""" Crypto Anomaly Detection Engine System (CADES)

Accumulation Analyzer Module
This module analyzes token accumulation patterns of whale wallets,
identifying strategic buying behavior and potential market manipulation.

Author: CADES Team
License: Proprietary
"""

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

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

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

@dataclass
class AccumulationPhase:
    """Data structure for token accumulation phases"""
    start_time: datetime
    end_time: Optional[datetime]
    initial_position: float
    current_position: float
    average_buy_price: float
    total_volume: float
    buy_frequency: float
    stealth_score: float
    phase_status: str  # 'active', 'completed', 'distributed'

@dataclass
class AccumulationPattern:
    """Data structure for accumulation patterns"""
    pattern_type: str  # 'stealth', 'aggressive', 'distributed'
    confidence: float
    start_price: float
    current_price: float
    volume_profile: Dict[str, float]
    time_weighted_average_price: float
    price_impact: float
    market_share: float

class AccumulationAnalyzer:
    """Analyzes token accumulation patterns"""
    
    def __init__(
        self,
        rpc_url: str,
        min_phase_duration: timedelta = timedelta(days=1),
        stealth_threshold: float = 0.15
    ):
        """Initialize the accumulation analyzer.
        
        Args:
            rpc_url: Solana RPC endpoint URL
            min_phase_duration: Minimum duration for accumulation phase
            stealth_threshold: Threshold for stealth buying detection
        """
        self.client = AsyncClient(rpc_url)
        self.min_phase_duration = min_phase_duration
        self.stealth_threshold = stealth_threshold
        self.active_phases: Dict[str, AccumulationPhase] = {}
        
    async def analyze_wallet(
        self,
        wallet_address: str,
        token_address: str,
        timeframe: timedelta = timedelta(days=30)
    ) -> Tuple[Optional[AccumulationPattern], List[AccumulationPhase]]:
        """Analyze accumulation pattern for a wallet.
        
        Args:
            wallet_address: Wallet address to analyze
            token_address: Token address to track
            timeframe: Analysis timeframe
            
        Returns:
            Tuple of current pattern and historical phases
        """
        try:
            transactions = await self._fetch_wallet_transactions(
                wallet_address,
                token_address,
                timeframe
            )
            
            phases = await self._identify_phases(transactions)
            pattern = await self._analyze_pattern(phases) if phases else None
            
            return pattern, phases
            
        except Exception as e:
            logger.error(f"Error analyzing wallet {wallet_address}: {e}")
            return None, []
            
    async def detect_stealth_accumulation(
        self,
        wallet_address: str,
        token_address: str
    ) -> Optional[AccumulationPattern]:
        """Detect stealth accumulation behavior.
        
        Args:
            wallet_address: Wallet address to analyze
            token_address: Token address to track
            
        Returns:
            AccumulationPattern if stealth behavior detected, None otherwise
        """
        try:
            pattern, phases = await self.analyze_wallet(
                wallet_address,
                token_address
            )
            
            if pattern and pattern.pattern_type == 'stealth' and pattern.confidence > 0.8:
                return pattern
            return None
            
        except Exception as e:
            logger.error(f"Error detecting stealth accumulation: {e}")
            return None
            
    async def calculate_market_impact(
        self,
        accumulation_pattern: AccumulationPattern,
        token_address: str
    ) -> Dict:
        """Calculate market impact of accumulation pattern.
        
        Args:
            accumulation_pattern: Pattern to analyze
            token_address: Token address
            
        Returns:
            Dict containing impact metrics
        """
        try:
            total_supply = await self._get_token_supply(token_address)
            market_cap = await self._get_market_cap(token_address)
            
            return {
                "price_impact": accumulation_pattern.price_impact,
                "market_share": accumulation_pattern.market_share,
                "supply_share": accumulation_pattern.current_price / total_supply,
                "cap_impact": (accumulation_pattern.total_volume / market_cap),
                "manipulation_risk": await self._calculate_manipulation_risk(
                    accumulation_pattern,
                    market_cap
                )
            }
            
        except Exception as e:
            logger.error(f"Error calculating market impact: {e}")
            return {}
            
    async def _fetch_wallet_transactions(
        self,
        wallet_address: str,
        token_address: str,
        timeframe: timedelta
    ) -> List[Dict]:
        """Fetch relevant wallet transactions.
        
