Yield Farming Cross-Chain Analysis: Multi-Network Capital Allocation Guide 2025

# Cross-chain yield analysis framework
import pandas as pd
import numpy as np

class CrossChainAnalyzer:
    def __init__(self):
        self.networks = ['ethereum', 'polygon', 'bsc', 'arbitrum']
        self.protocols = {}
        
    def calculate_real_yield(self, base_apy, bridge_cost, gas_fees, time_horizon):
        """
        Calculate actual yield after cross-chain costs
        
        Args:
            base_apy: Advertised APY (decimal)
            bridge_cost: One-way bridge fee (USD)
            gas_fees: Average gas cost per transaction (USD)
            time_horizon: Investment period (days)
        """
        # Convert APY to daily rate
        daily_rate = (1 + base_apy) ** (1/365) - 1
        
        # Calculate gross returns
        gross_return = daily_rate * time_horizon
        
        # Subtract bridge costs (both ways) and gas fees
        net_return = gross_return - (bridge_cost * 2) - gas_fees
        
        return net_return
    
    def risk_adjusted_yield(self, yield_rate, protocol_score, network_score):
        """
        Adjust yield for protocol and network risks
        
        Args:
            yield_rate: Base yield rate
            protocol_score: Risk score (1-10, 10 = safest)
            network_score: Network stability score (1-10)
        """
        risk_multiplier = (protocol_score * network_score) / 100
        return yield_rate * risk_multiplier

# Example usage
analyzer = CrossChainAnalyzer()

# Aave on Polygon vs Ethereum
polygon_yield = analyzer.calculate_real_yield(
    base_apy=0.08,      # 8% APY
    bridge_cost=15,     # $15 bridge fee
    gas_fees=2,         # $2 average gas
    time_horizon=90     # 90 days
)

ethereum_yield = analyzer.calculate_real_yield(
    base_apy=0.06,      # 6% APY  
    bridge_cost=0,      # No bridge needed
    gas_fees=25,        # $25 average gas
    time_horizon=90
)

print(f"Polygon Real Yield: {polygon_yield:.4f}")
print(f"Ethereum Real Yield: {ethereum_yield:.4f}")

// Multi-network yield data fetcher
const Web3 = require('web3');
const axios = require('axios');

class MultiNetworkYieldTracker {
    constructor() {
        this.networks = {
            ethereum: 'https://eth-mainnet.alchemyapi.io/v2/YOUR-KEY',
            polygon: 'https://polygon-mainnet.alchemyapi.io/v2/YOUR-KEY',
            bsc: 'https://bsc-dataseed.binance.org/'
        };
        this.protocols = [
            'aave', 'compound', 'curve', 'uniswap', 'pancakeswap'
        ];
    }

    async fetchYieldData(network, protocol) {
        try {
            // Example: Fetch Aave lending rates
            const response = await axios.get(
                `https://api.aave.com/data/rates?network=${network}`
            );
            
            return {
                network: network,
                protocol: protocol,
                apy: response.data.liquidityRate,
                tvl: response.data.totalLiquidity,
                timestamp: Date.now()
            };
        } catch (error) {
            console.error(`Failed to fetch ${protocol} data for ${network}:`, error);
            return null;
        }
    }

    async scanAllNetworks() {
        const yields = [];
        
        for (const network of Object.keys(this.networks)) {
            for (const protocol of this.protocols) {
                const data = await this.fetchYieldData(network, protocol);
                if (data) yields.push(data);
            }
        }
        
        return this.rankByEfficiency(yields);
    }

    rankByEfficiency(yields) {
        // Sort by risk-adjusted yield
        return yields.sort((a, b) => {
            const aScore = a.apy * this.getProtocolSafety(a.protocol);
            const bScore = b.apy * this.getProtocolSafety(b.protocol);
            return bScore - aScore;
        });
    }

    getProtocolSafety(protocol) {
        // Safety multipliers based on protocol maturity
        const safetyScores = {
            'aave': 0.95,
            'compound': 0.90,
            'curve': 0.85,
            'uniswap': 0.80,
            'pancakeswap': 0.70
        };
        return safetyScores[protocol] || 0.50;
    }
}

