How to Predict Market Crashes with Ollama: Black Swan Event Detection Guide

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

# Download models optimized for numerical analysis
ollama pull llama3.1:8b
ollama pull codellama:7b
ollama pull mistral:7b

# config.py
import ollama

class MarketPredictor:
    def __init__(self, model_name="llama3.1:8b"):
        self.client = ollama.Client()
        self.model = model_name
        self.context_window = 4096
        
    def generate_prediction(self, prompt, max_tokens=500):
        """Generate market prediction from prompt"""
        response = self.client.generate(
            model=self.model,
            prompt=prompt,
            options={
                'num_predict': max_tokens,
                'temperature': 0.1,  # Low temperature for consistent analysis
                'top_k': 10,
                'top_p': 0.9
            }
        )
        return response['response']

# black_swan_detector.py
import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json

class BlackSwanDetector:
    def __init__(self, predictor):
        self.predictor = predictor
        self.indicators = [
            'VIX',      # Volatility Index
            '^GSPC',    # S&P 500
            '^TNX',     # 10-Year Treasury
            'DXY',      # Dollar Index
            'GLD',      # Gold ETF
        ]
        
    def fetch_market_data(self, days=30):
        """Fetch recent market data for analysis"""
        data = {}
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days)
        
        for symbol in self.indicators:
            try:
                ticker = yf.Ticker(symbol)
                hist = ticker.history(start=start_date, end=end_date)
                data[symbol] = hist
            except Exception as e:
                print(f"Error fetching {symbol}: {e}")
                
        return data
        
    def calculate_anomaly_scores(self, data):
        """Calculate anomaly scores for each indicator"""
        scores = {}
        
        for symbol, df in data.items():
            if df.empty:
                continue
                
            # Calculate rolling statistics
            df['returns'] = df['Close'].pct_change()
            df['volatility'] = df['returns'].rolling(5).std()
            df['volume_spike'] = df['Volume'] / df['Volume'].rolling(20).mean()
            
            # Z-score based anomaly detection
            recent_vol = df['volatility'].iloc[-1]
            vol_mean = df['volatility'].mean()
            vol_std = df['volatility'].std()
            
            vol_zscore = (recent_vol - vol_mean) / vol_std if vol_std > 0 else 0
            
            scores[symbol] = {
                'volatility_zscore': vol_zscore,
                'volume_anomaly': df['volume_spike'].iloc[-1],
                'price_change': df['returns'].iloc[-1]
            }
            
        return scores

def analyze_market_patterns(self, scores, data):
    """Use Ollama to analyze market patterns for black swan indicators"""
    
    # Prepare context for AI analysis
    context = self.prepare_market_context(scores, data)
    
    prompt = f"""
    Analyze the following market data for potential black swan event indicators:

    {context}

    Focus on these warning signs:
    1. Unusual volatility spikes across multiple assets
    2. Inverse correlations breaking down
    3. Volume anomalies in safe-haven assets
    4. Credit spread widening indicators
    5. Cross-asset momentum divergences

    Provide:
    - Risk Level (1-10 scale)
    - Primary Concerns (max 3)
    - Recommended Actions
    - Confidence Level (1-100%)

    Format response as JSON.
    """
    
    response = self.predictor.generate_prediction(prompt)
    
    try:
        # Parse AI response
        analysis = json.loads(response)
        return analysis
    except json.JSONDecodeError:
        # Fallback to structured parsing
        return self.parse_text_response(response)
        
def prepare_market_context(self, scores, data):
    """Prepare market data context for AI analysis"""
    context_lines = []
    
    for symbol, metrics in scores.items():
        context_lines.append(f"{symbol}:")
        context_lines.append(f"  Volatility Z-Score: {metrics['volatility_zscore']:.2f}")
        context_lines.append(f"  Volume Anomaly: {metrics['volume_anomaly']:.2f}")
        context_lines.append(f"  Price Change: {metrics['price_change']:.4f}")
        