        Args:
            wallet_address: Wallet address to fetch
            token_address: Token address to filter
            timeframe: Time window to fetch
            
        Returns:
            List of transaction data
        """
        try:
            signatures = await self.client.get_signatures_for_address(
                Pubkey.from_string(wallet_address)
            )
            
            transactions = []
            for sig_info in signatures.value:
                if (datetime.now() - sig_info.block_time) > timeframe:
                    break
                    
                tx = await self.client.get_transaction(sig_info.signature)
                if await self._is_token_transaction(tx, token_address):
                    transactions.append(tx)
                    
            return transactions
            
        except Exception as e:
            logger.error(f"Error fetching transactions: {e}")
            return []

    async def _identify_phases(
        self,
        transactions: List[Dict]
    ) -> List[AccumulationPhase]:
        """Identify accumulation phases from transactions.
        
        The method analyzes transaction patterns to identify distinct accumulation phases
        based on buying behavior, volume, and timing.
        
        Args:
            transactions: List of transactions to analyze
            
        Returns:
            List of identified accumulation phases
        """
        phases = []
        current_phase = None
        
        # Sort transactions by timestamp
        sorted_txs = sorted(transactions, key=lambda x: x.block_time)
        
        for tx in sorted_txs:
            transfer_info = await self._extract_transfer_info(tx)
            if not transfer_info:
                continue
                
            amount, price = transfer_info
            
            if current_phase is None:
                # Start new phase
                current_phase = AccumulationPhase(
                    start_time=datetime.fromtimestamp(tx.block_time),
                    end_time=None,
                    initial_position=amount,
                    current_position=amount,
                    average_buy_price=price,
                    total_volume=amount,
                    buy_frequency=1,
                    stealth_score=0.0,
                    phase_status='active'
                )
            else:
                time_diff = datetime.fromtimestamp(tx.block_time) - current_phase.start_time
                
                # Check if this transaction belongs to current phase
                if time_diff <= self.min_phase_duration and self._is_same_pattern(
                    current_phase, amount, price
                ):
                    # Update current phase
                    current_phase.current_position += amount
                    current_phase.total_volume += amount
                    current_phase.average_buy_price = (
                        (current_phase.average_buy_price * current_phase.total_volume + price * amount) /
                        (current_phase.total_volume + amount)
                    )
                    current_phase.buy_frequency = (
                        current_phase.total_volume / time_diff.total_seconds()
                    )
                    current_phase.stealth_score = self._calculate_stealth_score(
                        current_phase, price, amount
                    )
                else:
                    # Close current phase and start new one
                    if current_phase:
                        current_phase.end_time = datetime.fromtimestamp(tx.block_time)
                        current_phase.phase_status = 'completed'
                        phases.append(current_phase)
                        
                    current_phase = AccumulationPhase(
                        start_time=datetime.fromtimestamp(tx.block_time),
                        end_time=None,
                        initial_position=amount,
                        current_position=amount,
                        average_buy_price=price,
                        total_volume=amount,
                        buy_frequency=1,
                        stealth_score=0.0,
                        phase_status='active'
                    )
        
        # Add final phase if exists
        if current_phase:
            current_phase.end_time = datetime.now()
            current_phase.phase_status = 'completed'
            phases.append(current_phase)
            
        return phases
        
    async def _analyze_pattern(
        self,
        phases: List[AccumulationPhase]
    ) -> Optional[AccumulationPattern]:
        """Analyze accumulation pattern from phases.
        
        Identifies the type of accumulation pattern and calculates confidence metrics
        based on historical phases.
        
