Regen Network MCP Server

"""Credit batch analysis prompt implementation.""" from typing import Optional async def credit_batch_analysis(batch_denom: Optional[str] = None) -> str: """ Deep dive analysis into credit batches. Helps users understand specific credit issuances and their lifecycle. """ # Customize based on whether a specific batch is provided if batch_denom: focus_section = f""" **🎯 Focus: Batch {batch_denom}** Let's analyze this specific credit batch in detail: • Supply and circulation metrics • Retirement patterns • Market activity • Project and vintage information """ else: focus_section = """ **🎯 Focus: Credit Batch Analysis** Let's explore how to analyze credit batches - the fundamental units of carbon credits on Regen Network! """ return f"""📦 **Credit Batch Deep Dive** {focus_section} **Understanding Credit Batches:** A credit batch represents a specific issuance of ecological credits from a project during a defined vintage period. Each batch is a unique, tradeable asset with its own lifecycle from issuance through retirement. **Batch Anatomy:** • **Denom**: Unique identifier (e.g., C01-001-20240101-20240131-001) • **Project ID**: Source project (e.g., C01-001) • **Vintage Period**: When ecological benefit occurred • **Issuance Date**: When credits were created • **Total Amount**: Initial credits issued • **Supply States**: Tradable, Retired, Cancelled --- ## 🔍 **Batch Identification** ### Decoding Batch Denoms ```python def decode_batch_denom(denom): \"\"\"Parse batch denom into components.\"\"\" # Format: PROJECT-STARTDATE-ENDDATE-SEQUENCE # Example: C01-001-20240101-20240131-001 parts = denom.split('-') if len(parts) >= 5: return {{ 'class_id': parts[0], 'project_sequence': parts[1], 'project_id': f"{{parts[0]}}-{{parts[1]}}", 'start_date': parts[2], 'end_date': parts[3], 'batch_sequence': parts[4] if len(parts) > 4 else '001', 'full_denom': denom }} return {{'error': 'Invalid denom format', 'denom': denom}} # Example batch_info = decode_batch_denom('C01-001-20240101-20240131-001') print(f"Project: {{batch_info['project_id']}}") print(f"Vintage: {{batch_info['start_date']}} to {{batch_info['end_date']}}") ``` ### Finding Batches ```python # Get all batches all_batches = get_all_batches() print(f"Total batches on chain: {{len(all_batches)}}") # Filter by various criteria def find_batches_by_criteria(class_id=None, project_id=None, year=None): batches = get_all_batches() filtered = [] for batch in batches: # Apply filters if class_id and not batch.get('class_id', '').startswith(class_id): continue if project_id and batch.get('project_id') != project_id: continue if year and not batch.get('start_date', '').startswith(str(year)): continue filtered.append(batch) return filtered # Find all 2024 vintage batches batches_2024 = find_batches_by_criteria(year=2024) ``` --- ## 📊 **Supply Analysis** ### Supply Breakdown ```python def analyze_batch_supply(batch_denom): \"\"\"Comprehensive supply analysis for a batch.\"\"\" # Get batch details and supply batch = get_batch(batch_denom) supply = get_batch_supply(batch_denom) # Calculate percentages total = float(supply.get('total_amount', 0)) tradable = float(supply.get('tradable_amount', 0)) retired = float(supply.get('retired_amount', 0)) cancelled = float(supply.get('cancelled_amount', 0)) tradable_pct = (tradable / total * 100) if total > 0 else 0 retired_pct = (retired / total * 100) if total > 0 else 0 cancelled_pct = (cancelled / total * 100) if total > 0 else 0 print(f"=== Supply Analysis: {{batch_denom}} ===") print(f"Total Issued: {{total:,.0f}} credits") print(f"") print(f"Current State:") print(f" Tradable: {{tradable:,.0f}} ({{tradable_pct:.1f}}%)") print(f" Retired: {{retired:,.0f}} ({{retired_pct:.1f}}%)") print(f" Cancelled: {{cancelled:,.0f}} ({{cancelled_pct:.1f}}%)") print(f"") print(f"Retirement Rate: {{retired_pct:.1f}}%") print(f"Active Circulation: {{tradable_pct:.1f}}%") return {{ 'total': total, 'tradable': tradable, 'retired': retired, 'cancelled': cancelled, 'retirement_rate': retired_pct, 'circulation_rate': tradable_pct }} # Analyze specific batch supply_stats = analyze_batch_supply('C01-001-20240101-20240131-001') ``` ### Supply Trends ```python def analyze_retirement_trends(project_id): \"\"\"Analyze retirement patterns across project batches.