Layer 2 Network Analysis with Ollama: Polygon, Arbitrum, and Optimism Comparison

# Download and install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the recommended model for Data Analysis
ollama pull llama2:13b

# Verify installation
ollama list

from web3 import Web3
import requests
import json
import time
from datetime import datetime, timedelta

class Layer2Analyzer:
    def __init__(self):
        # RPC endpoints for each network
        self.networks = {
            'polygon': {
                'rpc': 'https://polygon-rpc.com',
                'chain_id': 137,
                'name': 'Polygon'
            },
            'arbitrum': {
                'rpc': 'https://arb1.arbitrum.io/rpc',
                'chain_id': 42161,
                'name': 'Arbitrum One'
            },
            'optimism': {
                'rpc': 'https://mainnet.optimism.io',
                'chain_id': 10,
                'name': 'Optimism'
            }
        }
    
    def get_network_metrics(self, network_key):
        """Collect comprehensive network performance data"""
        network = self.networks[network_key]
        w3 = Web3(Web3.HTTPProvider(network['rpc']))
        
        # Get latest block information
        latest_block = w3.eth.get_block('latest')
        
        # Calculate average block time (last 100 blocks)
        block_times = []
        for i in range(100):
            block = w3.eth.get_block(latest_block.number - i)
            prev_block = w3.eth.get_block(latest_block.number - i - 1)
            block_times.append(block.timestamp - prev_block.timestamp)
        
        avg_block_time = sum(block_times) / len(block_times)
        
        # Get gas price data
        gas_price = w3.eth.gas_price
        
        return {
            'network': network['name'],
            'latest_block': latest_block.number,
            'avg_block_time': round(avg_block_time, 2),
            'gas_price_gwei': round(w3.from_wei(gas_price, 'gwei'), 4),
            'transactions_in_block': len(latest_block.transactions),
            'timestamp': datetime.now().isoformat()
        }

def analyze_polygon_performance():
    """Deep dive into Polygon network characteristics"""
    analyzer = Layer2Analyzer()
    polygon_data = analyzer.get_network_metrics('polygon')
    
    # Additional Polygon-specific metrics
    polygon_stats = {
        'daily_transactions': get_daily_tx_count('polygon'),
        'active_validators': get_validator_count(),
        'avg_gas_cost_usd': calculate_avg_gas_cost('polygon'),
        'popular_dapps': get_top_dapps('polygon')
    }
    
    # Prepare analysis prompt for Ollama
    analysis_prompt = f"""
    Analyze this Polygon network data:
    Block time: {polygon_data['avg_block_time']} seconds
    Gas price: {polygon_data['gas_price_gwei']} gwei
    Daily transactions: {polygon_stats['daily_transactions']}
    
    Provide insights on:
    1. Transaction speed compared to Ethereum mainnet
    2. Cost efficiency for different transaction types
    3. Network congestion patterns
    4. Suitability for high-frequency trading
    """
    
    return query_ollama(analysis_prompt)

def query_ollama(prompt):
    """Send analysis request to local Ollama instance"""
    response = requests.post('http://localhost:11434/api/generate', 
                           json={
                               'model': 'llama2:13b',
                               'prompt': prompt,
                               'stream': False
                           })
    return response.json()['response']

def analyze_arbitrum_rollup_efficiency():
    """Examine Arbitrum's rollup batching performance"""
    analyzer = Layer2Analyzer()
    arbitrum_data = analyzer.get_network_metrics('arbitrum')
    
    # Arbitrum-specific metrics
    rollup_metrics = {
        'batch_frequency': get_rollup_batch_frequency(),
        'tx_per_batch': get_average_transactions_per_batch(),
        'challenge_period': 604800,  # 7 days in seconds
        'fraud_proof_count': get_fraud_proof_history(),
        'sequencer_uptime': get_sequencer_performance()
    }
    
    analysis_prompt = f"""
    Analyze Arbitrum One performance data:
    Batch frequency: {rollup_metrics['batch_frequency']} batches/hour
    Transactions per batch: {rollup_metrics['tx_per_batch']}
    Gas efficiency: {arbitrum_data['gas_price_gwei']} gwei
    
