Cross-Chain Bitcoin Analysis: Ollama Wrapped BTC vs Native L2 Comparison

# Install base Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull Bitcoin-specialized models
ollama pull shreyshah/satoshi-7b-q4_k_m  # Bitcoin-focused analysis
ollama pull 0xroyce/plutus               # Financial analysis
ollama pull deepseek-r1:7b               # General reasoning

# Verify installation
ollama list

# bitcoin_analyzer.py
import ollama
import requests
import json
from datetime import datetime

class BitcoinAnalyzer:
    def __init__(self, model="shreyshah/satoshi-7b-q4_k_m"):
        self.model = model
        
    def analyze_wrapped_btc(self, market_data):
        """Analyze WBTC performance and risks"""
        prompt = f"""
        Analyze this Wrapped Bitcoin data:
        - Price: ${market_data['price']}
        - 24h Volume: ${market_data['volume']}B
        - Market Cap: ${market_data['market_cap']}B
        - Custodian: BitGo (centralized)
        - Blockchain: Ethereum + 30 other chains
        
        Evaluate:
        1. Custodial risk (1-10 scale)
        2. Liquidity advantages
        3. DeFi integration benefits
        4. Fee structure analysis
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        return response['message']['content']
    
    def analyze_native_l2(self, network_data):
        """Analyze native L2 solutions"""
        prompt = f"""
        Analyze these Bitcoin L2 networks:
        - Lightning Network: {network_data['lightning']['channels']} channels
        - Stacks: {network_data['stacks']['tvl']} TVL
        - Rootstock: {network_data['rootstock']['active_accounts']} accounts
        
        Compare:
        1. Security model vs Bitcoin mainnet
        2. Transaction speed and costs
        3. Smart contract capabilities
        4. Decentralization level
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        return response['message']['content']

# wbtc_analysis.py
def get_wbtc_data():
    """Fetch current WBTC market data"""
    # Using placeholder data - replace with actual API calls
    return {
        'price': 119709.12,
        'volume': 351.08,
        'market_cap': 15.46,
        'total_supply': 129018
    }

def analyze_wbtc_risks():
    analyzer = BitcoinAnalyzer()
    data = get_wbtc_data()
    
    analysis = analyzer.analyze_wrapped_btc(data)
    print("WBTC Risk Analysis:")
    print(analysis)
    
    # Example output analysis
    return {
        'custodial_risk': 7,  # High due to BitGo dependency
        'liquidity_score': 9, # Excellent DeFi integration
        'fee_efficiency': 6,  # Ethereum gas costs
        'decentralization': 3 # Low due to custodial model
    }

# l2_analyzer.py
def analyze_l2_ecosystem():
    """Comprehensive L2 analysis using multiple models"""
    
    # Lightning Network data
    lightning_data = {
        'channels': 50000,
        'capacity_btc': 5000,
        'avg_payment_size': 0.001
    }
    
    # Stacks data  
    stacks_data = {
        'tvl': 150000000,
        'active_contracts': 1200,
        'stx_stacked': 0.3  # 30% of supply
    }
    
    # Use financial analysis model
    analyzer = BitcoinAnalyzer(model="0xroyce/plutus")
    
    network_data = {
        'lightning': lightning_data,
        'stacks': stacks_data,
        'rootstock': {'active_accounts': 70000}
    }
    
    l2_analysis = analyzer.analyze_native_l2(network_data)
    print("Native L2 Analysis:")
    print(l2_analysis)
    
    return {
        'security_score': 9,      # Bitcoin-secured
        'scalability': 8,         # High TPS capability  
        'complexity': 7,          # Technical setup required
        'decentralization': 8     # No single custodian
    }

# monitoring_dashboard.py
import asyncio
from datetime import datetime

class CrossChainMonitor:
    def __init__(self):
        self.wbtc_analyzer = BitcoinAnalyzer("shreyshah/satoshi-7b-q4_k_m")
        self.defi_analyzer = BitcoinAnalyzer("0xroyce/plutus")
        
    async def compare_solutions(self):
        """Real-time comparison of WBTC vs L2 solutions"""
        
        # Fetch live data
        wbtc_metrics = await self.get_wbtc_metrics()
        l2_metrics = await self.get_l2_metrics()
        
        # AI-powered analysis
        comparison_prompt = f"""
        Compare these Bitcoin solutions:
        
        WBTC: ${wbtc_metrics['tvl']}B TVL, {wbtc_metrics['protocols']} protocols
        L2 Native: ${l2_metrics['combined_tvl']}B TVL, {l2_metrics['active_users']} users
        
