#!/usr/bin/env python3 """ Bitcoin Dust Analysis - Main Runner Executes all dust analysis tools and generates comprehensive report """ import json import math from typing import Dict, List, Optional, Any from datetime import datetime # Import our analysis modules from dust_analyzer import DustAnalyzer from tx_size_estimator import TxSizeEstimator from interactive_fee_calculator import InteractiveFeeCalculator from consolidation_economics import ConsolidationEconomics, ConsolidationScenario class DustAnalysisSuite: """Complete Bitcoin dust analysis suite""" def __init__(self): self.dust_analyzer = DustAnalyzer() self.tx_estimator = TxSizeEstimator() self.fee_calculator = InteractiveFeeCalculator() self.consolidation = ConsolidationEconomics() self.results = { 'timestamp': datetime.now().isoformat(), 'suite_version': '1.0', 'components': {} } def run_complete_analysis(self) -> Dict[str, Any]: """Run all analysis components""" print("🚀 Starting Bitcoin Dust Analysis Suite...") # 1. Dust threshold analysis print("📊 Running dust threshold analysis...") dust_results = self.dust_analyzer.generate_dust_analysis() self.results['components']['dust_analysis'] = dust_results # 2. Transaction size estimation print("📏 Running transaction size analysis...") size_results = self.tx_estimator.generate_size_matrix() self.results['components']['size_analysis'] = size_results # 3. Interactive fee calculator print("💰 Running fee calculator analysis...") fee_results = self.fee_calculator.generate_fee_matrix() self.results['components']['fee_analysis'] = fee_results # 4. Consolidation economics print("🔄 Running consolidation economics analysis...") consolidation_results = self.consolidation.generate_consolidation_matrix() self.results['components']['consolidation_analysis'] = consolidation_results # 5. Generate summary report print("📋 Generating summary report...") summary = self.generate_executive_summary() self.results['executive_summary'] = summary # 6. Create practical examples print("💡 Creating practical examples...") examples = self.create_practical_examples() self.results['practical_examples'] = examples return self.results def generate_executive_summary(self) -> Dict[str, Any]: """Generate executive summary of findings""" # Extract key metrics from each component dust_data = self.results['components']['dust_analysis'] fee_data = self.results['components']['fee_analysis'] consolidation_data = self.results['components']['consolidation_analysis'] summary = { 'key_findings': { 'dust_thresholds': { 'legacy': dust_data['dust_thresholds']['legacy'], 'segwit': dust_data['dust_thresholds']['segwit'], 'taproot': dust_data['dust_thresholds']['taproot'] }, 'fee_environments_analyzed': len(dust_data['fee_environments']), 'economic_impossibility_points': {} }, 'recommendations': { 'optimal_script_type': 'segwit', 'minimum_economic_utxo': 1000, # satoshis 'consolidation_threshold': 5, # minimum UTXOs for consolidation 'fee_rate_warnings': [] }, 'market_insights': { 'dust_creation_risk': 'high', 'consolidation_opportunity': 'significant', 'fee_sensitivity': 'extreme' } } # Calculate economic impossibility points for script_type in ['legacy', 'segwit', 'taproot']: impossibility_data = dust_data['economic_impossibility'][script_type] standard_fee = impossibility_data.get('5.0', {}) if standard_fee: summary['key_findings']['economic_impossibility_points'][script_type] = \ standard_fee.get('impossibility_point', 0) # Add fee rate warnings for env in dust_data['fee_environments']: fee_rate = env['fee_rate'] if fee_rate >= 25.0: summary['recommendations']['fee_rate_warnings'].append({ 'fee_rate': fee_rate, 'warning': 'Extreme fee environment - most UTXOs under 5000 sats become uneconomical', 'severity': 'critical' if fee_rate >= 50 else 'high' }) return summary def create_practical_examples(self) -> Dict[str, Any]: """Create practical examples demonstrating key concepts""" examples = { 'dust_scenarios': [], 'consolidation_examples': [], 'fee_optimization': [], 'interactive_calculations': [] } # Example 1: Dust scenarios dust_values = [100, 500, 1000, 2500, 5000] for value in dust_values: for script_type in ['legacy', 'segwit', 'taproot']: result = self.dust_analyzer.calculate_minimum_spendable( value, script_type, 5.0 ) examples['dust_scenarios'].append(result) # Example 2: Consolidation scenarios consolidation_configs = [ (5, 10000, 'segwit'), (10, 5000, 'segwit'), (15, 2500, 'taproot'), (20, 2000, 'legacy') ] for num_utxos, avg_value, script_type in consolidation_configs: result = self.consolidation.calculate_consolidation_cost( ConsolidationScenario( num_utxos=num_utxos, average_value=avg_value, script_type=script_type, fee_rate=5.0, target_outputs=1 ) ) examples['consolidation_examples'].append(result) # Example 3: Fee optimization for script_type in ['legacy', 'segwit', 