BlackRock Bitcoin ETF: Institutional Crypto Yield Strategies That Actually Work

import requests
import pandas as pd
from datetime import datetime, timedelta

class LiquidStakingAnalyzer:
    def __init__(self):
        self.protocols = {
            'lido': 'https://api.llama.fi/protocol/lido',
            'rocket_pool': 'https://api.llama.fi/protocol/rocket-pool',
            'frax_ether': 'https://api.llama.fi/protocol/frax-ether'
        }
    
    def get_staking_yields(self):
        """Fetch current staking yields from major liquid staking protocols"""
        yields = {}
        
        for name, url in self.protocols.items():
            try:
                response = requests.get(url)
                data = response.json()
                
                # Calculate 30-day average yield
                recent_data = data['chainTvls']['Ethereum'][-30:]
                yields[name] = {
                    'tvl': recent_data[-1]['totalLiquidityUSD'],
                    'avg_yield': self.calculate_yield(recent_data),
                    'risk_score': self.assess_risk(data)
                }
            except Exception as e:
                print(f"Error fetching {name}: {e}")
        
        return yields
    
    def calculate_yield(self, data_points):
        """Calculate annualized yield from TVL growth and rewards"""
        if len(data_points) < 2:
            return 0
        
        start_tvl = data_points[0]['totalLiquidityUSD']
        end_tvl = data_points[-1]['totalLiquidityUSD']
        days = len(data_points)
        
        # Simplified yield calculation (real implementation would include reward rates)
        daily_growth = (end_tvl / start_tvl) ** (1/days) - 1
        return daily_growth * 365 * 100
    
    def assess_risk(self, protocol_data):
        """Simple risk assessment based on protocol metrics"""
        tvl = protocol_data.get('tvl', 0)
        age_days = (datetime.now() - datetime.fromtimestamp(
            protocol_data.get('inception', 0))).days
        
        # Higher TVL and longer history = lower risk
        risk_score = max(1, 10 - (tvl / 1e9) - (age_days / 365))
        return min(risk_score, 10)

# Usage example
analyzer = LiquidStakingAnalyzer()
yields = analyzer.get_staking_yields()

for protocol, metrics in yields.items():
    print(f"{protocol.upper()}:")
    print(f"  TVL: ${metrics['tvl']:,.0f}")
    print(f"  Est. Yield: {metrics['avg_yield']:.2f}%")
    print(f"  Risk Score: {metrics['risk_score']:.1f}/10")
    print()

// Restaking optimization algorithm
class RestakingOptimizer {
    constructor(initialStake, riskTolerance) {
        this.stake = initialStake;
        this.riskTolerance = riskTolerance; // 1-10 scale
        this.protocols = [
            { name: 'EigenLayer', baseYield: 0.045, riskMultiplier: 1.2 },
            { name: 'Babylon', baseYield: 0.038, riskMultiplier: 1.0 },
            { name: 'Symbiotic', baseYield: 0.052, riskMultiplier: 1.5 }
        ];
    }
    
    calculateOptimalAllocation() {
        // Risk-adjusted yield calculation
        const scoredProtocols = this.protocols.map(protocol => {
            const riskAdjustedYield = protocol.baseYield / protocol.riskMultiplier;
            const riskScore = protocol.riskMultiplier * (11 - this.riskTolerance);
            
            return {
                ...protocol,
                riskAdjustedYield,
                riskScore,
                optimalWeight: this.calculateWeight(riskAdjustedYield, riskScore)
            };
        });
        
        // Normalize weights to sum to 1
        const totalWeight = scoredProtocols.reduce((sum, p) => sum + p.optimalWeight, 0);
        
        return scoredProtocols.map(protocol => ({
            name: protocol.name,
            allocation: (protocol.optimalWeight / totalWeight) * this.stake,
            expectedYield: protocol.baseYield,
            riskLevel: protocol.riskMultiplier
        }));
    }
    
    calculateWeight(yield, risk) {
        // Sharpe ratio-inspired weighting
        return Math.max(0, yield / risk);
    }
    
    simulateReturns(months = 12) {
        const allocation = this.calculateOptimalAllocation();
        let totalValue = this.stake;
        const monthlyReturns = [];
        
        for (let month = 0; month < months; month++) {
            let monthlyGain = 0;
            
            allocation.forEach(position => {
                // Add market volatility and compound interest
                const monthlyYield = position.expectedYield / 12;
                const volatility = position.riskLevel * 0.02 * (Math.random() - 0.5);
                const realizedReturn = monthlyYield + volatility;
                
