Real-World Asset Tokenization Tracker: Complete Ollama Property and Bond Analysis Guide

# Install Ollama on your system
curl -fsSL https://ollama.ai/install.sh | sh

# Download the finance-optimized model
ollama pull llama2:13b-chat

# Verify installation
ollama list

import ollama
import json
import requests
from datetime import datetime

class RWATracker:
    def __init__(self):
        self.client = ollama.Client()
        self.model = "llama2:13b-chat"
        
    def analyze_asset(self, asset_data, query_type):
        """
        Analyze tokenized asset data using Ollama
        asset_data: Dictionary containing token metrics
        query_type: 'performance', 'risk', or 'compliance'
        """
        prompt = self._build_analysis_prompt(asset_data, query_type)
        
        response = self.client.chat(
            model=self.model,
            messages=[{
                'role': 'user',
                'content': prompt
            }]
        )
        
        return response['message']['content']

class PropertyTokenTracker(RWATracker):
    def __init__(self):
        super().__init__()
        self.property_contracts = {}
        
    def fetch_property_metrics(self, token_address, blockchain="ethereum"):
        """
        Collect comprehensive property token data
        Returns: Dictionary with financial and operational metrics
        """
        # Fetch on-chain token data
        token_data = self._get_token_data(token_address, blockchain)
        
        # Get property-specific metrics
        property_metrics = {
            'token_address': token_address,
            'total_supply': token_data['totalSupply'],
            'market_cap': token_data['marketCap'],
            'trading_volume_24h': token_data['volume24h'],
            'holder_count': token_data['holderCount'],
            'rental_yield_annual': self._calculate_rental_yield(token_address),
            'occupancy_rate': self._get_occupancy_rate(token_address),
            'property_value_usd': self._get_property_valuation(token_address),
            'last_updated': datetime.now().isoformat()
        }
        
        return property_metrics
    
    def analyze_property_performance(self, property_metrics):
        """
        Use Ollama to analyze property token performance
        """
        analysis_prompt = f"""
        Analyze this tokenized property investment:
        
        Property Metrics:
        - Market Cap: ${property_metrics['market_cap']:,.2f}
        - Annual Rental Yield: {property_metrics['rental_yield_annual']:.2%}
        - Occupancy Rate: {property_metrics['occupancy_rate']:.1%}
        - 24h Trading Volume: ${property_metrics['trading_volume_24h']:,.2f}
        - Token Holders: {property_metrics['holder_count']:,}
        
        Provide analysis covering:
        1. Investment attractiveness (1-10 scale)
        2. Key risk factors
        3. Liquidity assessment
        4. Comparison to traditional real estate metrics
        5. Recommendation (buy/hold/sell)
        """
        
        return self.analyze_asset(property_metrics, 'performance')

def generate_property_dashboard(self, portfolio_addresses):
    """
    Create comprehensive property portfolio analysis
    """
    portfolio_data = []
    
    for address in portfolio_addresses:
        metrics = self.fetch_property_metrics(address)
        analysis = self.analyze_property_performance(metrics)
        
        portfolio_data.append({
            'address': address,
            'metrics': metrics,
            'ai_analysis': analysis
        })
    
    # Generate portfolio-level insights
    portfolio_summary = self._create_portfolio_summary(portfolio_data)
    
    return {
        'individual_properties': portfolio_data,
        'portfolio_summary': portfolio_summary,
        'generated_at': datetime.now().isoformat()
    }

class BondTokenTracker(RWATracker):
    def __init__(self):
        super().__init__()
        self.bond_contracts = {}
        
    def fetch_bond_metrics(self, bond_address, blockchain="ethereum"):
        """
        Collect comprehensive bond token data including yield calculations
        """
        # Get basic token information
        token_data = self._get_token_data(bond_address, blockchain)
        