        # Add price trend context
        if symbol in data:
            df = data[symbol]
            trend = "UP" if df['Close'].iloc[-1] > df['Close'].iloc[-5] else "DOWN"
            context_lines.append(f"  5-Day Trend: {trend}")
        
        context_lines.append("")
    
    return "\n".join(context_lines)

def multi_timeframe_analysis(self):
    """Analyze patterns across multiple timeframes"""
    timeframes = {
        'short': 7,    # 1 week
        'medium': 30,  # 1 month  
        'long': 90     # 3 months
    }
    
    results = {}
    
    for period_name, days in timeframes.items():
        data = self.fetch_market_data(days)
        scores = self.calculate_anomaly_scores(data)
        analysis = self.analyze_market_patterns(scores, data)
        
        results[period_name] = analysis
        
    # Cross-timeframe synthesis
    synthesis_prompt = f"""
    Analyze black swan risk across timeframes:
    
    Short-term (7 days): {results.get('short', {})}
    Medium-term (30 days): {results.get('medium', {})}
    Long-term (90 days): {results.get('long', {})}
    
    Identify:
    1. Consistent patterns across timeframes
    2. Divergences that signal regime changes
    3. Overall black swan probability
    
    Provide final risk assessment (1-10) with reasoning.
    """
    
    final_analysis = self.predictor.generate_prediction(synthesis_prompt)
    return final_analysis

def detect_correlation_breakdown(self, data):
    """Detect correlation breakdowns that signal market stress"""
    
    # Calculate correlation matrices for different periods
    returns_data = {}
    for symbol, df in data.items():
        if not df.empty:
            returns_data[symbol] = df['Close'].pct_change().dropna()
    
    if len(returns_data) < 2:
        return None
        
    returns_df = pd.DataFrame(returns_data)
    
    # Recent vs historical correlations
    recent_corr = returns_df.tail(10).corr()  # Last 10 days
    historical_corr = returns_df.head(-10).corr()  # Everything except last 10
    
    # Calculate correlation breakdown score
    corr_diff = (recent_corr - historical_corr).abs()
    breakdown_score = corr_diff.mean().mean()
    
    correlation_prompt = f"""
    Correlation Analysis for Black Swan Detection:
    
    Recent Correlations:
    {recent_corr.round(3).to_string()}
    
    Historical Correlations:
    {historical_corr.round(3).to_string()}
    
    Average Correlation Change: {breakdown_score:.3f}
    
    Analyze:
    1. Are traditional safe haven correlations breaking down?
    2. Is risk-on/risk-off correlation increasing abnormally?
    3. What does this suggest about market regime changes?
    
    Provide breakdown risk level (1-10) and key observations.
    """
    
    analysis = self.predictor.generate_prediction(correlation_prompt)
    return analysis

# monitor.py
import time
import schedule
from datetime import datetime
import logging

class RealTimeMonitor:
    def __init__(self, detector):
        self.detector = detector
        self.setup_logging()
        self.alert_thresholds = {
            'critical': 8,
            'warning': 6,
            'watch': 4
        }
        
    def setup_logging(self):
        """Configure logging for monitoring system"""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('market_monitor.log'),
                logging.StreamHandler()
            ]
        )
        self.logger = logging.getLogger(__name__)
        
    def run_detection_cycle(self):
        """Run one complete detection cycle"""
        try:
            self.logger.info("Starting detection cycle...")
            
