initial

README.md

@@ -0,0 +1,231 @@
+# Bitcoin Transaction Forensic Analyzer v2.0
+
+A comprehensive Python tool for forensic analysis of Bitcoin transactions with advanced change address detection using probabilistic heuristics.
+
+## Overview
+
+This tool performs in-depth forensic analysis of Bitcoin transactions by:
+- Fetching comprehensive transaction data from mempool APIs
+- Applying multiple heuristics to identify likely change addresses
+- Generating detailed forensic reports with probability assessments
+- Providing network context and spending status analysis
+
+## Features
+
+### Core Analysis
+- **Multi-heuristic change detection**: Combines 7+ different analytical approaches
+- **Probabilistic scoring**: Uses weighted feature analysis instead of arbitrary point systems
+- **Comprehensive data collection**: Fetches from multiple API endpoints
+- **Network context awareness**: Incorporates current fee environment and mempool state
+
+### Heuristics Implemented
+1. **Round Number Analysis**: Identifies payment patterns vs precise change amounts
+2. **Address History Analysis**: Examines address reuse patterns and transaction frequency
+3. **Relative Value Analysis**: Compares output values and percentages
+4. **Positional Pattern Analysis**: Considers wallet-specific change placement strategies
+5. **Script Type Analysis**: Checks for address type consistency across inputs/outputs
+6. **Fee Context Analysis**: Evaluates transaction urgency and batching patterns
+7. **Address Type Reuse**: Determines if outputs match input address types
+
+### Output Features
+- Detailed probability assessments with confidence levels
+- Comprehensive transaction breakdown
+- Spending status tracking
+- RBF (Replace-by-Fee) history analysis
+- Raw transaction data preservation
+- Feature contribution breakdown for each heuristic
+
+## Requirements
+
+### System Requirements
+- Python 3.7 or higher
+- Internet connection for API access
+- ~50MB available disk space
+
+### Python Dependencies
+```
+requests>=2.25.0
+```
+
+### API Access
+- Requires access to a mempool.space-compatible API
+- Default configuration uses `mempool.mikoshi.de`
+- No API key required for basic usage
+
+## Installation
+
+1. **Clone or download** the script file
+2. **Install dependencies**:
+   ```bash
+   pip install requests
+   ```
+3. **Verify setup**:
+   ```bash
+   python btc_forensic.py --help
+   ```
+
+## Usage
+
+### Basic Usage
+```bash
+python btc_forensic.py input_file.txt output_report.txt
+```
+
+### Command Line Options
+```bash
+python btc_forensic.py [options] input_file output_file
+
+Required Arguments:
+  input_file    Path to text file containing transaction IDs (one per line)
+  output_file   Path where forensic report will be saved
+
+Optional Arguments:
+  -h, --help           Show help message
+  -v, --verbose        Enable verbose output during processing
+  -d DELAY, --delay    Delay between API requests in seconds (default: 0.1)
+```
+
+### Input File Format
+Create a text file with one transaction ID per line:
+```
+15e10745f15593a899cef391191bdd3d7c12412cc4696b7bcb669d0feadc8521
+dba43fd04b7ae3df8e5b596f2e7fab247c58629d622e3a5213f03a5a09684430
+# Comments starting with # are ignored
+c4e53c2e37f4fac759fdb0d8380e4d49e6c7211233ae276a44ce7074a1d6d168
+```
+
+### Example Usage
+```bash
+# Basic analysis
+python btc_forensic.py transactions.txt forensic_report.txt
+
+# Verbose output with custom delay
+python btc_forensic.py -v -d 0.2 suspicious_txs.txt detailed_report.txt
+```
+
+## Understanding the Output
+
+### Change Address Analysis
+The script provides probability assessments for each output:
+- **Probability > 70%**: Likely change address
+- **Probability 50-70%**: Possible change address  
+- **Probability 30-50%**: Uncertain classification
+- **Probability < 30%**: Likely payment address
+
+### Confidence Levels
+- **HIGH**: Strong evidence supporting the assessment
+- **MEDIUM**: Moderate evidence, some uncertainty
+- **LOW**: Weak evidence, high uncertainty
+
+### Feature Breakdown
+Each output analysis includes detailed reasoning showing:
+- Individual heuristic scores and contributions
+- Weighted feature analysis
+- Specific evidence found (round numbers, address history, etc.)
+
+## Technical Details
+
+### API Endpoints Used
+- `/api/tx/{txid}` - Basic transaction data
+- `/api/tx/{txid}/outspends` - Spending status
+- `/api/v1/tx/{txid}/rbf` - RBF history
+- `/api/tx/{txid}/hex` - Raw transaction data
+- `/api/address/{address}` - Address statistics
+- `/api/address/{address}/txs` - Address transaction history
+- `/api/mempool` - Current mempool state
+- `/api/v1/fees/recommended` - Fee recommendations
+
+### Scoring Algorithm
+The probabilistic model uses weighted feature combination:
+1. Each heuristic produces a score (-1.0 to +1.0)
+2. Scores are weighted by empirical effectiveness
+3. Combined score is normalized using sigmoid function
+4. Result is converted to probability percentage
+
+### Rate Limiting
+- Default 100ms delay between requests
+- Configurable via `--delay` parameter
+- Designed to be respectful of API resources
+
+## Limitations and Considerations
+
+### Analytical Limitations
+- **Heuristic-based**: Not 100% accurate, provides probability estimates
+- **Privacy techniques**: May be less effective against advanced privacy wallets
+- **Exchange transactions**: Complex batching patterns may confuse analysis
+- **Network conditions**: Accuracy may vary based on fee environment
+
+### Technical Limitations
+- **API dependency**: Requires reliable internet connection
+- **Rate limits**: May need adjustment for different API providers
+- **Memory usage**: Large transaction sets may require significant RAM
+
+### Legal and Ethical Considerations
+- **Intended for legitimate forensic analysis only**
+- **Compliance**: Ensure usage complies with local laws and regulations
+- **Privacy**: Be mindful of privacy implications when analyzing transactions
+- **Data handling**: Secure storage and handling of forensic reports required
+
+## Troubleshooting
+
+### Common Issues
+
+**"Network error fetching data"**
+- Check internet connection
+- Verify API endpoint is accessible
+- Try increasing delay with `--delay` parameter
+
+**"No valid transaction IDs found"**
+- Verify input file format (one TXID per line)
+- Check for proper 64-character hex transaction IDs
+- Ensure file encoding is UTF-8
+
+**"Could not retrieve base transaction data"**
+- Transaction may not exist or be too recent
+- API may be temporarily unavailable
+- Try with a known valid transaction ID
+
+**High memory usage**
+- Process smaller batches of transactions
+- Monitor system resources during execution
+- Consider running analysis in segments
+
+### Performance Optimization
+- Use appropriate `--delay` setting for your network
+- Process transactions in smaller batches for large datasets
+- Monitor API response times and adjust accordingly
+
+## Version History
+
+### v2.0 (Current)
+- Advanced probabilistic change detection
+- Multi-heuristic analysis framework
+- Comprehensive forensic reporting
+- Network context awareness
+- Enhanced API utilization
+
+### v1.0 (Legacy)
+- Basic change detection using simple heuristics
+- Limited API endpoint usage
+- Basic reporting functionality
+
+## Support and Contributing
+
+### Reporting Issues
+When reporting issues, please include:
+- Python version and operating system
+- Complete error messages
+- Sample transaction IDs (if not sensitive)
+- Command line arguments used
+
+### Best Practices
+- Test with known transactions before production use
+- Validate results against other analytical tools
+- Keep transaction ID lists organized and documented
+- Regularly update the script for new features
+
+## Disclaimer
+
+This tool is provided for educational and legitimate forensic analysis purposes only. Users are responsible for ensuring compliance with applicable laws and regulations. The probabilistic nature of the analysis means results should be considered as investigative leads rather than definitive conclusions.
+
+Accuracy of change address detection varies based on wallet software, transaction patterns, and other factors. Always corroborate findings with additional analytical techniques and evidence.