// Usage example
const tracker = new MultiNetworkYieldTracker();
tracker.scanAllNetworks().then(results => {
    console.log('Top 5 Yield Opportunities:');
    results.slice(0, 5).forEach((opportunity, index) => {
        console.log(`${index + 1}. ${opportunity.protocol} on ${opportunity.network}: ${(opportunity.apy * 100).toFixed(2)}% APY`);
    });
});

import requests
import json

class BridgeCostAnalyzer:
    def __init__(self):
        self.bridges = {
            'polygon': {
                'provider': 'Polygon PoS Bridge',
                'base_fee': 0.001,  # ETH
                'percentage_fee': 0.0005  # 0.05%
            },
            'arbitrum': {
                'provider': 'Arbitrum Bridge',
                'base_fee': 0.002,  # ETH
                'percentage_fee': 0.0001  # 0.01%
            },
            'bsc': {
                'provider': 'Binance Bridge',
                'base_fee': 0.0005,  # BNB
                'percentage_fee': 0.001   # 0.1%
            }
        }
    
    def calculate_bridge_cost(self, amount_usd, source_network, target_network):
        """
        Calculate total bridge cost including fees and slippage
        
        Args:
            amount_usd: Amount to bridge in USD
            source_network: Source blockchain
            target_network: Target blockchain
        """
        if target_network not in self.bridges:
            return {'error': 'Unsupported bridge route'}
        
        bridge_config = self.bridges[target_network]
        
        # Get current ETH price
        eth_price = self.get_eth_price()
        
        # Calculate fees
        base_fee_usd = bridge_config['base_fee'] * eth_price
        percentage_fee_usd = amount_usd * bridge_config['percentage_fee']
        
        total_fee = base_fee_usd + percentage_fee_usd
        
        # Estimate time and add opportunity cost
        bridge_time_hours = self.get_bridge_time(source_network, target_network)
        opportunity_cost = amount_usd * 0.0001 * bridge_time_hours  # 1% annual rate
        
        return {
            'base_fee': base_fee_usd,
            'percentage_fee': percentage_fee_usd,
            'opportunity_cost': opportunity_cost,
            'total_cost': total_fee + opportunity_cost,
            'bridge_time_hours': bridge_time_hours,
            'cost_percentage': (total_fee + opportunity_cost) / amount_usd * 100
        }
    
    def get_eth_price(self):
        """Fetch current ETH price"""
        try:
            response = requests.get('https://api.coingecko.com/api/v3/simple/price?ids=ethereum&vs_currencies=usd')
            return response.json()['ethereum']['usd']
        except:
            return 2000  # Fallback price
    
    def get_bridge_time(self, source, target):
        """Estimate bridge completion time"""
        bridge_times = {
            ('ethereum', 'polygon'): 0.5,   # 30 minutes
            ('ethereum', 'arbitrum'): 0.25, # 15 minutes
            ('ethereum', 'bsc'): 0.1,       # 6 minutes
            ('polygon', 'ethereum'): 4,     # 4 hours (checkpoint)
            ('arbitrum', 'ethereum'): 168,  # 7 days (challenge period)
            ('bsc', 'ethereum'): 0.5        # 30 minutes
        }
        return bridge_times.get((source, target), 1)

# Example: Compare bridge costs for different amounts
analyzer = BridgeCostAnalyzer()
amounts = [1000, 5000, 10000, 50000]  # USD amounts

print("Bridge Cost Analysis (Ethereum to Polygon):")
print("Amount\tCost($)\tCost(%)\tTime(h)")
print("-" * 40)

for amount in amounts:
    cost_data = analyzer.calculate_bridge_cost(amount, 'ethereum', 'polygon')
    print(f"${amount:,}\t${cost_data['total_cost']:.2f}\t{cost_data['cost_percentage']:.3f}%\t{cost_data['bridge_time_hours']}")

import numpy as np
from scipy.optimize import minimize
import pandas as pd

class YieldPortfolioOptimizer:
    def __init__(self, risk_tolerance=0.3):
        self.risk_tolerance = risk_tolerance
        self.min_allocation = 0.05  # 5% minimum per position
        self.max_allocation = 0.40  # 40% maximum per position
        
    def optimize_allocation(self, opportunities, total_capital):
        """
        Optimize capital allocation across yield opportunities
        