            # Fetch fresh data
            data = self.detector.fetch_market_data()
            scores = self.detector.calculate_anomaly_scores(data)
            
            # Run AI analysis
            analysis = self.detector.analyze_market_patterns(scores, data)
            
            # Check correlation breakdown
            corr_analysis = self.detector.detect_correlation_breakdown(data)
            
            # Process alerts
            self.process_alerts(analysis, corr_analysis)
            
            self.logger.info("Detection cycle completed successfully")
            
        except Exception as e:
            self.logger.error(f"Detection cycle failed: {e}")
            
    def process_alerts(self, analysis, corr_analysis):
        """Process and send alerts based on analysis"""
        try:
            risk_level = analysis.get('risk_level', 0)
            
            if risk_level >= self.alert_thresholds['critical']:
                self.send_critical_alert(analysis, corr_analysis)
            elif risk_level >= self.alert_thresholds['warning']:
                self.send_warning_alert(analysis)
            elif risk_level >= self.alert_thresholds['watch']:
                self.send_watch_alert(analysis)
                
        except Exception as e:
            self.logger.error(f"Alert processing failed: {e}")
            
    def send_critical_alert(self, analysis, corr_analysis):
        """Send critical black swan alert"""
        alert_message = f"""
        🚨 CRITICAL BLACK SWAN ALERT 🚨
        
        Risk Level: {analysis.get('risk_level', 'Unknown')}/10
        Confidence: {analysis.get('confidence_level', 'Unknown')}%
        
        Primary Concerns:
        {self.format_concerns(analysis.get('primary_concerns', []))}
        
        Recommended Actions:
        {self.format_actions(analysis.get('recommended_actions', []))}
        
        Correlation Analysis:
        {corr_analysis}
        
        Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
        """
        
        self.logger.critical(alert_message)
        # Add your notification logic here (email, Slack, SMS, etc.)
        
    def start_monitoring(self):
        """Start continuous monitoring"""
        self.logger.info("Starting continuous market monitoring...")
        
        # Schedule regular checks
        schedule.every(15).minutes.do(self.run_detection_cycle)  # Every 15 minutes during market hours
        schedule.every(1).hours.do(self.run_detection_cycle)    # Every hour outside market hours
        
        while True:
            schedule.run_pending()
            time.sleep(60)  # Check every minute

def optimize_ollama_performance(self):
    """Optimize Ollama for real-time market analysis"""
    
    # Model-specific optimizations
    optimized_options = {
        'num_predict': 300,      # Shorter responses for speed
        'temperature': 0.05,     # Very low for consistent analysis
        'top_k': 5,             # Reduce for faster generation
        'top_p': 0.8,           # Balanced creativity vs speed
        'repeat_penalty': 1.1,   # Prevent repetition
        'num_thread': 8,        # Utilize multiple CPU cores
    }
    
    return optimized_options

def cache_market_contexts(self):
    """Cache frequently used market contexts"""
    # Implement caching for repeated analysis patterns
    # This reduces prompt processing time significantly
    pass

def analyze_covid_crash_signals():
    """Retrospective analysis of 2020 crash signals"""
    
    historical_prompt = """
    Analyze these market conditions from February 2020:
    
    VIX: Spiked from 15 to 40+ (167% increase)
    S&P 500: Down 7% in single day (Feb 27)
    Treasury yields: 10-year fell from 1.7% to 1.3%
    Gold: Volatile but initially down
    USD: Strengthening rapidly
    
    Additional context:
    - Novel virus spreading globally
    - Supply chain disruption reports
    - Central bank emergency language appearing
    
    What black swan indicators would this trigger?
    Rate the warning signals on a 1-10 scale.
    """
    
    # This analysis helps validate our detection system
    return historical_prompt

def analyze_2008_patterns():
    """Study 2008 crisis patterns for model training"""
    
    crisis_indicators = {
        'credit_spreads': 'TED spread widening dramatically',
        'bank_stocks': 'Financial sector underperforming',
        'yield_curve': 'Inversion and subsequent steepening',
        'housing_data': 'Home price indices declining',
        'commodity_prices': 'Oil and commodities crashing'
    }
    
