How to Build RWA Portfolio with Ollama: Diversification Strategy Framework

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

# Pull the model optimized for financial analysis
ollama pull llama2:13b-chat

# Verify installation
ollama list

# rwa_config.py
import ollama

class RWAAnalyzer:
    def __init__(self):
        self.model = "llama2:13b-chat"
        self.context_window = 4096
        
    def setup_rwa_context(self):
        """Configure Ollama for RWA-specific analysis"""
        system_prompt = """
        You are a specialized RWA portfolio analyst. Focus on:
        1. Asset correlation analysis
        2. Risk assessment across asset classes
        3. Tokenization impact on liquidity
        4. Geographic and sector diversification
        5. Regulatory compliance considerations
        """
        return system_prompt

# Initialize analyzer
analyzer = RWAAnalyzer()
context = analyzer.setup_rwa_context()

# asset_database.py
import pandas as pd
import json
from datetime import datetime

class RWADatabase:
    def __init__(self):
        self.assets = []
        self.categories = {
            'real_estate': ['residential', 'commercial', 'industrial'],
            'commodities': ['precious_metals', 'energy', 'agriculture'],
            'private_equity': ['growth', 'buyout', 'venture'],
            'infrastructure': ['renewable', 'transportation', 'utilities']
        }
    
    def add_asset(self, asset_data):
        """Add new RWA to database with validation"""
        required_fields = [
            'asset_id', 'category', 'subcategory', 'tokenization_status',
            'expected_return', 'risk_rating', 'liquidity_score',
            'geographic_exposure', 'market_cap', 'minimum_investment'
        ]
        
        if all(field in asset_data for field in required_fields):
            asset_data['timestamp'] = datetime.now().isoformat()
            self.assets.append(asset_data)
            return True
        return False
    
    def get_correlation_matrix(self):
        """Generate correlation matrix for portfolio optimization"""
        df = pd.DataFrame(self.assets)
        return df[['expected_return', 'risk_rating', 'liquidity_score']].corr()

# Example usage
db = RWADatabase()

# Add sample assets
sample_assets = [
    {
        'asset_id': 'RE001',
        'category': 'real_estate',
        'subcategory': 'commercial',
        'tokenization_status': 'fully_tokenized',
        'expected_return': 0.08,
        'risk_rating': 0.15,
        'liquidity_score': 0.6,
        'geographic_exposure': 'US_Northeast',
        'market_cap': 50000000,
        'minimum_investment': 1000
    },
    {
        'asset_id': 'COM001',
        'category': 'commodities',
        'subcategory': 'precious_metals',
        'tokenization_status': 'partially_tokenized',
        'expected_return': 0.06,
        'risk_rating': 0.12,
        'liquidity_score': 0.8,
        'geographic_exposure': 'Global',
        'market_cap': 100000000,
        'minimum_investment': 500
    }
]

for asset in sample_assets:
    db.add_asset(asset)

# risk_analyzer.py
import ollama
import json

class RWARiskAnalyzer:
    def __init__(self, model="llama2:13b-chat"):
        self.model = model
        self.risk_factors = [
            'market_risk', 'liquidity_risk', 'regulatory_risk',
            'tokenization_risk', 'geographic_risk', 'sector_risk'
        ]
    
    def analyze_asset_risk(self, asset_data):
        """Comprehensive risk analysis using Ollama"""
        prompt = f"""
        Analyze the risk profile for this RWA:
        
        Asset: {asset_data['asset_id']}
        Category: {asset_data['category']}
        Geographic Exposure: {asset_data['geographic_exposure']}
        Tokenization Status: {asset_data['tokenization_status']}
        Market Cap: ${asset_data['market_cap']:,}
        
        Provide risk assessment scores (0-1 scale) for:
        1. Market Risk
        2. Liquidity Risk
        3. Regulatory Risk
        4. Tokenization Risk
        5. Geographic Risk
        
        Format as JSON with explanations.
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        
        return self.parse_risk_response(response['message']['content'])
    
    def parse_risk_response(self, response_text):
        """Extract structured risk data from Ollama response"""
        try:
            # Extract JSON from response
            start = response_text.find('{')
            end = response_text.rfind('}') + 1
            json_str = response_text[start:end]
            return json.loads(json_str)
        except:
            return {'error': 'Could not parse risk analysis'}
    
    def portfolio_risk_aggregation(self, portfolio_risks):
        """Aggregate individual asset risks into portfolio risk"""
        aggregated_risks = {}
        
        for risk_factor in self.risk_factors:
            total_risk = sum(asset.get(risk_factor, 0) for asset in portfolio_risks)
            aggregated_risks[risk_factor] = total_risk / len(portfolio_risks)
        
        return aggregated_risks

# Example usage
risk_analyzer = RWARiskAnalyzer()

# Analyze sample asset
sample_asset = {
    'asset_id': 'RE001',
    'category': 'real_estate',
    'geographic_exposure': 'US_Northeast',
    'tokenization_status': 'fully_tokenized',
    'market_cap': 50000000
}

risk_profile = risk_analyzer.analyze_asset_risk(sample_asset)
print(f"Risk Analysis: {risk_profile}")

