Tokenized Securities Valuation with Ollama: $50 Billion Market Prediction

# Example: Processing multi-chain tokenized asset data
import ollama
import requests
from datetime import datetime

def get_tokenized_asset_data(contract_address, chain_id):
    """Fetch tokenized security data from multiple blockchains"""
    
    # Ethereum mainnet data
    eth_data = requests.get(f"https://api.etherscan.io/api?module=account&action=tokenbalance&contractaddress={contract_address}")
    
    # Polygon data  
    polygon_data = requests.get(f"https://api.polygonscan.com/api?module=account&action=tokenbalance&contractaddress={contract_address}")
    
    # BSC data
    bsc_data = requests.get(f"https://api.bscscan.com/api?module=account&action=tokenbalance&contractaddress={contract_address}")
    
    return {
        'ethereum': eth_data.json(),
        'polygon': polygon_data.json(),
        'bsc': bsc_data.json(),
        'timestamp': datetime.now()
    }

# Process data with Ollama
def analyze_tokenized_security(asset_data):
    """Use Ollama to analyze tokenized security valuation"""
    
    prompt = f"""
    Analyze this tokenized security data for valuation:
    
    Multi-chain holdings: {asset_data}
    
    Provide:
    1. Current fair value estimate
    2. Liquidity assessment
    3. Risk factors
    4. Price target range
    """
    
    response = ollama.chat(model='llama3', messages=[
        {'role': 'user', 'content': prompt}
    ])
    
    return response['message']['content']

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

# Pull financial analysis models
ollama pull llama3:8b
ollama pull codellama:7b
ollama pull mistral:7b

# requirements.txt
ollama>=0.1.7
web3>=6.0.0
pandas>=2.0.0
numpy>=1.24.0
requests>=2.31.0
streamlit>=1.25.0
plotly>=5.15.0

from web3 import Web3
import json

class TokenizedSecurityConnector:
    def __init__(self):
        self.networks = {
            'ethereum': Web3(Web3.HTTPProvider('https://mainnet.infura.io/v3/YOUR_KEY')),
            'polygon': Web3(Web3.HTTPProvider('https://polygon-mainnet.infura.io/v3/YOUR_KEY')),
            'bsc': Web3(Web3.HTTPProvider('https://bsc-dataseed.binance.org/'))
        }
    
    def get_token_metrics(self, contract_address, network='ethereum'):
        """Fetch comprehensive token metrics"""
        
        w3 = self.networks[network]
        
        # Standard ERC-20 contract ABI
        erc20_abi = json.loads('[{"constant":true,"inputs":[],"name":"totalSupply","outputs":[{"name":"","type":"uint256"}],"type":"function"}]')
        
        contract = w3.eth.contract(address=contract_address, abi=erc20_abi)
        
        metrics = {
            'total_supply': contract.functions.totalSupply().call(),
            'current_block': w3.eth.block_number,
            'network': network,
            'contract_address': contract_address
        }
        
        return metrics
    
    def get_trading_data(self, token_address, days=30):
        """Fetch trading data from DEX aggregators"""
        
        # Example using DeFiPulse API
        trading_data = requests.get(f"https://api.defipulse.com/v1/tokens/{token_address}/trades?days={days}")
        
        return trading_data.json()

class OllamaValuationEngine:
    def __init__(self, model_name='llama3'):
        self.model = model_name
        self.connector = TokenizedSecurityConnector()
    
    def dcf_valuation(self, asset_data):
        """Discounted Cash Flow valuation for tokenized securities"""
        
        prompt = f"""
        Perform DCF valuation for tokenized security:
        
        Asset Data: {asset_data}
        
        Calculate:
        1. Projected cash flows (5-year)
        2. Terminal value
        3. Discount rate adjustment for blockchain risks
        4. Present value calculation
        5. Per-token valuation
        
        Show detailed calculations and assumptions.
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'system', 'content': 'You are a blockchain financial analyst specializing in tokenized securities valuation.'},
            {'role': 'user', 'content': prompt}
        ])
        
        return self._parse_valuation_response(response['message']['content'])
    
    def comparable_analysis(self, target_asset, comparable_assets):
        """Comparative valuation analysis"""
        
        prompt = f"""
        Perform comparable analysis for tokenized security:
        
