Solana ETF Approval Predictor: Ollama Regulatory Timeline and Probability Analysis 2025

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

# Pull models for regulatory analysis
ollama pull llama3.2:latest
ollama pull mistral:7b
ollama pull codellama:13b

import requests
import pandas as pd
from datetime import datetime, timedelta
import ollama

class SolanaETFDataCollector:
    def __init__(self):
        self.sec_filings_api = "https://api.sec.gov/filings"
        self.regulatory_data = []
        
    def collect_sec_filings(self):
        """Gather recent SEC filings related to Solana ETF"""
        headers = {
            'User-Agent': 'ETF-Predictor/1.0',
            'Accept': 'application/json'
        }
        
        # Search for Solana ETF filings in last 90 days
        params = {
            'q': 'Solana ETF',
            'date_from': (datetime.now() - timedelta(days=90)).strftime('%Y-%m-%d'),
            'date_to': datetime.now().strftime('%Y-%m-%d')
        }
        
        response = requests.get(self.sec_filings_api, 
                              headers=headers, params=params)
        
        if response.status_code == 200:
            filings_data = response.json()
            return self.parse_filings(filings_data)
        
        return []
    
    def parse_filings(self, filings_data):
        """Extract relevant filing information"""
        parsed_filings = []
        
        for filing in filings_data.get('filings', []):
            parsed_filings.append({
                'date': filing.get('date'),
                'company': filing.get('company'),
                'form_type': filing.get('form'),
                'description': filing.get('description', ''),
                'filing_url': filing.get('url', '')
            })
            
        return parsed_filings
    
    def collect_market_sentiment(self):
        """Gather market sentiment indicators"""
        # Placeholder for sentiment analysis from social media, news
        sentiment_indicators = {
            'social_sentiment': 0.7,  # 0-1 scale
            'institutional_interest': 0.8,
            'regulatory_tone': 0.6,
            'market_volatility': 0.4
        }
        
        return sentiment_indicators

# Initialize data collector
collector = SolanaETFDataCollector()
filings = collector.collect_sec_filings()
sentiment = collector.collect_market_sentiment()

print(f"Collected {len(filings)} SEC filings")
print(f"Current sentiment score: {sentiment['social_sentiment']}")

class RegulatoryPatternAnalyzer:
    def __init__(self):
        self.ollama_client = ollama.Client()
        
    def analyze_sec_patterns(self, historical_data):
        """Use Ollama to identify SEC approval patterns"""
        
        # Prepare historical ETF approval data
        prompt = f"""
        Analyze these SEC ETF approval patterns:
        
        Historical Data:
        {historical_data}
        
        Current Solana ETF Status:
        - Multiple applications pending
        - Staking ETF already approved
        - October 2025 deadline
        - Security classification concerns
        
        Based on historical patterns, provide:
        1. Probability score (0-100) for approval
        2. Key risk factors
        3. Timeline prediction
        4. Comparison to Bitcoin/Ethereum ETF approvals
        
        Format response as structured analysis.
        """
        
        response = self.ollama_client.generate(
            model='llama3.2:latest',
            prompt=prompt
        )
        
        return self.parse_analysis(response['response'])
    
    def parse_analysis(self, ai_response):
        """Extract structured data from AI analysis"""
        # Parse probability score from response
        lines = ai_response.split('\n')
        analysis = {
            'probability_score': 0,
            'risk_factors': [],
            'timeline_prediction': '',
            'comparison_insights': ''
        }
        
        for line in lines:
            if 'probability' in line.lower() and '%' in line:
                # Extract percentage from line
                import re
                match = re.search(r'(\d+)%', line)
                if match:
                    analysis['probability_score'] = int(match.group(1))
            
            elif 'risk' in line.lower():
                analysis['risk_factors'].append(line.strip())
                
            elif 'timeline' in line.lower():
                analysis['timeline_prediction'] = line.strip()
                
        return analysis

# Historical ETF approval data
historical_etf_data = """
Bitcoin ETF: 10+ years from first application to approval (2024)
Ethereum ETF: 3 years from first application to approval (2024)
REX-Osprey Solana Staking ETF: Approved July 2025 (automatic approval)
Average approval time for crypto ETFs: 18-36 months
"""

analyzer = RegulatoryPatternAnalyzer()
pattern_analysis = analyzer.analyze_sec_patterns(historical_etf_data)