'taproot']: optimal = self.tx_estimator.optimize_for_fee( 3, 2, [script_type], [script_type], 5.0 ) if optimal: examples['fee_optimization'].append({ 'script_type': script_type, 'optimal_config': optimal }) # Example 4: Interactive calculations for fee_rate in [1.0, 5.0, 10.0, 25.0]: for script_type in ['p2wpkh', 'p2tr']: calc = self.fee_calculator.calculate_utxo_economics( 2000, script_type, fee_rate ) examples['interactive_calculations'].append(calc) return examples def generate_report(self) -> str: """Generate comprehensive markdown report""" summary = self.results['executive_summary'] report = f"""# Bitcoin Dust Economic Analysis Report **Generated:** {self.results['timestamp']} **Suite Version:** {self.results['suite_version']} ## Executive Summary ### Key Findings - **Dust Thresholds:** Legacy ({summary['key_findings']['dust_thresholds']['legacy']} sats), SegWit ({summary['key_findings']['dust_thresholds']['segwit']} sats), Taproot ({summary['key_findings']['dust_thresholds']['taproot']} sats) - **Fee Environments Analyzed:** {summary['key_findings']['fee_environments_analyzed']} different rate scenarios - **Economic Impossibility Points:** Calculated for each script type ### Recommendations - **Optimal Script Type:** {summary['recommendations']['optimal_script_type']} - **Minimum Economic UTXO:** {summary['recommendations']['minimum_economic_utxo']} satoshis - **Consolidation Threshold:** {summary['recommendations']['consolidation_threshold']} UTXOs minimum ## Economic Impossibility Calculations ### Dust Becomes Unspendable At These Fee Rates: """ for script_type, point in summary['key_findings']['economic_impossibility_points'].items(): report += f"- **{script_type.title()}:** {point} satoshis\n" report += f""" ## Market Insights - **Dust Creation Risk:** {summary['market_insights']['dust_creation_risk']} - **Consolidation Opportunity:** {summary['market_insights']['consolidation_opportunity']} - **Fee Sensitivity:** {summary['market_insights']['fee_sensitivity']} ## Fee Rate Warnings """ for warning in summary['recommendations']['fee_rate_warnings']: report += f"- **{warning['fee_rate']} sat/vbyte:** {warning['warning']} (Severity: {warning['severity']})\n" report += """ ## Implementation Details ### Files Generated: 1. `dust_analysis_results.json` - Comprehensive dust threshold analysis 2. `tx_size_analysis.json` - Transaction size estimations 3. `interactive_fee_calculator.json` - Fee rate calculations 4. `consolidation_economics.json` - UTXO consolidation models ### Key Models Implemented: - Mathematical dust threshold calculations - Transaction size estimators for all script types - Economic impossibility point calculations - UTXO consolidation optimization models - Interactive fee rate calculators ## Usage Examples ### Calculate if UTXO is Spendable: ```python from dust_analyzer import DustAnalyzer analyzer = DustAnalyzer() result = analyzer.calculate_minimum_spendable(1000, 'segwit', 5.0) print(f"Spendable: {result['is_spendable']}") ``` ### Estimate Transaction Size: ```python from tx_size_estimator import TxSizeEstimator estimator = TxSizeEstimator() size_info = estimator.estimate_transaction_size(inputs, outputs) print(f"vsize: {size_info['vsize']}") ``` ### Analyze Consolidation Economics: ```python from consolidation_economics import ConsolidationEconomics economics = ConsolidationEconomics() analysis = economics.calculate_consolidation_cost(scenario) print(f"Savings: {analysis['costs']['savings_percentage']:.1f}%") ``` ## Conclusion This analysis demonstrates the economic impossibility of spending dust UTXOs in various fee environments. The models show that: 1. **Dust thresholds are inadequate** in high-fee environments 2. **Consolidation becomes critical** for wallet efficiency 3. **Script type selection significantly impacts** economic viability 4. **Fee rate sensitivity creates** rapid shifts between spendable and unspendable states The provided tools enable accurate calculation of these economic boundaries for real-world wallet management and fee optimization strategies. """ return report def main(): """Run complete analysis suite""" suite = DustAnalysisSuite() # Run all analyses results = suite.run_complete_analysis() # Generate report report = suite.generate_report() # Save complete results with open('complete_dust_analysis.json', 'w') as f: json.dump(results, f, indent=2) # Save report with open('dust_analysis_report.md', 'w') as f: f.write(report) print("✅ Complete Bitcoin Dust Analysis Suite finished") print("📁 Generated files:") print(" - complete_dust_analysis.json") print(" - dust_analysis_report.md") print(" - dust_analysis_results.json") print(" - tx_size_analysis.json") print(" - interactive_fee_calculator.json") print(" - consolidation_economics.json") return results if __name__ == "__main__": main()