Corporate Bitcoin Treasury Tracker: Fortune 500 Bitcoin Adoption Analysis with Ollama

# Download and install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a suitable model for financial analysis
ollama pull llama3.2:latest

# Verify installation
ollama list

import json
import requests
import pandas as pd
from datetime import datetime
import ollama

class CorporateBitcoinTracker:
    def __init__(self):
        self.companies_data = []
        self.client = ollama.Client()
        
    def fetch_public_filings(self, company_symbol):
        """Fetch public SEC filings for Bitcoin mentions"""
        # Use SEC EDGAR API for public filings
        url = f"https://data.sec.gov/submissions/CIK{self.get_cik(company_symbol)}.json"
        headers = {
            'User-Agent': 'Corporate Analysis Tool corporate@example.com'
        }
        
        try:
            response = requests.get(url, headers=headers)
            return response.json() if response.status_code == 200 else None
        except Exception as e:
            print(f"Error fetching data for {company_symbol}: {e}")
            return None
    
    def analyze_bitcoin_mentions(self, filing_text):
        """Use Ollama to analyze Bitcoin-related content in filings"""
        prompt = f"""
        Analyze this corporate filing text for Bitcoin or cryptocurrency mentions.
        Extract any specific amounts, strategy statements, or future plans.
        
        Text: {filing_text[:4000]}  # Limit for context window
        
        Please provide:
        1. Bitcoin holdings amount (if mentioned)
        2. Strategic intent (hedge, investment, etc.)
        3. Risk factors mentioned
        4. Future acquisition plans
        
        Format as JSON.
        """
        
        response = self.client.generate(
            model='llama3.2:latest',
            prompt=prompt,
            stream=False
        )
        
        return response['response']

class Fortune500BitcoinAnalyzer:
    def __init__(self):
        self.fortune_500_list = self.load_fortune_500()
        self.bitcoin_adopters = {}
        
    def load_fortune_500(self):
        """Load current Fortune 500 company list"""
        # Fortune 500 companies with their ticker symbols
        companies = [
            {"name": "Apple Inc.", "symbol": "AAPL", "rank": 1},
            {"name": "Amazon.com Inc.", "symbol": "AMZN", "rank": 2},
            {"name": "Tesla Inc.", "symbol": "TSLA", "rank": 3},
            # Add all Fortune 500 companies
        ]
        return companies
    
    def scan_bitcoin_adoption(self):
        """Scan Fortune 500 companies for Bitcoin adoption signals"""
        for company in self.fortune_500_list:
            print(f"Analyzing {company['name']}...")
            
            # Check multiple data sources
            adoption_data = {
                'company': company['name'],
                'symbol': company['symbol'],
                'rank': company['rank'],
                'bitcoin_holdings': self.check_bitcoin_holdings(company['symbol']),
                'blockchain_initiatives': self.check_blockchain_projects(company['symbol']),
                'treasury_strategy': self.analyze_treasury_strategy(company['symbol']),
                'risk_factors': self.extract_crypto_risks(company['symbol']),
                'last_updated': datetime.now().isoformat()
            }
            
            self.bitcoin_adopters[company['symbol']] = adoption_data
    
    def analyze_adoption_trends(self):
        """Use Ollama to analyze adoption trends across Fortune 500"""
        trend_data = json.dumps(self.bitcoin_adopters, indent=2)
        
        prompt = f"""
        Analyze Fortune 500 Bitcoin adoption patterns from this data:
        
        {trend_data[:8000]}  # Limit for model context
        
        Provide insights on:
        1. Adoption velocity trends
        2. Industry sector patterns
        3. Market cap correlations
        4. Geographic distributions
        5. Predicted next adopters
        
        Focus on actionable business intelligence.
        """
        
        response = ollama.generate(
            model='llama3.2:latest',
            prompt=prompt,
            stream=False
        )
        
        return response['response']

import streamlit as st
import plotly.express as px
import plotly.graph_objects as go

class BitcoinAdoptionDashboard:
    def __init__(self, tracker_data):
        self.data = tracker_data
        
    def render_dashboard(self):
        st.set_page_config(
            page_title="Fortune 500 Bitcoin Treasury Tracker",
            page_icon="₿",
            layout="wide"
        )
        
        st.title("🏢 Fortune 500 Bitcoin Treasury Tracker")
        st.markdown("*Powered by Ollama Local AI Analysis*")
        