print(f"Approval Probability: {pattern_analysis['probability_score']}%")
print(f"Timeline Prediction: {pattern_analysis['timeline_prediction']}")

from datetime import datetime, timedelta
import numpy as np

class ETFTimelinePredictor:
    def __init__(self):
        self.ollama_client = ollama.Client()
        self.key_dates = {
            'current_date': datetime.now(),
            'sec_deadline': datetime(2025, 10, 10),
            'refiling_deadline': datetime(2025, 7, 31),
            'election_impact': datetime(2025, 1, 20)  # Inauguration impact
        }
    
    def predict_approval_timeline(self, pattern_analysis, market_data):
        """Generate timeline prediction using ensemble AI approach"""
        
        # Calculate days to key milestones
        days_to_deadline = (self.key_dates['sec_deadline'] - 
                          self.key_dates['current_date']).days
        
        timeline_prompt = f"""
        Predict Solana ETF approval timeline based on:
        
        Current Analysis:
        - Approval probability: {pattern_analysis['probability_score']}%
        - Days to SEC deadline: {days_to_deadline}
        - Market sentiment: {market_data.get('social_sentiment', 0) * 100}%
        
        Key Factors:
        - REX-Osprey staking ETF already trading
        - SEC requested July refilings
        - Multiple major firms applied
        - Security classification concerns remain
        
        Provide specific date predictions for:
        1. Most likely approval date
        2. Best-case scenario date
        3. Worst-case scenario date
        4. Key milestone dates to watch
        
        Use YYYY-MM-DD format for dates.
        """
        
        response = self.ollama_client.generate(
            model='mistral:7b',
            prompt=timeline_prompt
        )
        
        return self.extract_timeline_dates(response['response'])
    
    def extract_timeline_dates(self, ai_response):
        """Parse date predictions from AI response"""
        import re
        
        # Extract dates in YYYY-MM-DD format
        date_pattern = r'(\d{4}-\d{2}-\d{2})'
        dates = re.findall(date_pattern, ai_response)
        
        timeline = {
            'most_likely_date': dates[0] if len(dates) > 0 else None,
            'best_case_date': dates[1] if len(dates) > 1 else None,
            'worst_case_date': dates[2] if len(dates) > 2 else None,
            'confidence_score': pattern_analysis['probability_score'],
            'raw_analysis': ai_response
        }
        
        return timeline

# Generate timeline prediction
timeline_predictor = ETFTimelinePredictor()
timeline_forecast = timeline_predictor.predict_approval_timeline(
    pattern_analysis, sentiment
)

print(f"Most Likely Approval: {timeline_forecast['most_likely_date']}")
print(f"Confidence Score: {timeline_forecast['confidence_score']}%")

class RiskAssessmentEngine:
    def __init__(self):
        self.ollama_client = ollama.Client()
        self.risk_weights = {
            'regulatory': 0.4,
            'market': 0.3,
            'technical': 0.2,
            'political': 0.1
        }
    
    def calculate_risk_score(self, data_inputs):
        """Calculate comprehensive risk score for ETF approval"""
        
        risk_prompt = f"""
        Calculate risk assessment for Solana ETF approval:
        
        Regulatory Factors:
        - SEC security classification concerns
        - Lack of CME futures market
        - Need for regulatory clarity
        
        Market Factors:
        - Current SOL price: ~$166
        - Market cap: ~$90B (180-day average)
        - Daily trading volume: $2B average
        
        Technical Factors:
        - Network uptime and stability
        - Decentralization metrics
        - Infrastructure maturity
        
        Political Factors:
        - Pro-crypto administration
        - Congressional support for digital assets
        - International regulatory trends
        
        Rate each category (1-10) and provide overall risk score.
        Include specific mitigation strategies.
        """
        
        response = self.ollama_client.generate(
            model='llama3.2:latest',
            prompt=risk_prompt
        )
        
        return self.parse_risk_assessment(response['response'])
    
    def parse_risk_assessment(self, ai_response):
        """Extract risk scores and recommendations"""
        import re
        
        # Extract numerical scores from response
        score_pattern = r'(\w+):\s*(\d+(?:\.\d+)?)'
        scores = re.findall(score_pattern, ai_response.lower())
        
        risk_assessment = {
            'regulatory_risk': 6.5,  # Default scores if parsing fails
            'market_risk': 4.2,
            'technical_risk': 3.8,
            'political_risk': 2.1,
            'overall_risk': 4.1,
            'mitigation_strategies': [],
            'raw_analysis': ai_response
        }
        
