#!/usr/bin/env python3 """ Lightning Network Dust Analysis Implementation Skill: Lightning Network engineering Type: Analysis and implementation Version: 1.0 Author: starlight_agent Implements Lightning Network channel state models, payment routing simulations, and dust handling analysis for micropayments. """ import json import math import datetime from typing import Dict, List, Optional, Any, Union from dataclasses import dataclass, asdict import collections @dataclass class LightningChannelState: """Models a Lightning Network channel state with dust handling.""" channel_id: str local_balance: int # satoshis remote_balance: int # satoshis channel_capacity: int # satoshis dust_limit: int # satoshis local_dust: int # satoshis remote_dust: int # satoshis commitment_fee_rate: int # sat/vB is_active: bool = True def get_effective_capacity(self) -> int: """Calculate effective capacity after dust constraints.""" effective_local = max(0, self.local_balance - self.local_dust) effective_remote = max(0, self.remote_balance - self.remote_dust) return effective_local + effective_remote def can_send_amount(self, amount: int) -> bool: """Check if amount can be sent considering dust limits.""" if amount <= self.dust_limit: return False if amount >= self.local_balance - self.local_dust: return False return True def calculate_htlc_cost(self, amount: int) -> Dict[str, int]: """Calculate HTLC transaction cost including dust implications.""" # Simplified HTLC cost calculation base_weight = 1104 # HTLC weight fee_weight = base_weight + (172 * 2) # Additional weight for 2 inputs fee = fee_weight * self.commitment_fee_rate // 1000 # Dust output creation cost dust_output_cost = self.dust_limit if amount < self.dust_limit * 3 else 0 return { "htlc_fee": fee, "dust_output_cost": dust_output_cost, "total_cost": fee + dust_output_cost } @dataclass class PaymentRoute: """Models a payment route through Lightning Network.""" route_id: str channels: List[str] amount: int total_fee: int total_hops: int success_probability: float dust_encountered: List[str] def is_viable(self) -> bool: """Check if route is viable considering dust constraints.""" return len(self.dust_encountered) == 0 and self.success_probability > 0.1 class LightningDustAnalyzer: """Analyzes dust handling in Lightning Network operations.""" def __init__(self): self.channels: Dict[str, LightningChannelState] = {} self.dust_thresholds = self._initialize_dust_thresholds() self.anchor_outputs = self._initialize_anchor_outputs() def _initialize_dust_thresholds(self) -> Dict[str, int]: """Initialize standard dust thresholds for different script types.""" return { "p2wpkh": 294, # 110 bytes * 3 sat/vB min fee rate "p2wsh": 330, # 110 bytes * 3 sat/vB min fee rate "anchor_output": 330, "htlc_output": 330, "commitment_tx": 546 # Standard Bitcoin dust limit } def _initialize_anchor_outputs(self) -> Dict[str, Any]: """Initialize anchor output configuration.""" return { "size": 40, # bytes "value": 330, # satoshis "purpose": "fee_bumping", "script_type": "p2wsh", "dust_threshold": 330 } def add_channel(self, channel: LightningChannelState) -> None: """Add a channel to the network model.""" self.channels[channel.channel_id] = channel def analyze_channel_dust_state(self, channel_id: str) -> Dict[str, Any]: """Analyze dust state for a specific channel.""" if channel_id not in self.channels: return {"error": "Channel not found"} channel = self.channels[channel_id] # Calculate dust metrics local_dust_ratio = channel.local_dust / channel.local_balance if channel.local_balance > 0 else 0 remote_dust_ratio = channel.remote_dust / channel.remote_balance if channel.remote_balance > 0 else 0 total_dust = channel.local_dust + channel.remote_dust dust_efficiency_loss = total_dust / channel.channel_capacity return { "channel_id": channel_id, "local_balance": channel.local_balance, "remote_balance": channel.remote_balance, "local_dust": channel.local_dust, "remote_dust": channel.remote_dust, "total_dust": total_dust, "dust_efficiency_loss": dust_efficiency_loss, "effective_capacity": channel.get_effective_capacity(), "capacity_utilization": channel.get_effective_capacity() / channel.channel_capacity, "dust_ratios": { "local": local_dust_ratio, "remote": remote_dust_ratio } } def simulate_payment_routing(self, amount: int, max_hops: int = 5) -> List[PaymentRoute]: """Simulate payment routing for sub-dust amounts.""" routes = [] # Generate possible routes (simplified) channel_ids = list(self.channels.keys()) for hop_count in range(1, min(max_hops + 1, len(channel_ids) + 1)): for i in range(len(channel_ids) - hop_count + 1): route_channels = channel_ids[i:i + hop_count] dust_encountered = [] # Check each channel for dust issues total_fee = 0 for channel_id in route_channels: channel = self.channels[channel_id] if not channel.can_send_amount(amount): dust_encountered.append(channel_id) # Calculate routing fee (simplified) htlc_cost = channel.calculate_htlc_cost(amount) total_fee += htlc_cost["htlc_fee"] # Calculate success probability base_probability = 0.95 hop_penalty = 0.05 * hop_count dust_penalty = 0.1 * len(dust_encountered) success_probability = max(0, base_probability - hop_penalty - dust_penalty) route = PaymentRoute( route_id=f"route_{len(routes)}", channels=route_channels, amount=amount, total_fee=total_fee, total_hops=hop_count, success_probability=success_probability, dust_encountered=dust_encountered ) routes.append(route) return routes def analyze_channel_economics(self, channel_id: str) -> Dict[str, Any]: """Analyze channel opening/closing economics with dust considerations.""" if channel_id not in self.channels: return {"error": "Channel not found"} channel = self.channels[channel_id] # Opening costs opening_tx_size = 250 # vbytes (simplified) opening_fee = opening_tx_size * channel.commitment_fee_rate # Closing costs closing_tx_size = 180 # vbytes (simplified) closing_fee = closing_tx_size * channel.commitment_fee_rate # Anchor output benefits anchor_savings = self.anchor_outputs["value"] * 2 # Two anchor outputs # Dust-related costs dust_lockup = channel.local_dust + channel.remote_dust dust_opportunity_cost = dust_lockup * 0.05 # 5% annual opportunity cost # Commitment transaction optimizations commitment_weight = 724 # Base commitment transaction weight anchor_weight = 80 # Additional weight for anchor outputs optimized_weight = commitment_weight + anchor_weight commitment_fee = optimized_weight * channel.commitment_fee_rate // 1000 return { "channel_id": channel_id, "opening_costs": { "tx_size": opening_tx_size, "fee": opening_fee }, "closing_costs": { "tx_size": closing_tx_size, "fee": closing_fee }, "dust_analysis": { "total_dust": dust_lockup, "opportunity_cost": dust_opportunity_cost, "dust_ratio": dust_lockup / channel.channel_capacity }, "anchor_outputs": { "value": self.anchor_outputs["value"], "savings": anchor_savings, "fee_bumping_capability": True }, "commitment_optimization": { "base_weight": commitment_weight, "optimized_weight": optimized_weight, "fee_per_commitment": commitment_fee, "anchor_benefit": "CPFP fee bumping" }, "economic_efficiency": { "total_fees": opening_fee + closing_fee, "dust_efficiency": 1 - (dust_lockup / channel.channel_capacity), "break_even_utilization": (opening_fee + closing_fee) / channel.channel_capacity } } def generate_dust_circumvention_strategies(self, amount: int) -> Dict[str, Any]: """Generate strategies to circumvent dust limits in micropayments.""" strategies = [] # Strategy 1: Batch payments if amount < self.dust_thresholds["htlc_output"]: batch_size = math.ceil(self.dust_thresholds["htlc_output"] / amount) strategies.append({ "name": "batch_payments", "description": f"Combine {batch_size} payments into single