Bitcoin L2 Transaction Speed Analysis: Ollama Latency and Throughput Metrics

# Basic latency breakdown
Time_to_confirmation = Route_discovery + Channel_updates + Settlement_time

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

# Configure for Bitcoin L2 testing
ollama pull bitcoin-l2-analyzer
ollama create bitcoin-metrics -f ./Modelfile

FROM bitcoin-l2-analyzer
PARAMETER temperature 0.1
PARAMETER num_ctx 4096

# System prompt for L2 analysis
SYSTEM """
You are a Bitcoin Layer 2 performance analyzer. 
Focus on transaction speed, latency, and throughput metrics.
Provide numerical data and avoid speculation.
"""

import ollama
import time
import json
from datetime import datetime

class BitcoinL2SpeedTest:
    def __init__(self, l2_network="lightning"):
        self.network = l2_network
        self.client = ollama.Client()
        self.metrics = []
    
    def measure_transaction_latency(self, amount_sats, route_hops):
        """Measure end-to-end transaction time"""
        start_time = time.time()
        
        # Simulate L2 transaction with Ollama analysis
        response = self.client.chat(
            model='bitcoin-metrics',
            messages=[{
                'role': 'user',
                'content': f'Analyze {self.network} transaction: {amount_sats} sats, {route_hops} hops'
            }]
        )
        
        end_time = time.time()
        latency = (end_time - start_time) * 1000  # Convert to milliseconds
        
        return {
            'latency_ms': latency,
            'amount': amount_sats,
            'hops': route_hops,
            'timestamp': datetime.now().isoformat()
        }

def test_multiple_routes(self):
    """Test transaction speeds across different route configurations"""
    test_scenarios = [
        {'amount': 1000, 'hops': 1, 'description': 'Direct channel'},
        {'amount': 1000, 'hops': 3, 'description': 'Standard route'},
        {'amount': 1000, 'hops': 6, 'description': 'Complex route'},
        {'amount': 100000, 'hops': 3, 'description': 'Large payment'},
        {'amount': 100, 'hops': 3, 'description': 'Micropayment'}
    ]
    
    results = []
    for scenario in test_scenarios:
        # Run 10 tests per scenario for statistical accuracy
        scenario_results = []
        for i in range(10):
            result = self.measure_transaction_latency(
                scenario['amount'], 
                scenario['hops']
            )
            scenario_results.append(result['latency_ms'])
            time.sleep(0.1)  # Prevent rate limiting
        
        # Calculate statistics
        avg_latency = sum(scenario_results) / len(scenario_results)
        min_latency = min(scenario_results)
        max_latency = max(scenario_results)
        
        results.append({
            'scenario': scenario['description'],
            'average_latency_ms': avg_latency,
            'min_latency_ms': min_latency,
            'max_latency_ms': max_latency,
            'amount_sats': scenario['amount'],
            'route_hops': scenario['hops']
        })
    
    return results

def stress_test_throughput(self, duration_seconds=60):
    """Measure sustained transaction throughput"""
    start_time = time.time()
    transaction_count = 0
    failed_transactions = 0
    
    while (time.time() - start_time) < duration_seconds:
        try:
            # Send rapid transactions
            result = self.measure_transaction_latency(1000, 2)
            if result['latency_ms'] < 5000:  # Success if under 5 seconds
                transaction_count += 1
            else:
                failed_transactions += 1
        except Exception as e:
            failed_transactions += 1
        
        time.sleep(0.01)  # 100 transactions per second attempt rate
    
    actual_duration = time.time() - start_time
    throughput_tps = transaction_count / actual_duration
    failure_rate = failed_transactions / (transaction_count + failed_transactions)
    
    return {
        'throughput_tps': throughput_tps,
        'successful_transactions': transaction_count,
        'failed_transactions': failed_transactions,
        'failure_rate_percent': failure_rate * 100,
        'test_duration_seconds': actual_duration
    }

class LiquidSpeedTest(BitcoinL2SpeedTest):
    def __init__(self):
        super().__init__("liquid")
        self.block_time = 60  # Liquid 1-minute blocks
    
    def measure_liquid_confirmation(self, amount_lbtc):
        """Measure Liquid transaction confirmation time"""
        start_time = time.time()
        
