Ollama SEC and CFTC Announcement Processing: Complete Guide to Local Regulatory Impact Analysis

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

# Verify installation
ollama --version

# Start Ollama service
ollama serve

# For general regulatory analysis - balanced performance
ollama pull llama3.3:70b

# For legal document processing - specialized understanding
ollama pull mistral:7b

# For financial compliance - numerical analysis capabilities  
ollama pull qwen2.5:14b

# For code generation from regulations
ollama pull codellama:13b

import requests
import json
from pathlib import Path
import PyPDF2
from docx import Document

class RegulatoryDocumentProcessor:
    def __init__(self, ollama_url="http://localhost:11434"):
        self.ollama_url = ollama_url
        self.model = "llama3.3:70b"
    
    def extract_text(self, file_path):
        """Extract text from various document formats"""
        file_path = Path(file_path)
        
        if file_path.suffix.lower() == '.pdf':
            return self._extract_from_pdf(file_path)
        elif file_path.suffix.lower() in ['.doc', '.docx']:
            return self._extract_from_docx(file_path)
        elif file_path.suffix.lower() == '.txt':
            return file_path.read_text(encoding='utf-8')
        else:
            raise ValueError(f"Unsupported file format: {file_path.suffix}")
    
    def _extract_from_pdf(self, pdf_path):
        """Extract text from PDF documents"""
        text = ""
        with open(pdf_path, 'rb') as file:
            pdf_reader = PyPDF2.PdfReader(file)
            for page in pdf_reader.pages:
                text += page.extract_text() + "\n"
        return text
    
    def _extract_from_docx(self, docx_path):
        """Extract text from Word documents"""
        doc = Document(docx_path)
        return "\n".join([paragraph.text for paragraph in doc.paragraphs])
    
    def analyze_regulatory_impact(self, document_text, analysis_type="general"):
        """Analyze regulatory document for business impact"""
        prompts = {
            "general": """
            Analyze this regulatory document and provide:
            1. Key regulatory changes
            2. Implementation timeline
            3. Business impact assessment
            4. Required actions
            5. Risk implications
            
            Document:
            {text}
            
            Provide structured analysis with specific actionable insights.
            """,
            
            "compliance": """
            Review this regulatory update for compliance implications:
            1. New compliance requirements
            2. Modified existing obligations  
            3. Enforcement timeline
            4. Penalties for non-compliance
            5. Resources needed for implementation
            
            Document:
            {text}
            
            Focus on practical compliance steps.
            """,
            
            "risk_assessment": """
            Conduct risk analysis of this regulatory change:
            1. Operational risks
            2. Financial exposure
            3. Reputational considerations
            4. Market impact
            5. Mitigation strategies
            
            Document:
            {text}
            
            Prioritize by risk severity and likelihood.
            """
        }
        
        prompt = prompts.get(analysis_type, prompts["general"]).format(text=document_text)
        
        # Send request to local Ollama instance
        response = requests.post(
            f"{self.ollama_url}/api/generate",
            json={
                "model": self.model,
                "prompt": prompt,
                "stream": False,
                "options": {
                    "temperature": 0.1,  # Low temperature for consistent analysis
                    "top_p": 0.9
                }
            }
        )
        
        if response.status_code == 200:
            return response.json()['response']
        else:
            raise Exception(f"Ollama request failed: {response.status_code}")

# Usage example
processor = RegulatoryDocumentProcessor()

# Process SEC announcement
sec_text = processor.extract_text("sec_announcement_2025.pdf")
impact_analysis = processor.analyze_regulatory_impact(sec_text, "compliance")

print("Regulatory Impact Analysis:")
print(impact_analysis)

import feedparser
import schedule
import time
from datetime import datetime, timedelta

class RegulatoryMonitor:
    def __init__(self, processor):
        self.processor = processor
        self.monitored_feeds = {
            'SEC': 'https://www.sec.gov/news/pressreleases.rss',
            'CFTC': 'https://www.cftc.gov/PressRoom/PressReleases/rss.xml'
        }
        self.last_check = {}
    
    def check_feeds(self):
        """Check RSS feeds for new regulatory announcements"""
        for agency, feed_url in self.monitored_feeds.items():
            try:
                feed = feedparser.parse(feed_url)
                
                # Get last check time for this agency
                last_check = self.last_check.get(agency, datetime.now() - timedelta(days=1))
                
                new_entries = []
                for entry in feed.entries:
                    pub_date = datetime(*entry.published_parsed[:6])
                    if pub_date > last_check:
                        new_entries.append(entry)
                
                # Process new entries
                for entry in new_entries:
                    self.process_announcement(agency, entry)
                