fetch_tx_details.py

@@ -1,109 +1,780 @@
 import requests
-import json
+import argparse
 import sys
+import json
+import math
 from datetime import datetime
+from collections import Counter, defaultdict
+from typing import Dict, List, Optional, Tuple, Any
+import time
 
 # --- Configuration ---
-# The base URL for your mempool API instance.
-BASE_URL = "https://mempool.mikoshi.de/api/tx/"
+BASE_URL_V1 = "https://mempool.mikoshi.de/api/v1"
+BASE_URL_DEFAULT = "https://mempool.mikoshi.de/api"
+SESSION = requests.Session()
+SESSION.headers.update({
+    'User-Agent': 'Bitcoin-Forensic-Analyzer/2.0'
+})
 
-def format_transaction_details(tx_data):
-    """Formats the JSON data from the API into a human-readable string."""
+# Rate limiting (even though not needed, good practice)
+REQUEST_DELAY = 0.1
 
-    txid = tx_data.get("txid", "N/A")
-    fee = tx_data.get("fee", 0)
-    size = tx_data.get("size", 0)
-    weight = tx_data.get("weight", 0)
+class TransactionAnalyzer:
+    def __init__(self):
+        self.mempool_stats = None
+        self.fee_recommendations = None
+        self._load_network_context()
     
