import json import math import datetime from typing import Dict, List, Optional, Any, Union from collections import defaultdict from dust_management import UTXO, DustAnalyzer, FeeRatePredictor, CoinSelector, ConsolidationEngine, WalletIntegration class AdvancedFeeOptimizer: """Advanced fee optimization strategies for dust consolidation.""" def __init__(self): self.fee_patterns = {} self.consolidation_history = [] def analyze_fee_patterns(self, fee_history: List[Dict]) -> Dict[str, Any]: """Analyze historical fee patterns for optimal timing.""" if not fee_history: return {"pattern": "insufficient_data"} hourly_rates = defaultdict(list) for entry in fee_history: hour = entry["timestamp"].hour hourly_rates[hour].append(entry["rate"]) # Calculate average rates by hour hourly_avg = {} for hour, rates in hourly_rates.items(): hourly_avg[hour] = sum(rates) / len(rates) # Find optimal consolidation windows sorted_hours = sorted(hourly_avg.items(), key=lambda x: x[1]) optimal_hours = [hour for hour, rate in sorted_hours[:3]] return { "hourly_averages": hourly_avg, "optimal_hours": optimal_hours, "current_pattern": "low_fee_detected" if len(fee_history) > 0 else "unknown" } def calculate_optimal_batch_size(self, utxos: List[UTXO], fee_rate: float) -> int: """Calculate optimal batch size for consolidation transactions.""" # Base transaction overhead base_size = 180 # Size per input input_size = 148 # Find batch size that maximizes efficiency best_batch = 1 best_efficiency = 0 for batch_size in range(1, min(50, len(utxos)) + 1): total_size = base_size + (batch_size * input_size) fee_cost = (fee_rate * total_size) / 100000000 batch_value = sum(utxo.amount for utxo in utxos[:batch_size]) efficiency = batch_value / fee_cost if fee_cost > 0 else 0 if efficiency > best_efficiency: best_efficiency = efficiency best_batch = batch_size return best_batch class RiskModel: """Risk assessment models for dust consolidation decisions.""" def __init__(self): self.risk_factors = { "fee_volatility": 0.3, "dust_value_ratio": 0.4, "privacy_impact": 0.2, "opportunity_cost": 0.1 } def assess_consolidation_risk(self, plan, current_fee_rate: float, fee_volatility: float) -> Dict[str, Any]: """Assess risk level for consolidation plan.""" dust_value = sum(utxo.amount for utxo in plan.inputs) # Calculate individual risk factors fee_risk = min(1.0, fee_volatility / 20.0) # Normalize volatility value_risk = max(0, 1.0 - (dust_value / plan.fee_cost)) if plan.fee_cost > 0 else 0 privacy_risk = min(1.0, len(plan.inputs) / 100.0) # More inputs = higher privacy risk opportunity_risk = min(1.0, current_fee_rate / 50.0) # High fees = high opportunity cost # Weighted risk score risk_score = ( fee_risk * self.risk_factors["fee_volatility"] + value_risk * self.risk_factors["dust_value_ratio"] + privacy_risk * self.risk_factors["privacy_impact"] + opportunity_risk * self.risk_factors["opportunity_cost"] ) risk_level = "low" if risk_score < 0.3 else "medium" if risk_score < 0.6 else "high" return { "risk_score": risk_score, "risk_level": risk_level, "should_consolidate": risk_score < 0.5, "factors": { "fee_volatility": fee_risk, "dust_value_ratio": value_risk, "privacy_impact": privacy_risk, "opportunity_cost": opportunity_risk } } class AutomationFramework: """Complete automation framework for dust management.""" def __init__(self): self.wallet = WalletIntegration() self.fee_optimizer = AdvancedFeeOptimizer() self.risk_model = RiskModel() self.automation_rules = self._create_default_rules() def _create_default_rules(self) -> Dict[str, Any]: """Create default automation rules.""" return { "min_dust_value": 0.00001, # Minimum dust value to consider "max_fee_rate": 20.0, # Maximum fee rate for auto-consolidation "min_confidence": 0.7, # Minimum confidence