Cross-Chain Bridge Analysis with Ollama: Multi-Blockchain Asset Tracking

# Verify Ollama installation
ollama --version

# Pull the recommended model for blockchain analysis
ollama pull llama3.1:8b

# Test the model
ollama run llama3.1:8b "Analyze this transaction hash: 0x..."

import requests
import json
from datetime import datetime
import subprocess

class MultiBridgeAnalyzer:
    def __init__(self):
        # Configure RPC endpoints for major chains
        self.rpc_endpoints = {
            'ethereum': 'https://eth-mainnet.g.alchemy.com/v2/YOUR_KEY',
            'polygon': 'https://polygon-mainnet.g.alchemy.com/v2/YOUR_KEY',
            'bsc': 'https://bsc-dataseed.binance.org/',
            'arbitrum': 'https://arb1.arbitrum.io/rpc'
        }
        
        # Popular bridge contract addresses
        self.bridge_contracts = {
            'polygon_pos': '0x40ec5B33f54e0E8A33A975908C5BA1c14e5BbbDf',
            'arbitrum_bridge': '0x8315177aB297bA92A06054cE80a67Ed4DBd7ed3a',
            'bsc_bridge': '0xd4e7C1F3DA1144c9E2CfD1b015eDA7652b4a4399'
        }
    
    def query_ollama(self, prompt):
        """Send analysis request to Ollama"""
        result = subprocess.run([
            'ollama', 'run', 'llama3.1:8b', prompt
        ], capture_output=True, text=True)
        return result.stdout.strip()

def monitor_bridge_transactions(self, wallet_address, time_window_hours=24):
    """Monitor bridge transactions for specific wallet"""
    
    transactions = []
    
    for chain_name, rpc_url in self.rpc_endpoints.items():
        # Fetch recent transactions
        chain_txs = self.fetch_chain_transactions(
            wallet_address, chain_name, time_window_hours
        )
        
        # Filter bridge-related transactions
        bridge_txs = [
            tx for tx in chain_txs 
            if any(bridge in tx['to'] for bridge in self.bridge_contracts.values())
        ]
        
        transactions.extend(bridge_txs)
    
    # Analyze transactions with Ollama
    analysis_prompt = f"""
    Analyze these cross-chain bridge transactions:
    {json.dumps(transactions, indent=2)}
    
    Identify:
    1. Asset flow patterns between chains
    2. Unusual transaction amounts or frequencies  
    3. Potential security risks or suspicious activity
    4. Bridge efficiency and gas cost analysis
    
    Provide actionable insights for asset tracking.
    """
    
    analysis = self.query_ollama(analysis_prompt)
    return {
        'transactions': transactions,
        'analysis': analysis,
        'timestamp': datetime.now().isoformat()
    }

def track_multi_chain_balances(self, wallet_address, target_tokens):
    """Track token balances across all chains"""
    
    balances = {}
    
    for chain_name, rpc_url in self.rpc_endpoints.items():
        chain_balances = {}
        
        for token_symbol, token_contracts in target_tokens.items():
            if chain_name in token_contracts:
                balance = self.get_token_balance(
                    wallet_address, 
                    token_contracts[chain_name], 
                    rpc_url
                )
                chain_balances[token_symbol] = balance
        
        balances[chain_name] = chain_balances
    
    # Generate balance analysis
    balance_prompt = f"""
    Analyze this multi-chain token portfolio:
    Wallet: {wallet_address}
    Balances: {json.dumps(balances, indent=2)}
    
    Provide insights on:
    1. Asset distribution across chains
    2. Potential arbitrage opportunities
    3. Bridge efficiency recommendations
    4. Risk assessment for each chain
    """
    
    analysis = self.query_ollama(balance_prompt)
    
    return {
        'balances': balances,
        'total_usd_value': self.calculate_total_value(balances),
        'analysis': analysis
    }

def analyze_bridge_performance(self, bridge_name, time_period_days=7):
    """Analyze bridge performance metrics"""
    
    # Fetch bridge transaction data
    bridge_data = self.fetch_bridge_metrics(bridge_name, time_period_days)
    
    performance_prompt = f"""
    Analyze this bridge performance data for {bridge_name}:
    