        # Key metrics
        col1, col2, col3, col4 = st.columns(4)
        
        with col1:
            adopters_count = len([c for c in self.data.values() 
                                if c.get('bitcoin_holdings', 0) > 0])
            st.metric("Bitcoin Adopters", adopters_count)
        
        with col2:
            total_holdings = sum([c.get('bitcoin_holdings', 0) 
                                for c in self.data.values()])
            st.metric("Total BTC Holdings", f"{total_holdings:,.0f}")
        
        with col3:
            blockchain_projects = len([c for c in self.data.values() 
                                     if c.get('blockchain_initiatives')])
            st.metric("Blockchain Projects", blockchain_projects)
        
        with col4:
            adoption_rate = (adopters_count / len(self.data)) * 100
            st.metric("Adoption Rate", f"{adoption_rate:.1f}%")
        
        # Adoption timeline
        st.subheader("📈 Adoption Timeline")
        self.render_adoption_timeline()
        
        # Industry breakdown
        st.subheader("🏭 Industry Analysis")
        self.render_industry_breakdown()
        
        # AI-Generated Insights
        st.subheader("🤖 AI Analysis")
        self.render_ai_insights()
    
    def render_adoption_timeline(self):
        """Create timeline visualization of Bitcoin adoption"""
        # Implementation for timeline chart
        pass
    
    def render_industry_breakdown(self):
        """Show adoption by industry sector"""
        # Implementation for industry analysis
        pass
    
    def render_ai_insights(self):
        """Display Ollama-generated insights"""
        with st.spinner("Generating AI insights..."):
            insights = self.generate_insights()
            st.markdown(insights)
    
    def generate_insights(self):
        """Generate insights using Ollama"""
        prompt = f"""
        Based on Fortune 500 Bitcoin adoption data, provide:
        
        1. Three key trends emerging in corporate Bitcoin adoption
        2. Recommendations for companies considering Bitcoin treasury strategy
        3. Potential risks and mitigation strategies
        4. Market timing considerations
        
        Keep insights actionable and specific to Fortune 500 context.
        """
        
        response = ollama.generate(
            model='llama3.2:latest',
            prompt=prompt,
            stream=False
        )
        
        return response['response']

class AdoptionPredictor:
    def __init__(self, historical_data):
        self.data = historical_data
        
    def predict_next_adopters(self):
        """Predict which companies might adopt Bitcoin next"""
        prediction_prompt = f"""
        Based on these Fortune 500 companies' characteristics, predict 
        which are most likely to adopt Bitcoin for treasury purposes:
        
        Consider factors:
        - Cash position and treasury size
        - CEO/leadership statements on innovation
        - Industry sector (tech companies more likely)
        - Geographic presence (regulatory considerations)
        - Competitive pressures
        - Previous blockchain/crypto initiatives
        
        Rank top 10 most likely adopters with reasoning.
        """
        
        response = ollama.generate(
            model='llama3.2:latest',
            prompt=prediction_prompt,
            stream=False
        )
        
        return response['response']

def analyze_adoption_risks(company_data):
    """Analyze risks and benefits of Bitcoin adoption for specific companies"""
    risk_prompt = f"""
    Analyze Bitcoin treasury adoption risks for this Fortune 500 company:
    
    Company Profile: {json.dumps(company_data, indent=2)}
    
    Assess:
    1. Regulatory risks by jurisdiction
    2. Volatility impact on earnings
    3. Shareholder sentiment considerations
    4. Competitive positioning effects
    5. Implementation complexity
    
    Provide risk mitigation strategies.
    """
    
    response = ollama.generate(
        model='llama3.2:latest',
        prompt=risk_prompt,
        stream=False
    )
    
    return response['response']

class ComplianceFramework:
    def __init__(self):
        self.audit_log = []
        
    def log_analysis_activity(self, user, action, data_type):
        """Log all analysis activities for compliance"""
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'user': user,
            'action': action,
            'data_type': data_type,
            'local_processing': True  # Ollama ensures local processing
        }
        self.audit_log.append(log_entry)
    
    def generate_compliance_report(self):
        """Generate compliance report for regulators"""
        report_prompt = f"""
        Generate a compliance report for our Bitcoin tracking system:
        
        Activities logged: {len(self.audit_log)}
        Local processing: 100% (via Ollama)
        Data exposure: 0% (no cloud transmission)
        
        Summarize compliance posture and data protection measures.
        """
        
        response = ollama.generate(
            model='llama3.2:latest',
            prompt=report_prompt,
            stream=False
        )
        
        return response['response']

# Optimize Ollama for large-scale Fortune 500 analysis
def optimize_for_enterprise():
    """Configure Ollama for enterprise-scale analysis"""
    config = {
        'model_concurrency': 4,  # Process multiple companies simultaneously
        'context_window': 8192,  # Handle larger document analysis
        'gpu_acceleration': True,  # Use available GPU resources
        'memory_allocation': '16GB'  # Allocate sufficient memory
    }
    return config