-    # Status details
-    status = tx_data.get("status", {})
-    confirmed = status.get("confirmed", False)
-    if confirmed:
-        block_height = status.get("block_height", "N/A")
-        block_time_unix = status.get("block_time", 0)
-        block_time_str = datetime.utcfromtimestamp(block_time_unix).strftime('%Y-%m-%d %H:%M:%S UTC')
-        status_str = f"✅ Confirmed in block {block_height} at {block_time_str}"
+    def _load_network_context(self):
+        """Load current network context for analysis."""
+        try:
+            # Get current mempool state
+            mempool_resp = SESSION.get(f"{BASE_URL_DEFAULT}/mempool", timeout=10)
+            if mempool_resp.status_code == 200:
+                self.mempool_stats = mempool_resp.json()
+            
+            # Get current fee recommendations
+            fee_resp = SESSION.get(f"{BASE_URL_V1}/fees/recommended", timeout=10)
+            if fee_resp.status_code == 200:
+                self.fee_recommendations = fee_resp.json()
+                
+        except requests.RequestException:
+            print("   - Warning: Could not load network context")
+    
+    def analyze_round_numbers(self, value_sats: int) -> Tuple[float, str]:
+        """Analyze if value appears to be a round number (indicates payment)."""
+        value_btc = value_sats / 100_000_000
+        
+        # Very round BTC amounts (strong payment indicators)
+        round_btc_amounts = [0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 5.0, 10.0, 20.0, 50.0, 100.0]
+        for amount in round_btc_amounts:
+            if abs(value_btc - amount) < 0.000001:
+                return -0.4, f"Exact round amount: {amount} BTC"
+        
+        # Round satoshi amounts
+        if value_sats >= 100000 and value_sats % 100000 == 0:
+            return -0.3, f"Round 100k sats: {value_sats}"
+        if value_sats >= 10000 and value_sats % 10000 == 0:
+            return -0.2, f"Round 10k sats: {value_sats}"
+        if value_sats >= 1000 and value_sats % 1000 == 0:
+            return -0.1, f"Round 1k sats: {value_sats}"
+        
+        # Very precise amounts suggest change
+        value_str = str(value_sats)
+        if len(value_str) >= 8 and not value_str.endswith('0000'):
+            return 0.2, f"Precise amount: {value_sats} sats"
+        
+        return 0.0, "No clear round number pattern"
+    
+    def analyze_output_values(self, outputs: List[Dict]) -> List[Tuple[float, str]]:
+        """Analyze relative value patterns across outputs."""
+        if len(outputs) < 2:
+            return [(0.0, "Single output - no relative analysis")] * len(outputs)
+        
+        values = [out['value'] for out in outputs]
+        total_value = sum(values)
+        
+        scores = []
+        for i, value in enumerate(values):
+            score = 0.0
+            reasons = []
+            
+            percentage = (value / total_value) * 100
+            
+            # Very small outputs often change (especially in consolidations)
+            if percentage < 5:
+                score += 0.15
+                reasons.append(f"Small output: {percentage:.1f}% of total")
+            
+            # Very large outputs rarely change
+            elif percentage > 80:
+                score -= 0.25
+                reasons.append(f"Dominant output: {percentage:.1f}% of total")
+            
+            # Check if smallest output (common change pattern)
+            if value == min(values) and len(values) > 2:
+                score += 0.1
+                reasons.append("Smallest output")
+            
+            # Check if second smallest (also common for change)
+            sorted_values = sorted(values)
+            if len(values) > 2 and value == sorted_values[1]:
+                score += 0.05
+                reasons.append("Second smallest output")
+            
+            reason_text = "; ".join(reasons) if reasons else "No relative value indicators"
+            scores.append((score, reason_text))
+        
+        return scores
+    
+    def analyze_position_patterns(self, outputs: List[Dict], input_count: int) -> List[Tuple[float, str]]:
+        """Analyze positional and structural patterns."""
+        scores = []
+        output_count = len(outputs)
+        
+        for i, output in enumerate(outputs):
+            score = 0.0
+            reasons = []
+            
+            # Last position often change (but not always)
+            if i == output_count - 1 and output_count > 1:
+                score += 0.1
+                reasons.append("Last position")
+            
+            # Two-output transactions - second often change
+            if output_count == 2 and i == 1:
+                score += 0.15
+                reasons.append("Second output in 2-output tx")
+            
+            # Consolidation pattern (many inputs, few outputs)
+            if input_count >= 5 and output_count <= 2:
+                score += 0.2
+                reasons.append(f"Consolidation pattern: {input_count} inputs → {output_count} outputs")
+            
+            # Fan-out pattern (few inputs, many outputs) - change usually first or last
+            if input_count <= 2 and output_count >= 5:
+                if i == 0 or i == output_count - 1:
+                    score += 0.1
+                    reasons.append("Fan-out pattern: position suggests change")
+            
+            reason_text = "; ".join(reasons) if reasons else "No positional indicators"
+            scores.append((score, reason_text))
+        
+        return scores
+    
+    def analyze_address_advanced(self, address: str) -> Tuple[float, str]:
+        """Enhanced address history analysis."""
+        if not address or address == "N/A":
+            return 0.0, "No address to analyze"
+        
+        try:
+            time.sleep(REQUEST_DELAY)
+            
+            # Get comprehensive address info
+            addr_resp = SESSION.get(f"{BASE_URL_DEFAULT}/address/{address}", timeout=15)
+            if addr_resp.status_code != 200:
+                return 0.0, "Could not fetch address info"
+            
+            addr_info = addr_resp.json()
+            chain_stats = addr_info.get('chain_stats', {})
+            mempool_stats = addr_info.get('mempool_stats', {})
+            
+            tx_count = chain_stats.get('tx_count', 0)
+            funded_count = chain_stats.get('funded_txo_count', 0)
+            spent_count = chain_stats.get('spent_txo_count', 0)
+            
+            # Brand new addresses are very likely change
+            if tx_count == 0:
+                return 0.4, "Brand new address (0 transactions)"
+            
