Build a Crypto Regulation Tracker with Ollama: Monitor Global Policy Changes in Real-Time

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

# Pull the recommended model for document analysis
ollama pull llama2:13b

# Verify installation
ollama list

import ollama
import json

def configure_ollama_for_legal():
    """Configure Ollama for optimal legal document processing"""
    
    # Model configuration for regulatory analysis
    model_config = {
        "model": "llama2:13b",
        "options": {
            "temperature": 0.1,  # Low temperature for consistent analysis
            "top_p": 0.9,
            "context_length": 4096,  # Handle long regulatory documents
            "num_predict": 1024
        }
    }
    
    return model_config

# Test configuration with sample regulatory text
config = configure_ollama_for_legal()
print("Ollama configured for regulatory document analysis")

import requests
import feedparser
from datetime import datetime, timedelta
import sqlite3

class RegulatoryScraper:
    def __init__(self):
        self.sources = {
            'SEC': 'https://www.sec.gov/news/pressreleases.rss',
            'CFTC': 'https://www.cftc.gov/PressRoom/PressReleases/rss.xml',
            'FINRA': 'https://www.finra.org/about/press-releases/rss',
            'EU_ESMA': 'https://www.esma.europa.eu/news-and-events/esma-news'
        }
        self.db_connection = sqlite3.connect('crypto_regulations.db')
        self.setup_database()
    
    def setup_database(self):
        """Initialize database for storing regulatory documents"""
        cursor = self.db_connection.cursor()
        cursor.execute('''
            CREATE TABLE IF NOT EXISTS regulations (
                id INTEGER PRIMARY KEY,
                source TEXT,
                title TEXT,
                content TEXT,
                published_date DATE,
                crypto_relevance_score REAL,
                processed BOOLEAN DEFAULT FALSE
            )
        ''')
        self.db_connection.commit()
    
    def scrape_sec_releases(self):
        """Scrape SEC press releases for crypto-related content"""
        feed = feedparser.parse(self.sources['SEC'])
        crypto_keywords = ['crypto', 'bitcoin', 'ethereum', 'blockchain', 
                          'digital asset', 'virtual currency', 'stablecoin']
        
        for entry in feed.entries:
            # Check if entry contains crypto-related keywords
            title_lower = entry.title.lower()
            summary_lower = entry.summary.lower()
            
            crypto_mentions = sum(1 for keyword in crypto_keywords 
                                if keyword in title_lower or keyword in summary_lower)
            
            if crypto_mentions > 0:
                self.store_regulation({
                    'source': 'SEC',
                    'title': entry.title,
                    'content': entry.summary,
                    'published_date': entry.published,
                    'crypto_relevance_score': crypto_mentions / len(crypto_keywords)
                })
    
    def store_regulation(self, regulation_data):
        """Store regulatory document in database"""
        cursor = self.db_connection.cursor()
        cursor.execute('''
            INSERT INTO regulations 
            (source, title, content, published_date, crypto_relevance_score)
            VALUES (?, ?, ?, ?, ?)
        ''', (
            regulation_data['source'],
            regulation_data['title'],
            regulation_data['content'],
            regulation_data['published_date'],
            regulation_data['crypto_relevance_score']
        ))
        self.db_connection.commit()

# Initialize and run scraper
scraper = RegulatoryScraper()
scraper.scrape_sec_releases()
print("Regulatory data collection complete")

class GlobalRegulatoryScraper(RegulatoryScraper):
    def __init__(self):
        super().__init__()
        self.international_sources = {
            'UK_FCA': 'https://www.fca.org.uk/news/news.rss',
            'Japan_FSA': 'https://www.fsa.go.jp/en/news/index.html',
            'Singapore_MAS': 'https://www.mas.gov.sg/news',
            'Canada_OSC': 'https://www.osc.ca/en/news-events/news/rss.xml'
        }
        
    def scrape_all_sources(self):
        """Comprehensive scraping across global regulatory bodies"""
        for source_name, source_url in self.sources.items():
            try:
                self.scrape_generic_rss(source_name, source_url)
                print(f"✓ Scraped {source_name}")
            except Exception as e:
                print(f"✗ Failed to scrape {source_name}: {e}")
        