        Target Asset: {target_asset}
        Comparable Assets: {comparable_assets}
        
        Analyze:
        1. Trading multiples (P/E, P/B, EV/EBITDA)
        2. Blockchain-specific metrics
        3. Liquidity adjustments
        4. Risk premium calculations
        5. Fair value range
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': prompt}
        ])
        
        return response['message']['content']
    
    def risk_assessment(self, asset_data):
        """Comprehensive risk analysis"""
        
        prompt = f"""
        Assess risks for tokenized security:
        
        Asset Information: {asset_data}
        
        Evaluate:
        1. Smart contract risks
        2. Regulatory compliance
        3. Market liquidity risks
        4. Technology risks
        5. Counterparty risks
        6. Overall risk score (1-10)
        """
        
        response = ollama.chat(model=self.model, messages=[
            {'role': 'user', 'content': prompt}
        ])
        
        return response['message']['content']

import streamlit as st
import plotly.graph_objects as go
from datetime import datetime, timedelta

def create_valuation_dashboard():
    """Main dashboard for tokenized securities valuation"""
    
    st.title("🚀 Tokenized Securities Valuation Dashboard")
    st.sidebar.header("Configuration")
    
    # Asset selection
    asset_address = st.sidebar.text_input("Token Contract Address")
    network = st.sidebar.selectbox("Blockchain Network", ['ethereum', 'polygon', 'bsc'])
    
    if asset_address:
        # Initialize components
        connector = TokenizedSecurityConnector()
        valuation_engine = OllamaValuationEngine()
        
        # Fetch asset data
        with st.spinner("Fetching blockchain data..."):
            asset_metrics = connector.get_token_metrics(asset_address, network)
            trading_data = connector.get_trading_data(asset_address)
        
        # Display current metrics
        col1, col2, col3 = st.columns(3)
        
        with col1:
            st.metric("Total Supply", f"{asset_metrics['total_supply']:,}")
        
        with col2:
            st.metric("Current Block", f"{asset_metrics['current_block']:,}")
        
        with col3:
            st.metric("Network", network.upper())
        
        # Valuation analysis
        st.header("💰 AI-Powered Valuation Analysis")
        
        analysis_type = st.selectbox("Analysis Type", ['DCF Valuation', 'Comparable Analysis', 'Risk Assessment'])
        
        if st.button("Run Analysis"):
            with st.spinner("Running AI analysis..."):
                if analysis_type == 'DCF Valuation':
                    result = valuation_engine.dcf_valuation(asset_metrics)
                elif analysis_type == 'Comparable Analysis':
                    result = valuation_engine.comparable_analysis(asset_metrics, {})
                else:
                    result = valuation_engine.risk_assessment(asset_metrics)
                
                st.markdown(result)
        
        # Trading chart
        st.header("📈 Trading Analysis")
        
        if trading_data:
            fig = go.Figure()
            fig.add_trace(go.Scatter(
                x=trading_data.get('timestamps', []),
                y=trading_data.get('prices', []),
                mode='lines+markers',
                name='Price'
            ))
            
            fig.update_layout(
                title="Token Price History",
                xaxis_title="Time",
                yaxis_title="Price (USD)"
            )
            
            st.plotly_chart(fig, use_container_width=True)

if __name__ == "__main__":
    create_valuation_dashboard()

import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split

class MLEnhancedValuation:
    def __init__(self):
        self.model = RandomForestRegressor(n_estimators=100, random_state=42)
        self.is_trained = False
    
    def prepare_features(self, historical_data):
        """Prepare ML features from tokenized security data"""
        
        df = pd.DataFrame(historical_data)
        
        # Technical indicators
        df['ma_7'] = df['price'].rolling(window=7).mean()
        df['ma_30'] = df['price'].rolling(window=30).mean()
        df['volatility'] = df['price'].rolling(window=14).std()
        
        # Blockchain-specific features
        df['transaction_volume'] = df['transactions'] * df['price']
        df['holder_count_change'] = df['holder_count'].pct_change()
        df['liquidity_ratio'] = df['liquidity'] / df['market_cap']
        