        Args:
            opportunities: List of yield opportunities with expected returns and risks
            total_capital: Total capital to allocate (USD)
        """
        n_assets = len(opportunities)
        
        # Extract expected returns and risks
        returns = np.array([opp['expected_return'] for opp in opportunities])
        risks = np.array([opp['risk_score'] for opp in opportunities])
        
        # Covariance matrix (simplified - assumes some correlation)
        correlation_matrix = np.full((n_assets, n_assets), 0.3)
        np.fill_diagonal(correlation_matrix, 1.0)
        cov_matrix = np.outer(risks, risks) * correlation_matrix
        
        # Objective function: maximize return - risk penalty
        def objective(weights):
            portfolio_return = np.dot(weights, returns)
            portfolio_risk = np.sqrt(np.dot(weights, np.dot(cov_matrix, weights)))
            return -(portfolio_return - self.risk_tolerance * portfolio_risk)
        
        # Constraints
        constraints = [
            {'type': 'eq', 'fun': lambda w: np.sum(w) - 1},  # Weights sum to 1
        ]
        
        # Bounds for each weight
        bounds = [(self.min_allocation, self.max_allocation) for _ in range(n_assets)]
        
        # Initial guess (equal weights)
        initial_weights = np.array([1/n_assets] * n_assets)
        
        # Optimize
        result = minimize(
            objective,
            initial_weights,
            method='SLSQP',
            bounds=bounds,
            constraints=constraints
        )
        
        if result.success:
            optimal_weights = result.x
            return self.format_allocation(opportunities, optimal_weights, total_capital)
        else:
            raise ValueError("Optimization failed")
    
    def format_allocation(self, opportunities, weights, total_capital):
        """Format optimization results"""
        allocations = []
        
        for i, (opp, weight) in enumerate(zip(opportunities, weights)):
            allocation_amount = total_capital * weight
            
            allocations.append({
                'network': opp['network'],
                'protocol': opp['protocol'],
                'pair': opp['pair'],
                'weight': weight,
                'amount_usd': allocation_amount,
                'expected_return': opp['expected_return'],
                'risk_score': opp['risk_score'],
                'apy': opp['apy']
            })
        
        # Sort by allocation amount
        allocations.sort(key=lambda x: x['amount_usd'], reverse=True)
        return allocations

# Example usage
optimizer = YieldPortfolioOptimizer(risk_tolerance=0.25)

# Sample yield opportunities
opportunities = [
    {
        'network': 'ethereum',
        'protocol': 'aave',
        'pair': 'USDC',
        'apy': 0.045,
        'expected_return': 0.042,  # After costs
        'risk_score': 0.05
    },
    {
        'network': 'polygon',
        'protocol': 'aave',
        'pair': 'USDC',
        'apy': 0.065,
        'expected_return': 0.058,
        'risk_score': 0.08
    },
    {
        'network': 'arbitrum',
        'protocol': 'curve',
        'pair': 'USDC-USDT',
        'apy': 0.075,
        'expected_return': 0.068,
        'risk_score': 0.12
    },
    {
        'network': 'bsc',
        'protocol': 'pancakeswap',
        'pair': 'BUSD-USDT',
        'apy': 0.120,
        'expected_return': 0.095,
        'risk_score': 0.25
    }
]