        # Custom scrapers for sources without RSS feeds
        self.scrape_custom_sources()
    
    def scrape_custom_sources(self):
        """Handle regulatory sources that require custom scraping logic"""
        # Implementation for non-RSS sources
        pass

global_scraper = GlobalRegulatoryScraper()
global_scraper.scrape_all_sources()

import ollama
import re
from typing import Dict, List

class RegulatoryAnalyzer:
    def __init__(self):
        self.model_config = {
            "model": "llama2:13b",
            "options": {
                "temperature": 0.1,
                "top_p": 0.9,
                "context_length": 4096
            }
        }
    
    def analyze_regulatory_document(self, document_text: str) -> Dict:
        """Analyze regulatory document for crypto impact"""
        
        analysis_prompt = f"""
        Analyze this regulatory document for cryptocurrency impact:

        Document: {document_text}

        Provide analysis in this format:
        IMPACT_LEVEL: [HIGH/MEDIUM/LOW]
        AFFECTED_ASSETS: [List specific cryptocurrencies or categories]
        KEY_CHANGES: [Main regulatory changes]
        COMPLIANCE_REQUIREMENTS: [New requirements for crypto businesses]
        TIMELINE: [Implementation dates or deadlines]
        MARKET_IMPLICATIONS: [Potential market effects]
        """
        
        try:
            response = ollama.generate(
                model=self.model_config["model"],
                prompt=analysis_prompt,
                options=self.model_config["options"]
            )
            
            return self.parse_analysis_response(response['response'])
            
        except Exception as e:
            print(f"Analysis error: {e}")
            return self.generate_fallback_analysis(document_text)
    
    def parse_analysis_response(self, response_text: str) -> Dict:
        """Parse structured analysis from Ollama response"""
        analysis = {}
        
        patterns = {
            'impact_level': r'IMPACT_LEVEL:\s*([^\n]+)',
            'affected_assets': r'AFFECTED_ASSETS:\s*([^\n]+)',
            'key_changes': r'KEY_CHANGES:\s*([^\n]+)',
            'compliance_requirements': r'COMPLIANCE_REQUIREMENTS:\s*([^\n]+)',
            'timeline': r'TIMELINE:\s*([^\n]+)',
            'market_implications': r'MARKET_IMPLICATIONS:\s*([^\n]+)'
        }
        
        for key, pattern in patterns.items():
            match = re.search(pattern, response_text, re.IGNORECASE)
            analysis[key] = match.group(1).strip() if match else "Not specified"
        
        return analysis
    
    def assess_market_impact(self, analysis: Dict) -> float:
        """Calculate numerical market impact score (0-1)"""
        impact_weights = {
            'HIGH': 0.8,
            'MEDIUM': 0.5,
            'LOW': 0.2
        }
        
        base_score = impact_weights.get(analysis['impact_level'], 0.3)
        
        # Adjust score based on affected assets scope
        if 'bitcoin' in analysis['affected_assets'].lower():
            base_score += 0.1
        if 'all cryptocurrencies' in analysis['affected_assets'].lower():
            base_score += 0.2
            
        return min(base_score, 1.0)

# Example usage
analyzer = RegulatoryAnalyzer()

sample_document = """
The Securities and Exchange Commission today announced new reporting 
requirements for cryptocurrency exchanges operating in the United States. 
Starting January 1, 2025, all digital asset platforms must submit 
quarterly compliance reports and maintain segregated customer funds.
"""

analysis_result = analyzer.analyze_regulatory_document(sample_document)
market_impact = analyzer.assess_market_impact(analysis_result)

print(f"Analysis complete. Market impact score: {market_impact}")

class GlobalTrendAnalyzer:
    def __init__(self, db_connection):
        self.db = db_connection
        self.trend_patterns = {
            'stablecoin_regulation': ['stablecoin', 'stable coin', 'cbdc'],
            'exchange_licensing': ['license', 'registration', 'authorization'],
            'tax_compliance': ['tax', 'reporting', 'capital gains'],
            'consumer_protection': ['investor protection', 'consumer', 'disclosure']
        }
    
