4. Whale Detection System

Accumulation Pattern Recognition

The Whale Detection System implements sophisticated algorithms for identifying strategic accumulation behaviors utilizing advanced pattern recognition methodologies. The system processes on-chain data through multiple analysis layers for comprehensive whale activity detection.

Detection Parameters

WHALE_THRESHOLDS = {
    'min_whale_size_usd': 50000,
    'analysis_window': 24 * 60 * 60,  # 24 hours
    'update_interval': 60,  # seconds
    'confidence_threshold': 0.75
}

Pattern Recognition Implementation

class WhalePatternRecognizer:
    def __init__(
        self,
        rpc_client: AsyncClient,
        min_whale_size_usd: float = 50000,
        analysis_window: int = 24 * 60 * 60,
        update_interval: int = 60,
        confidence_threshold: float = 0.75
    ):
        self.thresholds = {
            'stealth_accumulation': {
                'min_transactions': 5,
                'max_size_ratio': 0.1,
                'time_window': 3600
            },
            'distribution': {
                'min_transactions': 10,
                'min_unique_receivers': 5,
                'time_window': 7200
            },
            'wash_trading': {
                'min_cycle_length': 3,
                'max_time_between': 300,
                'min_volume': 1000
            }
        }

Distribution Phase Analysis

The system implements sophisticated metrics for distribution detection:

Analysis Metrics

DISTRIBUTION_METRICS = {
    'volume_threshold': 0.05,  # 5% of total supply
    'time_window': 7200,      # 2 hours
    'receiver_diversity': 0.7, # Minimum unique receiver ratio
    'volume_distribution': {
        'min_transactions': 10,
        'max_size_variance': 0.3
    }
}

Stealth Movement Detection

Implementation of sophisticated algorithms for detecting concealed accumulation:

async def _detect_stealth_accumulation(
    self,
    token_address: str
) -> List[WhalePattern]:
    """Detect stealth accumulation patterns.
    
    Args:
        token_address: Token contract address
        
    Returns:
        List of detected whale patterns
    """
    patterns = []
    
    wallet_transactions = self._group_transactions_by_wallet(
        token_address,
        self.thresholds['stealth_accumulation']['time_window']
    )
    
    for wallet_address, transactions in wallet_transactions.items():
        if self._is_stealth_pattern(
            transactions,
            self.thresholds['stealth_accumulation']
        ):
            pattern = WhalePattern(
                pattern_type=WhaleActivityType.STEALTH_ACCUMULATION,
                confidence_score=self._calculate_stealth_confidence(
                    transactions
                ),
                risk_score=self._calculate_pattern_risk(
                    WhaleActivityType.STEALTH_ACCUMULATION,
                    transactions
                )
            )
            patterns.append(pattern)
    
    return patterns

Performance Optimization

Resource Management

Resource Allocation:
    Memory Footprint: 4GB per instance
    CPU Utilization: 40-60% optimal
    Network Bandwidth: 50Mbps sustained

Processing Metrics:
    Transaction Analysis: <100ms
    Pattern Detection: <200ms
    Alert Generation: <50ms

Caching Strategy

CACHE_CONFIG = {
    'wallet_data': {
        'ttl': 300,      # 5 minutes
        'max_size': 5000 # entries
    },
    'pattern_data': {
        'ttl': 600,      # 10 minutes
        'max_size': 1000 # entries
    },
    'metrics': {
        'ttl': 60,       # 1 minute
        'max_size': 10000 # entries
    }
}

System Monitoring

The engine exposes comprehensive metrics through standardized endpoints:

Prometheus Metrics:
    - whale_detection_latency_seconds
    - pattern_recognition_duration
    - wallet_analysis_queue_size
    - memory_utilization_bytes
    - cache_hit_ratio

Alert Configurations:
    - PatternDetectionDelay: >500ms
    - HighMemoryUsage: >85% utilization
    - ProcessingQueueOverflow: >1000 pending
    - ErrorRateHigh: >2% error rate

Error Handling

ERROR_HANDLING = {
    'max_retries': 3,
    'backoff_factor': 2,
    'timeout': 30,
    'circuit_breaker': {
        'failure_threshold': 5,
        'reset_timeout': 60,
        'half_open_timeout': 30
    }
}

The system implements sophisticated error recovery mechanisms with exponential backoff strategies and circuit breakers to prevent cascade failures during high-load scenarios.