                # Update last check time
                self.last_check[agency] = datetime.now()
                
            except Exception as e:
                print(f"Error checking {agency} feed: {e}")
    
    def process_announcement(self, agency, entry):
        """Process individual regulatory announcement"""
        print(f"\nNew {agency} announcement: {entry.title}")
        
        # Extract content
        content = entry.description if hasattr(entry, 'description') else entry.summary
        
        # Analyze with Ollama
        try:
            analysis = self.processor.analyze_regulatory_impact(
                f"Title: {entry.title}\n\nContent: {content}", 
                "general"
            )
            
            # Store or alert based on analysis
            self.handle_analysis_result(agency, entry.title, analysis)
            
        except Exception as e:
            print(f"Error analyzing announcement: {e}")
    
    def handle_analysis_result(self, agency, title, analysis):
        """Handle analysis results - store, alert, or forward"""
        # Example: Save to database, send alerts, etc.
        timestamp = datetime.now().isoformat()
        
        result = {
            'timestamp': timestamp,
            'agency': agency,
            'title': title,
            'analysis': analysis
        }
        
        # Save to file (replace with database in production)
        with open(f'regulatory_analysis_{timestamp.split("T")[0]}.json', 'a') as f:
            f.write(json.dumps(result) + '\n')
        
        print(f"Analysis saved for: {title}")

# Set up monitoring
processor = RegulatoryDocumentProcessor()
monitor = RegulatoryMonitor(processor)

# Schedule regular checks
schedule.every(30).minutes.do(monitor.check_feeds)

# Run monitoring loop
print("Starting regulatory monitoring...")
while True:
    schedule.run_pending()
    time.sleep(60)

import numpy as np
from sentence_transformers import SentenceTransformer
import faiss

class RegulatorySemanticSearch:
    def __init__(self, embedding_model="all-MiniLM-L6-v2"):
        self.embedding_model = SentenceTransformer(embedding_model)
        self.documents = []
        self.embeddings = None
        self.index = None
    
    def add_documents(self, documents):
        """Add regulatory documents to search index"""
        self.documents.extend(documents)
        
        # Generate embeddings
        doc_embeddings = self.embedding_model.encode([doc['content'] for doc in documents])
        
        if self.embeddings is None:
            self.embeddings = doc_embeddings
        else:
            self.embeddings = np.vstack([self.embeddings, doc_embeddings])
        
        # Build FAISS index
        dimension = self.embeddings.shape[1]
        self.index = faiss.IndexFlatL2(dimension)
        self.index.add(self.embeddings.astype(np.float32))
    
    def search_similar(self, query, top_k=5):
        """Find similar regulatory documents"""
        if self.index is None:
            return []
        
        # Encode query
        query_embedding = self.embedding_model.encode([query])
        
        # Search
        distances, indices = self.index.search(query_embedding.astype(np.float32), top_k)
        
        results = []
        for i, (distance, idx) in enumerate(zip(distances[0], indices[0])):
            if idx < len(self.documents):
                results.append({
                    'document': self.documents[idx],
                    'similarity_score': 1 / (1 + distance),  # Convert distance to similarity
                    'rank': i + 1
                })
        
        return results

# Usage example
search_engine = RegulatorySemanticSearch()

# Add historical regulatory documents
historical_docs = [
    {
        'id': 'sec_2024_001',
        'title': 'Form PF Amendments',
        'content': 'SEC and CFTC announce amendments to Form PF reporting requirements...',
        'date': '2024-02-08'
    },
    {
        'id': 'cftc_2024_ai',
        'title': 'AI Advisory for CFTC Entities',
        'content': 'CFTC staff advisory on artificial intelligence use in regulated markets...',
        'date': '2024-12-05'
    }
]

search_engine.add_documents(historical_docs)

# Search for related documents
query = "artificial intelligence compliance requirements"
similar_docs = search_engine.search_similar(query)

for result in similar_docs:
    print(f"Similar document: {result['document']['title']}")
    print(f"Similarity: {result['similarity_score']:.3f}")

class RegulatoryRiskScorer:
    def __init__(self, processor):
        self.processor = processor
        self.risk_keywords = {
            'high': ['penalty', 'enforcement', 'violation', 'cease and desist', 'fine'],
            'medium': ['compliance', 'review', 'examination', 'requirement'],
            'low': ['guidance', 'clarification', 'update', 'extension']
        }
    
    def score_announcement(self, text, title=""):
        """Score regulatory announcement for risk level"""
        