    def identify_global_trends(self, days_back: int = 30) -> Dict:
        """Analyze recent regulations for emerging global trends"""
        
        cursor = self.db.cursor()
        cursor.execute('''
            SELECT source, title, content, published_date, crypto_relevance_score
            FROM regulations 
            WHERE published_date >= date('now', '-{} days')
            AND crypto_relevance_score > 0.3
            ORDER BY published_date DESC
        '''.format(days_back))
        
        recent_regulations = cursor.fetchall()
        trend_analysis = {}
        
        for trend_name, keywords in self.trend_patterns.items():
            trend_count = 0
            jurisdictions = set()
            
            for reg in recent_regulations:
                source, title, content, date, score = reg
                text_to_search = f"{title} {content}".lower()
                
                if any(keyword in text_to_search for keyword in keywords):
                    trend_count += 1
                    jurisdictions.add(source)
            
            trend_analysis[trend_name] = {
                'frequency': trend_count,
                'jurisdictions': list(jurisdictions),
                'global_momentum': len(jurisdictions) / len(set(r[0] for r in recent_regulations))
            }
        
        return trend_analysis
    
    def generate_trend_report(self, trends: Dict) -> str:
        """Generate human-readable trend analysis report"""
        
        report_prompt = f"""
        Based on this regulatory trend data, create a concise analysis:
        
        {trends}
        
        Focus on:
        1. Most significant emerging trends
        2. Jurisdictions leading regulatory development
        3. Potential impact on crypto markets
        4. Recommended compliance actions
        
        Keep analysis under 200 words.
        """
        
        response = ollama.generate(
            model="llama2:13b",
            prompt=report_prompt,
            options={"temperature": 0.2}
        )
        
        return response['response']

# Generate trend analysis
trend_analyzer = GlobalTrendAnalyzer(scraper.db_connection)
current_trends = trend_analyzer.identify_global_trends(30)
trend_report = trend_analyzer.generate_trend_report(current_trends)

print("Global Regulatory Trends:")
print(trend_report)

import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart
import json

class CryptoRegulatoryAlerts:
    def __init__(self, config_file='alert_config.json'):
        self.load_alert_config(config_file)
        self.analyzer = RegulatoryAnalyzer()
    
    def load_alert_config(self, config_file: str):
        """Load alert configuration from JSON file"""
        try:
            with open(config_file, 'r') as f:
                self.config = json.load(f)
        except FileNotFoundError:
            self.config = self.create_default_config()
            self.save_alert_config(config_file)
    
    def create_default_config(self) -> Dict:
        """Create default alert configuration"""
        return {
            "email_settings": {
                "smtp_server": "smtp.gmail.com",
                "smtp_port": 587,
                "username": "",
                "password": "",
                "recipients": []
            },
            "alert_thresholds": {
                "high_impact": 0.7,
                "medium_impact": 0.4,
                "immediate_alert": ["SEC", "CFTC", "EU_ESMA"]
            },
            "keywords_priority": {
                "bitcoin": 0.8,
                "ethereum": 0.7,
                "stablecoin": 0.9,
                "exchange": 0.6
            }
        }
    
    def process_new_regulation(self, regulation_data: Dict):
        """Process new regulation and determine if alert is needed"""
        
        # Analyze the regulation
        analysis = self.analyzer.analyze_regulatory_document(regulation_data['content'])
        impact_score = self.analyzer.assess_market_impact(analysis)
        
        # Calculate priority score
        priority_score = self.calculate_priority(regulation_data, analysis, impact_score)
        
        # Determine alert level
        if priority_score >= self.config['alert_thresholds']['high_impact']:
            self.send_urgent_alert(regulation_data, analysis, priority_score)
        elif priority_score >= self.config['alert_thresholds']['medium_impact']:
            self.send_standard_alert(regulation_data, analysis, priority_score)
        
        # Log all regulations for daily digest
        self.log_for_digest(regulation_data, analysis, priority_score)
    
    def calculate_priority(self, regulation_data: Dict, analysis: Dict, impact_score: float) -> float:
        """Calculate alert priority based on multiple factors"""
        
        base_priority = impact_score
        
        # Source priority boost
        if regulation_data['source'] in self.config['alert_thresholds']['immediate_alert']:
            base_priority += 0.2
        