                monthlyGain += position.allocation * realizedReturn;
            });
            
            totalValue += monthlyGain;
            monthlyReturns.push({
                month: month + 1,
                totalValue,
                monthlyGain,
                totalReturn: ((totalValue - this.stake) / this.stake) * 100
            });
        }
        
        return monthlyReturns;
    }
}

// Example usage for institutional portfolio
const institution = new RestakingOptimizer(10000000, 7); // $10M stake, moderate-high risk tolerance
const allocation = institution.calculateOptimalAllocation();
const projectedReturns = institution.simulateReturns(12);

console.log('Optimal Restaking Allocation:');
allocation.forEach(position => {
    console.log(`${position.name}: $${position.allocation.toLocaleString()} (${position.expectedYield * 100:.1f}% APY)`);
});

console.log(`\nProjected 12-month return: ${projectedReturns[11].totalReturn.toFixed(2)}%`);

import numpy as np
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class YieldPosition:
    asset: str
    amount: float
    apy: float
    risk_factor: float
    liquidity_score: float

class DeltaNeutralFarmer:
    def __init__(self, initial_capital: float):
        self.capital = initial_capital
        self.positions: List[YieldPosition] = []
        self.hedge_ratio = 0.95  # 95% hedged to maintain delta neutrality
        
    def add_farming_position(self, asset: str, allocation_pct: float, 
                           expected_apy: float, risk_factor: float = 1.0):
        """Add a yield farming position with automatic hedging"""
        amount = self.capital * (allocation_pct / 100)
        
        # Create the farming position
        position = YieldPosition(
            asset=asset,
            amount=amount,
            apy=expected_apy,
            risk_factor=risk_factor,
            liquidity_score=self.calculate_liquidity_score(asset)
        )
        
        self.positions.append(position)
        
        # Automatically create hedge position
        hedge_amount = amount * self.hedge_ratio
        self.create_hedge(asset, hedge_amount)
        
    def create_hedge(self, asset: str, amount: float):
        """Create perpetual futures hedge to maintain delta neutrality"""
        hedge_cost = amount * 0.001  # 0.1% funding rate assumption
        
        print(f"Creating hedge for {asset}: ${amount:,.0f}")
        print(f"Estimated monthly hedge cost: ${hedge_cost:,.0f}")
        
        return {
            'asset': f"{asset}-PERP",
            'size': -amount,  # Short position
            'funding_rate': 0.001,
            'maintenance_margin': amount * 0.05
        }
    
    def calculate_liquidity_score(self, asset: str) -> float:
        """Calculate liquidity score based on market data"""
        # Simplified scoring - real implementation would use order book depth
        liquidity_scores = {
            'ETH': 0.95,
            'BTC': 0.98,
            'USDC': 0.99,
            'USDT': 0.97,
            'DAI': 0.85
        }
        return liquidity_scores.get(asset, 0.5)
    
    def optimize_allocation(self) -> Dict:
        """Optimize position sizes based on risk-adjusted returns"""
        if not self.positions:
            return {}
            
        # Calculate efficiency ratio for each position
        efficiency_ratios = []
        for pos in self.positions:
            # Risk-adjusted yield accounting for liquidity
            adjusted_yield = (pos.apy / pos.risk_factor) * pos.liquidity_score
            efficiency_ratios.append(adjusted_yield)
        
        # Normalize to create optimal weights
        total_efficiency = sum(efficiency_ratios)
        optimal_weights = [ratio / total_efficiency for ratio in efficiency_ratios]
        
        # Reallocate based on optimal weights
        reallocation_plan = {}
        for i, pos in enumerate(self.positions):
            optimal_amount = self.capital * optimal_weights[i]
            current_amount = pos.amount
            
            reallocation_plan[pos.asset] = {
                'current': current_amount,
                'optimal': optimal_amount,
                'adjustment': optimal_amount - current_amount,
                'expected_yield': pos.apy * optimal_amount / 100
            }
        
        return reallocation_plan
    
    def calculate_portfolio_metrics(self) -> Dict:
        """Calculate comprehensive portfolio metrics"""
        total_value = sum(pos.amount for pos in self.positions)
        weighted_apy = sum(pos.apy * (pos.amount / total_value) for pos in self.positions)
        