        Market conditions: {self.get_market_sentiment()}
        
        Recommend the optimal choice for:
        1. Long-term hodlers seeking yield
        2. Active DeFi traders
        3. Bitcoin purists prioritizing decentralization
        4. Institutions requiring compliance
        """
        
        analysis = ollama.chat(
            model="deepseek-r1:7b",
            messages=[{'role': 'user', 'content': comparison_prompt}]
        )
        
        return analysis['message']['content']
    
    def get_market_sentiment(self):
        """Analyze current Bitcoin market sentiment"""
        # Placeholder for sentiment analysis
        return "Bullish with high volatility"

# security_analyzer.py
def assess_security_risks():
    """Comprehensive security risk assessment"""
    
    wbtc_risks = {
        'custodial_failure': 0.05,    # 5% BitGo risk
        'smart_contract': 0.02,       # 2% contract risk  
        'regulatory': 0.15,           # 15% regulatory risk
        'liquidity': 0.01             # 1% liquidity risk
    }
    
    l2_risks = {
        'protocol_bugs': 0.08,        # 8% protocol risk
        'network_attacks': 0.03,      # 3% attack risk
        'liquidity_fragmentation': 0.12, # 12% liquidity risk
        'adoption': 0.20              # 20% adoption risk
    }
    
    # Calculate composite risk scores
    wbtc_total_risk = sum(wbtc_risks.values())
    l2_total_risk = sum(l2_risks.values())
    
    return {
        'wbtc_risk': wbtc_total_risk,
        'l2_risk': l2_total_risk,
        'recommendation': 'L2 Native' if l2_total_risk < wbtc_total_risk else 'WBTC'
    }

# performance_benchmarks.py
def benchmark_transaction_speeds():
    """Measure and compare transaction speeds"""
    
    benchmarks = {
        'bitcoin_mainnet': 600,      # 10 minutes average
        'wbtc_ethereum': 15,         # 15 seconds average
        'wbtc_arbitrum': 2,          # 2 seconds average
        'lightning_network': 0.001,  # Milliseconds
        'stacks': 10,                # 10 seconds average
        'rootstock': 30              # 30 seconds average
    }
    
    # Generate performance report
    analyzer = BitcoinAnalyzer("0xroyce/plutus")
    
    report_prompt = f"""
    Analyze these Bitcoin transaction speeds:
    {json.dumps(benchmarks, indent=2)}
    
    Consider:
    1. User experience impact
    2. Trading strategy implications  
    3. Cost-speed trade-offs
    4. Use case optimization
    """
    
    response = ollama.chat(
        model="0xroyce/plutus", 
        messages=[{'role': 'user', 'content': report_prompt}]
    )
    
    return response['message']['content']

# strategy_optimizer.py
def optimize_btc_allocation(risk_tolerance, investment_amount):
    """AI-powered portfolio optimization"""
    
    strategy_prompt = f"""
    Optimize Bitcoin allocation:
    - Risk tolerance: {risk_tolerance}/10
    - Investment: ${investment_amount}
    - Goal: Maximize risk-adjusted returns
    
    Options:
    1. Native BTC (0% yield, 100% security)
    2. WBTC DeFi (5-15% APY, custodial risk)
    3. Lightning channels (2-8% routing fees)
    4. Stacks stacking (6-12% BTC rewards)
    
    Provide specific allocation percentages.
    """
    
    analyzer = BitcoinAnalyzer("0xroyce/plutus")
    optimization = analyzer.analyze_wrapped_btc({
        'risk_tolerance': risk_tolerance,
        'amount': investment_amount,
        'market_conditions': 'current'
    })
    
    return optimization

# trend_predictor.py
def predict_market_trends():
    """AI-powered trend prediction"""
    
    prediction_prompt = """
    Analyze Bitcoin cross-chain trends for 2025-2026:
    
    Current data:
    - WBTC dominance: 85% of wrapped Bitcoin market
    - L2 growth: 2,767% TVL increase in BTCFi
    - Lightning adoption: 1,212% transaction growth
    
    Predict:
    1. Market share shifts between solutions
    2. New technological developments
    3. Regulatory impact on each approach
    4. Investment flow patterns
    """
    
    analyzer = BitcoinAnalyzer("deepseek-r1:7b")
    response = ollama.chat(
        model="deepseek-r1:7b",
        messages=[{'role': 'user', 'content': prediction_prompt}]
    )
    
    return response['message']['content']