        # Override with parsed scores if available
        for category, score in scores:
            if 'regulatory' in category:
                risk_assessment['regulatory_risk'] = float(score)
            elif 'market' in category:
                risk_assessment['market_risk'] = float(score)
            elif 'technical' in category:
                risk_assessment['technical_risk'] = float(score)
            elif 'political' in category:
                risk_assessment['political_risk'] = float(score)
        
        # Calculate weighted overall risk
        weighted_risk = (
            risk_assessment['regulatory_risk'] * self.risk_weights['regulatory'] +
            risk_assessment['market_risk'] * self.risk_weights['market'] +
            risk_assessment['technical_risk'] * self.risk_weights['technical'] +
            risk_assessment['political_risk'] * self.risk_weights['political']
        )
        
        risk_assessment['overall_risk'] = round(weighted_risk, 1)
        
        return risk_assessment

# Generate risk assessment
risk_engine = RiskAssessmentEngine()
risk_analysis = risk_engine.calculate_risk_score({
    'current_data': sentiment,
    'pattern_analysis': pattern_analysis,
    'timeline': timeline_forecast
})

print(f"Overall Risk Score: {risk_analysis['overall_risk']}/10")
print(f"Regulatory Risk: {risk_analysis['regulatory_risk']}/10")
print(f"Market Risk: {risk_analysis['market_risk']}/10")

class EnsembleETFPredictor:
    def __init__(self):
        self.models = ['llama3.2:latest', 'mistral:7b', 'codellama:13b']
        self.ollama_client = ollama.Client()
    
    def ensemble_prediction(self, data_package):
        """Generate predictions from multiple models and combine results"""
        
        predictions = []
        
        for model in self.models:
            prediction = self.single_model_prediction(model, data_package)
            predictions.append(prediction)
        
        # Combine predictions using weighted average
        ensemble_result = self.combine_predictions(predictions)
        
        return ensemble_result
    
    def single_model_prediction(self, model, data):
        """Get prediction from single model"""
        
        prompt = f"""
        Based on all available data, predict Solana ETF approval:
        
        Data Summary: {data}
        
        Provide only:
        1. Approval probability (0-100)
        2. Most likely date (YYYY-MM-DD)
        3. Confidence level (0-100)
        """
        
        response = self.ollama_client.generate(
            model=model,
            prompt=prompt
        )
        
        return self.parse_single_prediction(response['response'])
    
    def parse_single_prediction(self, response):
        """Parse individual model prediction"""
        import re
        
        # Extract probability percentage
        prob_match = re.search(r'(\d+)%', response)
        probability = int(prob_match.group(1)) if prob_match else 50
        
        # Extract date
        date_match = re.search(r'(\d{4}-\d{2}-\d{2})', response)
        predicted_date = date_match.group(1) if date_match else '2025-10-01'
        
        # Extract confidence
        conf_match = re.search(r'confidence:?\s*(\d+)', response.lower())
        confidence = int(conf_match.group(1)) if conf_match else 70
        
        return {
            'probability': probability,
            'predicted_date': predicted_date,
            'confidence': confidence
        }
    
    def combine_predictions(self, predictions):
        """Combine multiple model predictions"""
        
        # Weight predictions by confidence scores
        total_weight = sum(p['confidence'] for p in predictions)
        
        weighted_probability = sum(
            p['probability'] * p['confidence'] for p in predictions
        ) / total_weight
        
        # Use highest confidence prediction for date
        best_prediction = max(predictions, key=lambda x: x['confidence'])
        
        ensemble = {
            'final_probability': round(weighted_probability, 1),
            'predicted_date': best_prediction['predicted_date'],
            'confidence_level': round(total_weight / len(predictions), 1),
            'model_agreement': self.calculate_agreement(predictions)
        }
        
        return ensemble
    
    def calculate_agreement(self, predictions):
        """Calculate how much models agree"""
        probabilities = [p['probability'] for p in predictions]
        std_dev = np.std(probabilities)
        
        # Lower standard deviation = higher agreement
        agreement = max(0, 100 - (std_dev * 2))
        return round(agreement, 1)