# Optimize for $100,000 portfolio
allocation = optimizer.optimize_allocation(opportunities, 100000)

print("Optimal Capital Allocation:")
print("Network\t\tProtocol\tAmount\t\tWeight\tAPY")
print("-" * 60)
for alloc in allocation:
    print(f"{alloc['network']:<12}\t{alloc['protocol']:<8}\t${alloc['amount_usd']:,.0f}\t\t{alloc['weight']:.1%}\t{alloc['apy']:.1%}")

class ProtocolRiskAnalyzer:
    def __init__(self):
        self.risk_factors = {
            'audit_score': 0.3,      # Weight for audit quality
            'tvl_stability': 0.25,   # TVL volatility
            'time_tested': 0.2,      # Protocol age
            'team_reputation': 0.15, # Team track record
            'governance': 0.1        # Governance decentralization
        }
    
    def calculate_risk_score(self, protocol_data):
        """
        Calculate comprehensive risk score for a protocol
        Returns score from 0 (highest risk) to 100 (lowest risk)
        """
        scores = {}
        
        # Audit score based on security audits
        audit_firms = protocol_data.get('audit_firms', [])
        scores['audit_score'] = min(len(audit_firms) * 25, 100)
        
        # TVL stability (lower volatility = higher score)
        tvl_volatility = protocol_data.get('tvl_volatility', 0.5)
        scores['tvl_stability'] = max(0, 100 - tvl_volatility * 200)
        
        # Time tested (protocols >2 years get full points)
        age_months = protocol_data.get('age_months', 0)
        scores['time_tested'] = min(age_months / 24 * 100, 100)
        
        # Team reputation
        team_score = protocol_data.get('team_score', 50)  # 0-100
        scores['team_reputation'] = team_score
        
        # Governance decentralization
        governance_score = protocol_data.get('governance_score', 30)
        scores['governance'] = governance_score
        
        # Calculate weighted average
        total_score = sum(
            scores[factor] * weight 
            for factor, weight in self.risk_factors.items()
        )
        
        return {
            'total_score': total_score,
            'breakdown': scores,
            'risk_level': self.get_risk_level(total_score)
        }
    
    def get_risk_level(self, score):
        """Convert numeric score to risk level"""
        if score >= 80:
            return 'Low Risk'
        elif score >= 60:
            return 'Medium Risk'
        elif score >= 40:
            return 'High Risk'
        else:
            return 'Very High Risk'

# Example risk assessment
risk_analyzer = ProtocolRiskAnalyzer()

aave_data = {
    'audit_firms': ['Consensys', 'OpenZeppelin', 'Certik', 'PeckShield'],
    'tvl_volatility': 0.15,  # 15% volatility
    'age_months': 36,        # 3 years old
    'team_score': 90,        # High team reputation
    'governance_score': 85   # Well decentralized
}

risk_assessment = risk_analyzer.calculate_risk_score(aave_data)
print(f"Aave Risk Assessment: {risk_assessment['total_score']:.1f}/100 ({risk_assessment['risk_level']})")

class DynamicRebalancer:
    def __init__(self, rebalance_threshold=0.02):
        self.rebalance_threshold = rebalance_threshold  # 2% yield difference
        self.min_rebalance_amount = 1000  # Minimum $1000 to rebalance
        
    def should_rebalance(self, current_portfolio, market_opportunities):
        """
        Determine if portfolio should be rebalanced
        """
        rebalance_actions = []
        
        for position in current_portfolio:
            current_yield = position['current_apy']
            network = position['network']
            protocol = position['protocol']
            
            # Find better opportunities in same asset class
            better_opportunities = [
                opp for opp in market_opportunities
                if (opp['asset'] == position['asset'] and 
                    opp['expected_yield'] > current_yield + self.rebalance_threshold)
            ]
            
            if better_opportunities:
                best_opportunity = max(better_opportunities, key=lambda x: x['expected_yield'])
                
