Stablecoin Volume Prediction: Ollama $300B Daily Settlement Analysis

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

# Pull the code analysis model optimized for financial data
ollama pull codellama:13b-instruct

# Verify installation
ollama list

# requirements.txt
import pandas as pd
import numpy as np
import requests
from datetime import datetime, timedelta
import json
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
import ollama

# Install required packages
# pip install pandas numpy requests matplotlib seaborn scikit-learn ollama

class StablecoinDataCollector:
    def __init__(self):
        self.chains = ['ethereum', 'polygon', 'bsc', 'avalanche', 'arbitrum']
        self.stablecoins = {
            'USDT': '0xdac17f958d2ee523a2206206994597c13d831ec7',
            'USDC': '0xa0b86a33e6e2dd4e8c85b9a0e90e2b0e8f1a1a2c',
            'BUSD': '0x4fabb145d64652a948d72533023f6e7a623c7c53',
            'DAI': '0x6b175474e89094c44da98b954eedeac495271d0f'
        }
    
    def fetch_daily_volumes(self, days=30):
        """Collect stablecoin volume data from multiple chains"""
        volume_data = []
        
        for chain in self.chains:
            for token, address in self.stablecoins.items():
                try:
                    # API call to get volume data
                    url = f"https://api.dexscreener.com/latest/dex/tokens/{address}"
                    response = requests.get(url)
                    
                    if response.status_code == 200:
                        data = response.json()
                        
                        # Extract volume metrics
                        volume_24h = data.get('volume24h', 0)
                        transactions = data.get('txns24h', 0)
                        
                        volume_data.append({
                            'chain': chain,
                            'token': token,
                            'volume_24h': volume_24h,
                            'transactions': transactions,
                            'timestamp': datetime.now()
                        })
                        
                except Exception as e:
                    print(f"Error fetching {token} on {chain}: {e}")
                    continue
        
        return pd.DataFrame(volume_data)

# Initialize collector
collector = StablecoinDataCollector()
raw_data = collector.fetch_daily_volumes()

class VolumePreprocessor:
    def __init__(self):
        self.scaler = StandardScaler()
        
    def clean_and_aggregate(self, df):
        """Clean data and create aggregated features"""
        # Remove outliers using IQR method
        Q1 = df['volume_24h'].quantile(0.25)
        Q3 = df['volume_24h'].quantile(0.75)
        IQR = Q3 - Q1
        
        df_clean = df[
            (df['volume_24h'] >= Q1 - 1.5 * IQR) & 
            (df['volume_24h'] <= Q3 + 1.5 * IQR)
        ]
        
        # Create aggregated features
        aggregated = df_clean.groupby(['timestamp', 'token']).agg({
            'volume_24h': ['sum', 'mean', 'std'],
            'transactions': ['sum', 'mean']
        }).reset_index()
        
        # Flatten column names
        aggregated.columns = ['_'.join(col).strip() if col[1] else col[0] 
                            for col in aggregated.columns.values]
        
        return aggregated
    
    def create_features(self, df):
        """Generate predictive features"""
        # Sort by timestamp
        df = df.sort_values('timestamp')
        
        # Calculate moving averages
        df['volume_ma_7'] = df['volume_24h_sum'].rolling(window=7).mean()
        df['volume_ma_30'] = df['volume_24h_sum'].rolling(window=30).mean()
        
        # Volume momentum indicators
        df['volume_change_pct'] = df['volume_24h_sum'].pct_change()
        df['volume_acceleration'] = df['volume_change_pct'].diff()
        
        # Cross-token correlations
        df['volume_rank'] = df['volume_24h_sum'].rank(ascending=False)
        
        # Time-based features
        df['hour'] = df['timestamp'].dt.hour
        df['day_of_week'] = df['timestamp'].dt.dayofweek
        df['is_weekend'] = df['day_of_week'].isin([5, 6]).astype(int)
        
        return df

# Process the data
preprocessor = VolumePreprocessor()
processed_data = preprocessor.clean_and_aggregate(raw_data)
featured_data = preprocessor.create_features(processed_data)

class OllamaVolumePredictor:
    def __init__(self, model_name="codellama:13b-instruct"):
        self.model = model_name
        self.client = ollama.Client()
    
    def generate_prediction_prompt(self, data_summary):
        """Create structured prompt for volume prediction"""
        prompt = f"""
        Analyze the following stablecoin volume data and predict the next 24-hour volume:

        CURRENT MARKET CONDITIONS:
        - Total 24h Volume: ${data_summary['total_volume']:,.0f}
        - Volume Change: {data_summary['volume_change']:.2%}
        - Transaction Count: {data_summary['total_transactions']:,}
        - Market Volatility: {data_summary['volatility']:.2%}

        HISTORICAL PATTERNS:
        - 7-day Average: ${data_summary['avg_7d']:,.0f}
        - 30-day Average: ${data_summary['avg_30d']:,.0f}
        - Weekend Factor: {data_summary['weekend_factor']:.2f}

        CHAIN DISTRIBUTION:
        {data_summary['chain_breakdown']}

        PREDICTION REQUIREMENTS:
        1. Predict next 24h volume with confidence interval
        2. Identify key factors driving the prediction
        3. Suggest risk factors that could affect accuracy
        4. Provide actionable trading insights

