_speed(token_address)
                    if removal_speed > thresholds['removal_speed']:
                        return True

            return False

        except Exception as e:
            logger.error(f"Error detecting liquidity risks: {e}")
            return False

    def _assess_risk(
        self,
        metrics: MemeTokenMetrics,
        patterns: List[MemeTokenPattern]
    ) -> Dict:
        """Generate comprehensive risk assessment."""
        try:
            # Calculate risk scores
            liquidity_risk = self._calculate_liquidity_risk(metrics)
            social_risk = self._calculate_social_risk(metrics)
            pattern_risk = self._calculate_pattern_risk(patterns)

            # Combine risk scores
            total_risk = (
                liquidity_risk * 0.4 +
                social_risk * 0.3 +
                pattern_risk * 0.3
            )

            return {
                "risk_level": self._get_risk_level(total_risk),
                "risk_scores": {
                    "liquidity_risk": liquidity_risk,
                    "social_risk": social_risk,
                    "pattern_risk": pattern_risk,
                    "total_risk": total_risk
                },
                "warning_signals": self._generate_warnings(metrics, patterns)
            }

        except Exception as e:
            logger.error(f"Error assessing risk: {e}")
            return {}

    @staticmethod
    def _get_risk_level(risk_score: float) -> str:
        """Convert risk score to risk level."""
        if risk_score >= 0.8:
            return "CRITICAL"
        elif risk_score >= 0.6:
            return "HIGH"
        elif risk_score >= 0.4:
            return "MEDIUM"
        return "LOW"

    def _generate_warnings(
        self,
        metrics: MemeTokenMetrics,
        patterns: List[MemeTokenPattern]
    ) -> List[str]:
        """Generate warning signals based on analysis."""
        warnings = []
        try:
            # Liquidity warnings
            if metrics.current_liquidity_usd < self.min_liquidity_usd * 2:
                warnings.append("LOW_LIQUIDITY")
            
            liquidity_change = (
                metrics.current_liquidity_usd / metrics.initial_liquidity_usd
                if metrics.initial_liquidity_usd > 0 else 0
            )
            if liquidity_change < 0.5:
                warnings.append("SIGNIFICANT_LIQUIDITY_LOSS")

            # Pattern-based warnings
            for pattern in patterns:
                if pattern.pattern_type == "pump_and_dump":
                    warnings.append("PUMP_AND_DUMP_RISK")
                elif pattern.pattern_type == "social_manipulation":
                    warnings.append("SOCIAL_MANIPULATION_RISK")
                elif pattern.pattern_type == "liquidity_risk":
                    warnings.append("LIQUIDITY_MANIPULATION_RISK")

            # Social signal warnings
            if metrics.social_signals.get('bot_activity', 0) > 0.7:
                warnings.append("HIGH_BOT_ACTIVITY")
            if metrics.social_signals.get('coordination', 0) > 0.8:
                warnings.append("COORDINATED_ACTIVITY")

            return warnings

        except Exception as e:
            logger.error(f"Error generating warnings: {e}")
            return []

if __name__ == "__main__":
    async def main():
        # Initialize RPC client
        client = AsyncClient("https://api.mainnet-beta.solana.com")

        # Create detector instance
        detector = MemecoinDetector(
            rpc_client=client,
            min_liquidity_usd=10000,
            social_signal_threshold=0.6
        )

        # Example token analysis
        token_address = "ExampleToken123"
        analysis = await detector.analyze_token(token_address)

        if analysis:
            print("\nToken Analysis:")
            print(f"Name: {analysis['token_info']['name']}")
            print(f"Symbol: {analysis['token_info']['symbol']}")
            print(f"Risk Level: {analysis['risk_assessment']['risk_level']}")
            print("\nWarning Signals:")
            for warning in analysis['risk_assessment']['warning_signals']:
                print(f"- {warning}")
            
            print("\nDetected Patterns:")
            for pattern in analysis['patterns']:
                print(f"- {pattern['pattern_type']} "
                      f"(Confidence: {pattern['confidence']:.2f})")
            
            print("\nRisk Scores:")
            risk_scores = analysis['risk_assessment']['risk_scores']
            for score_type, value in risk_scores.items():
                print(f"- {score_type}: {value:.2f}")

    import asyncio
    asyncio.run(main())