        # Keyword priority adjustment
        content_lower = regulation_data['content'].lower()
        for keyword, weight in self.config['keywords_priority'].items():
            if keyword in content_lower:
                base_priority += weight * 0.1
        
        # Recency boost (newer = higher priority)
        # Implementation depends on your date handling
        
        return min(base_priority, 1.0)
    
    def send_urgent_alert(self, regulation_data: Dict, analysis: Dict, priority_score: float):
        """Send immediate email alert for high-priority regulations"""
        
        subject = f"🚨 URGENT: {regulation_data['source']} Crypto Regulation Alert"
        
        body = f"""
        HIGH PRIORITY REGULATORY UPDATE
        
        Source: {regulation_data['source']}
        Impact Level: {analysis['impact_level']}
        Priority Score: {priority_score:.2f}
        
        Title: {regulation_data['title']}
        
        Key Changes:
        {analysis['key_changes']}
        
        Affected Assets:
        {analysis['affected_assets']}
        
        Compliance Requirements:
        {analysis['compliance_requirements']}
        
        Timeline:
        {analysis['timeline']}
        
        Market Implications:
        {analysis['market_implications']}
        
        ---
        This is an automated alert from your Crypto Regulation Tracker.
        """
        
        self.send_email(subject, body)
        
        # Also send to Slack if configured
        if 'slack_webhook' in self.config:
            self.send_slack_alert(regulation_data, analysis, priority_score)
    
    def send_email(self, subject: str, body: str):
        """Send email alert to configured recipients"""
        
        email_config = self.config['email_settings']
        
        if not email_config['recipients']:
            print("No email recipients configured")
            return
        
        try:
            msg = MIMEMultipart()
            msg['From'] = email_config['username']
            msg['To'] = ', '.join(email_config['recipients'])
            msg['Subject'] = subject
            
            msg.attach(MIMEText(body, 'plain'))
            
            server = smtplib.SMTP(email_config['smtp_server'], email_config['smtp_port'])
            server.starttls()
            server.login(email_config['username'], email_config['password'])
            
            server.send_message(msg)
            server.quit()
            
            print(f"Alert sent to {len(email_config['recipients'])} recipients")
            
        except Exception as e:
            print(f"Failed to send email alert: {e}")

# Example alert system usage
alert_system = CryptoRegulatoryAlerts()

# Process a sample regulation
sample_regulation = {
    'source': 'SEC',
    'title': 'SEC Proposes New Digital Asset Custody Rules',
    'content': 'The SEC today proposed comprehensive custody requirements for digital assets...',
    'published_date': '2024-07-10'
}

alert_system.process_new_regulation(sample_regulation)

from flask import Flask, render_template, jsonify
import sqlite3
from datetime import datetime, timedelta

app = Flask(__name__)

class RegulationDashboard:
    def __init__(self, db_path):
        self.db_path = db_path
    
    def get_recent_regulations(self, days=7):
        """Get recent regulations for dashboard display"""
        conn = sqlite3.connect(self.db_path)
        cursor = conn.cursor()
        
        cursor.execute('''
            SELECT source, title, published_date, crypto_relevance_score
            FROM regulations 
            WHERE published_date >= date('now', '-{} days')
            ORDER BY crypto_relevance_score DESC, published_date DESC
            LIMIT 20
        '''.format(days))
        
        regulations = cursor.fetchall()
        conn.close()
        
        return [
            {
                'source': reg[0],
                'title': reg[1],
                'date': reg[2],
                'relevance': reg[3]
            }
            for reg in regulations
        ]
    
    def get_trend_summary(self):
        """Get regulatory trend summary for dashboard"""
        trend_analyzer = GlobalTrendAnalyzer(sqlite3.connect(self.db_path))
        trends = trend_analyzer.identify_global_trends(30)
        
        return {
            'most_active_trend': max(trends.items(), key=lambda x: x[1]['frequency'])[0],
            'total_regulations': sum(trend['frequency'] for trend in trends.values()),
            'active_jurisdictions': len(set().union(*[trend['jurisdictions'] for trend in trends.values()]))
        }

dashboard = RegulationDashboard('crypto_regulations.db')