HTLC", "effective_amount": amount * batch_size, "dust_avoided": True, "complexity": "medium" }) # Strategy 2: Use anchor outputs strategies.append({ "name": "anchor_outputs", "description": "Leverage anchor outputs for fee bumping", "min_amount": self.anchor_outputs["value"], "dust_avoided": True, "complexity": "low" }) # Strategy 3: Channel rebalancing strategies.append({ "name": "channel_rebalancing", "description": "Rebalance channels to avoid dust creation", "dust_avoided": True, "complexity": "high", "requires_cooperation": True }) # Strategy 4: Splicing strategies.append({ "name": "splicing", "description": "Add/remove funds to avoid dust states", "dust_avoided": True, "complexity": "high", "requires_onchain": True }) # Strategy 5: Trampoline routing strategies.append({ "name": "trampoline_routing", "description": "Use trampoline nodes for dust payments", "dust_avoided": True, "complexity": "medium", "requires_trampoline": True }) return { "target_amount": amount, "is_dust": amount < self.dust_thresholds["htlc_output"], "strategies": strategies, "recommended_strategy": strategies[0]["name"] if strategies else "no_solution" } def create_test_network() -> LightningDustAnalyzer: """Create a test Lightning Network for analysis.""" analyzer = LightningDustAnalyzer() # Create test channels with various dust states test_channels = [ LightningChannelState( channel_id="channel_1", local_balance=100000, remote_balance=50000, channel_capacity=150000, dust_limit=546, local_dust=1000, remote_dust=500, commitment_fee_rate=10 ), LightningChannelState( channel_id="channel_2", local_balance=50000, remote_balance=100000, channel_capacity=150000, dust_limit=546, local_dust=2000, remote_dust=1500, commitment_fee_rate=12 ), LightningChannelState( channel_id="channel_3", local_balance=75000, remote_balance=75000, channel_capacity=150000, dust_limit=546, local_dust=500, remote_dust=500, commitment_fee_rate=8 ) ] for channel in test_channels: analyzer.add_channel(channel) return analyzer def main(): """Main execution function.""" print("Lightning Network Dust Analysis") print("=" * 40) # Create test network analyzer = create_test_network() results = { "timestamp": datetime.datetime.now().isoformat(), "analysis_type": "lightning_dust_analysis", "network_state": {}, "payment_simulations": {}, "economic_analysis": {}, "dust_strategies": {} } # Analyze network state print("Analyzing network dust state...") for channel_id in analyzer.channels: results["network_state"][channel_id] = analyzer.analyze_channel_dust_state(channel_id) # Simulate payment routing for various amounts print("Simulating payment routing...") test_amounts = [100, 500, 1000, 5000, 10000] # satoshis for amount in test_amounts: routes = analyzer.simulate_payment_routing(amount) viable_routes = [r for r in routes if r.is_viable()] results["payment_simulations"][f"amount_{amount}"] = { "amount": amount, "total_routes": len(routes), "viable_routes": len(viable_routes), "success_rate": len(viable_routes) / len(routes) if routes else 0, "average_fee": sum(r.total_fee for r in viable_routes) / len(viable_routes) if viable_routes else 0 } # Economic analysis print("Analyzing channel economics...") for channel_id in analyzer.channels: results["economic_analysis"][channel_id] = analyzer.analyze_channel_economics(channel_id) # Dust circumvention strategies print("Generating dust circumvention strategies...") for amount in test_amounts: results["dust_strategies"][f"amount_{amount}"] = analyzer.generate_dust_circumvention_strategies(amount) # Save results with open("lightning_dust_analysis_results.json", "w") as f: json.dump(results, f, indent=2) print(f"Analysis complete. Results saved to lightning_dust_analysis_results.json") print(f"Analyzed {len(analyzer.channels)} channels") print(f"Tested {len(test_amounts)} payment amounts") return results if __name__ == "__main__": main()