        Args:
            phases: List of accumulation phases
            
        Returns:
            AccumulationPattern if pattern identified, None otherwise
        """
        if not phases:
            return None
            
        # Calculate pattern metrics
        total_volume = sum(phase.total_volume for phase in phases)
        time_weighted_price = sum(
            phase.average_buy_price * phase.total_volume for phase in phases
        ) / total_volume
        
        # Analyze volume profile
        volume_profile = {
            'large_trades': 0.0,
            'medium_trades': 0.0,
            'small_trades': 0.0
        }
        
        for phase in phases:
            avg_trade_size = phase.total_volume / phase.buy_frequency
            if avg_trade_size > 10000:  # Arbitrary thresholds, adjust as needed
                volume_profile['large_trades'] += phase.total_volume
            elif avg_trade_size > 1000:
                volume_profile['medium_trades'] += phase.total_volume
            else:
                volume_profile['small_trades'] += phase.total_volume
                
        # Normalize volume profile
        for key in volume_profile:
            volume_profile[key] /= total_volume
            
        # Determine pattern type and confidence
        pattern_type = self._determine_pattern_type(phases, volume_profile)
        confidence = self._calculate_pattern_confidence(phases, pattern_type)
        
        return AccumulationPattern(
            pattern_type=pattern_type,
            confidence=confidence,
            start_price=phases[0].average_buy_price,
            current_price=phases[-1].average_buy_price,
            volume_profile=volume_profile,
            time_weighted_average_price=time_weighted_price,
            price_impact=self._calculate_price_impact(phases),
            market_share=self._calculate_market_share(phases[-1].current_position)
        )
        
    async def _calculate_manipulation_risk(
        self,
        pattern: AccumulationPattern,
        market_cap: float
    ) -> float:
        """Calculate risk of market manipulation.
        
        Evaluates various risk factors to determine the likelihood of market manipulation.
        
        Args:
            pattern: Accumulation pattern to analyze
            market_cap: Token market cap
            
        Returns:
            Risk score between 0 and 1
        """
        # Weight factors for risk calculation
        weights = {
            'volume_concentration': 0.3,
            'price_impact': 0.25,
            'market_share': 0.25,
            'pattern_confidence': 0.2
        }
        
        # Calculate volume concentration risk
        volume_concentration = (
            pattern.volume_profile['large_trades'] * 1.0 +
            pattern.volume_profile['medium_trades'] * 0.5 +
            pattern.volume_profile['small_trades'] * 0.2
        )
        
        # Calculate price impact risk
        price_impact_risk = min(1.0, pattern.price_impact / 0.1)  # 10% price impact = max risk
        
        # Calculate market share risk
        market_share_risk = min(1.0, pattern.market_share / 0.05)  # 5% market share = max risk
        
        # Calculate pattern confidence risk
        pattern_confidence_risk = pattern.confidence if pattern.pattern_type == 'stealth' else pattern.confidence * 0.5
        
        # Calculate weighted risk score
        risk_score = (
            weights['volume_concentration'] * volume_concentration +
            weights['price_impact'] * price_impact_risk +
            weights['market_share'] * market_share_risk +
            weights['pattern_confidence'] * pattern_confidence_risk
        )
        
        return min(1.0, max(0.0, risk_score))
        
    async def _extract_transfer_info(self, tx: Dict) -> Optional[Tuple[float, float]]:
        """Extract transfer amount and price from transaction."""
        try:
            # Implementation depends on specific token program and transaction structure
            # This is a simplified example
            for ix in tx.transaction.message.instructions:
                if self._is_transfer_instruction(ix):
                    amount = self._decode_amount(ix.data)
                    price = await self._get_token_price_at_time(
                        tx.block_time
                    )
                    return amount, price
            return None
        except Exception as e:
            logger.error(f"Error extracting transfer info: {e}")
            return None
            
    def _is_same_pattern(
        self,
        phase: AccumulationPhase,
        amount: float,
        price: float
    ) -> bool:
        """Check if new transaction follows the same pattern as current phase."""
        price_deviation = abs(price - phase.average_buy_price) / phase.average_buy_price
        volume_deviation = abs(amount - phase.total_volume / phase.buy_frequency)
        
        return (
            price_deviation <= self.stealth_threshold and
            volume_deviation <= phase.total_volume * 0.2  # 20% volume deviation threshold
        )
        