+            if tx_count == 1:
+                return 0.35, "Address used only once before"
+            
+            # Get transaction history for pattern analysis
+            time.sleep(REQUEST_DELAY)
+            txs_resp = SESSION.get(f"{BASE_URL_DEFAULT}/address/{address}/txs", timeout=15)
+            
+            if txs_resp.status_code == 200:
+                txs = txs_resp.json()
+                
+                # Analyze usage patterns
+                if len(txs) <= 3:
+                    score = 0.25
+                    reason = f"Low usage: {len(txs)} transactions"
+                elif len(txs) <= 5:
+                    score = 0.1
+                    reason = f"Moderate usage: {len(txs)} transactions"
+                else:
+                    # Check if it's an exchange/service address pattern
+                    if len(txs) > 100:
+                        score = -0.3
+                        reason = f"High activity address: {len(txs)} transactions (likely service)"
+                    else:
+                        score = -0.1
+                        reason = f"Regular usage: {len(txs)} transactions"
+                
+                # Check for rapid reuse (suggests not change)
+                if len(txs) >= 2:
+                    recent_txs = [tx for tx in txs if tx.get('status', {}).get('confirmed', False)]
+                    if len(recent_txs) >= 2:
+                        time_diff = recent_txs[0].get('status', {}).get('block_time', 0) - recent_txs[1].get('status', {}).get('block_time', 0)
+                        if abs(time_diff) < 3600:  # Less than 1 hour between uses
+                            score -= 0.15
+                            reason += "; rapid reuse detected"
+                
+                return score, reason
+            
+            # Fallback to basic analysis
+            if tx_count <= 2:
+                return 0.25, f"Low transaction count: {tx_count}"
+            elif tx_count <= 10:
+                return 0.0, f"Moderate transaction count: {tx_count}"
+            else:
+                return -0.2, f"High transaction count: {tx_count}"
+                
+        except requests.RequestException as e:
+            return 0.0, f"Network error analyzing address: {str(e)}"
+    
+    def analyze_script_complexity(self, output: Dict) -> Tuple[float, str]:
+        """Analyze script type and complexity."""
+        script_type = output.get('scriptpubkey_type', 'unknown')
+        script_hex = output.get('scriptpubkey', '')
+        
+        # Standard single-sig types are common for change
+        if script_type in ['p2pkh', 'p2wpkh']:
+            return 0.1, f"Standard single-sig: {script_type}"
+        
+        # Wrapped segwit also common for change
+        if script_type == 'p2sh':
+            return 0.05, "P2SH (possibly wrapped segwit)"
+        
+        # Native segwit
+        if script_type in ['p2wsh', 'p2tr']:
+            return 0.0, f"Advanced script type: {script_type}"
+        
+        # Multi-sig and complex scripts less likely to be change
+        if script_type == 'v0_p2wsh' or 'multisig' in script_type.lower():
+            return -0.2, f"Complex script: {script_type}"
+        
+        # OP_RETURN and non-standard outputs definitely not change
+        if script_type in ['op_return', 'nulldata']:
+            return -1.0, "OP_RETURN or nulldata output"
+        
+        return 0.0, f"Unknown script type: {script_type}"
+    
+    def analyze_fee_context(self, base_data: Dict) -> Tuple[float, str]:
+        """Analyze transaction in context of current fee environment."""
+        if not self.fee_recommendations:
+            return 0.0, "No fee context available"
+        
+        fee = base_data.get('fee', 0)
+        weight = base_data.get('weight', 1)
+        fee_rate = fee / (weight / 4) if weight > 0 else 0
+        
+        # Compare to recommended fees
+        fast_fee = self.fee_recommendations.get('fastestFee', 1)
+        hour_fee = self.fee_recommendations.get('hourFee', 1)
+        economy_fee = self.fee_recommendations.get('economyFee', 1)
+        
+        if fee_rate > fast_fee * 2:
+            return 0.1, f"High fee rate: {fee_rate:.1f} sat/vB (suggests urgent payment)"
+        elif fee_rate < economy_fee * 0.5:
+            return 0.05, f"Very low fee rate: {fee_rate:.1f} sat/vB (suggests batching/consolidation)"
+        
+        return 0.0, f"Normal fee rate: {fee_rate:.1f} sat/vB"
+    
+    def calculate_change_probability(self, features: Dict[str, Tuple[float, str]]) -> Tuple[float, Dict[str, Any]]:
+        """Calculate change probability using weighted features."""
+        
+        # Feature weights based on empirical effectiveness
+        weights = {
+            'round_number': 1.0,        # Strong indicator
+            'address_history': 0.8,     # Very reliable
+            'relative_value': 0.6,      # Good indicator
+            'position': 0.4,            # Moderate indicator
+            'script_complexity': 0.3,   # Weak but useful
+            'fee_context': 0.2,         # Minor indicator
+            'address_type_reuse': 0.7   # Strong indicator
+        }
+        
+        weighted_score = 0.0
+        total_weight = 0.0
+        details = {}
+        
+        for feature_name, (score, reason) in features.items():
+            if feature_name in weights:
+                weight = weights[feature_name]
+                weighted_score += score * weight
+                total_weight += weight
+                details[feature_name] = {
+                    'score': score,
+                    'weight': weight,
+                    'contribution': score * weight,
+                    'reason': reason
+                }
+        
+        # Normalize score
+        if total_weight > 0:
+            normalized_score = weighted_score / total_weight
+        else:
+            normalized_score = 0.0
+        
+        # Convert to probability using sigmoid function
+        probability = 1 / (1 + math.exp(-normalized_score * 3))  # Scale factor of 3
+        
+        return probability, details
+    
+    def analyze_address_type_reuse(self, base_data: Dict, output_index: int) -> Tuple[float, str]:
+        """Check if output address type matches input types."""
+        if not base_data or 'vin' not in base_data or 'vout' not in base_data:
+            return 0.0, "Insufficient data for address type analysis"
+        
+        inputs = base_data.get('vin', [])
+        outputs = base_data.get('vout', [])
+        
+        if output_index >= len(outputs):
+            return 0.0, "Invalid output index"
+        
+        output = outputs[output_index]