    Transaction Volume: {bridge_data['total_volume']} USD
    Average Transaction Time: {bridge_data['avg_time']} minutes
    Success Rate: {bridge_data['success_rate']}%
    Average Gas Cost: {bridge_data['avg_gas']} USD
    Failed Transactions: {bridge_data['failed_count']}
    
    Peak Usage Hours: {bridge_data['peak_hours']}
    Supported Assets: {bridge_data['supported_assets']}
    
    Provide analysis covering:
    1. Performance bottlenecks and optimization opportunities
    2. Cost efficiency compared to alternatives
    3. Reliability assessment for large transfers
    4. Best times to use this bridge
    5. Risk factors and mitigation strategies
    """
    
    analysis = self.query_ollama(performance_prompt)
    
    return {
        'bridge_name': bridge_name,
        'metrics': bridge_data,
        'analysis': analysis,
        'recommendations': self.extract_recommendations(analysis)
    }

def assess_transaction_risk(self, transaction_hash, source_chain, target_chain):
    """Assess risk level for bridge transaction"""
    
    # Fetch detailed transaction data
    tx_data = self.get_transaction_details(transaction_hash, source_chain)
    
    # Gather contextual information
    context = {
        'sender_history': self.get_wallet_history(tx_data['from']),
        'bridge_status': self.check_bridge_health(source_chain, target_chain),
        'recent_incidents': self.check_recent_bridge_incidents(),
        'transaction_amount': tx_data['value'],
        'gas_price': tx_data['gasPrice']
    }
    
    risk_prompt = f"""
    Assess the risk level for this cross-chain bridge transaction:
    
    Transaction: {transaction_hash}
    Route: {source_chain} → {target_chain}
    Amount: {tx_data['value']} tokens
    Sender: {tx_data['from']}
    
    Context:
    - Sender transaction history: {context['sender_history']}
    - Bridge health status: {context['bridge_status']}
    - Recent security incidents: {context['recent_incidents']}
    - Gas price vs network average: {context['gas_price']}
    
    Provide risk assessment:
    1. Overall risk level (Low/Medium/High/Critical)
    2. Specific risk factors identified
    3. Recommended actions
    4. Monitoring suggestions
    """
    
    risk_analysis = self.query_ollama(risk_prompt)
    
    return {
        'transaction_hash': transaction_hash,
        'risk_level': self.extract_risk_level(risk_analysis),
        'analysis': risk_analysis,
        'context': context
    }

def monitor_bridge_security(self):
    """Continuous security monitoring for bridges"""
    
    security_events = []
    
    for bridge_name, contract_address in self.bridge_contracts.items():
        # Check for unusual activity patterns
        recent_txs = self.get_recent_bridge_transactions(contract_address)
        
        # Analyze transaction patterns
        pattern_analysis = self.analyze_transaction_patterns(recent_txs)
        
        security_prompt = f"""
        Analyze these bridge transactions for security threats:
        Bridge: {bridge_name}
        Contract: {contract_address}
        
        Recent activity:
        {json.dumps(pattern_analysis, indent=2)}
        
        Detect:
        1. Unusual transaction volumes or frequencies
        2. Potential flash loan attacks
        3. Suspicious wallet clusters
        4. Failed transaction spikes
        5. Gas price manipulation attempts
        
        Rate threat level and provide immediate actions.
        """
        
        security_analysis = self.query_ollama(security_prompt)
        
        if self.detect_high_risk(security_analysis):
            security_events.append({
                'bridge': bridge_name,
                'threat_level': 'HIGH',
                'analysis': security_analysis,
                'timestamp': datetime.now().isoformat()
            })
    
    return security_events

def assess_portfolio_risk(self, wallet_address):
    """Comprehensive portfolio risk assessment"""
    