    # Use these patterns to train better detection algorithms
    return crisis_indicators

class TradingIntegration:
    def __init__(self, detector):
        self.detector = detector
        self.position_limits = {
            'max_hedge_percent': 20,  # Maximum portfolio hedge
            'cash_reserve_target': 15  # Target cash percentage
        }
        
    def execute_black_swan_protocol(self, risk_level):
        """Execute predefined trading responses to black swan signals"""
        
        if risk_level >= 8:
            # Critical: Maximum defensive positioning
            self.increase_cash_position(self.position_limits['cash_reserve_target'])
            self.add_volatility_hedge(self.position_limits['max_hedge_percent'])
            self.reduce_leverage()
            
        elif risk_level >= 6:
            # Warning: Moderate defensive measures
            self.increase_cash_position(10)
            self.add_volatility_hedge(10)
            
        elif risk_level >= 4:
            # Watch: Prepare for potential moves
            self.prepare_hedge_orders()
            
    def increase_cash_position(self, target_percent):
        """Increase cash allocation to target percentage"""
        # Implement your broker's API calls here
        pass
        
    def add_volatility_hedge(self, hedge_percent):
        """Add volatility protection through VIX calls or put spreads"""
        # Implement volatility hedging strategy
        pass

def update_risk_models(self, black_swan_probability):
    """Update risk models based on black swan probability"""
    
    # Adjust VaR calculations
    var_multiplier = 1 + (black_swan_probability / 10)
    
    # Update correlation assumptions
    stress_correlations = self.calculate_stress_correlations()
    
    # Modify position limits
    adjusted_limits = self.calculate_adjusted_limits(black_swan_probability)
    
    return {
        'var_multiplier': var_multiplier,
        'stress_correlations': stress_correlations,
        'position_limits': adjusted_limits
    }

def analyze_market_sentiment(self):
    """Analyze market sentiment for additional black swan signals"""
    
    sentiment_prompt = """
    Analyze current market sentiment indicators:
    
    - Fed officials' language becoming more dovish/hawkish
    - Corporate earnings call sentiment
    - Financial media fear/greed indicators
    - Social media sentiment trends
    - Insider trading patterns
    
    How do these sentiment factors correlate with technical indicators?
    What additional black swan risk do they suggest?
    """
    
    return self.predictor.generate_prediction(sentiment_prompt)

from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler

class EnsembleDetector:
    def __init__(self, ollama_predictor):
        self.ollama = ollama_predictor
        self.isolation_forest = IsolationForest(contamination=0.1)
        self.scaler = StandardScaler()
        
    def train_ensemble(self, historical_data):
        """Train traditional ML models on historical data"""
        features = self.extract_features(historical_data)
        scaled_features = self.scaler.fit_transform(features)
        self.isolation_forest.fit(scaled_features)
        
    def combined_prediction(self, current_data):
        """Combine Ollama analysis with ML model predictions"""
        
        # Get Ollama analysis
        ollama_analysis = self.ollama.analyze_market_patterns(current_data)
        
        # Get ML model prediction
        features = self.extract_features(current_data)
        scaled_features = self.scaler.transform(features)
        anomaly_score = self.isolation_forest.decision_function(scaled_features)
        
        # Combine predictions
        combined_risk = self.combine_scores(ollama_analysis, anomaly_score)
        
        return combined_risk

class PerformanceTracker:
    def __init__(self):
        self.predictions = []
        self.outcomes = []
        
    def record_prediction(self, prediction, timestamp):
        """Record a prediction for later evaluation"""
        self.predictions.append({
            'prediction': prediction,
            'timestamp': timestamp,
            'risk_level': prediction.get('risk_level', 0)
        })
        
    def evaluate_performance(self, lookback_days=30):
        """Evaluate prediction accuracy over time"""
        
        # Calculate metrics
        accuracy_metrics = {
            'true_positives': 0,
            'false_positives': 0,
            'true_negatives': 0,
            'false_negatives': 0
        }
        
        # Implement evaluation logic
        return accuracy_metrics
        
    def generate_performance_report(self):
        """Generate detailed performance report"""
        
        report_prompt = f"""
        Analyze black swan detection system performance:
        