+        output_type = output.get('scriptpubkey_type')
+        
+        if not output_type:
+            return 0.0, "No output script type"
+        
+        # Collect input types
+        input_types = []
+        for inp in inputs:
+            prevout = inp.get('prevout', {})
+            inp_type = prevout.get('scriptpubkey_type')
+            if inp_type:
+                input_types.append(inp_type)
+        
+        if not input_types:
+            return 0.0, "No input script types available"
+        
+        # Find dominant input type
+        type_counts = Counter(input_types)
+        dominant_type, dominant_count = type_counts.most_common(1)[0]
+        
+        if output_type == dominant_type:
+            reuse_percentage = (dominant_count / len(input_types)) * 100
+            return 0.3, f"Address type '{output_type}' matches {reuse_percentage:.0f}% of inputs"
+        else:
+            return -0.1, f"Address type '{output_type}' differs from dominant input type '{dominant_type}'"
+
+
+def fetch_comprehensive_details(txid: str, analyzer: TransactionAnalyzer) -> Optional[Dict]:
+    """Fetch comprehensive transaction details from multiple endpoints."""
+    print(f"Fetching comprehensive data for {txid}...")
+    
+    results = {}
+    
+    # Primary endpoints
+    endpoints = {
+        'base': f"{BASE_URL_DEFAULT}/tx/{txid}",
+        'outspends': f"{BASE_URL_DEFAULT}/tx/{txid}/outspends",
+        'rbf': f"{BASE_URL_V1}/tx/{txid}/rbf",
+        'hex': f"{BASE_URL_DEFAULT}/tx/{txid}/hex",
+        'status': f"{BASE_URL_DEFAULT}/tx/{txid}/status"
+    }
+    
+    for key, url in endpoints.items():
+        try:
+            time.sleep(REQUEST_DELAY)
+            response = SESSION.get(url, timeout=15)
+            if response.status_code == 200:
+                results[key] = response.text if key == 'hex' else response.json()
+                print(f"   ✓ Fetched {key}")
+            else:
+                results[key] = None
+                print(f"   - {key} not available (Status: {response.status_code})")
+        except requests.RequestException as e:
+            print(f"   - Network error fetching {key}: {e}")
+            results[key] = None
+            if key == 'base':
+                return None
+    
+    return results
+
+
+def perform_comprehensive_change_analysis(base_data: Dict, analyzer: TransactionAnalyzer) -> Optional[List[Dict]]:
+    """Perform comprehensive change address analysis using multiple heuristics."""
+    if not base_data or 'vout' not in base_data:
+        return None
+    
+    outputs = base_data.get('vout', [])
+    inputs = base_data.get('vin', [])
+    
+    print(f"   - Analyzing {len(outputs)} outputs using advanced heuristics...")
+    
+    analysis_results = []
+    
+    # Run value analysis once for all outputs
+    value_scores = analyzer.analyze_output_values(outputs)
+    position_scores = analyzer.analyze_position_patterns(outputs, len(inputs))
+    
+    for i, output in enumerate(outputs):
+        print(f"     → Analyzing output {i}")
+        
+        address = output.get('scriptpubkey_address', 'N/A')
+        value = output.get('value', 0)
+        
+        # Skip non-standard outputs
+        if not address or address == 'N/A':
+            analysis_results.append({
+                'index': i,
+                'address': 'N/A',
+                'value': value,
+                'probability': 0.0,
+                'confidence': 'HIGH',
+                'reasoning': 'Non-standard output (e.g., OP_RETURN)',
+                'details': {}
+            })
+            continue
+        
+        # Collect all features
+        features = {}
+        
+        # Round number analysis
+        round_score, round_reason = analyzer.analyze_round_numbers(value)
+        features['round_number'] = (round_score, round_reason)
+        
+        # Address history analysis
+        addr_score, addr_reason = analyzer.analyze_address_advanced(address)
+        features['address_history'] = (addr_score, addr_reason)
+        
+        # Relative value analysis
+        rel_score, rel_reason = value_scores[i]
+        features['relative_value'] = (rel_score, rel_reason)
+        
+        # Position analysis
+        pos_score, pos_reason = position_scores[i]
+        features['position'] = (pos_score, pos_reason)
+        
+        # Script complexity analysis
+        script_score, script_reason = analyzer.analyze_script_complexity(output)
+        features['script_complexity'] = (script_score, script_reason)
+        
+        # Fee context analysis
+        fee_score, fee_reason = analyzer.analyze_fee_context(base_data)
+        features['fee_context'] = (fee_score, fee_reason)
+        
+        # Address type reuse analysis
+        type_score, type_reason = analyzer.analyze_address_type_reuse(base_data, i)
+        features['address_type_reuse'] = (type_score, type_reason)
+        
+        # Calculate final probability
+        probability, feature_details = analyzer.calculate_change_probability(features)
+        
+        # Determine confidence level
+        confidence = "MEDIUM"
+        if probability > 0.8 or probability < 0.2:
+            confidence = "HIGH"
+        elif probability > 0.6 or probability < 0.4:
+            confidence = "MEDIUM"
+        else:
+            confidence = "LOW"
+        
+        analysis_results.append({
+            'index': i,
+            'address': address,
+            'value': value,
+            'probability': probability,
+            'confidence': confidence,
+            'reasoning': f"Change probability: {probability:.1%}",
+            'details': feature_details
+        })
+    
+    return analysis_results
+
+
+def format_comprehensive_report(all_data: Dict, change_analysis: Optional[List[Dict]], analyzer: TransactionAnalyzer) -> str:
+    """Format comprehensive forensic report."""
+    base_data = all_data.get('base')
+    if not base_data:
+        return "Could not retrieve base transaction data.\n\n"
+    
+    txid = base_data.get("txid", "N/A")
+    fee = base_data.get("fee", 0)
+    weight = base_data.get("weight", 1)
+    size = base_data.get("size", 0)
+    
+    output = ["=" * 100, f"COMPREHENSIVE FORENSIC ANALYSIS: {txid}", "=" * 100]
+    
+    # Transaction Overview
+    status = base_data.get("status", {})
+    if status.get("confirmed", False):
+        block_time = datetime.utcfromtimestamp(status.get("block_time", 0)).strftime('%Y-%m-%d %H:%M:%S UTC')
+        status_str = f"Confirmed in block {status.get('block_height', 'N/A')} at {block_time}"
     else:
-        status_str = "⌛ Unconfirmed"
+        status_str = "Unconfirmed (in mempool)"
     
