/lyra

Whale Scanner Agent

The Whale Scanner Agent implements advanced on-chain analysis capabilities, monitoring large wallet movements and identifying trading patterns across multiple blockchains.

Technical Implementation

from langchain.agents import AgentExecutor
from langchain.tools import BaseTool
from typing import Dict, List, Optional
import asyncio

class WhaleAnalysisAgent:
    def __init__(self, config: Dict):
        self.config = config
        self.helius_client = HeliusRPCClient(config['helius_api_key'])
        self.memory_store = VectorMemoryStore(config['vector_db'])
        self.llm = ChatAnthropic(model="claude-3-sonnet")
        
    async def analyze_wallet_cluster(self, wallet_addresses: List[str]) -> Dict:
        """Analyzes a cluster of whale wallets for coordinated behavior"""
        analysis_tasks = []
        
        for wallet in wallet_addresses[:10]:  # Rate limiting
            task = self.process_wallet_transactions(wallet)
            analysis_tasks.append(task)
        
        # Parallel processing with rate limiting
        results = await asyncio.gather(*analysis_tasks, return_exceptions=True)
        
        # Constitutional AI validation
        validated_results = await self.constitutional_validation(results)
        
        return {
            "cluster_analysis": validated_results,
            "coordination_score": self.calculate_coordination_score(results),
            "risk_assessment": self.assess_cluster_risk(results),
            "recommendations": self.generate_recommendations(results)
        }
        
    async def process_wallet_transactions(self, wallet_address: str) -> Dict:
        """Deep analysis of individual wallet transaction patterns"""
        # Fetch transaction history
        transactions = await self.helius_client.get_transactions(
            wallet_address, 
            limit=50
        )
        
        # Extract token rotation patterns
        rotation_analysis = self.analyze_token_rotations(transactions)
        
        # Calculate timing urgency metrics
        urgency_metrics = self.calculate_urgency_metrics(transactions)
        
        # Store in vector memory for pattern recognition
        await self.memory_store.store_wallet_patterns(
            wallet_address, 
            rotation_analysis
        )
        
        return {
            "wallet": wallet_address,
            "transaction_count": len(transactions),
            "rotation_patterns": rotation_analysis,
            "urgency_score": urgency_metrics['urgency_score'],
            "volume_patterns": urgency_metrics['volume_patterns']
        }
        
    def analyze_token_rotations(self, transactions: List[Dict]) -> Dict:
        """Identifies token rotation patterns and trading behaviors"""
        token_flows = {}
        rotation_events = []
        
        for tx in transactions:
            if tx.get('tokenTransfers'):
                for transfer in tx['tokenTransfers']:
                    mint = transfer['mint']
                    if mint not in token_flows:
                        token_flows[mint] = {
                            'inflows': 0,
                            'outflows': 0,
                            'net_flow': 0,
                            'first_seen': tx['timestamp'],
                            'last_seen': tx['timestamp']
                        }
                        
                    if transfer['changeType'] == 'increase':
                        token_flows[mint]['inflows'] += transfer['amount']
                    else:
                        token_flows[mint]['outflows'] += transfer['amount']
                        
                    token_flows[mint]['net_flow'] = (
                        token_flows[mint]['inflows'] - 
                        token_flows[mint]['outflows']
                    )
                    token_flows[mint]['last_seen'] = tx['timestamp']
                    
        # Identify rotation patterns
        for mint, flow_data in token_flows.items():
            if (flow_data['inflows'] > 0 and 
                flow_data['outflows'] > 0 and 
                abs(flow_data['net_flow']) < flow_data['inflows'] * 0.1):
                
                rotation_events.append({
                    'token_mint': mint,
                    'rotation_volume': min(flow_data['inflows'],
                                          flow_data['outflows']),
                    'duration': flow_data['last_seen'] - flow_data['first_seen'],
                    'pattern_type': 'rotation'
                })
                
        return {
            'token_flows': token_flows,
            'rotation_events': rotation_events,
            'rotation_score': len(rotation_events) / len(token_flows) if token_flows else 0
        }

Key Features

• Real-Time Transaction Monitoring: Sub-second blockchain event processing

• Pattern Recognition: ML-based identification of whale trading patterns

• Coordination Detection: Multi-wallet behavior correlation analysis

• Risk Scoring: Constitutional AI-based risk assessment