                # Calculate potential profit after costs
                yield_improvement = best_opportunity['expected_yield'] - current_yield
                bridge_cost = self.estimate_bridge_cost(
                    position['amount_usd'], 
                    network, 
                    best_opportunity['network']
                )
                
                if yield_improvement * position['amount_usd'] > bridge_cost * 2:  # 2x cost coverage
                    rebalance_actions.append({
                        'action': 'migrate',
                        'from': position,
                        'to': best_opportunity,
                        'expected_profit': yield_improvement * position['amount_usd'] - bridge_cost,
                        'urgency': yield_improvement / self.rebalance_threshold
                    })
        
        return rebalance_actions
    
    def estimate_bridge_cost(self, amount, from_network, to_network):
        """Estimate bridge cost for rebalancing"""
        # Simplified bridge cost estimation
        base_costs = {
            ('ethereum', 'polygon'): 25,
            ('polygon', 'ethereum'): 35,
            ('ethereum', 'arbitrum'): 30,
            ('arbitrum', 'ethereum'): 40,
            ('ethereum', 'bsc'): 20,
            ('bsc', 'ethereum'): 25
        }
        
        base_cost = base_costs.get((from_network, to_network), 50)
        percentage_cost = amount * 0.001  # 0.1% of amount
        
        return base_cost + percentage_cost

# Example rebalancing decision
rebalancer = DynamicRebalancer()

current_portfolio = [
    {
        'network': 'ethereum',
        'protocol': 'compound',
        'asset': 'USDC',
        'amount_usd': 50000,
        'current_apy': 0.035
    }
]

market_opportunities = [
    {
        'network': 'polygon',
        'protocol': 'aave',
        'asset': 'USDC',
        'expected_yield': 0.065,
        'risk_score': 0.08
    },
    {
        'network': 'arbitrum',
        'protocol': 'curve',
        'asset': 'USDC',
        'expected_yield': 0.058,
        'risk_score': 0.06
    }
]

rebalance_suggestions = rebalancer.should_rebalance(current_portfolio, market_opportunities)

if rebalance_suggestions:
    print("Rebalancing Recommendations:")
    for suggestion in rebalance_suggestions:
        print(f"Migrate ${suggestion['from']['amount_usd']:,} from {suggestion['from']['network']} to {suggestion['to']['network']}")
        print(f"Expected additional profit: ${suggestion['expected_profit']:,.2f}")
else:
    print("No profitable rebalancing opportunities found.")

// Example: Secure cross-chain yield farming contract
pragma solidity ^0.8.19;

import "@openzeppelin/contracts/security/ReentrancyGuard.sol";
import "@openzeppelin/contracts/security/Pausable.sol";
import "@openzeppelin/contracts/access/Ownable.sol";

contract CrossChainYieldManager is ReentrancyGuard, Pausable, Ownable {
    struct Position {
        address asset;
        uint256 amount;
        address protocol;
        uint256 networkId;
        uint256 entryTime;
        uint256 lastRebalance;
    }
    
    mapping(address => Position[]) public userPositions;
    mapping(address => uint256) public maxPositionSize;
    
    // Emergency pause for specific networks
    mapping(uint256 => bool) public networkPaused;
    
    modifier validNetwork(uint256 networkId) {
        require(!networkPaused[networkId], "Network operations paused");
        _;
    }
    
    modifier positionSizeCheck(address asset, uint256 amount) {
        require(amount <= maxPositionSize[asset], "Position too large");
        _;
    }
    
    function depositToNetwork(
        address asset,
        uint256 amount,
        address protocol,
        uint256 targetNetwork
    ) 
        external 
        nonReentrant 
        whenNotPaused 
        validNetwork(targetNetwork)
        positionSizeCheck(asset, amount)
    {
        // Implementation for cross-chain deposit
        // Include bridge validation and slippage protection
    }
    
    function emergencyPauseNetwork(uint256 networkId) external onlyOwner {
        networkPaused[networkId] = true;
        emit NetworkPaused(networkId);
    }
    
    function setMaxPositionSize(address asset, uint256 maxSize) external onlyOwner {
        maxPositionSize[asset] = maxSize;
        emit MaxPositionSizeUpdated(asset, maxSize);
    }
    
    event NetworkPaused(uint256 indexed networkId);
    event MaxPositionSizeUpdated(address indexed asset, uint256 maxSize);
}