# diversification_engine.py
import numpy as np
import pandas as pd
from scipy.optimize import minimize
import ollama

class DiversificationEngine:
    def __init__(self, rwa_database):
        self.database = rwa_database
        self.model = "llama2:13b-chat"
        
    def calculate_portfolio_metrics(self, weights, returns, cov_matrix):
        """Calculate portfolio return and risk"""
        portfolio_return = np.sum(weights * returns)
        portfolio_risk = np.sqrt(np.dot(weights.T, np.dot(cov_matrix, weights)))
        return portfolio_return, portfolio_risk
    
    def optimize_portfolio(self, target_return=None, risk_tolerance=0.2):
        """Optimize portfolio allocation using modern portfolio theory"""
        assets_df = pd.DataFrame(self.database.assets)
        
        # Extract returns and calculate covariance matrix
        returns = assets_df['expected_return'].values
        risks = assets_df['risk_rating'].values
        
        # Simplified covariance matrix (in practice, use historical data)
        cov_matrix = np.outer(risks, risks) * 0.5 + np.eye(len(risks)) * 0.1
        
        n_assets = len(returns)
        
        # Objective function (minimize risk for given return)
        def objective(weights):
            return self.calculate_portfolio_metrics(weights, returns, cov_matrix)[1]
        
        # Constraints
        constraints = [
            {'type': 'eq', 'fun': lambda x: np.sum(x) - 1},  # Weights sum to 1
        ]
        
        if target_return:
            constraints.append({
                'type': 'eq', 
                'fun': lambda x: self.calculate_portfolio_metrics(x, returns, cov_matrix)[0] - target_return
            })
        
        # Bounds (no short selling)
        bounds = [(0, 1) for _ in range(n_assets)]
        
        # Initial guess (equal weights)
        initial_weights = np.array([1/n_assets] * n_assets)
        
        # Optimize
        result = minimize(
            objective, 
            initial_weights, 
            method='SLSQP', 
            bounds=bounds, 
            constraints=constraints
        )
        
        return result.x if result.success else None
    
    def get_diversification_recommendations(self, current_portfolio):
        """Use Ollama to generate diversification recommendations"""
        portfolio_summary = self.summarize_portfolio(current_portfolio)
        
        prompt = f"""
        Current RWA portfolio composition:
        {portfolio_summary}
        
        Analyze diversification gaps and recommend:
        1. Asset categories to increase/decrease
        2. Geographic diversification opportunities
        3. Risk concentration issues
        4. Liquidity balance improvements
        
        Provide specific, actionable recommendations.
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        
        return response['message']['content']
    
    def summarize_portfolio(self, portfolio):
        """Create portfolio summary for analysis"""
        total_value = sum(holding['value'] for holding in portfolio)
        
        summary = "Portfolio Breakdown:\n"
        category_weights = {}
        
        for holding in portfolio:
            category = holding['category']
            weight = holding['value'] / total_value
            
            if category in category_weights:
                category_weights[category] += weight
            else:
                category_weights[category] = weight
        
        for category, weight in category_weights.items():
            summary += f"- {category}: {weight:.1%}\n"
        
        return summary

# Example usage
diversification_engine = DiversificationEngine(db)

# Current portfolio example
current_portfolio = [
    {'asset_id': 'RE001', 'category': 'real_estate', 'value': 50000},
    {'asset_id': 'COM001', 'category': 'commodities', 'value': 30000},
]

# Get optimization recommendations
recommendations = diversification_engine.get_diversification_recommendations(current_portfolio)
print(f"Diversification Recommendations:\n{recommendations}")

# Optimize portfolio allocation
optimal_weights = diversification_engine.optimize_portfolio(target_return=0.08)
if optimal_weights is not None:
    print(f"Optimal Weights: {optimal_weights}")