        # Keyword-based initial scoring
        keyword_score = self._calculate_keyword_score(text + " " + title)
        
        # LLM-based detailed analysis
        llm_analysis = self._llm_risk_analysis(text)
        
        # Combine scores
        final_score = self._combine_scores(keyword_score, llm_analysis)
        
        return {
            'risk_score': final_score,
            'risk_level': self._get_risk_level(final_score),
            'keyword_indicators': keyword_score,
            'llm_analysis': llm_analysis
        }
    
    def _calculate_keyword_score(self, text):
        """Calculate risk score based on keywords"""
        text_lower = text.lower()
        scores = {'high': 0, 'medium': 0, 'low': 0}
        
        for level, keywords in self.risk_keywords.items():
            for keyword in keywords:
                scores[level] += text_lower.count(keyword)
        
        # Weight scores
        weighted_score = (scores['high'] * 3 + scores['medium'] * 2 + scores['low'] * 1)
        return min(weighted_score / 10, 1.0)  # Normalize to 0-1
    
    def _llm_risk_analysis(self, text):
        """Use LLM for detailed risk analysis"""
        prompt = f"""
        Analyze this regulatory text for business risk on a scale of 0-100:
        
        Consider:
        - Enforcement implications
        - Implementation complexity
        - Financial impact potential
        - Timeline urgency
        - Industry scope
        
        Text: {text}
        
        Respond with only a number between 0-100 representing risk level.
        """
        
        try:
            response = self.processor.analyze_regulatory_impact(prompt, "risk_assessment")
            # Extract numeric score from response
            import re
            scores = re.findall(r'\b(\d{1,2}|100)\b', response)
            return float(scores[0]) / 100 if scores else 0.5
        except:
            return 0.5  # Default moderate risk if analysis fails
    
    def _combine_scores(self, keyword_score, llm_score):
        """Combine keyword and LLM scores"""
        return (keyword_score * 0.3) + (llm_score * 0.7)
    
    def _get_risk_level(self, score):
        """Convert numeric score to risk level"""
        if score >= 0.7:
            return "HIGH"
        elif score >= 0.4:
            return "MEDIUM"
        else:
            return "LOW"

# Usage with monitoring system
class EnhancedRegulatoryMonitor(RegulatoryMonitor):
    def __init__(self, processor):
        super().__init__(processor)
        self.risk_scorer = RegulatoryRiskScorer(processor)
        self.alert_thresholds = {
            'HIGH': 'immediate',
            'MEDIUM': 'daily_digest',
            'LOW': 'weekly_summary'
        }
    
    def process_announcement(self, agency, entry):
        """Enhanced processing with risk scoring"""
        content = entry.description if hasattr(entry, 'description') else entry.summary
        
        # Score risk level
        risk_data = self.risk_scorer.score_announcement(content, entry.title)
        
        # Generate detailed analysis
        analysis = self.processor.analyze_regulatory_impact(
            f"Title: {entry.title}\n\nContent: {content}", 
            "compliance"
        )
        
        # Create comprehensive result
        result = {
            'timestamp': datetime.now().isoformat(),
            'agency': agency,
            'title': entry.title,
            'risk_level': risk_data['risk_level'],
            'risk_score': risk_data['risk_score'],
            'analysis': analysis,
            'alert_priority': self.alert_thresholds[risk_data['risk_level']]
        }
        
        # Handle based on risk level
        self.handle_risk_based_alert(result)
    
    def handle_risk_based_alert(self, result):
        """Handle alerts based on risk level"""
        if result['risk_level'] == 'HIGH':
            self.send_immediate_alert(result)
        elif result['risk_level'] == 'MEDIUM':
            self.add_to_daily_digest(result)
        else:
            self.add_to_weekly_summary(result)
    
    def send_immediate_alert(self, result):
        """Send high-priority immediate alert"""
        print(f"🚨 HIGH RISK REGULATORY ALERT 🚨")
        print(f"Agency: {result['agency']}")
        print(f"Title: {result['title']}")
        print(f"Risk Score: {result['risk_score']:.2f}")
        print(f"Analysis: {result['analysis'][:200]}...")
        
        # In production: send email, Slack notification, etc.

# Dockerfile for Ollama regulatory analysis
FROM ollama/ollama:latest

# Install Python and dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    python3-venv \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Create working directory
WORKDIR /app

# Copy requirements
COPY requirements.txt .

# Install Python dependencies
RUN pip3 install -r requirements.txt

# Copy application code
COPY . .

# Pull required models
RUN ollama pull llama3.3:70b
RUN ollama pull mistral:7b

# Expose ports
EXPOSE 11434 8000

# Start script
COPY start.sh .
RUN chmod +x start.sh

CMD ["./start.sh"]