        Predictions made: {len(self.predictions)}
        Accuracy metrics: {self.evaluate_performance()}
        
        Identify:
        1. Patterns in false positives/negatives
        2. Optimal risk level thresholds
        3. Recommended system improvements
        
        Provide actionable recommendations for enhancement.
        """
        
        return report_prompt

def backtest_detection_system(start_date, end_date):
    """Backtest the detection system over historical period"""
    
    results = {
        'total_signals': 0,
        'correct_predictions': 0,
        'false_alarms': 0,
        'missed_events': 0
    }
    
    # Implement backtesting logic here
    # Compare predictions against known historical events
    
    return results

# deploy.py
import docker
from kubernetes import client, config

class ProductionDeployment:
    def __init__(self):
        self.docker_client = docker.from_env()
        
    def create_dockerfile(self):
        """Create optimized Docker container"""
        dockerfile_content = """
        FROM python:3.11-slim
        
        # Install Ollama
        RUN curl -fsSL https://ollama.ai/install.sh | sh
        
        # Copy application
        COPY . /app
        WORKDIR /app
        
        # Install dependencies
        RUN pip install -r requirements.txt
        
        # Download models
        RUN ollama pull llama3.1:8b
        
        # Start services
        CMD ["python", "monitor.py"]
        """
        
        return dockerfile_content
        
    def deploy_to_kubernetes(self):
        """Deploy to Kubernetes cluster"""
        # Kubernetes deployment configuration
        pass

class MultiMarketDetector:
    def __init__(self):
        self.market_regions = ['US', 'EU', 'ASIA']
        self.detectors = {}
        
    def setup_regional_detectors(self):
        """Setup detectors for different market regions"""
        for region in self.market_regions:
            self.detectors[region] = BlackSwanDetector(
                model_name=f"llama3.1:8b",
                region=region
            )
            
    def cross_market_analysis(self):
        """Analyze contagion risk across markets"""
        regional_risks = {}
        
        for region, detector in self.detectors.items():
            regional_risks[region] = detector.run_detection_cycle()
            
        # Analyze cross-market correlations
        contagion_risk = self.analyze_contagion(regional_risks)
        
        return contagion_risk

def troubleshoot_model_performance():
    """Common troubleshooting steps"""
    
    issues_and_solutions = {
        'slow_inference': [
            'Reduce context window size',
            'Use smaller model (7B instead of 13B)',
            'Optimize prompt length',
            'Enable GPU acceleration'
        ],
        'inconsistent_predictions': [
            'Lower temperature setting',
            'Use more specific prompts',
            'Add more context examples',
            'Implement response validation'
        ],
        'memory_issues': [
            'Implement prompt caching',
            'Batch process requests',
            'Use model quantization',
            'Increase system RAM'
        ]
    }
    
    return issues_and_solutions

def validate_market_data(data):
    """Validate market data quality before analysis"""
    
    quality_checks = {
        'completeness': all(len(df) > 0 for df in data.values()),
        'freshness': max(df.index[-1] for df in data.values() if len(df) > 0),
        'consistency': check_price_consistency(data)
    }
    
    return quality_checks

def handle_data_gaps(data):
    """Handle missing or incomplete data"""
    # Implement gap-filling strategies
    pass

class SecurityManager:
    def __init__(self):
        self.encryption_key = self.generate_encryption_key()
        
    def encrypt_sensitive_data(self, data):
        """Encrypt sensitive market data"""
        # Implement encryption for data at rest
        pass
        
    def secure_api_access(self):
        """Implement secure API access patterns"""
        # API key rotation, rate limiting, etc.
        pass
        
    def audit_data_access(self):
        """Log and audit data access patterns"""
        # Implement comprehensive logging
        pass

def anonymize_trading_data(data):
    """Remove personally identifiable information"""
    # Implement data anonymization
    pass

def implement_data_retention_policy():
    """Implement automated data retention policies"""
    # Auto-delete old data per compliance requirements
    pass