Cross-Chain MEV: Optimizing Bridge Latency for Arbitrage

# Cross-chain arbitrage monitoring system
import asyncio
from web3 import Web3
from datetime import datetime, timedelta
import pandas as pd

class BridgeArbitrageMonitor:
    def __init__(self):
        # Initialize connections to major bridges
        self.bridges = {
            'stargate': {
                'contract': '0x8731fE554B7dA7F0B05C3C3A0D04BbC6D9d80E6',
                'chain_id': 1,
                'latency': 15.2  # seconds
            },
            'hop': {
                'contract': '0xB8901acB5Ed3Bcd6B03f4bfC8B16a8F32CD4eD6',
                'chain_id': 1,
                'latency': 8.7
            },
            'arbitrum_bridge': {
                'contract': '0x72Ce9c846789fdEe6f7bc1E14F9398E538cb9B53',
                'chain_id': 1,
                'latency': 12.3
            }
        }
        
        self.minimum_profit_threshold = 0.005  # 0.5% minimum profit
        self.max_slippage = 0.01  # 1% max slippage
        
    async def monitor_price_differential(self):
        """Monitor price differences across chains for arbitrage opportunities"""
        while True:
            prices = await self.fetch_cross_chain_prices()
            arbitrage_opportunities = []
            
            for asset in prices:
                for bridge in self.bridges:
                    for target_chain in [137, 42161, 10]:  # Polygon, Arbitrum, Optimism
                        if (bridge['chain_id'] == 1 and 
                            target_chain in prices[asset] and
                            asset in prices[target_chain]):
                            
                            diff = prices[target_chain][asset] - prices[asset][asset]
                            pct_diff = diff / prices[asset][asset]
                            
                            if pct_diff > self.minimum_profit_threshold:
                                # Calculate bridge latency impact
                                time_to_settlement = self.bridges[bridge]['latency']
                                opportunity_cost = time_to_settlement * 0.0001  # hypothetical
                                adjusted_profit = pct_diff - opportunity_cost
                                
                                if adjusted_profit > self.minimum_profit_threshold:
                                    arbitrage_opportunities.append({
                                        'asset': asset,
                                        'bridge': bridge,
                                        'source_chain': 1,
                                        'target_chain': target_chain,
                                        'price_diff': pct_diff,
                                        'adjusted_profit': adjusted_profit,
                                        'timestamp': datetime.now(),
                                        'latency': time_to_settlement
                                    })
            
            if arbitrage_opportunities:
                await self.execute_arbitrage_opportunities(arbitrage_opportunities)
            
            await asyncio.sleep(1)  # 1-second monitoring interval
    
    async def execute_arbitrage_opportunities(self, opportunities):
        """Execute cross-chain arbitrage transactions"""
        for opportunity in opportunities:
            try:
                # Calculate optimal amount based on gas costs and profit margin
                gas_cost = await self.estimate_bridge_gas_cost(opportunity['bridge'])
                min_amount = (gas_cost * 10) / opportunity['adjusted_profit']
                
                # Execute bridge transaction
                tx_hash = await self.bridge_transaction(opportunity, min_amount)
                print(f"Executed arbitrage: {tx_hash}")
                
            except Exception as e:
                print(f"Arbitrage failed: {e}")
                continue

# Initialize and run the monitor
monitor = BridgeArbitrageMonitor()
asyncio.run(monitor.monitor_price_differential())