        # Drop NaN values
        df = df.dropna()
        
        return df
    
    def train_model(self, training_data):
        """Train ML model on historical tokenized security data"""
        
        df = self.prepare_features(training_data)
        
        features = ['ma_7', 'ma_30', 'volatility', 'transaction_volume', 
                   'holder_count_change', 'liquidity_ratio']
        
        X = df[features]
        y = df['price']
        
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        self.model.fit(X_train, y_train)
        self.is_trained = True
        
        # Return model performance
        train_score = self.model.score(X_train, y_train)
        test_score = self.model.score(X_test, y_test)
        
        return {
            'train_score': train_score,
            'test_score': test_score,
            'feature_importance': dict(zip(features, self.model.feature_importances_))
        }
    
    def predict_valuation(self, current_data):
        """Predict token valuation using trained ML model"""
        
        if not self.is_trained:
            raise ValueError("Model must be trained before making predictions")
        
        features_df = self.prepare_features([current_data])
        prediction = self.model.predict(features_df.iloc[-1:])
        
        return {
            'predicted_price': prediction[0],
            'confidence_interval': self._calculate_confidence_interval(prediction[0])
        }
    
    def _calculate_confidence_interval(self, prediction, confidence=0.95):
        """Calculate confidence interval for prediction"""
        
        # Simplified confidence interval calculation
        margin = prediction * 0.1  # 10% margin
        
        return {
            'lower_bound': prediction - margin,
            'upper_bound': prediction + margin
        }

class ArbitrageDetector:
    def __init__(self):
        self.networks = ['ethereum', 'polygon', 'bsc', 'avalanche']
        self.min_profit_threshold = 0.02  # 2% minimum profit
    
    def detect_arbitrage_opportunities(self, token_addresses):
        """Detect cross-chain arbitrage opportunities"""
        
        opportunities = []
        
        for token_group in token_addresses:
            prices = {}
            
            # Fetch prices across all networks
            for network in self.networks:
                if network in token_group:
                    price = self._get_token_price(token_group[network], network)
                    prices[network] = price
            
            # Find arbitrage opportunities
            if len(prices) >= 2:
                max_price_network = max(prices, key=prices.get)
                min_price_network = min(prices, key=prices.get)
                
                profit_margin = (prices[max_price_network] - prices[min_price_network]) / prices[min_price_network]
                
                if profit_margin > self.min_profit_threshold:
                    opportunities.append({
                        'token_group': token_group,
                        'buy_network': min_price_network,
                        'sell_network': max_price_network,
                        'profit_margin': profit_margin,
                        'estimated_profit': profit_margin * 1000  # Assuming $1000 trade
                    })
        
        return opportunities
    
    def _get_token_price(self, token_address, network):
        """Fetch current token price from DEX"""
        
        # Implementation depends on specific DEX APIs
        # This is a placeholder for actual price fetching logic
        import random
        return random.uniform(0.5, 2.0)  # Mock price

# Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    curl \
    git \
    && rm -rf /var/lib/apt/lists/*

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

# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose port
EXPOSE 8501

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD curl -f http://localhost:8501/health || exit 1

# Start application
CMD ["streamlit", "run", "dashboard.py", "--server.port=8501", "--server.address=0.0.0.0"]

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: tokenized-securities-valuation
spec:
  replicas: 3
  selector:
    matchLabels:
      app: tokenized-securities-valuation
  template:
    metadata:
      labels:
        app: tokenized-securities-valuation
    spec:
      containers:
      - name: valuation-app
        image: your-registry/tokenized-securities-valuation:latest
        ports:
        - containerPort: 8501
        env:
        - name: OLLAMA_HOST
          value: "ollama-service:11434"
        resources:
          requests:
            memory: "2Gi"
            cpu: "1000m"
          limits:
            memory: "4Gi"
            cpu: "2000m"
---
apiVersion: v1
kind: Service
metadata:
  name: valuation-service
spec:
  selector:
    app: tokenized-securities-valuation
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8501
  type: LoadBalancer