        # Calculate portfolio risk (simplified VaR)
        daily_volatilities = [pos.risk_factor * 0.02 for pos in self.positions]
        weights = [pos.amount / total_value for pos in self.positions]
        
        portfolio_volatility = np.sqrt(
            sum(w**2 * vol**2 for w, vol in zip(weights, daily_volatilities))
        )
        
        # 95% VaR (1-day)
        var_95 = total_value * portfolio_volatility * 1.65
        
        return {
            'total_value': total_value,
            'weighted_apy': weighted_apy,
            'daily_var_95': var_95,
            'sharpe_estimate': weighted_apy / (portfolio_volatility * np.sqrt(252) * 100),
            'hedge_efficiency': self.hedge_ratio
        }

# Example: Institutional yield farming setup
farmer = DeltaNeutralFarmer(initial_capital=50_000_000)  # $50M portfolio

# Add diverse yield farming positions
farmer.add_farming_position('ETH', 30, 5.2, 1.2)    # 30% ETH, 5.2% APY
farmer.add_farming_position('BTC', 25, 3.8, 1.0)    # 25% BTC, 3.8% APY  
farmer.add_farming_position('USDC', 25, 8.5, 0.8)   # 25% USDC, 8.5% APY
farmer.add_farming_position('DAI', 20, 7.2, 0.9)    # 20% DAI, 7.2% APY

# Optimize and analyze
reallocation = farmer.optimize_allocation()
metrics = farmer.calculate_portfolio_metrics()

print("INSTITUTIONAL YIELD FARMING ANALYSIS")
print("=" * 50)
print(f"Portfolio Value: ${metrics['total_value']:,.0f}")
print(f"Weighted APY: {metrics['weighted_apy']:.2f}%")
print(f"Daily VaR (95%): ${metrics['daily_var_95']:,.0f}")
print(f"Estimated Sharpe Ratio: {metrics['sharpe_estimate']:.2f}")
print(f"Hedge Efficiency: {metrics['hedge_efficiency']*100:.1f}%")

class InstitutionalRiskManager:
    def __init__(self, max_drawdown=0.15, correlation_limit=0.7):
        self.max_drawdown = max_drawdown
        self.correlation_limit = correlation_limit
        self.positions = {}
        
    def add_position_limit(self, asset, max_allocation_pct):
        """Set position limits for risk management"""
        self.positions[asset] = {
            'max_allocation': max_allocation_pct / 100,
            'current_allocation': 0
        }
    
    def validate_allocation(self, proposed_allocation: Dict) -> Dict:
        """Validate proposed allocation against risk limits"""
        violations = []
        recommendations = []
        
        total_allocation = sum(proposed_allocation.values())
        
        for asset, amount in proposed_allocation.items():
            allocation_pct = amount / total_allocation
            
            # Check position limits
            if asset in self.positions:
                max_allowed = self.positions[asset]['max_allocation']
                if allocation_pct > max_allowed:
                    violations.append(f"{asset} allocation {allocation_pct:.1%} exceeds limit {max_allowed:.1%}")
                    recommendations.append(f"Reduce {asset} to {max_allowed:.1%} of portfolio")
        
        # Check correlation concentration (simplified)
        high_corr_assets = ['BTC', 'ETH']  # Assume these are highly correlated
        high_corr_allocation = sum(
            amount for asset, amount in proposed_allocation.items() 
            if asset in high_corr_assets
        ) / total_allocation
        
        if high_corr_allocation > self.correlation_limit:
            violations.append(f"High correlation asset concentration: {high_corr_allocation:.1%}")
            recommendations.append("Diversify into uncorrelated assets or stablecoins")
        
        return {
            'valid': len(violations) == 0,
            'violations': violations,
            'recommendations': recommendations
        }

# Example institutional risk setup
risk_manager = InstitutionalRiskManager(max_drawdown=0.10, correlation_limit=0.60)

# Set institutional position limits
risk_manager.add_position_limit('BTC', 30)  # Max 30% BTC
risk_manager.add_position_limit('ETH', 25)  # Max 25% ETH
risk_manager.add_position_limit('IBIT', 40) # Max 40% IBIT

# Validate a proposed allocation
proposed = {
    'IBIT': 20_000_000,    # $20M
    'ETH_STAKING': 15_000_000,  # $15M  
    'BTC_FARMING': 10_000_000,  # $10M
    'USDC_YIELD': 5_000_000     # $5M
}

validation = risk_manager.validate_allocation(proposed)
print("Risk Analysis:", validation)