-    # --- Start building the output string ---
-    output = []
-    output.append("=" * 80)
-    output.append(f"TRANSACTION DETAILS FOR: {txid}")
-    output.append("=" * 80)
+    fee_rate = fee / (weight / 4) if weight > 0 else 0
     
-    # General Info
-    output.append("\n--- General ---")
-    output.append(f"Status: {status_str}")
-    output.append(f"Fee: {fee} sats")
-    output.append(f"Size: {size} bytes")
-    output.append(f"Weight: {weight} vB")
-    output.append(f"Fee Rate: {fee/(weight/4):.2f} sat/vB")
+    output.extend([
+        "\n" + "─" * 50 + " TRANSACTION OVERVIEW " + "─" * 50,
+        f"Status: {status_str}",
+        f"Fee: {fee:,} sats ({fee/100000000:.8f} BTC)",
+        f"Size: {size:,} bytes | Weight: {weight:,} vB | Fee Rate: {fee_rate:.2f} sat/vB",
+        f"Version: {base_data.get('version', 'N/A')} | Locktime: {base_data.get('locktime', 'N/A')}"
+    ])
+    
+    # Network Context
+    if analyzer.fee_recommendations:
+        output.append(f"\nCurrent Network Fees - Fast: {analyzer.fee_recommendations.get('fastestFee', 'N/A')} | "
+                     f"Hour: {analyzer.fee_recommendations.get('hourFee', 'N/A')} | "
+                     f"Economy: {analyzer.fee_recommendations.get('economyFee', 'N/A')} sat/vB")
+    
+    # Input Analysis
+    vin = base_data.get("vin", [])
+    output.append("\n" + "─" * 50 + f" INPUTS ({len(vin)}) " + "─" * 50)
     
