""" Crypto Anomaly Detection Engine System (CADES)

Whale Tracker Module
This module monitors and analyzes large wallet movements on Solana,
identifying whale activity patterns and potential market impacts.

Author: CADES Team
License: Proprietary
"""

import asyncio
import logging
from dataclasses import dataclass
from typing import Dict, List, Optional, Set
from datetime import datetime, timedelta

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

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class WhaleMovement:
    """Data structure for whale transaction movements"""
    wallet_address: str
    transaction_signature: str
    token_address: str
    amount: float
    usd_value: float
    movement_type: str  # 'accumulate', 'distribute', 'transfer'
    timestamp: datetime
    related_transactions: List[str]
    impact_score: float

@dataclass
class WhaleProfile:
    """Data structure for whale wallet profiles"""
    wallet_address: str
    total_holdings_usd: float
    tokens_held: Dict[str, float]
    average_transaction_size: float
    activity_score: float
    influence_rating: float
    last_active: datetime
    known_associates: Set[str]
    movement_pattern: str

class WhaleTracker:
    """Tracks and analyzes whale wallet activity"""
    
    def __init__(
        self,
        rpc_url: str,
        min_whale_threshold_usd: float = 100000,
        track_window: timedelta = timedelta(days=30)
    ):
        """Initialize the whale tracker.
        
        Args:
            rpc_url: Solana RPC endpoint URL
            min_whale_threshold_usd: Minimum USD value to classify as whale
            track_window: Time window for tracking whale activity
        """
        self.client = AsyncClient(rpc_url)
        self.min_whale_threshold_usd = min_whale_threshold_usd
        self.track_window = track_window
        self.whale_profiles: Dict[str, WhaleProfile] = {}
        self.recent_movements: List[WhaleMovement] = []
        
    async def track_wallet(
        self,
        wallet_address: str,
        token_address: Optional[str] = None
    ) -> Optional[WhaleProfile]:
        """Start tracking a potential whale wallet.
        
        Args:
            wallet_address: Wallet address to track
            token_address: Optional specific token to track
            
        Returns:
            WhaleProfile if wallet qualifies as whale, None otherwise
        """
        try:
            holdings = await self._calculate_holdings(wallet_address, token_address)
            if holdings < self.min_whale_threshold_usd:
                return None
                
            profile = await self._create_whale_profile(wallet_address, token_address)
            self.whale_profiles[wallet_address] = profile
            return profile
            
        except Exception as e:
            logger.error(f"Error tracking wallet {wallet_address}: {e}")
            return None
            
    async def analyze_movement(
        self,
        movement: WhaleMovement
    ) -> Dict:
        """Analyze a whale movement and its potential market impact.
        
        Args:
            movement: WhaleMovement to analyze
            
        Returns:
            Dict containing analysis results
        """
        try:
            market_impact = await self._calculate_market_impact(movement)
            pattern_match = await self._match_movement_pattern(movement)
            network_effect = await self._analyze_network_effect(movement)
            
            return {
                "market_impact": market_impact,
                "pattern_match": pattern_match,
                "network_effect": network_effect,
                "total_impact_score": (
                    market_impact * 0.4 +
                    pattern_match * 0.3 +
                    network_effect * 0.3
                )
            }
            
        except Exception as e:
            logger.error(f"Error analyzing movement: {e}")
            return {}
            
    async def get_active_whales(
        self,
        token_address: Optional[str] = None,
        min_activity_score: float = 0.5
    ) -> List[WhaleProfile]:
        """Get list of currently active whale wallets.
        
        Args:
            token_address: Optional specific token to filter by
            min_activity_score: Minimum activity score threshold
            
        Returns:
            List of active WhaleProfiles
        """
        active_whales = []
        
        for profile in self.whale_profiles.values():
            if profile.activity_score >= min_activity_score:
                if token_address:
                    if token_address in profile.tokens_held:
                        active_whales.append(profile)
                else:
                    active_whales.append(profile)
                    
        return sorted(
            active_whales,
            key=lambda x: x.influence_rating,
            reverse=True
        )
        
    async def _calculate_holdings(
        self,
        wallet_address: str,
        token_address: Optional[str]
    ) -> float:
        """Calculate total holdings value for a wallet.
        
        Args:
            wallet_address: Wallet address to calculate
            token_address: Optional specific token to calculate
            
        Returns:
            Total holdings value in USD
        """
        try:
            account = await self.client.get_account_info(
                Pubkey.from_string(wallet_address)
            )
            if not account.value:
                return 0.0
                
            # Calculate holdings (implementation depends on token program)
            # This is a placeholder for the actual calculation logic
            return 0.0
            
        except Exception as e:
            logger.error(f"Error calculating holdings for {wallet_address}: {e}")
            return 0.0
            
    async def _create_whale_profile(
        self,
        wallet_address: str,
        token_address: Optional[str]
    ) -> WhaleProfile:
        """Create a new whale profile with historical analysis.
        