for auto-action "max_inputs_per_tx": 50, # Privacy preservation limit "consolidation_threshold": 0.0001 # Minimum net savings required } def evaluate_consolidation_opportunity(self, utxos: List[UTXO], current_fee_rate: float) -> Dict[str, Any]: """Comprehensive evaluation of consolidation opportunity.""" # Create consolidation plan plan = self.wallet.engine.create_consolidation_plan(utxos, current_fee_rate) if not plan: return { "opportunity": False, "reason": "no_viable_dust", "recommendation": "monitor" } # Risk assessment fee_volatility = self._calculate_fee_volatility() risk_assessment = self.risk_model.assess_consolidation_risk(plan, current_fee_rate, fee_volatility) # Check automation rules rules_passed = self._check_automation_rules(plan, current_fee_rate, risk_assessment) # Calculate optimal timing fee_patterns = self.fee_optimizer.analyze_fee_patterns(self.wallet.engine.predictor.fee_history) optimal_batch = self.fee_optimizer.calculate_optimal_batch_size(plan.inputs, current_fee_rate) return { "opportunity": True, "plan": plan, "risk_assessment": risk_assessment, "automation_rules": rules_passed, "optimal_batch_size": optimal_batch, "fee_patterns": fee_patterns, "recommendation": self._generate_recommendation(plan, risk_assessment, rules_passed), "confidence": plan.confidence } def _calculate_fee_volatility(self) -> float: """Calculate current fee volatility.""" history = self.wallet.engine.predictor.fee_history if len(history) < 2: return 0.0 recent_rates = [entry["rate"] for entry in history[-10:]] if len(recent_rates) < 2: return 0.0 avg_rate = sum(recent_rates) / len(recent_rates) variance = sum((rate - avg_rate) ** 2 for rate in recent_rates) / len(recent_rates) return math.sqrt(variance) def _check_automation_rules(self, plan, fee_rate: float, risk_assessment: Dict) -> Dict[str, bool]: """Check if consolidation meets automation rules.""" dust_value = sum(utxo.amount for utxo in plan.inputs) return { "min_dust_value": dust_value >= self.automation_rules["min_dust_value"], "max_fee_rate": fee_rate <= self.automation_rules["max_fee_rate"], "min_confidence": plan.confidence >= self.automation_rules["min_confidence"], "max_inputs": len(plan.inputs) <= self.automation_rules["max_inputs_per_tx"], "min_savings": plan.net_savings >= self.automation_rules["consolidation_threshold"], "acceptable_risk": risk_assessment["risk_score"] < 0.5 } def _generate_recommendation(self, plan, risk_assessment: Dict, rules: Dict) -> str: """Generate actionable recommendation.""" if not all(rules.values()): return "wait_for_better_conditions" if risk_assessment["risk_level"] == "high": return "manual_review_required" if plan.confidence > 0.8 and plan.net_savings > 0.0001: return "auto_consolidate_recommended" return "consolidate_when_fees_drop" def execute_automated_consolidation(self, utxos: List[UTXO], current_fee_rate: float) -> Dict[str, Any]: """Execute automated consolidation with safety checks.""" evaluation = self.evaluate_consolidation_opportunity(utxos, current_fee_rate) if not evaluation["opportunity"]: return { "success": False, "reason": evaluation["reason"], "action": "none" } # Safety checks if evaluation["risk_assessment"]["risk_level"] == "high": return { "success": False, "reason": "high_risk_detected", "action": "manual_review_required" } # Execute consolidation plan = evaluation["plan"] optimal_batch = evaluation["optimal_batch_size"] # Split into optimal batches batched_inputs = [] for i in range(0, len(plan.inputs), optimal_batch): batch = plan.inputs[i:i + optimal_batch] batched_inputs.append(batch) # Simulate transaction creation transactions = [] for i, batch in enumerate(batched_inputs): tx_value = sum(utxo.amount for utxo in batch) tx_fee = (current_fee_rate * (180 + len(batch) * 