        # Fetch bond-specific smart contract data
        bond_contract = self._get_contract(bond_address, blockchain)
        
        bond_metrics = {
            'token_address': bond_address,
            'face_value': bond_contract.functions.faceValue().call(),
            'coupon_rate': bond_contract.functions.couponRate().call() / 10000,  # Convert basis points
            'maturity_date': bond_contract.functions.maturityDate().call(),
            'current_price': token_data['price'],
            'yield_to_maturity': self._calculate_ytm(bond_address),
            'duration': self._calculate_duration(bond_address),
            'credit_rating': self._get_credit_rating(bond_address),
            'payment_frequency': bond_contract.functions.paymentFrequency().call(),
            'next_payment_date': bond_contract.functions.nextPaymentDate().call(),
            'total_payments_made': bond_contract.functions.totalPaymentsMade().call()
        }
        
        return bond_metrics
    
    def _calculate_ytm(self, bond_address):
        """
        Calculate yield to maturity for tokenized bond
        Uses Newton-Raphson method for accurate YTM calculation
        """
        bond_data = self._get_contract(bond_address)
        
        face_value = bond_data.functions.faceValue().call()
        current_price = self._get_current_price(bond_address)
        coupon_rate = bond_data.functions.couponRate().call() / 10000
        periods_remaining = self._get_periods_to_maturity(bond_address)
        
        # Newton-Raphson iteration for YTM
        ytm_estimate = 0.05  # Starting estimate of 5%
        
        for _ in range(100):  # Maximum iterations
            pv_calc = self._present_value(face_value, coupon_rate, ytm_estimate, periods_remaining)
            pv_derivative = self._pv_derivative(face_value, coupon_rate, ytm_estimate, periods_remaining)
            
            ytm_new = ytm_estimate - (pv_calc - current_price) / pv_derivative
            
            if abs(ytm_new - ytm_estimate) < 0.000001:  # Convergence check
                break
                
            ytm_estimate = ytm_new
            
        return ytm_estimate

def analyze_bond_risk(self, bond_metrics):
    """
    Comprehensive bond risk analysis using AI
    """
    risk_prompt = f"""
    Analyze the risk profile of this tokenized bond:
    
    Bond Details:
    - Face Value: ${bond_metrics['face_value']:,.2f}
    - Coupon Rate: {bond_metrics['coupon_rate']:.2%}
    - Current Price: ${bond_metrics['current_price']:.2f}
    - Yield to Maturity: {bond_metrics['yield_to_maturity']:.2%}
    - Duration: {bond_metrics['duration']:.2f} years
    - Credit Rating: {bond_metrics['credit_rating']}
    - Days to Maturity: {self._days_to_maturity(bond_metrics['maturity_date'])}
    
    Provide detailed analysis including:
    1. Interest rate risk assessment
    2. Credit risk evaluation
    3. Liquidity risk factors
    4. Smart contract risk considerations
    5. Overall risk score (1-10, where 10 is highest risk)
    6. Risk-adjusted return recommendation
    """
    
    return self.analyze_asset(bond_metrics, 'risk')

def monitor_bond_payments(self, bond_address):
    """
    Track and verify automated bond payments
    """
    payment_history = self._get_payment_events(bond_address)
    expected_payments = self._calculate_expected_payments(bond_address)
    
    payment_analysis = {
        'payments_made': len(payment_history),
        'payments_expected': len(expected_payments),
        'payment_reliability': len(payment_history) / len(expected_payments),
        'average_payment_delay': self._calculate_avg_delay(payment_history),
        'next_payment_due': expected_payments[0] if expected_payments else None
    }
    
    return payment_analysis

class ComprehensiveRWATracker:
    def __init__(self):
        self.property_tracker = PropertyTokenTracker()
        self.bond_tracker = BondTokenTracker()
        self.supported_chains = ['ethereum', 'polygon', 'avalanche', 'binance']
        
    def track_full_portfolio(self, wallet_address):
        """
        Analyze complete tokenized asset portfolio across multiple chains
        """
        portfolio = {
            'wallet_address': wallet_address,
            'properties': [],
            'bonds': [],
            'other_assets': [],
            'total_value_usd': 0,
            'diversification_score': 0,
            'risk_metrics': {}
        }
        