        # Analyze Liquid transaction with Ollama
        response = self.client.chat(
            model='bitcoin-metrics',
            messages=[{
                'role': 'user',
                'content': f'Analyze Liquid transaction: {amount_lbtc} L-BTC confirmation time'
            }]
        )
        
        # Liquid transactions typically confirm in 1-2 blocks
        estimated_confirmation = self.block_time * 1.5  # Average 1.5 blocks
        
        return {
            'estimated_confirmation_seconds': estimated_confirmation,
            'amount_lbtc': amount_lbtc,
            'block_time': self.block_time,
            'analysis': response['message']['content']
        }

def compare_l2_solutions(self):
    """Compare performance across different L2 networks"""
    
    # Lightning Network test
    lightning_test = BitcoinL2SpeedTest("lightning")
    lightning_results = lightning_test.test_multiple_routes()
    
    # Liquid Network test  
    liquid_test = LiquidSpeedTest()
    liquid_results = []
    for amount in [0.001, 0.01, 0.1, 1.0]:
        result = liquid_test.measure_liquid_confirmation(amount)
        liquid_results.append(result)
    
    # State channel test (simplified)
    statechan_latency = 50  # Typical state channel update time in ms
    
    comparison = {
        'lightning_avg_latency_ms': sum(r['average_latency_ms'] for r in lightning_results) / len(lightning_results),
        'liquid_avg_confirmation_sec': sum(r['estimated_confirmation_seconds'] for r in liquid_results) / len(liquid_results),
        'state_channel_latency_ms': statechan_latency,
        'recommendations': self.generate_recommendations(lightning_results, liquid_results)
    }
    
    return comparison

def generate_recommendations(self, lightning_data, liquid_data):
    """Generate usage recommendations based on performance data"""
    recommendations = []
    
    avg_lightning_ms = sum(r['average_latency_ms'] for r in lightning_data) / len(lightning_data)
    
    if avg_lightning_ms < 1000:
        recommendations.append("Lightning Network: Excellent for instant micropayments")
    elif avg_lightning_ms < 3000:
        recommendations.append("Lightning Network: Good for small retail transactions")
    else:
        recommendations.append("Lightning Network: Consider route optimization")
    
    recommendations.append("Liquid Network: Best for medium-value transactions requiring privacy")
    recommendations.append("State Channels: Optimal for high-frequency trading between known parties")
    
    return recommendations

def optimize_l2_performance(self, network_type, current_metrics):
    """Suggest performance optimizations based on measured data"""
    
    optimizations = []
    
    if network_type == "lightning":
        if current_metrics['average_latency_ms'] > 2000:
            optimizations.extend([
                "Use direct channels for frequent counterparties",
                "Implement route caching to reduce discovery time",
                "Monitor channel liquidity to prevent routing failures"
            ])
        
        if current_metrics.get('failure_rate_percent', 0) > 10:
            optimizations.extend([
                "Increase channel capacity for reliable routing",
                "Use multiple smaller payments instead of large ones",
                "Implement retry logic with exponential backoff"
            ])
    
    elif network_type == "liquid":
        if current_metrics['confirmation_time'] > 180:  # Over 3 minutes
            optimizations.extend([
                "Increase transaction fee for faster confirmation",
                "Use Liquid's confidential transactions for privacy without speed impact",
                "Batch multiple transactions to improve efficiency"
            ])
    
    return optimizations

import asyncio
import websockets
import json

class L2PerformanceMonitor:
    def __init__(self):
        self.metrics_history = []
        self.alert_thresholds = {
            'lightning_latency_ms': 5000,
            'liquid_confirmation_sec': 300,
            'failure_rate_percent': 15
        }
    
    async def monitor_network_health(self):
        """Continuously monitor L2 network performance"""
        while True:
            # Test Lightning Network
            lightning_test = BitcoinL2SpeedTest("lightning")
            lightning_sample = lightning_test.measure_transaction_latency(1000, 3)
            