-    # Inputs (vin)
-    vin = tx_data.get("vin", [])
-    output.append(f"\n--- Inputs ({len(vin)}) ---")
     if not vin:
-        output.append("No inputs found (likely a coinbase transaction).")
-    for i, an_input in enumerate(vin, 1):
-        prev_txid = an_input.get("txid", "N/A")
-        prev_vout = an_input.get("vout", "N/A")
-        prevout = an_input.get("prevout", {})
-        value = prevout.get("value", "N/A")
-        address = prevout.get("scriptpubkey_address", "Address not available")
-        output.append(f"  {i}. From TX: {prev_txid[:20]}... | Output Index: {prev_vout}")
-        output.append(f"     Value: {value} sats | Spent by: {address}")
+        output.append("No inputs found (coinbase transaction)")
+    else:
+        total_input_value = 0
+        for i, inp in enumerate(vin, 1):
+            prevout = inp.get("prevout", {})
+            value = prevout.get('value', 0)
+            total_input_value += value
+            script_type = prevout.get('scriptpubkey_type', 'unknown')
             
-    # Outputs (vout)
-    vout = tx_data.get("vout", [])
-    output.append(f"\n--- Outputs ({len(vout)}) ---")
-    for i, an_output in enumerate(vout, 1):
-        value = an_output.get("value", "N/A")
-        address = an_output.get("scriptpubkey_address", "Address not available")
-        output.append(f"  {i}. Value: {value} sats | To Address: {address}")
+            output.append(f"  {i}. TXID: {inp.get('txid', 'N/A')[:16]}...")
+            output.append(f"     Value: {value:,} sats | Address: {prevout.get('scriptpubkey_address', 'N/A')}")
+            output.append(f"     Script Type: {script_type}")
+    
+    # Output Analysis
+    vout = base_data.get("vout", [])
+    output.append("\n" + "─" * 50 + f" OUTPUTS ({len(vout)}) " + "─" * 50)
+    
+    total_output_value = 0
+    for i, out in enumerate(vout, 1):
+        value = out.get('value', 0)
+        total_output_value += value
+        script_type = out.get('scriptpubkey_type', 'unknown')
+        
+        output.append(f"  {i}. Value: {value:,} sats ({value/100000000:.8f} BTC)")
+        output.append(f"     Address: {out.get('scriptpubkey_address', 'N/A')}")
+        output.append(f"     Script Type: {script_type}")
+    
+    # Fee verification
+    calculated_fee = total_input_value - total_output_value
+    output.append(f"\nFee Verification: Calculated={calculated_fee:,} sats | Reported={fee:,} sats")
+    if abs(calculated_fee - fee) > 1:
+        output.append("⚠️  WARNING: Fee mismatch detected!")
+    
+    # Change Address Analysis
+    output.append("\n" + "=" * 100)
+    output.append("ADVANCED CHANGE ADDRESS ANALYSIS")
+    output.append("=" * 100)
+    output.append("\nMethodology: Multi-heuristic probabilistic model analyzing:")
+    output.append("• Round number patterns (payments often use round amounts)")
+    output.append("• Address reuse and history (change addresses often new)")
+    output.append("• Relative output values (change often smaller/different)")
+    output.append("• Positional patterns (change position varies by wallet)")
+    output.append("• Script type consistency (wallets reuse address types)")
+    output.append("• Transaction context (fee rates, timing, structure)")
+    
+    if change_analysis:
+        # Sort by probability for easy identification
+        sorted_analysis = sorted(change_analysis, key=lambda x: x['probability'], reverse=True)
+        
+        output.append("\n" + "─" * 80 + " RESULTS " + "─" * 80)
+        
+        for result in sorted_analysis:
+            prob = result['probability']
+            conf = result['confidence']
+            
+            # Determine label
+            if prob > 0.7:
+                label = "🟢 LIKELY CHANGE"
+            elif prob > 0.5:
+                label = "🟡 POSSIBLE CHANGE"
+            elif prob < 0.3:
+                label = "🔴 LIKELY PAYMENT"
+            else:
+                label = "⚪ UNCERTAIN"
+            
+            output.append(f"\nOutput {result['index']} - {result['address'][:20]}{'...' if len(result['address']) > 20 else ''}")
+            output.append(f"Value: {result['value']:,} sats | Probability: {prob:.1%} | Confidence: {conf}")
+            output.append(f"Assessment: {label}")
+            output.append("Detailed Analysis:")
+            
+            for feature, details in result['details'].items():
+                score = details['score']
+                contribution = details['contribution']
+                reason = details['reason']
+                
+                indicator = "+" if score > 0 else "-" if score < 0 else "="
+                output.append(f"  {indicator} {feature.replace('_', ' ').title()}: {reason}")
+                output.append(f"    Score: {score:+.2f} | Weight: {details['weight']:.1f} | Contribution: {contribution:+.2f}")
+        
+        # Summary
+        most_likely_change = max(sorted_analysis, key=lambda x: x['probability'])
+        if most_likely_change['probability'] > 0.5:
+            output.append("\n" + "─" * 80 + " SUMMARY " + "─" * 80)
+            output.append(f"Most Likely Change: Output {most_likely_change['index']} "
+                         f"({most_likely_change['probability']:.1%} probability)")
+            output.append(f"Address: {most_likely_change['address']}")
+            output.append(f"Value: {most_likely_change['value']:,} sats")
+        else:
+            output.append("\n" + "─" * 80 + " SUMMARY " + "─" * 80)
+            output.append("⚠️  No clear change address identified - all outputs show low change probability")
+            output.append("This may indicate: multiple payments, exchange transaction, or privacy technique")
+    
+    else:
+        output.append("\n❌ Could not perform change address analysis due to insufficient data")
+    
+    # Spending Status Analysis
+    outspends_data = all_data.get('outspends')
+    output.append("\n" + "─" * 50 + " SPENDING STATUS " + "─" * 50)
+    
+    if outspends_data:
+        for i, spend_info in enumerate(outspends_data):
+            if spend_info and spend_info.get('spent'):
+                spend_txid = spend_info.get('txid', 'N/A')
+                spend_vin = spend_info.get('vin', 'N/A')
+                spend_status = spend_info.get('status', {})
+                
+                if spend_status.get('confirmed'):
+                    spend_height = spend_status.get('block_height', 'N/A')
+                    output.append(f"  Output {i}: ✅ Spent in TX {spend_txid} (input {spend_vin}) at block {spend_height}")
+                else:
+                    output.append(f"  Output {i}: 🟡 Spent in unconfirmed TX {spend_txid}")
+            else:
+                output.append(f"  Output {i}: 💰 Unspent (UTXO)")
+    else:
+        output.append("Could not retrieve spending status information")
+    
+    # RBF Analysis
+    rbf_data = all_data.get('rbf')
+    output.append("\n" + "─" * 50 + " RBF HISTORY " + "─" * 50)
+    
+    if rbf_data:
+        replacements = rbf_data.get('replacements')
+        replaces = rbf_data.get('replaces', [])
+        
+        if replaces:
+            output.append("🔄 This transaction REPLACED the following:")
+            for replaced_txid in replaces:
+                output.append(f"  ← {replaced_txid}")
+        
+        if replacements:
+            output.append("🔄 This transaction was REPLACED by:")
+            output.append(f"  → {replacements.get('tx', {}).get('txid', 'N/A')}")
+        
+        if not replaces and not replacements:
+            output.append("No RBF activity detected")
+    else:
+        output.append("No RBF history available")
+    
+    # Raw Transaction Data
+    hex_data = all_data.get('hex')
+    output.append("\n" + "─" * 50 + " RAW TRANSACTION " + "─" * 50)
+    if hex_data:
+        output.append(f"Raw Hex ({len(hex_data)} characters):")
+        output.append(hex_data)
+    else:
+        output.append("Raw hex data not available")
+    
+    output.append("\n" + "=" * 100)
+    output.append(f"Report generated at: {datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S')} UTC")
+    output.append("=" * 100 + "\n\n")
     