\"\"\" batches = get_batches_by_project(project_id) retirement_data = [] for batch in batches: supply = get_batch_supply(batch['denom']) total = float(supply.get('total_amount', 0)) retired = float(supply.get('retired_amount', 0)) retirement_rate = (retired / total * 100) if total > 0 else 0 retirement_data.append({{ 'batch': batch['denom'], 'vintage': f"{{batch.get('start_date', '')}} to {{batch.get('end_date', '')}}", 'total': total, 'retired': retired, 'rate': retirement_rate }}) # Sort by vintage retirement_data.sort(key=lambda x: x['vintage']) # Calculate averages avg_rate = sum(b['rate'] for b in retirement_data) / len(retirement_data) if retirement_data else 0 print(f"=== Retirement Trends: {{project_id}} ===") print(f"Average Retirement Rate: {{avg_rate:.1f}}%") print(f"") print("Vintage | Retirement Rate") print("--------|----------------") for batch in retirement_data: print(f"{{batch['vintage'][:7]}}... | {{batch['rate']:>6.1f}}%") return retirement_data ``` --- ## 💼 **Holder Analysis** ### Distribution Analysis ```python def analyze_batch_holders(batch_denom, top_n=10): \"\"\"Analyze credit distribution among holders.\"\"\" # Note: This would require querying all addresses # Simplified example showing the pattern holders = [] # Would be populated with actual holder data # Example holder analysis structure holder_analysis = {{ 'batch': batch_denom, 'unique_holders': len(holders), 'concentration': 0, # Top 10 holders percentage 'largest_holder': None, 'distribution': 'even' # even, concentrated, highly_concentrated }} # Classification based on concentration if holder_analysis['concentration'] > 80: holder_analysis['distribution'] = 'highly_concentrated' elif holder_analysis['concentration'] > 50: holder_analysis['distribution'] = 'concentrated' return holder_analysis ``` --- ## 📈 **Market Integration** ### Batch Market Activity ```python def analyze_batch_market(batch_denom): \"\"\"Analyze market activity for a specific batch.\"\"\" # Get batch info batch = get_batch(batch_denom) supply = get_batch_supply(batch_denom) # Get market orders orders = get_sell_orders_by_batch(batch_denom) if not orders: return {{ 'status': 'Not on market', 'batch': batch_denom, 'tradable_supply': supply.get('tradable_amount', 0) }} # Analyze orders total_for_sale = sum(float(o.get('quantity', 0)) for o in orders) prices = [float(o.get('ask_price', 0)) for o in orders] tradable = float(supply.get('tradable_amount', 0)) market_depth = (total_for_sale / tradable * 100) if tradable > 0 else 0 return {{ 'batch': batch_denom, 'status': 'Active on market', 'num_orders': len(orders), 'total_for_sale': total_for_sale, 'tradable_supply': tradable, 'market_depth_pct': market_depth, 'price_range': {{ 'min': min(prices) if prices else 0, 'max': max(prices) if prices else 0, 'avg': sum(prices) / len(prices) if prices else 0 }}, 'sellers': list(set(o.get('seller', '') for o in orders)) }} # Check market presence market_status = analyze_batch_market('C01-001-20240101-20240131-001') print(f"Market Status: {{market_status['status']}}") if market_status['num_orders']: print(f"Orders: {{market_status['num_orders']}}") print(f"For Sale: {{market_status['total_for_sale']:,.0f}} ({{market_status['market_depth_pct']:.1f}}% of tradable)") ``` --- ## 🔬 **Batch Comparison** ### Compare Multiple Batches ```python def compare_batches(batch_denoms): \"\"\"Compare multiple batches side by side.\"\"\" comparison = [] for denom in batch_denoms: batch = get_batch(denom) supply = get_batch_supply(denom) orders = get_sell_orders_by_batch(denom) # Calculate metrics total = float(supply.get('total_amount', 0)) retired_pct = (float(supply.get('retired_amount', 0)) / total * 100) if total > 0 else 0 comparison.append({{ 'denom': denom, 'project': batch.get('project_id', 'Unknown'), 'vintage_start': batch.get('start_date', 'Unknown'), 'total_amount': total, 'retirement_rate': retired_pct, 'on_market': len(orders) > 0, 'num_orders': len(orders) }}) # Sort by total amount comparison.sort(key=lambda x: x['total_amount'], reverse=True) print("Batch Comparison") print("=" * 60) for batch in comparison: print(f"{{batch['denom'][:30]}}...") print(f" Total: {{batch['total_amount']:,.0f}} | Retired: {{batch['retirement_rate']:.1f}}%") print(f" Market: {{'Yes' if batch['on_market'] else 'No'}} ({{batch['num_orders']}} orders)") print() return comparison ``` ### Vintage Performance ```python def analyze_vintage_performance(class_id, year): \"\"\"Compare all batches from a specific vintage year.