        Args:
            wallet_address: Wallet address to profile
            token_address: Optional specific token to focus on
            
        Returns:
            Newly created WhaleProfile
        """
        # Implementation for creating whale profile
        pass
        
    async def _calculate_market_impact(
        self,
        movement: WhaleMovement
    ) -> float:
        """Calculate potential market impact of a whale movement.
        
        Args:
            movement: WhaleMovement to analyze
            
        Returns:
            Impact score between 0 and 1
        """
        # Implementation for market impact calculation
        pass
        
    async def _match_movement_pattern(
        self,
        movement: WhaleMovement
    ) -> float:
        """Match movement against known whale patterns.
        
        Args:
            movement: WhaleMovement to analyze
            
        Returns:
            Pattern match score between 0 and 1
        """
        # Implementation for pattern matching
        pass
        
async def _analyze_network_effect(
        self,
        movement: WhaleMovement
    ) -> float:
        """Analyze the network effect of a whale movement.
        
        Args:
            movement: WhaleMovement to analyze
            
        Returns:
            Network effect score between 0 and 1
        """
        try:
            # Get related wallets
            related_wallets = await self._get_related_wallets(movement.wallet_address)
            
            # Calculate network density
            network_size = len(related_wallets)
            if network_size == 0:
                return 0.0
            
            # Calculate activity synchronization
            sync_score = await self._calculate_synchronization(
                movement,
                related_wallets
            )
            
            # Calculate influence spread
            influence_score = await self._calculate_influence_spread(
                movement.wallet_address,
                related_wallets
            )
            
            # Combine scores
            return min(1.0, (
                sync_score * 0.4 +
                influence_score * 0.4 +
                min(1.0, network_size / 10) * 0.2
            ))
            
        except Exception as e:
            logger.error(f"Error analyzing network effect: {e}")
            return 0.0

    async def _calculate_holdings(
        self,
        wallet_address: str,
        token_address: Optional[str]
    ) -> float:
        """Calculate total holdings value for a wallet."""
        try:
            # Get account info
            account = await self.client.get_account_info(
                Pubkey.from_string(wallet_address)
            )
            if not account.value:
                return 0.0

            # Get token accounts
            token_accounts = await self.client.get_token_accounts_by_owner(
                Pubkey.from_string(wallet_address),
                {"programId": Pubkey.from_string("TokenkegQfeZyiNwAJbNbGKPFXCWuBvf9Ss623VQ5DA")}
            )

            total_value = 0.0
            for account in token_accounts.value:
                balance = account.account.data.parsed['info']['tokenAmount']['uiAmount']
                mint = account.account.data.parsed['info']['mint']
                
                if token_address and mint != token_address:
                    continue
                    
                # Get token price (implement price fetching logic)
                price = await self._get_token_price(mint)
                total_value += balance * price

            return total_value

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

    async def _create_whale_profile(
        self,
        wallet_address: str,
        token_address: Optional[str]
    ) -> WhaleProfile:
        """Create a new whale profile with historical analysis."""
        try:
            # Get historical transactions
            transactions = await self._get_historical_transactions(
                wallet_address,
                self.track_window
            )

            # Calculate metrics
            total_holdings = await self._calculate_holdings(wallet_address, token_address)
            tokens_held = await self._get_token_holdings(wallet_address)
            avg_tx_size = self._calculate_average_transaction_size(transactions)
            activity_score = self._calculate_activity_score(transactions)
            influence = await self._calculate_influence_rating(
                wallet_address,
                transactions
            )
            
            # Get known associates
            associates = await self._get_related_wallets(wallet_address)
            
            # Analyze movement patterns
            pattern = self._analyze_movement_patterns(transactions)

            return WhaleProfile(
                wallet_address=wallet_address,
                total_holdings_usd=total_holdings,
                tokens_held=tokens_held,
                average_transaction_size=avg_tx_size,
                activity_score=activity_score,
                influence_rating=influence,
                last_active=transactions[-1]['timestamp'] if transactions else datetime.now(),
                known_associates=associates,
                movement_pattern=pattern
            )

        except Exception as e:
            logger.error(f"Error creating whale profile: {e}")
            raise

    async def _get_historical_transactions(
        self,
        wallet_address: str,
        time_window: timedelta
    ) -> List[Dict]:
        """Get historical transactions for a wallet."""
        try:
            signatures = await self.client.get_signatures_for_address(
                Pubkey.from_string(wallet_address),
                limit=1000
            )
            
            transactions = []
            for sig_info in signatures.value:
                if (datetime.now() - sig_info.block_time) > time_window:
                    break
                    
                tx = await self.client.get_transaction(
                    sig_info.signature,
                    encoding="jsonParsed"
                )
                if tx:
                    transactions.append(tx)
                    
            return transactions
            
        except Exception as e:
            logger.error(f"Error fetching historical transactions: {e}")
            return []

    async def _calculate_market_impact(
        self,
        movement: WhaleMovement
    ) -> float:
        """Calculate potential market impact of a whale movement."""
        try:
            # Get token liquidity
            liquidity = await self._get_token_liquidity(movement.token_address)
            if liquidity == 0:
                return 1.0
            