    # Gather portfolio data
    portfolio_data = {
        'balances': self.track_multi_chain_balances(wallet_address, {
            'USDC': {
                'ethereum': '0xA0b86a33E6441e',
                'polygon': '0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174',
                'arbitrum': '0xFF970A61A04b1cA14834A43f5dE4533eBDDB5CC8'
            },
            'WETH': {
                'ethereum': '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2',
                'polygon': '0x7ceB23fD6bC0adD59E62ac25578270cFf1b9f619'
            }
        }),
        'pending_bridges': self.get_pending_bridge_transactions(wallet_address),
        'bridge_exposure': self.calculate_bridge_exposure(wallet_address)
    }
    
    risk_prompt = f"""
    Assess portfolio risk for multi-chain assets:
    
    Wallet: {wallet_address}
    Portfolio: {json.dumps(portfolio_data, indent=2)}
    
    Analyze:
    1. Chain concentration risk
    2. Bridge dependency risk  
    3. Liquidity risk across chains
    4. Smart contract risk exposure
    5. Slippage risk for large movements
    
    Provide risk score (1-10) and mitigation strategies.
    """
    
    risk_assessment = self.query_ollama(risk_prompt)
    
    return {
        'wallet': wallet_address,
        'portfolio_data': portfolio_data,
        'risk_assessment': risk_assessment,
        'risk_score': self.extract_risk_score(risk_assessment)
    }

class BridgeArbitrageMonitor:
    def __init__(self, analyzer):
        self.analyzer = analyzer
        
    def find_arbitrage_opportunities(self, token_pairs):
        """Find profitable arbitrage opportunities"""
        
        opportunities = []
        
        for token_symbol, contracts in token_pairs.items():
            prices = {}
            
            # Get prices on each chain
            for chain, contract in contracts.items():
                price = self.get_token_price(chain, contract)
                prices[chain] = price
            
            # Analyze arbitrage potential
            arb_prompt = f"""
            Analyze arbitrage opportunities for {token_symbol}:
            
            Prices across chains:
            {json.dumps(prices, indent=2)}
            
            Consider:
            1. Price differences between chains
            2. Bridge fees and gas costs
            3. Transaction time requirements
            4. Liquidity availability
            5. Slippage impact
            
            Calculate profitable opportunities with minimum 2% profit margin.
            """
            
            analysis = self.analyzer.query_ollama(arb_prompt)
            
            if self.is_profitable_opportunity(analysis):
                opportunities.append({
                    'token': token_symbol,
                    'prices': prices,
                    'analysis': analysis,
                    'profit_estimate': self.extract_profit_estimate(analysis)
                })
        
        return opportunities

def monitor_defi_bridge_integration(self, protocol_name, user_positions):
    """Monitor DeFi protocol positions across bridges"""
    
    # Track positions across all chains
    position_data = {}
    
    for chain in self.rpc_endpoints.keys():
        chain_positions = self.get_protocol_positions(
            protocol_name, user_positions, chain
        )
        position_data[chain] = chain_positions
    
    integration_prompt = f"""
    Analyze DeFi positions across chains for {protocol_name}:
    
    Positions: {json.dumps(position_data, indent=2)}
    
    Provide insights on:
    1. Cross-chain yield optimization opportunities
    2. Risk factors for bridged positions
    3. Impermanent loss across chains
    4. Optimal rebalancing strategies
    5. Emergency exit procedures
    """
    
    analysis = self.analyzer.query_ollama(integration_prompt)
    
    return {
        'protocol': protocol_name,
        'positions': position_data,
        'analysis': analysis,
        'recommendations': self.extract_action_items(analysis)
    }

# Complete bridge analyzer implementation
def main():
    # Initialize the analyzer
    analyzer = MultiBridgeAnalyzer()
    
    # Target wallet and tokens to track
    wallet = "0x742d35Cc6064Bc5aA5c3B7e0D8E3e0D8E3e0D8E3"
    tokens = {
        'USDC': {
            'ethereum': '0xA0b86a33E6441e',
            'polygon': '0x2791Bca1f2de4661ED88A30C99A7a9449Aa84174'
        }
    }
    
    # Run comprehensive analysis
    print("🔍 Starting Cross-Chain Bridge Analysis...")
    
    # 1. Monitor recent bridge transactions
    bridge_activity = analyzer.monitor_bridge_transactions(wallet)
    print(f"✅ Found {len(bridge_activity['transactions'])} bridge transactions")
    