# Generate ensemble prediction
ensemble_predictor = EnsembleETFPredictor()

# Combine all collected data
data_package = {
    'filings': filings,
    'sentiment': sentiment,
    'patterns': pattern_analysis,
    'timeline': timeline_forecast,
    'risks': risk_analysis
}

final_prediction = ensemble_predictor.ensemble_prediction(data_package)

print("=== FINAL SOLANA ETF PREDICTION ===")
print(f"Approval Probability: {final_prediction['final_probability']}%")
print(f"Predicted Date: {final_prediction['predicted_date']}")
print(f"Confidence Level: {final_prediction['confidence_level']}%")
print(f"Model Agreement: {final_prediction['model_agreement']}%")

import schedule
import time
from datetime import datetime

class ETFMonitoringSystem:
    def __init__(self):
        self.predictor = EnsembleETFPredictor()
        self.last_prediction = None
        self.alert_threshold = 10  # Trigger alert for 10% probability change
    
    def daily_prediction_update(self):
        """Run daily prediction update"""
        
        print(f"Running daily update at {datetime.now()}")
        
        # Collect fresh data
        collector = SolanaETFDataCollector()
        new_filings = collector.collect_sec_filings()
        new_sentiment = collector.collect_market_sentiment()
        
        # Generate new prediction
        current_data = {
            'filings': new_filings,
            'sentiment': new_sentiment,
            'date': datetime.now().isoformat()
        }
        
        new_prediction = self.predictor.ensemble_prediction(current_data)
        
        # Check for significant changes
        if self.last_prediction:
            prob_change = abs(
                new_prediction['final_probability'] - 
                self.last_prediction['final_probability']
            )
            
            if prob_change >= self.alert_threshold:
                self.send_alert(new_prediction, prob_change)
        
        self.last_prediction = new_prediction
        self.log_prediction(new_prediction)
        
        return new_prediction
    
    def send_alert(self, prediction, change):
        """Send alert for significant changes"""
        
        alert_message = f"""
        🚨 SOLANA ETF PREDICTION ALERT 🚨
        
        Significant change detected:
        New Probability: {prediction['final_probability']}%
        Change: {change}%
        Predicted Date: {prediction['predicted_date']}
        Confidence: {prediction['confidence_level']}%
        
        Time: {datetime.now()}
        """
        
        print(alert_message)
        # Add email/SMS integration here
    
    def log_prediction(self, prediction):
        """Log prediction to file for historical analysis"""
        
        log_entry = {
            'timestamp': datetime.now().isoformat(),
            'probability': prediction['final_probability'],
            'predicted_date': prediction['predicted_date'],
            'confidence': prediction['confidence_level']
        }
        
        # Append to CSV log file
        import csv
        with open('etf_predictions.csv', 'a', newline='') as f:
            writer = csv.DictWriter(f, fieldnames=log_entry.keys())
            if f.tell() == 0:  # Write header if file is empty
                writer.writeheader()
            writer.writerow(log_entry)

# Set up automated monitoring
monitor = ETFMonitoringSystem()

# Schedule daily updates
schedule.every().day.at("09:00").do(monitor.daily_prediction_update)
schedule.every().day.at("15:30").do(monitor.daily_prediction_update)  # After market close

print("Starting automated monitoring...")
print("Daily updates scheduled for 9:00 AM and 3:30 PM")

# Run initial prediction
initial_prediction = monitor.daily_prediction_update()

class AdvancedDataIntegration:
    def __init__(self):
        self.data_sources = {
            'sec_filings': 'https://api.sec.gov/filings',
            'crypto_prices': 'https://api.coingecko.com/api/v3',
            'sentiment_data': 'social_media_apis',
            'regulatory_news': 'news_apis'
        }
    
    def fetch_comprehensive_data(self):
        """Gather data from all sources"""
        
        # Current SOL price and market data
        sol_data = self.get_solana_market_data()
        
        # ETF industry trends
        etf_trends = self.get_etf_industry_trends()
        
        # Regulatory sentiment analysis
        regulatory_sentiment = self.analyze_regulatory_sentiment()
        
        # Institutional activity tracking
        institutional_data = self.track_institutional_activity()
        
        comprehensive_data = {
            'market_data': sol_data,
            'etf_trends': etf_trends,
            'regulatory_sentiment': regulatory_sentiment,
            'institutional_activity': institutional_data,
            'timestamp': datetime.now().isoformat()
        }
        
        return comprehensive_data
    
    def get_solana_market_data(self):
        """Fetch current Solana market metrics"""
        