# rebalancing_system.py
import pandas as pd
from datetime import datetime, timedelta
import ollama

class RebalancingSystem:
    def __init__(self, portfolio, target_allocations):
        self.portfolio = portfolio
        self.target_allocations = target_allocations
        self.rebalancing_threshold = 0.05  # 5% deviation triggers rebalancing
        self.model = "llama2:13b-chat"
    
    def check_rebalancing_need(self):
        """Check if portfolio needs rebalancing"""
        current_allocations = self.calculate_current_allocations()
        deviations = {}
        
        for category, target in self.target_allocations.items():
            current = current_allocations.get(category, 0)
            deviation = abs(current - target)
            deviations[category] = deviation
        
        max_deviation = max(deviations.values())
        return max_deviation > self.rebalancing_threshold, deviations
    
    def calculate_current_allocations(self):
        """Calculate current portfolio allocations"""
        total_value = sum(asset['current_value'] for asset in self.portfolio)
        allocations = {}
        
        for asset in self.portfolio:
            # Category breakdown
            category = asset['category']
            if category in summary['category_breakdown']:
                summary['category_breakdown'][category] += asset['current_value']
            else:
                summary['category_breakdown'][category] = asset['current_value']
            
            # Geographic breakdown
            geo = asset.get('geographic_exposure', 'Unknown')
            if geo in summary['geographic_breakdown']:
                summary['geographic_breakdown'][geo] += asset['current_value']
            else:
                summary['geographic_breakdown'][geo] = asset['current_value']
            
            # Tokenization status
            token_status = asset.get('tokenization_status', 'Unknown')
            if token_status in summary['tokenization_status']:
                summary['tokenization_status'][token_status] += asset['current_value']
            else:
                summary['tokenization_status'][token_status] = asset['current_value']
        
        # Convert to percentages
        for category in ['category_breakdown', 'geographic_breakdown', 'tokenization_status']:
            for key in summary[category]:
                summary[category][key] = summary[category][key] / total_value
        
        return summary

# Example usage
compliance_monitor = ComplianceMonitor(portfolio, 'US')
compliance_status = compliance_monitor.check_compliance_status()
compliance_report = compliance_monitor.generate_compliance_report()
print(f"Compliance Report:\n{compliance_report}")

# market_regime_detector.py
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import ollama

class MarketRegimeDetector:
    def __init__(self, market_data):
        self.market_data = market_data
        self.model = "llama2:13b-chat"
        
    def detect_current_regime(self):
        """Detect current market regime using multiple indicators"""
        indicators = self.calculate_market_indicators()
        
        prompt = f"""
        Market regime analysis based on current indicators:
        
        Indicators:
        {json.dumps(indicators, indent=2)}
        
        Classify current market regime as one of:
        1. Growth/Expansion
        2. Contraction/Recession
        3. High Inflation
        4. Deflationary
        5. High Volatility/Crisis
        6. Low Volatility/Stable
        
        Provide:
        - Primary regime classification
        - Confidence level (1-10)
        - Key supporting evidence
        - Implications for RWA allocation
        
        Format as structured analysis.
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        
        return response['message']['content']
    
    def calculate_market_indicators(self):
        """Calculate key market indicators"""
        # Simplified indicators (in practice, use real market data)
        indicators = {
            'volatility_index': 0.25,
            'interest_rate_trend': 'rising',
            'inflation_rate': 0.035,
            'credit_spreads': 0.015,
            'equity_momentum': 0.08,
            'commodity_momentum': 0.12,
            'real_estate_momentum': 0.06
        }
        
        return indicators
    
    def generate_regime_strategy(self, current_regime):
        """Generate strategy recommendations for current regime"""
        prompt = f"""
        Portfolio strategy for {current_regime} market regime:
        
        Current RWA allocation principles should focus on:
        1. Asset category preferences
        2. Geographic allocation adjustments
        3. Liquidity management
        4. Risk management priorities
        5. Rebalancing frequency
        
        Provide specific, actionable recommendations for RWA portfolio optimization.
        """
        
        response = ollama.chat(
            model=self.model,
            messages=[{'role': 'user', 'content': prompt}]
        )
        
        return response['message']['content']

# Example usage
market_data = {}  # Placeholder for real market data
regime_detector = MarketRegimeDetector(market_data)
current_regime = regime_detector.detect_current_regime()
strategy = regime_detector.generate_regime_strategy("High Inflation")
print(f"Market Regime Strategy:\n{strategy}")

# Data validation example
def validate_asset_data(asset_data):
    required_fields = ['asset_id', 'category', 'current_value', 'last_updated']
    missing_fields = [field for field in required_fields if field not in asset_data]
    
    if missing_fields:
        return False, f"Missing fields: {missing_fields}"
    
    # Validate data freshness
    last_updated = datetime.fromisoformat(asset_data['last_updated'])
    if (datetime.now() - last_updated).days > 30:
        return False, "Data too old"
    
    return True, "Valid"