    Compare to Polygon and evaluate:
    1. Rollup efficiency vs sidechain approach
    2. Security trade-offs with 7-day challenge period
    3. Cost predictability for users
    4. Decentralization benefits
    """
    
    return query_ollama(analysis_prompt)

def get_rollup_batch_frequency():
    """Calculate how often Arbitrum posts batches to L1"""
    # Implementation to track L1 batch submissions
    pass

def get_sequencer_performance():
    """Monitor Arbitrum sequencer uptime and performance"""
    # Implementation to track sequencer metrics
    pass

def analyze_optimism_equivalence():
    """Evaluate Optimism's EVM equivalence benefits"""
    analyzer = Layer2Analyzer()
    optimism_data = analyzer.get_network_metrics('optimism')
    
    # Optimism-specific features
    op_metrics = {
        'bedrock_upgrade_impact': measure_bedrock_performance(),
        'op_token_governance': get_governance_participation(),
        'retroactive_funding': get_retropgf_impact(),
        'developer_adoption': count_deployed_contracts(),
        'evm_equivalence_score': calculate_compatibility_score()
    }
    
    comparison_prompt = f"""
    Analyze Optimism network characteristics:
    EVM equivalence: {op_metrics['evm_equivalence_score']}%
    Developer adoption: {op_metrics['developer_adoption']} new contracts/month
    Gas efficiency: {optimism_data['gas_price_gwei']} gwei
    
    Compare against Polygon and Arbitrum for:
    1. Developer migration ease
    2. Long-term sustainability model
    3. Governance token utility
    4. Public goods funding impact
    """
    
    return query_ollama(comparison_prompt)

def measure_bedrock_performance():
    """Track performance improvements from Bedrock upgrade"""
    # Implementation to measure post-Bedrock metrics
    pass

def get_governance_participation():
    """Monitor OP token governance activity"""
    # Implementation to track governance metrics
    pass

def create_comparison_dashboard():
    """Generate comprehensive L2 network comparison"""
    analyzer = Layer2Analyzer()
    
    # Collect data from all networks
    networks_data = {}
    for network in ['polygon', 'arbitrum', 'optimism']:
        networks_data[network] = analyzer.get_network_metrics(network)
    
    # Generate comparative analysis
    comparison_prompt = f"""
    Compare these Layer 2 networks using the data:
    
    Polygon: {json.dumps(networks_data['polygon'], indent=2)}
    Arbitrum: {json.dumps(networks_data['arbitrum'], indent=2)}
    Optimism: {json.dumps(networks_data['optimism'], indent=2)}
    
    Provide recommendations for:
    1. High-frequency trading applications
    2. NFT marketplace deployment
    3. Complex DeFi protocol development
    4. Cost-sensitive consumer applications
    5. Long-term ecosystem investment
    
    Include specific metrics and reasoning for each recommendation.
    """
    
    return query_ollama(comparison_prompt)

def generate_cost_analysis():
    """Calculate transaction costs across all networks"""
    cost_scenarios = {
        'simple_transfer': {'gas_units': 21000},
        'erc20_transfer': {'gas_units': 65000},
        'uniswap_swap': {'gas_units': 150000},
        'nft_mint': {'gas_units': 200000},
        'complex_defi': {'gas_units': 500000}
    }
    
    # Calculate costs for each network and scenario
    cost_comparison = {}
    for network in ['polygon', 'arbitrum', 'optimism']:
        network_data = analyzer.get_network_metrics(network)
        gas_price_wei = w3.to_wei(network_data['gas_price_gwei'], 'gwei')
        
        cost_comparison[network] = {}
        for scenario, details in cost_scenarios.items():
            gas_cost = gas_price_wei * details['gas_units']
            cost_comparison[network][scenario] = {
                'gas_cost_eth': w3.from_wei(gas_cost, 'ether'),
                'gas_cost_usd': convert_eth_to_usd(w3.from_wei(gas_cost, 'ether'))
            }
    
    return cost_comparison

def analyze_mev_opportunities():
    """Compare MEV extraction potential across networks"""
    mev_data = {}
    
    for network in ['polygon', 'arbitrum', 'optimism']:
        mev_data[network] = {
            'flashloan_volume': get_flashloan_metrics(network),
            'arbitrage_frequency': count_arbitrage_transactions(network),
            'sandwich_attacks': detect_sandwich_patterns(network),
            'liquidation_events': track_liquidations(network)
        }
    