#!/bin/bash
# start.sh - Startup script for regulatory analysis container

# Start Ollama service
ollama serve &

# Wait for Ollama to be ready
sleep 10

# Start monitoring application
python3 regulatory_monitor.py &

# Keep container running
wait

# Performance optimization configuration
OLLAMA_CONFIG = {
    "models": {
        "llama3.3:70b": {
            "context_length": 4096,
            "batch_size": 1,
            "gpu_layers": 35,  # Adjust based on your GPU
            "threads": 8
        },
        "mistral:7b": {
            "context_length": 8192,
            "batch_size": 4,
            "gpu_layers": 32,
            "threads": 6
        }
    },
    "cache_settings": {
        "enable_caching": True,
        "cache_size": "2GB",
        "cache_expiry": 3600  # 1 hour
    }
}

class OptimizedProcessor(RegulatoryDocumentProcessor):
    def __init__(self, config=OLLAMA_CONFIG):
        super().__init__()
        self.config = config
        self.setup_optimization()
    
    def setup_optimization(self):
        """Configure Ollama for optimal performance"""
        # Set environment variables for optimization
        import os
        os.environ['OLLAMA_NUM_PARALLEL'] = '2'
        os.environ['OLLAMA_MAX_LOADED_MODELS'] = '3'
        os.environ['OLLAMA_FLASH_ATTENTION'] = '1'
    
    def batch_analyze_documents(self, documents, analysis_type="general"):
        """Process multiple documents efficiently"""
        results = []
        
        # Group documents by size for optimal batching
        small_docs = [doc for doc in documents if len(doc) < 2000]
        large_docs = [doc for doc in documents if len(doc) >= 2000]
        
        # Process small documents in batches
        for i in range(0, len(small_docs), 4):
            batch = small_docs[i:i+4]
            batch_text = "\n---DOCUMENT SEPARATOR---\n".join(batch)
            
            result = self.analyze_regulatory_impact(batch_text, analysis_type)
            results.extend(self.split_batch_results(result, len(batch)))
        
        # Process large documents individually
        for doc in large_docs:
            result = self.analyze_regulatory_impact(doc, analysis_type)
            results.append(result)
        
        return results

from flask import Flask, request, jsonify
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address

app = Flask(__name__)
limiter = Limiter(
    app,
    key_func=get_remote_address,
    default_limits=["100 per hour"]
)

processor = OptimizedProcessor()

@app.route('/analyze/document', methods=['POST'])
@limiter.limit("10 per minute")
def analyze_document():
    """Analyze uploaded regulatory document"""
    try:
        data = request.get_json()
        
        if 'text' not in data:
            return jsonify({'error': 'Missing text field'}), 400
        
        analysis_type = data.get('type', 'general')
        text = data['text']
        
        result = processor.analyze_regulatory_impact(text, analysis_type)
        
        return jsonify({
            'status': 'success',
            'analysis': result,
            'timestamp': datetime.now().isoformat()
        })
        
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/monitor/status', methods=['GET'])
def monitor_status():
    """Get monitoring system status"""
    return jsonify({
        'status': 'active',
        'models_loaded': ['llama3.3:70b', 'mistral:7b'],
        'last_check': datetime.now().isoformat()
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=8000, debug=False)

class ModelManager:
    def __init__(self):
        self.models = {}
        self.model_versions = {}
    
    def update_model(self, model_name, new_version):
        """Safely update models with validation"""
        # Test new model on validation set
        if self.validate_model_performance(model_name, new_version):
            # Backup current model
            self.backup_model(model_name)
            
            # Pull new version
            os.system(f"ollama pull {model_name}:{new_version}")
            
            # Update tracking
            self.model_versions[model_name] = new_version
            
            return True
        return False
    
    def validate_model_performance(self, model_name, version):
        """Validate model performance on test regulatory documents"""
        # Implementation depends on your validation criteria
        test_docs = self.load_test_documents()
        
        for doc in test_docs:
            result = self.test_model_analysis(model_name, version, doc)
            if not self.meets_quality_threshold(result):
                return False
        
        return True

class ComplianceMetrics:
    def __init__(self):
        self.metrics = {
            'documents_processed': 0,
            'alerts_generated': 0,
            'high_risk_alerts': 0,
            'processing_time_avg': 0,
            'accuracy_score': 0
        }
    
    def generate_weekly_report(self):
        """Generate compliance system performance report"""
        return {
            'period': 'week',
            'documents_analyzed': self.metrics['documents_processed'],
            'risk_alerts': {
                'total': self.metrics['alerts_generated'],
                'high_priority': self.metrics['high_risk_alerts']
            },
            'performance': {
                'avg_processing_time': f"{self.metrics['processing_time_avg']:.2f}s",
                'accuracy_rate': f"{self.metrics['accuracy_score']:.1%}"
            },
            'recommendations': self.generate_recommendations()
        }