@app.route('/')
def index():
    """Main dashboard page"""
    recent_regs = dashboard.get_recent_regulations()
    trend_summary = dashboard.get_trend_summary()
    
    return render_template('dashboard.html', 
                         regulations=recent_regs,
                         trends=trend_summary)

@app.route('/api/regulations')
def api_regulations():
    """API endpoint for regulation data"""
    return jsonify(dashboard.get_recent_regulations())

if __name__ == '__main__':
    app.run(debug=True, port=5000)

# docker-compose.yml for production deployment
"""
version: '3.8'

services:
  crypto-tracker:
    build: .
    ports:
      - "5000:5000"
    environment:
      - DATABASE_URL=postgresql://user:pass@db:5432/crypto_regs
      - OLLAMA_HOST=ollama:11434
    depends_on:
      - db
      - ollama
    volumes:
      - ./logs:/app/logs
      
  ollama:
    image: ollama/ollama:latest
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    command: ollama serve
    
  db:
    image: postgres:13
    environment:
      - POSTGRES_DB=crypto_regs
      - POSTGRES_USER=user
      - POSTGRES_PASSWORD=pass
    volumes:
      - postgres_data:/var/lib/postgresql/data
      
  redis:
    image: redis:alpine
    ports:
      - "6379:6379"

volumes:
  ollama_data:
  postgres_data:
"""

# Production monitoring script
class ProductionMonitor:
    def __init__(self):
        self.metrics = {
            'regulations_processed': 0,
            'alerts_sent': 0,
            'analysis_errors': 0,
            'last_successful_scrape': None
        }
    
    def health_check(self):
        """Comprehensive system health check"""
        health_status = {
            'database': self.check_database_connection(),
            'ollama': self.check_ollama_status(),
            'recent_activity': self.check_recent_activity(),
            'disk_space': self.check_disk_space()
        }
        
        return all(health_status.values()), health_status
    
    def check_ollama_status(self):
        """Verify Ollama is responding"""
        try:
            response = ollama.generate(
                model="llama2:13b",
                prompt="Test prompt",
                options={"num_predict": 1}
            )
            return True
        except:
            return False
    
    def generate_daily_report(self):
        """Generate daily system performance report"""
        # Implementation for daily reporting
        pass

monitor = ProductionMonitor()
health_ok, health_details = monitor.health_check()
print(f"System Health: {'OK' if health_ok else 'ISSUES'}")

# Batch processing for improved performance
class OptimizedProcessor:
    def __init__(self, batch_size=10):
        self.batch_size = batch_size
        self.processing_queue = []
    
    def batch_process_regulations(self, regulations):
        """Process regulations in batches for better performance"""
        
        for i in range(0, len(regulations), self.batch_size):
            batch = regulations[i:i + self.batch_size]
            
            # Parallel processing within batch
            with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
                futures = [
                    executor.submit(self.process_single_regulation, reg)
                    for reg in batch
                ]
                
                results = [future.result() for future in futures]
            
            # Batch insert results to database
            self.batch_insert_results(results)
    
    def cache_frequent_analyses(self):
        """Cache common analysis patterns to improve response time"""
        # Implementation for intelligent caching
        pass

# Memory usage optimization
import gc
from memory_profiler import profile

@profile
def memory_efficient_analysis(document_batch):
    """Analyze documents with minimal memory footprint"""
    
    results = []
    
    for doc in document_batch:
        # Process one document at a time
        analysis = analyzer.analyze_regulatory_document(doc['content'])
        results.append(analysis)
        