    # Analyze MEV landscape with Ollama
    mev_analysis_prompt = f"""
    Analyze MEV (Maximal Extractable Value) opportunities:
    {json.dumps(mev_data, indent=2)}
    
    Evaluate:
    1. Which network offers best MEV protection for users?
    2. Arbitrage opportunity frequency and profitability
    3. Flashloan availability and costs
    4. Impact on regular user transaction costs
    """
    
    return query_ollama(mev_analysis_prompt)

def compare_developer_experience():
    """Evaluate development tools and resources"""
    dev_metrics = {
        'polygon': {
            'rpc_reliability': measure_rpc_uptime('polygon'),
            'documentation_quality': rate_docs_quality('polygon'),
            'developer_tools': count_available_tools('polygon'),
            'testnet_faucet_availability': check_faucet_status('polygon'),
            'debugging_capabilities': assess_debugging_tools('polygon')
        },
        'arbitrum': {
            'rpc_reliability': measure_rpc_uptime('arbitrum'),
            'documentation_quality': rate_docs_quality('arbitrum'),
            'developer_tools': count_available_tools('arbitrum'),
            'testnet_faucet_availability': check_faucet_status('arbitrum'),
            'debugging_capabilities': assess_debugging_tools('arbitrum')
        },
        'optimism': {
            'rpc_reliability': measure_rpc_uptime('optimism'),
            'documentation_quality': rate_docs_quality('optimism'),
            'developer_tools': count_available_tools('optimism'),
            'testnet_faucet_availability': check_faucet_status('optimism'),
            'debugging_capabilities': assess_debugging_tools('optimism')
        }
    }
    
    return dev_metrics

import schedule
import time
from datetime import datetime

def setup_continuous_monitoring():
    """Set up automated Layer 2 network monitoring"""
    
    def run_analysis():
        print(f"Running analysis at {datetime.now()}")
        
        # Collect fresh data
        dashboard_data = create_comparison_dashboard()
        cost_analysis = generate_cost_analysis()
        
        # Store results with timestamp
        save_analysis_results({
            'timestamp': datetime.now().isoformat(),
            'dashboard': dashboard_data,
            'costs': cost_analysis
        })
        
        # Alert on significant changes
        check_for_alerts(dashboard_data)
    
    # Schedule analysis every hour
    schedule.every().hour.do(run_analysis)
    
    # Run initial analysis
    run_analysis()
    
    # Keep monitoring
    while True:
        schedule.run_pending()
        time.sleep(300)  # Check every 5 minutes

def check_for_alerts(data):
    """Monitor for significant network changes"""
    alert_conditions = {
        'high_gas_prices': {'threshold': 100, 'unit': 'gwei'},
        'slow_block_times': {'threshold': 10, 'unit': 'seconds'},
        'network_congestion': {'threshold': 90, 'unit': 'percent'}
    }
    
    # Implementation for alert logic
    pass

if __name__ == "__main__":
    setup_continuous_monitoring()

def handle_rpc_failures():
    """Implement fallback RPC endpoints"""
    fallback_rpcs = {
        'polygon': [
            'https://polygon-rpc.com',
            'https://rpc-mainnet.matic.network',
            'https://matic-mainnet.chainstacklabs.com'
        ],
        'arbitrum': [
            'https://arb1.arbitrum.io/rpc',
            'https://arbitrum-mainnet.infura.io/v3/YOUR_KEY',
            'https://rpc.ankr.com/arbitrum'
        ],
        'optimism': [
            'https://mainnet.optimism.io',
            'https://optimism-mainnet.infura.io/v3/YOUR_KEY',
            'https://rpc.ankr.com/optimism'
        ]
    }
    
    # Implementation for RPC failover logic
    pass

def validate_metrics():
    """Cross-reference data with multiple sources"""
    validation_sources = {
        'l2beat': 'https://l2beat.com/api/tvl',
        'defillama': 'https://api.llama.fi/chains',
        'dune_analytics': 'Custom SQL queries'
    }
    
    # Compare internal metrics with external sources
    # Flag discrepancies for manual review
    pass