    def _calculate_stealth_score(
        self,
        phase: AccumulationPhase,
        price: float,
        amount: float
    ) -> float:
        """Calculate stealth score based on trading behavior."""
        price_stability = 1 - (
            abs(price - phase.average_buy_price) / phase.average_buy_price
        )
        volume_consistency = 1 - (
            abs(amount - phase.total_volume / phase.buy_frequency) /
            (phase.total_volume / phase.buy_frequency)
        )
        time_distribution = min(
            1.0,
            phase.buy_frequency * 86400 / phase.total_volume  # Normalize to daily volume
        )
        
        return (price_stability * 0.4 + volume_consistency * 0.4 + time_distribution * 0.2)
        
    def _determine_pattern_type(
        self,
        phases: List[AccumulationPhase],
        volume_profile: Dict[str, float]
    ) -> str:
        """Determine the type of accumulation pattern."""
        if volume_profile['small_trades'] > 0.7:  # 70% small trades
            return 'stealth'
        elif volume_profile['large_trades'] > 0.5:  # 50% large trades
            return 'aggressive'
        else:
            return 'distributed'
            
    def _calculate_pattern_confidence(
        self,
        phases: List[AccumulationPhase],
        pattern_type: str
    ) -> float:
        """Calculate confidence score for identified pattern."""
        avg_stealth_score = sum(p.stealth_score for p in phases) / len(phases)
        consistency_score = self._calculate_consistency_score(phases)
        duration_score = min(1.0, len(phases) / 10)  # More phases = higher confidence
        
        if pattern_type == 'stealth':
            return (avg_stealth_score * 0.5 + consistency_score * 0.3 + duration_score * 0.2)
        else:
            return (avg_stealth_score * 0.3 + consistency_score * 0.5 + duration_score * 0.2)
            
    def _calculate_consistency_score(self, phases: List[AccumulationPhase]) -> float:
        """Calculate consistency score based on phase patterns."""
        if len(phases) < 2:
            return 0.5
            
        price_variations = []
        volume_variations = []
        
        for i in range(1, len(phases)):
            price_var = abs(
                phases[i].average_buy_price - phases[i-1].average_buy_price
            ) / phases[i-1].average_buy_price
            volume_var = abs(
                phases[i].total_volume - phases[i-1].total_volume
            ) / phases[i-1].total_volume
            
            price_variations.append(price_var)
            volume_variations.append(volume_var)
            
        avg_price_consistency = 1 - min(1.0, sum(price_variations) / len(price_variations))
        avg_volume_consistency = 1 - min(1.0, sum(volume_variations) / len(volume_variations))
        
        return (avg_price_consistency * 0.6 + avg_volume_consistency * 0.4)
        
    def _calculate_price_impact(self, phases: List[AccumulationPhase]) -> float:
        """Calculate price impact of accumulation."""
        if not phases:
            return 0.0
            
        initial_price = phases[0].average_buy_price
        final_price = phases[-1].average_buy_price
        
        return (final_price - initial_price) / initial_price
        
    def _calculate_market_share(self, position: float) -> float:
        """Calculate market share based on position size."""
        # This would typically involve fetching total supply and calculating
        # position / total_supply, but for now we'll use a simplified version
        return min(1.0, position / 1_000_000)  # Simplified example
        
    async def _get_token_supply(self, token_address: str) -> float:
        """Get total token supply."""
        try:
            # Implement token supply fetching logic here
            # This would typically involve querying the token's mint info
            supply_info = await self.client.get_token_supply(
                Pubkey.from_string(token_address)
            )
            return float(supply_info.value.amount)
        except Exception as e:
            logger.error(f"Error getting token supply: {e}")
            return 1_000_000  # Default fallback value
            

if __name__ == "__main__":
    # Example usage
    async def main():
        analyzer = AccumulationAnalyzer("https://api.mainnet-beta.solana.com")
        pattern, phases = await analyzer.analyze_wallet(
            "wallet_address",
            "token_address"
        )
        
        if pattern:
            print(f"Pattern detected: {pattern.pattern_type}")
            print(f"Confidence: {pattern.confidence}")
            print(f"Market share: {pattern.market_share}")
            
            impact = await analyzer.calculate_market_impact(
                pattern,
                "token_address"
            )
            print(f"Market impact: {impact}")
            
    asyncio.run(main())