    # 2. Track multi-chain balances
    balance_report = analyzer.track_multi_chain_balances(wallet, tokens)
    print(f"💰 Total portfolio value: ${balance_report['total_usd_value']:,.2f}")
    
    # 3. Assess security risks
    security_events = analyzer.monitor_bridge_security()
    if security_events:
        print(f"⚠️  {len(security_events)} security events detected")
    
    # 4. Generate comprehensive report
    report = {
        'timestamp': datetime.now().isoformat(),
        'wallet_address': wallet,
        'bridge_activity': bridge_activity,
        'balances': balance_report,
        'security_status': security_events
    }
    
    # Save report
    with open('bridge_analysis_report.json', 'w') as f:
        json.dump(report, f, indent=2)
    
    print("📊 Analysis complete! Report saved to bridge_analysis_report.json")

if __name__ == "__main__":
    main()

import time
from functools import wraps

def cache_with_ttl(ttl_seconds=300):
    """Cache function results with time-to-live"""
    def decorator(func):
        cache = {}
        
        @wraps(func)
        def wrapper(*args, **kwargs):
            key = str(args) + str(kwargs)
            now = time.time()
            
            if key in cache:
                result, timestamp = cache[key]
                if now - timestamp < ttl_seconds:
                    return result
            
            result = func(*args, **kwargs)
            cache[key] = (result, now)
            return result
        
        return wrapper
    return decorator

class OptimizedBridgeAnalyzer(MultiBridgeAnalyzer):
    @cache_with_ttl(ttl_seconds=60)  # Cache for 1 minute
    def get_token_price(self, chain, token_contract):
        """Get token price with caching"""
        return super().get_token_price(chain, token_contract)
    
    @cache_with_ttl(ttl_seconds=300)  # Cache for 5 minutes
    def get_bridge_metrics(self, bridge_name, time_period):
        """Get bridge metrics with caching"""
        return super().get_bridge_metrics(bridge_name, time_period)

import asyncio
import aiohttp

class AsyncBridgeAnalyzer:
    async def fetch_all_chain_data(self, wallet_address):
        """Fetch data from all chains simultaneously"""
        
        async with aiohttp.ClientSession() as session:
            tasks = []
            
            for chain_name, rpc_url in self.rpc_endpoints.items():
                task = self.fetch_chain_data_async(
                    session, wallet_address, chain_name, rpc_url
                )
                tasks.append(task)
            
            # Execute all requests concurrently
            results = await asyncio.gather(*tasks)
            
            return dict(zip(self.rpc_endpoints.keys(), results))
    
    async def fetch_chain_data_async(self, session, wallet, chain, rpc_url):
        """Async chain data fetching"""
        payload = {
            "jsonrpc": "2.0",
            "method": "eth_getBalance", 
            "params": [wallet, "latest"],
            "id": 1
        }
        
        async with session.post(rpc_url, json=payload) as response:
            data = await response.json()
            return data['result']

def robust_rpc_call(self, rpc_url, payload, max_retries=3):
    """Make RPC calls with automatic retry logic"""
    
    for attempt in range(max_retries):
        try:
            response = requests.post(
                rpc_url, 
                json=payload, 
                timeout=30,
                headers={'Content-Type': 'application/json'}
            )
            
            if response.status_code == 200:
                return response.json()
            else:
                print(f"RPC call failed with status {response.status_code}")
                
        except requests.exceptions.RequestException as e:
            print(f"RPC attempt {attempt + 1} failed: {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
    
    raise Exception(f"All RPC attempts failed for {rpc_url}")

def ensure_ollama_ready(self):
    """Ensure Ollama is running and model is available"""
    
    try:
        # Test basic Ollama connectivity
        result = subprocess.run([
            'ollama', 'list'
        ], capture_output=True, text=True, timeout=10)
        
        if 'llama3.1:8b' not in result.stdout:
            print("Downloading required model...")
            subprocess.run(['ollama', 'pull', 'llama3.1:8b'])
        
        return True
        
    except (subprocess.TimeoutExpired, FileNotFoundError):
        print("❌ Ollama not available. Please install Ollama first.")
        return False