        # Simulated market data (replace with real API calls)
        market_data = {
            'price': 166.00,
            'market_cap': 90_000_000_000,
            'daily_volume': 2_000_000_000,
            'price_change_24h': 2.5,
            'volatility_index': 0.45,
            'institutional_holdings': 1_300_000_000
        }
        
        return market_data
    
    def get_etf_industry_trends(self):
        """Analyze broader ETF industry trends"""
        
        trends = {
            'crypto_etf_inflows': 1_200_000_000,  # YTD inflows
            'total_crypto_etfs': 12,
            'avg_approval_time': 24,  # months
            'success_rate': 0.65,
            'regulatory_momentum': 0.8  # 0-1 scale
        }
        
        return trends
    
    def analyze_regulatory_sentiment(self):
        """Analyze regulatory communication sentiment"""
        
        sentiment = {
            'sec_tone_score': 0.6,  # 0-1 positive scale
            'congressional_support': 0.75,
            'industry_confidence': 0.8,
            'media_sentiment': 0.65,
            'expert_predictions': 0.82
        }
        
        return sentiment
    
    def track_institutional_activity(self):
        """Monitor institutional crypto adoption"""
        
        institutional = {
            'new_filings_count': 8,
            'major_firms_involved': [
                'Fidelity', 'Grayscale', 'VanEck', 
                'Franklin Templeton', 'Bitwise'
            ],
            'total_aum_seeking_approval': 50_000_000_000,
            'lobbying_activity_score': 0.9
        }
        
        return institutional

# Enhanced prediction with comprehensive data
data_integrator = AdvancedDataIntegration()
comprehensive_data = data_integrator.fetch_comprehensive_data()

# Use enhanced data in prediction
enhanced_prediction = ensemble_predictor.ensemble_prediction(comprehensive_data)

print("=== ENHANCED PREDICTION WITH COMPREHENSIVE DATA ===")
print(f"Approval Probability: {enhanced_prediction['final_probability']}%")
print(f"Predicted Date: {enhanced_prediction['predicted_date']}")
print(f"Confidence Level: {enhanced_prediction['confidence_level']}%")

# Production deployment setup
# Create Docker container for consistent environment

cat > Dockerfile << EOF
FROM ubuntu:22.04

# Install Ollama and dependencies
RUN apt-get update && apt-get install -y curl python3 python3-pip
RUN curl -fsSL https://ollama.ai/install.sh | sh

# Install Python requirements
COPY requirements.txt /app/
RUN pip3 install -r /app/requirements.txt

# Copy prediction system
COPY *.py /app/
WORKDIR /app

# Pull required models
RUN ollama pull llama3.2:latest
RUN ollama pull mistral:7b
RUN ollama pull codellama:13b

# Expose API port
EXPOSE 8000

# Start prediction service
CMD ["python3", "api_server.py"]
EOF

# Build and run container
docker build -t solana-etf-predictor .
docker run -p 8000:8000 solana-etf-predictor

class OptimizedPredictor:
    def __init__(self):
        self.cache = {}
        self.cache_timeout = 3600  # 1 hour
        
    def cached_prediction(self, data_hash):
        """Use caching to avoid redundant AI calls"""
        
        current_time = time.time()
        
        if data_hash in self.cache:
            cached_result, timestamp = self.cache[data_hash]
            
            if current_time - timestamp < self.cache_timeout:
                return cached_result
        
        # Generate new prediction if not cached or expired
        new_prediction = self.generate_fresh_prediction()
        self.cache[data_hash] = (new_prediction, current_time)
        
        return new_prediction
    
    def batch_predictions(self, multiple_scenarios):
        """Process multiple scenarios efficiently"""
        
        results = []
        
        for scenario in multiple_scenarios:
            prediction = self.cached_prediction(
                hash(str(scenario))
            )
            results.append(prediction)
        
        return results

# Performance monitoring
import time
start_time = time.time()

# Run optimized prediction
optimized_predictor = OptimizedPredictor()
prediction = optimized_predictor.cached_prediction(
    hash(str(comprehensive_data))
)

processing_time = time.time() - start_time
print(f"Prediction generated in {processing_time:.2f} seconds")