        # Scan each supported blockchain
        for chain in self.supported_chains:
            tokens = self._get_wallet_tokens(wallet_address, chain)
            
            for token in tokens:
                asset_type = self._identify_asset_type(token['address'], chain)
                
                if asset_type == 'property':
                    property_data = self.property_tracker.fetch_property_metrics(
                        token['address'], chain
                    )
                    property_data['balance'] = token['balance']
                    property_data['chain'] = chain
                    portfolio['properties'].append(property_data)
                    
                elif asset_type == 'bond':
                    bond_data = self.bond_tracker.fetch_bond_metrics(
                        token['address'], chain
                    )
                    bond_data['balance'] = token['balance']
                    bond_data['chain'] = chain
                    portfolio['bonds'].append(bond_data)
                    
                else:
                    # Handle other RWA types (commodities, infrastructure, etc.)
                    other_data = self._analyze_generic_rwa(token['address'], chain)
                    portfolio['other_assets'].append(other_data)
        
        # Calculate portfolio-level metrics
        portfolio['total_value_usd'] = self._calculate_total_value(portfolio)
        portfolio['diversification_score'] = self._calculate_diversification(portfolio)
        portfolio['risk_metrics'] = self._calculate_portfolio_risk(portfolio)
        
        return portfolio
    
    def generate_ai_insights(self, portfolio):
        """
        Generate comprehensive portfolio insights using Ollama
        """
        insight_prompt = f"""
        Analyze this tokenized asset portfolio:
        
        Portfolio Summary:
        - Total Value: ${portfolio['total_value_usd']:,.2f}
        - Properties: {len(portfolio['properties'])} tokens
        - Bonds: {len(portfolio['bonds'])} tokens
        - Other Assets: {len(portfolio['other_assets'])} tokens
        - Diversification Score: {portfolio['diversification_score']:.1f}/10
        - Blockchains Used: {len(set(asset.get('chain') for assets in [portfolio['properties'], portfolio['bonds']] for asset in assets))}
        
        Risk Metrics:
        - Portfolio Beta: {portfolio['risk_metrics'].get('beta', 'N/A')}
        - Value at Risk (95%): {portfolio['risk_metrics'].get('var_95', 'N/A')}
        - Sharpe Ratio: {portfolio['risk_metrics'].get('sharpe_ratio', 'N/A')}
        
        Provide strategic analysis including:
        1. Portfolio strength assessment
        2. Diversification recommendations
        3. Risk management suggestions
        4. Rebalancing opportunities
        5. Market timing considerations
        6. Regulatory compliance status
        """
        
        response = self.property_tracker.client.chat(
            model=self.property_tracker.model,
            messages=[{'role': 'user', 'content': insight_prompt}]
        )
        
        return response['message']['content']

def monitor_compliance(self, portfolio):
    """
    Check tokenized assets against regulatory requirements
    """
    compliance_report = {
        'kyc_status': self._check_kyc_compliance(portfolio),
        'aml_flags': self._scan_aml_violations(portfolio),
        'accredited_investor_only': self._check_accreditation_requirements(portfolio),
        'geographic_restrictions': self._verify_geographic_eligibility(portfolio),
        'holding_period_restrictions': self._check_holding_periods(portfolio),
        'reporting_requirements': self._identify_reporting_obligations(portfolio)
    }
    
    # Use AI to interpret compliance status
    compliance_analysis = self._analyze_compliance_with_ai(compliance_report)
    
    return {
        'report': compliance_report,
        'analysis': compliance_analysis,
        'action_required': self._identify_required_actions(compliance_report)
    }

def build_price_prediction_model(self, asset_address, historical_days=365):
    """
    Create ML model for tokenized asset price prediction
    """
    # Collect historical data
    price_history = self._get_price_history(asset_address, historical_days)
    on_chain_metrics = self._get_historical_metrics(asset_address, historical_days)
    macro_factors = self._get_macro_economic_data(historical_days)
    
    # Feature engineering
    features = self._engineer_features(price_history, on_chain_metrics, macro_factors)
    
    # Train prediction model
    model = self._train_prediction_model(features)
    
    # Generate predictions with Ollama interpretation
    predictions = model.predict(features[-30:])  # 30-day forecast
    
    prediction_prompt = f"""
    Interpret these tokenized asset price predictions:
    
    Asset: {asset_address}
    Current Price: ${price_history[-1]:.2f}
    30-day Predictions: {[f'${p:.2f}' for p in predictions]}
    