            # Calculate impact ratio
            impact_ratio = movement.usd_value / liquidity
            
            # Calculate price impact using square root formula
            # This better models market impact than linear relationship
            price_impact = min(1.0, np.sqrt(impact_ratio))
            
            # Adjust for movement type
            type_multiplier = {
                'accumulate': 0.8,  # Accumulation usually has less impact
                'distribute': 1.2,  # Distribution typically has more impact
                'transfer': 1.0     # Neutral impact for transfers
            }.get(movement.movement_type, 1.0)
            
            return min(1.0, price_impact * type_multiplier)
            
        except Exception as e:
            logger.error(f"Error calculating market impact: {e}")
            return 0.0

    async def _match_movement_pattern(
        self,
        movement: WhaleMovement
    ) -> float:
        """Match movement against known whale patterns."""
        try:
            # Get recent movements
            recent = [m for m in self.recent_movements
                     if (movement.timestamp - m.timestamp).total_seconds() < 3600]
            
            # Pattern matching scores
            pattern_scores = []
            
            # Check for accumulation pattern
            if movement.movement_type == 'accumulate':
                acc_score = self._match_accumulation_pattern(movement, recent)
                pattern_scores.append(acc_score)
            
            # Check for distribution pattern
            if movement.movement_type == 'distribute':
                dist_score = self._match_distribution_pattern(movement, recent)
                pattern_scores.append(dist_score)
            
            # Check for wash trading pattern
            wash_score = self._match_wash_trading_pattern(movement, recent)
            pattern_scores.append(wash_score)
            
            return max(pattern_scores) if pattern_scores else 0.0
            
        except Exception as e:
            logger.error(f"Error matching movement pattern: {e}")
            return 0.0

    def _match_accumulation_pattern(
        self,
        movement: WhaleMovement,
        recent_movements: List[WhaleMovement]
    ) -> float:
        """Match against accumulation pattern."""
        try:
            if not recent_movements:
                return 0.0
                
            # Look for steady buying pattern
            buy_movements = [m for m in recent_movements 
                           if m.movement_type == 'accumulate']
            
            if len(buy_movements) < 3:
                return 0.0
                
            # Calculate buy size consistency
            sizes = [m.amount for m in buy_movements]
            size_variance = np.std(sizes) / np.mean(sizes)
            
            # Calculate time regularity
            times = [m.timestamp.timestamp() for m in buy_movements]
            time_diffs = np.diff(times)
            time_variance = np.std(time_diffs) / np.mean(time_diffs)
            
            # Score based on consistency
            consistency_score = 1 - min(1.0, (size_variance + time_variance) / 2)
            
            return consistency_score
            
        except Exception as e:
            logger.error(f"Error matching accumulation pattern: {e}")
            return 0.0

    async def _calculate_synchronization(
        self,
        movement: WhaleMovement,
        related_wallets: Set[str]
    ) -> float:
        """Calculate activity synchronization with related wallets."""
        try:
            if not related_wallets:
                return 0.0
                
            # Get recent transactions for related wallets
            sync_window = timedelta(hours=1)
            synced_movements = 0
            
            for wallet in related_wallets:
                recent_txs = await self._get_historical_transactions(
                    wallet,
                    sync_window
                )
                
                # Check for similar movements
                for tx in recent_txs:
                    if self._is_similar_movement(movement, tx):
                        synced_movements += 1
                        break
            
            return min(1.0, synced_movements / len(related_wallets))
            
        except Exception as e:
            logger.error(f"Error calculating synchronization: {e}")
            return 0.0

    async def _calculate_influence_spread(
        self,
        wallet_address: str,
        related_wallets: Set[str]
    ) -> float:
        """Calculate influence spread through the network."""
        try:
            if not related_wallets:
                return 0.0
                
            # Calculate average holdings of related wallets
            total_holdings = 0.0
            for wallet in related_wallets:
                holdings = await self._calculate_holdings(wallet, None)
                total_holdings += holdings
            
            avg_holdings = total_holdings / len(related_wallets)
            
            # Calculate influence based on average holdings
            influence = min(1.0, avg_holdings / self.min_whale_threshold_usd)
            
            return influence
            
        except Exception as e:
            logger.error(f"Error calculating influence spread: {e}")
            return 0.0

if __name__ == "__main__":
    # Example usage
    async def main():
        tracker = WhaleTracker("https://api.mainnet-beta.solana.com")
        
        # Track a whale wallet
        profile = await tracker.track_wallet(
            "FZMpxEd4qmDGeGpDit5cQcT5MFRsZyGbEPurhMeHvXh8",  # Example address
            "So11111111111111111111111111111111111111112"    # SOL token
        )
        
        if profile:
            print(f"Whale detected: {profile.wallet_address}")
            print(f"Holdings: ${profile.total_holdings_usd:,.2f}")
            print(f"Influence rating: {profile.influence_rating}")
            print(f"Activity score: {profile.activity_score}")
            print(f"Movement pattern: {profile.movement_pattern}")
            print(f"Known associates: {len(profile.known_associates)}")
            
    asyncio.run(main())