            # Test Liquid Network
            liquid_test = LiquidSpeedTest()
            liquid_sample = liquid_test.measure_liquid_confirmation(0.01)
            
            # Check against thresholds
            alerts = self.check_alert_conditions(lightning_sample, liquid_sample)
            
            if alerts:
                await self.send_performance_alerts(alerts)
            
            # Store metrics for trending
            self.metrics_history.append({
                'timestamp': time.time(),
                'lightning': lightning_sample,
                'liquid': liquid_sample
            })
            
            # Keep only last 24 hours of data
            cutoff_time = time.time() - (24 * 60 * 60)
            self.metrics_history = [m for m in self.metrics_history if m['timestamp'] > cutoff_time]
            
            await asyncio.sleep(60)  # Check every minute
    
    def check_alert_conditions(self, lightning_data, liquid_data):
        """Check if performance metrics exceed alert thresholds"""
        alerts = []
        
        if lightning_data['latency_ms'] > self.alert_thresholds['lightning_latency_ms']:
            alerts.append(f"Lightning Network latency high: {lightning_data['latency_ms']}ms")
        
        if liquid_data['estimated_confirmation_seconds'] > self.alert_thresholds['liquid_confirmation_sec']:
            alerts.append(f"Liquid confirmation slow: {liquid_data['estimated_confirmation_seconds']}s")
        
        return alerts
    
    async def send_performance_alerts(self, alerts):
        """Send alerts when performance degrades"""
        alert_message = {
            'timestamp': datetime.now().isoformat(),
            'alerts': alerts,
            'severity': 'warning'
        }
        
        # In production, send to monitoring system
        print(f"PERFORMANCE ALERT: {json.dumps(alert_message, indent=2)}")

# Production deployment script
#!/bin/bash

# Start Bitcoin node
bitcoind -daemon -testnet

# Initialize Lightning node
lnd --bitcoin.active --bitcoin.testnet --debuglevel=info

# Start Liquid daemon
liquidd -daemon -chain=testnet

# Deploy monitoring service
python3 l2_monitor.py --config=production.json --log-level=INFO

class L2MetricsAPI:
    """REST API for Bitcoin L2 performance metrics"""
    
    def __init__(self, monitor):
        self.monitor = monitor
        self.app = Flask(__name__)
        self.setup_routes()
    
    def setup_routes(self):
        @self.app.route('/api/v1/metrics/latest')
        def get_latest_metrics():
            """Return most recent performance data"""
            if not self.monitor.metrics_history:
                return jsonify({'error': 'No data available'}), 404
            
            latest = self.monitor.metrics_history[-1]
            return jsonify({
                'timestamp': latest['timestamp'],
                'lightning_latency_ms': latest['lightning']['latency_ms'],
                'liquid_confirmation_sec': latest['liquid']['estimated_confirmation_seconds'],
                'status': 'healthy' if not self.monitor.check_alert_conditions(
                    latest['lightning'], latest['liquid']) else 'degraded'
            })
        
        @self.app.route('/api/v1/metrics/history/<int:hours>')
        def get_historical_metrics(hours):
            """Return historical performance data"""
            cutoff_time = time.time() - (hours * 60 * 60)
            historical_data = [
                m for m in self.monitor.metrics_history 
                if m['timestamp'] > cutoff_time
            ]
            
            return jsonify({
                'data_points': len(historical_data),
                'metrics': historical_data,
                'summary': self.calculate_historical_summary(historical_data)
            })
    
    def calculate_historical_summary(self, data):
        """Calculate performance summary statistics"""
        if not data:
            return {}
        
        lightning_latencies = [d['lightning']['latency_ms'] for d in data]
        liquid_confirmations = [d['liquid']['estimated_confirmation_seconds'] for d in data]
        
        return {
            'lightning_avg_latency_ms': sum(lightning_latencies) / len(lightning_latencies),
            'lightning_max_latency_ms': max(lightning_latencies),
            'liquid_avg_confirmation_sec': sum(liquid_confirmations) / len(liquid_confirmations),
            'uptime_percent': len(data) / (len(data) + self.count_missing_data_points()) * 100
        }