    Model Accuracy: {model.score_:.2%}
    Key Features: {model.feature_importance_}
    
    Provide interpretation including:
    1. Prediction confidence assessment
    2. Key driving factors
    3. Potential catalysts or risks
    4. Trading strategy recommendations
    5. Model limitation warnings
    """
    
    interpretation = self.property_tracker.client.chat(
        model=self.property_tracker.model,
        messages=[{'role': 'user', 'content': prediction_prompt}]
    )
    
    return {
        'predictions': predictions.tolist(),
        'model_accuracy': model.score_,
        'interpretation': interpretation['message']['content']
    }

def detect_arbitrage_opportunities(self, asset_addresses):
    """
    Identify price discrepancies across different blockchains
    """
    arbitrage_opportunities = []
    
    for asset in asset_addresses:
        cross_chain_prices = {}
        
        # Check price on each supported chain
        for chain in self.supported_chains:
            if self._asset_exists_on_chain(asset, chain):
                price = self._get_current_price(asset, chain)
                liquidity = self._get_liquidity_depth(asset, chain)
                gas_costs = self._estimate_transaction_costs(chain)
                
                cross_chain_prices[chain] = {
                    'price': price,
                    'liquidity': liquidity,
                    'gas_costs': gas_costs
                }
        
        # Calculate arbitrage potential
        if len(cross_chain_prices) > 1:
            opportunity = self._calculate_arbitrage_profit(cross_chain_prices)
            
            if opportunity['profit_percentage'] > 0.5:  # Minimum 0.5% profit threshold
                arbitrage_opportunities.append({
                    'asset': asset,
                    'opportunity': opportunity,
                    'execution_plan': self._create_arbitrage_plan(opportunity)
                })
    
    return arbitrage_opportunities

def generate_monthly_report(self, portfolio, report_type='investor'):
    """
    Create comprehensive monthly portfolio report
    """
    report_data = {
        'reporting_period': self._get_current_month(),
        'portfolio_performance': self._calculate_monthly_performance(portfolio),
        'asset_allocation': self._analyze_allocation_changes(portfolio),
        'income_generation': self._calculate_monthly_income(portfolio),
        'compliance_status': self.monitor_compliance(portfolio),
        'market_commentary': self._generate_market_analysis(portfolio)
    }
    
    # Customize report based on audience
    if report_type == 'investor':
        template = self._get_investor_report_template()
    elif report_type == 'regulatory':
        template = self._get_regulatory_report_template()
    else:
        template = self._get_management_report_template()
    
    # Generate AI-powered executive summary
    exec_summary = self._create_executive_summary(report_data)
    
    return {
        'report_data': report_data,
        'executive_summary': exec_summary,
        'formatted_report': self._format_report(report_data, template)
    }

def setup_alert_system(self, portfolio, alert_config):
    """
    Configure automated alerts for portfolio events
    """
    alert_triggers = {
        'price_change': alert_config.get('price_threshold', 0.05),  # 5% default
        'liquidity_drop': alert_config.get('liquidity_threshold', 0.20),  # 20% default
        'compliance_violation': True,
        'payment_delays': True,
        'smart_contract_upgrades': True,
        'regulatory_changes': True
    }
    
    return self._configure_monitoring(portfolio, alert_triggers)

def validate_data_quality(self, asset_data):
    """
    Ensure data accuracy and completeness
    """
    validation_results = {
        'completeness_score': self._check_data_completeness(asset_data),
        'accuracy_checks': self._verify_data_accuracy(asset_data),
        'freshness_score': self._assess_data_freshness(asset_data),
        'consistency_checks': self._validate_cross_source_consistency(asset_data)
    }
    
    overall_quality = sum(validation_results.values()) / len(validation_results)
    
    if overall_quality < 0.8:  # 80% quality threshold
        raise DataQualityError(f"Data quality score {overall_quality:.1%} below threshold")
    
    return validation_results

def implement_security_measures(self):
    """
    Apply security best practices for asset tracking
    """
    security_config = {
        'api_rate_limiting': self._setup_rate_limits(),
        'data_encryption': self._configure_encryption(),
        'access_controls': self._implement_rbac(),
        'audit_logging': self._setup_audit_trail(),
        'backup_procedures': self._configure_backups()
    }
    
    return security_config