        # Clear intermediate variables
        del analysis
        gc.collect()
    
    return results

class CustomRegulatoryCategories:
    def __init__(self):
        self.categories = {
            'defi_protocols': {
                'keywords': ['defi', 'decentralized finance', 'dex', 'yield farming'],
                'risk_level': 'high',
                'monitoring_frequency': 'hourly'
            },
            'nft_regulations': {
                'keywords': ['nft', 'non-fungible token', 'digital collectible'],
                'risk_level': 'medium',
                'monitoring_frequency': 'daily'
            },
            'cbdc_development': {
                'keywords': ['cbdc', 'central bank digital currency', 'digital dollar'],
                'risk_level': 'high',
                'monitoring_frequency': 'hourly'
            }
        }
    
    def classify_regulation(self, document_text):
        """Classify regulation into custom categories"""
        
        text_lower = document_text.lower()
        matches = []
        
        for category_name, category_config in self.categories.items():
            keyword_matches = sum(
                1 for keyword in category_config['keywords']
                if keyword in text_lower
            )
            
            if keyword_matches > 0:
                matches.append({
                    'category': category_name,
                    'confidence': keyword_matches / len(category_config['keywords']),
                    'risk_level': category_config['risk_level']
                })
        
        return sorted(matches, key=lambda x: x['confidence'], reverse=True)

# Usage example
categorizer = CustomRegulatoryCategories()
categories = categorizer.classify_regulation(sample_document)
print(f"Document categories: {categories}")

class TradingIntegration:
    def __init__(self, exchange_api_keys):
        self.exchanges = exchange_api_keys
        self.compliance_rules = self.load_compliance_rules()
    
    def check_asset_compliance(self, asset_symbol, jurisdiction):
        """Check if asset trading complies with current regulations"""
        
        # Get recent regulations affecting this asset
        relevant_regs = self.get_asset_regulations(asset_symbol, jurisdiction)
        
        compliance_status = {
            'trading_allowed': True,
            'restrictions': [],
            'compliance_requirements': []
        }
        
        for reg in relevant_regs:
            analysis = analyzer.analyze_regulatory_document(reg['content'])
            
            if 'prohibited' in analysis['key_changes'].lower():
                compliance_status['trading_allowed'] = False
                compliance_status['restrictions'].append(reg['title'])
        
        return compliance_status
    
    def automated_compliance_check(self):
        """Run automated compliance checks on active trading pairs"""
        
        # Get active trading pairs from exchanges
        active_pairs = self.get_active_trading_pairs()
        
        compliance_issues = []
        
        for pair in active_pairs:
            status = self.check_asset_compliance(pair['base'], pair['jurisdiction'])
            
            if not status['trading_allowed']:
                compliance_issues.append({
                    'pair': pair,
                    'issue': 'Trading prohibited by recent regulation',
                    'action_required': 'Suspend trading immediately'
                })
        
        return compliance_issues

# Monitor compliance for active trades
trading_monitor = TradingIntegration({'binance': 'api_key', 'coinbase': 'api_key'})
compliance_issues = trading_monitor.automated_compliance_check()

if compliance_issues:
    print(f"⚠️  {len(compliance_issues)} compliance issues detected")
    for issue in compliance_issues:
        print(f"Issue: {issue['pair']} - {issue['action_required']}")

def calculate_tracker_roi(
    manual_hours_saved_monthly: int,
    average_hourly_cost: float,
    prevented_compliance_issues: int,
    average_issue_cost: float,
    system_operating_cost: float
):
    """Calculate ROI for crypto regulation tracker"""
    
    monthly_savings = manual_hours_saved_monthly * average_hourly_cost
    annual_labor_savings = monthly_savings * 12
    
    annual_risk_savings = prevented_compliance_issues * average_issue_cost
    annual_operating_cost = system_operating_cost * 12
    
    total_annual_savings = annual_labor_savings + annual_risk_savings
    roi_percentage = ((total_annual_savings - annual_operating_cost) / annual_operating_cost) * 100
    
    return {
        'annual_savings': total_annual_savings,
        'annual_costs': annual_operating_cost,
        'roi_percentage': roi_percentage,
        'payback_months': annual_operating_cost / (total_annual_savings / 12)
    }

# Example ROI calculation
roi_analysis = calculate_tracker_roi(
    manual_hours_saved_monthly=80,  # Hours saved per month
    average_hourly_cost=75,         # Cost per hour for compliance staff
    prevented_compliance_issues=2,   # Issues prevented per year
    average_issue_cost=50000,       # Average cost of compliance issue
    system_operating_cost=2000      # Monthly system costs
)

print(f"Annual ROI: {roi_analysis['roi_percentage']:.1f}%")
print(f"Payback period: {roi_analysis['payback_months']:.1f} months")