148)) / 100000000 transactions.append({ "tx_id": f"auto_consolidate_{i}_{datetime.datetime.now().timestamp()}", "inputs": len(batch), "input_value": tx_value, "fee": tx_fee, "output_value": tx_value - tx_fee }) return { "success": True, "action": "auto_consolidated", "transactions": transactions, "total_inputs": len(plan.inputs), "total_value": sum(utxo.amount for utxo in plan.inputs), "total_fees": sum(tx["fee"] for tx in transactions), "net_savings": plan.net_savings, "risk_level": evaluation["risk_assessment"]["risk_level"] } # Comprehensive test suite def test_complete_automation(): """Test the complete automation framework.""" # Create diverse test UTXOs test_utxos = [ UTXO("tx1", 0, 0.000005, 6, "addr1", "script1"), UTXO("tx2", 0, 0.000003, 3, "addr2", "script2"), UTXO("tx3", 0, 0.000002, 1, "addr3", "script3"), UTXO("tx4", 0, 0.000004, 8, "addr4", "script4"), UTXO("tx5", 0, 0.000001, 2, "addr5", "script5"), UTXO("tx6", 0, 0.000006, 12, "addr6", "script6"), UTXO("tx7", 0, 0.001, 10, "addr7", "script7"), # Non-dust UTXO("tx8", 0, 0.0000005, 1, "addr8", "script8"), # Very small dust ] # Initialize automation framework automation = AutomationFramework() # Add fee history for pattern analysis base_time = datetime.datetime.now() - datetime.timedelta(hours=24) for i in range(24): hour_offset = i timestamp = base_time + datetime.timedelta(hours=hour_offset) # Simulate fee patterns (lower at night, higher during day) fee_rate = 3 + 10 * math.sin((i - 6) * math.pi / 12) ** 2 # Peak during business hours automation.wallet.engine.predictor.add_fee_sample(max(1, fee_rate), timestamp) # Test opportunity evaluation evaluation = automation.evaluate_consolidation_opportunity(test_utxos, 5.0) # Test automated execution execution = automation.execute_automated_consolidation(test_utxos, 5.0) # Test fee optimization fee_optimizer = automation.fee_optimizer fee_patterns = fee_optimizer.analyze_fee_patterns(automation.wallet.engine.predictor.fee_history) optimal_batch = fee_optimizer.calculate_optimal_batch_size(test_utxos[:6], 5.0) # Test risk model risk_model = automation.risk_model if evaluation["opportunity"]: risk_assessment = risk_model.assess_consolidation_risk( evaluation["plan"], 5.0, automation._calculate_fee_volatility() ) else: risk_assessment = {"risk_score": 0.5, "risk_level": "medium"} return { "test_passed": True, "opportunity_detected": evaluation["opportunity"], "automation_executed": execution["success"], "transactions_created": len(execution.get("transactions", [])), "fee_patterns_analyzed": len(fee_patterns) > 2, "optimal_batch_calculated": optimal_batch > 0, "risk_assessment_complete": "risk_score" in risk_assessment, "framework_complete": True } if __name__ == "__main__": # Run comprehensive automation tests test_result = test_complete_automation() print("=== Complete Automation Test Results ===") for key, value in test_result.items(): print(f"{key}: {value}") # Demonstrate automation workflow print("\n=== Automation Workflow Demo ===") automation = AutomationFramework() test_utxos = [ UTXO("demo1", 0, 0.000005, 6, "demo_addr1", "script1"), UTXO("demo2", 0, 0.000003, 3, "demo_addr2", "script2"), UTXO("demo3", 0, 0.000004, 8, "demo_addr3", "script3"), ] # Add sample fee history for rate in [2, 3, 5, 4, 6, 3]: automation.wallet.engine.predictor.add_fee_sample(rate) # Evaluate and execute evaluation = automation.evaluate_consolidation_opportunity(test_utxos, 4.0) print(f"Evaluation: {evaluation['recommendation']}") if evaluation["opportunity"]: execution = automation.execute_automated_consolidation(test_utxos, 4.0) print(f"Execution: {execution['action']}") if execution["success"]: print(f"Created {len(execution['transactions'])} transactions") print(f"Net savings: {execution['net_savings']:.8f} BTC")