\"\"\" # Find all batches from this vintage all_batches = get_all_batches() vintage_batches = [] for batch in all_batches: if (batch.get('class_id', '') == class_id and batch.get('start_date', '').startswith(str(year))): vintage_batches.append(batch) # Analyze performance total_issued = 0 total_retired = 0 market_active = 0 for batch in vintage_batches: supply = get_batch_supply(batch['denom']) orders = get_sell_orders_by_batch(batch['denom']) total_issued += float(supply.get('total_amount', 0)) total_retired += float(supply.get('retired_amount', 0)) if orders: market_active += 1 retirement_rate = (total_retired / total_issued * 100) if total_issued > 0 else 0 market_participation = (market_active / len(vintage_batches) * 100) if vintage_batches else 0 print(f"=== {{class_id}} Vintage {{year}} Performance ===") print(f"Total Batches: {{len(vintage_batches)}}") print(f"Total Credits: {{total_issued:,.0f}}") print(f"Retirement Rate: {{retirement_rate:.1f}}%") print(f"Market Active: {{market_participation:.1f}}% of batches") return {{ 'vintage': f"{{class_id}}-{{year}}", 'batch_count': len(vintage_batches), 'total_credits': total_issued, 'retirement_rate': retirement_rate, 'market_participation': market_participation }} ``` --- ## 🎯 **Advanced Batch Analytics** ### Lifecycle Tracking ```python def track_batch_lifecycle(batch_denom): \"\"\"Track complete lifecycle of a credit batch.\"\"\" batch = get_batch(batch_denom) supply = get_batch_supply(batch_denom) # Calculate age (simplified) issuance_date = batch.get('issuance_date', '') lifecycle = {{ 'batch': batch_denom, 'stages': {{ 'issued': {{ 'date': issuance_date, 'amount': batch.get('total_amount', 0) }}, 'current': {{ 'tradable': supply.get('tradable_amount', 0), 'retired': supply.get('retired_amount', 0), 'cancelled': supply.get('cancelled_amount', 0) }}, 'market': {{ 'first_listing': None, # Would need historical data 'current_orders': len(get_sell_orders_by_batch(batch_denom)) }} }} }} return lifecycle ``` ### Price Discovery ```python def analyze_batch_pricing(batch_denom): \"\"\"Analyze pricing for a specific batch.\"\"\" orders = get_sell_orders_by_batch(batch_denom) if not orders: return {{'status': 'No pricing data available'}} prices = [float(o.get('ask_price', 0)) for o in orders] quantities = [float(o.get('quantity', 0)) for o in orders] # Volume-weighted average price vwap = sum(p * q for p, q in zip(prices, quantities)) / sum(quantities) if quantities else 0 # Price distribution price_distribution = {{ 'min': min(prices), 'max': max(prices), 'avg': sum(prices) / len(prices), 'vwap': vwap, 'spread': max(prices) - min(prices), 'spread_pct': ((max(prices) - min(prices)) / min(prices) * 100) if min(prices) > 0 else 0 }} print(f"=== Price Analysis: {{batch_denom[:30]}}... ===") print(f"Price Range: ${{price_distribution['min']:.2f}} - ${{price_distribution['max']:.2f}}") print(f"Average: ${{price_distribution['avg']:.2f}}") print(f"VWAP: ${{price_distribution['vwap']:.2f}}") print(f"Spread: {{price_distribution['spread_pct']:.1f}}%") return price_distribution ``` --- ## 💡 **Batch Analysis Best Practices** **Key Metrics to Track:** • **Retirement Rate**: Indicates actual environmental impact • **Market Depth**: Percentage of tradable supply on market • **Price Stability**: Spread between min and max prices • **Holder Concentration**: Distribution of ownership **Analysis Workflow:** 1. Identify batch using denom 2. Check supply status and retirement rate 3. Analyze market activity if applicable 4. Compare with similar vintages 5. Track trends over time **Red Flags:** • Very high retirement rate (might indicate low tradability) • No market activity despite tradable supply • Extreme price spreads • Single holder concentration Ready to analyze credit batches? Which batch or vintage would you like to explore?"""