-    output.append("\n" * 2) # Add space before the next transaction
     return "\n".join(output)
 
 
 def main():
-    """Main function to run the script."""
-    if len(sys.argv) != 3:
-        print("Usage: python fetch_tx_details.py <input_file.txt> <output_file.txt>")
-        sys.exit(1)
+    """Main function with enhanced argument parsing and execution."""
+    parser = argparse.ArgumentParser(
+        description="Advanced Bitcoin Transaction Forensic Analyzer v2.0",
+        formatter_class=argparse.RawTextHelpFormatter,
+        epilog="""
+Features:
+• Multi-heuristic change address detection
+• Comprehensive transaction analysis  
+• Network context awareness
+• Probabilistic scoring system
+• Detailed forensic reporting
 
-    input_filename = sys.argv[1]
-    output_filename = sys.argv[2]
+Example:
+  python btc_forensic.py transactions.txt report.txt
+        """
+    )
     
+    parser.add_argument("input_file", 
+                       help="Path to input file containing transaction IDs (one per line)")
+    parser.add_argument("output_file", 
+                       help="Path to output file for the forensic report")
+    parser.add_argument("--verbose", "-v", action="store_true",
+                       help="Enable verbose output")
+    parser.add_argument("--delay", "-d", type=float, default=0.1,
+                       help="Delay between API requests in seconds (default: 0.1)")
+    
+    args = parser.parse_args()
+    
+    # Update delay if specified
+    global REQUEST_DELAY
+    REQUEST_DELAY = args.delay
+    
+    # Load transaction IDs
     try:
-        with open(input_filename, 'r') as f:
-            txids = [line.strip() for line in f if line.strip()]
+        with open(args.input_file, 'r', encoding='utf-8') as f:
+            txids = [line.strip() for line in f if line.strip() and not line.startswith('#')]
     except FileNotFoundError:
-        print(f"Error: Input file '{input_filename}' not found.")
+        print(f"❌ Error: Input file '{args.input_file}' not found.")
+        sys.exit(1)
+    except Exception as e:
+        print(f"❌ Error reading input file: {e}")
         sys.exit(1)
     
-    print(f"Found {len(txids)} transaction IDs. Fetching details...")
+    if not txids:
+        print("❌ Error: No valid transaction IDs found in input file.")
+        sys.exit(1)
+    
+    print(f"🔍 Starting comprehensive forensic analysis of {len(txids)} transactions...")
+    print("📊 Using enhanced heuristics with probabilistic scoring")
+    print(f"🌐 Target API: {BASE_URL_DEFAULT}")
+    
+    # Initialize analyzer
+    analyzer = TransactionAnalyzer()
+    
+    # Process transactions
+    with open(args.output_file, 'w', encoding='utf-8') as out_file:
+        successful = 0
+        failed = 0
+        
+        for i, txid in enumerate(txids, 1):
+            print(f"\n[{i}/{len(txids)}] Processing: {txid}")
+            print("-" * 80)
             
-    with open(output_filename, 'w') as out_file:
-        for i, txid in enumerate(txids):
-            print(f"({i+1}/{len(txids)}) Fetching {txid}...")
             try:
-                response = requests.get(f"{BASE_URL}{txid}", timeout=10)
-                # Check if the request was successful
-                if response.status_code == 200:
-                    data = response.json()
-                    formatted_data = format_transaction_details(data)
-                    out_file.write(formatted_data)
-                else:
-                    error_message = f"Failed to fetch {txid}. Status Code: {response.status_code}. Response: {response.text}\n\n"
-                    print(error_message)
-                    out_file.write(error_message)
-            except requests.RequestException as e:
-                error_message = f"An error occurred while fetching {txid}: {e}\n\n"
-                print(error_message)
-                out_file.write(error_message)
+                # Fetch comprehensive data
+                all_details = fetch_comprehensive_details(txid, analyzer)
                 
-    print(f"\n✅ Done! All details have been saved to '{output_filename}'.")
+                if all_details and all_details.get('base'):
+                    # Perform advanced analysis
+                    change_analysis = perform_comprehensive_change_analysis(
+                        all_details['base'], analyzer
+                    )
+                    
+                    # Generate report
+                    formatted_report = format_comprehensive_report(
+                        all_details, change_analysis, analyzer
+                    )
+                    
+                    out_file.write(formatted_report)
+                    out_file.flush()  # Ensure data is written
+                    
+                    successful += 1
+                    print("✅ Analysis completed successfully")
+                    
+                else:
+                    error_msg = f"❌ Failed to fetch critical data for {txid}\n\n"
+                    out_file.write(error_msg)
+                    failed += 1
+                    print("❌ Failed to fetch critical data")
+                    
+            except Exception as e:
+                error_msg = f"❌ Error processing {txid}: {str(e)}\n\n"
+                out_file.write(error_msg)
+                failed += 1
+                print(f"❌ Error: {str(e)}")
+    
+    # Final summary
+    print("\n" + "=" * 80)
+    print("📋 ANALYSIS SUMMARY")
+    print("=" * 80)
+    print(f"✅ Successfully analyzed: {successful} transactions")
+    print(f"❌ Failed to analyze: {failed} transactions") 
+    print(f"📄 Comprehensive report saved to: {args.output_file}")
+    print(f"🕒 Total processing time: {datetime.utcnow().strftime('%H:%M:%S')} UTC")
+    
+    if successful > 0:
+        print("\n🎯 Advanced forensic analysis complete!")
+        print("   Report includes probabilistic change detection,")
+        print("   comprehensive transaction analysis, and detailed")
+        print("   heuristic breakdowns for enhanced investigation.")
 
 
 if __name__ == "__main__":