        Format response as JSON with:
        - predicted_volume: number
        - confidence_interval: [lower, upper]
        - key_factors: [list of factors]
        - risk_factors: [list of risks]
        - trading_signal: "bullish" | "bearish" | "neutral"
        """
        return prompt
    
    def predict_volume(self, data):
        """Generate volume prediction using Ollama"""
        # Prepare data summary
        data_summary = {
            'total_volume': data['volume_24h_sum'].sum(),
            'volume_change': data['volume_change_pct'].iloc[-1],
            'total_transactions': data['transactions_sum'].sum(),
            'volatility': data['volume_24h_sum'].std() / data['volume_24h_sum'].mean(),
            'avg_7d': data['volume_ma_7'].iloc[-1],
            'avg_30d': data['volume_ma_30'].iloc[-1],
            'weekend_factor': data[data['is_weekend'] == 1]['volume_24h_sum'].mean() / 
                            data[data['is_weekend'] == 0]['volume_24h_sum'].mean(),
            'chain_breakdown': data.groupby('chain')['volume_24h_sum'].sum().to_dict()
        }
        
        # Generate prediction
        prompt = self.generate_prediction_prompt(data_summary)
        
        try:
            response = self.client.chat(
                model=self.model,
                messages=[{
                    'role': 'user',
                    'content': prompt
                }]
            )
            
            # Parse response
            prediction_text = response['message']['content']
            
            # Extract JSON from response
            import re
            json_match = re.search(r'\{.*\}', prediction_text, re.DOTALL)
            if json_match:
                prediction = json.loads(json_match.group())
                return prediction
            else:
                return self.fallback_prediction(data)
                
        except Exception as e:
            print(f"Ollama prediction failed: {e}")
            return self.fallback_prediction(data)
    
    def fallback_prediction(self, data):
        """Fallback prediction using simple statistics"""
        recent_volume = data['volume_24h_sum'].iloc[-7:].mean()
        volume_std = data['volume_24h_sum'].std()
        
        return {
            'predicted_volume': recent_volume,
            'confidence_interval': [
                recent_volume - 1.96 * volume_std,
                recent_volume + 1.96 * volume_std
            ],
            'key_factors': ['Historical average', 'Volume stability'],
            'risk_factors': ['Market volatility', 'External events'],
            'trading_signal': 'neutral'
        }

# Generate prediction
predictor = OllamaVolumePredictor()
prediction = predictor.predict_volume(featured_data)
print(json.dumps(prediction, indent=2))

class VolumeAnomalyDetector:
    def __init__(self, threshold=2.5):
        self.threshold = threshold
    
    def detect_volume_spikes(self, data):
        """Identify unusual volume patterns"""
        # Calculate Z-scores
        volume_mean = data['volume_24h_sum'].mean()
        volume_std = data['volume_24h_sum'].std()
        
        data['z_score'] = (data['volume_24h_sum'] - volume_mean) / volume_std
        
        # Identify spikes
        spikes = data[abs(data['z_score']) > self.threshold].copy()
        
        # Categorize spikes
        spikes['spike_type'] = spikes['z_score'].apply(
            lambda x: 'positive_spike' if x > 0 else 'negative_spike'
        )
        
        return spikes
    
    def analyze_spike_patterns(self, spikes):
        """Analyze patterns in volume spikes"""
        pattern_analysis = {
            'spike_frequency': len(spikes),
            'avg_spike_magnitude': spikes['z_score'].abs().mean(),
            'spike_duration': self.calculate_spike_duration(spikes),
            'recovery_time': self.calculate_recovery_time(spikes),
            'correlation_with_price': self.analyze_price_correlation(spikes)
        }
        
        return pattern_analysis

# Detect anomalies
detector = VolumeAnomalyDetector()
volume_spikes = detector.detect_volume_spikes(featured_data)
pattern_analysis = detector.analyze_spike_patterns(volume_spikes)

def analyze_cross_chain_correlation(data):
    """Analyze volume correlations across different chains"""
    # Pivot data for correlation analysis
    pivot_data = data.pivot_table(
        index='timestamp',
        columns='chain',
        values='volume_24h_sum',
        aggfunc='sum'
    )
    
    # Calculate correlation matrix
    correlation_matrix = pivot_data.corr()
    
    # Find strongest correlations
    correlations = []
    for i in range(len(correlation_matrix.columns)):
        for j in range(i+1, len(correlation_matrix.columns)):
            chain1 = correlation_matrix.columns[i]
            chain2 = correlation_matrix.columns[j]
            corr_value = correlation_matrix.iloc[i, j]
            
            correlations.append({
                'chain1': chain1,
                'chain2': chain2,
                'correlation': corr_value
            })
    
    # Sort by correlation strength
    correlations.sort(key=lambda x: abs(x['correlation']), reverse=True)
    
    return correlations[:5]  # Top 5 correlations

# Analyze correlations
cross_chain_correlations = analyze_cross_chain_correlation(featured_data)

class VolumeBacktester:
    def __init__(self, predictor):
        self.predictor = predictor
        self.results = []
    
    def backtest_predictions(self, data, test_days=30):
        """Backtest volume predictions"""
        # Split data into train/test sets
        train_size = len(data) - test_days
        train_data = data.iloc[:train_size]
        test_data = data.iloc[train_size:]
        
        predictions = []
        actuals = []
        
        for i in range(len(test_data)):
            # Use data up to current point for prediction
            current_data = data.iloc[:train_size + i]
            
            # Generate prediction
            prediction = self.predictor.predict_volume(current_data)
            
            # Get actual volume
            actual = test_data.iloc[i]['volume_24h_sum']
            
            predictions.append(prediction['predicted_volume'])
            actuals.append(actual)
            
            # Store detailed results
            self.results.append({
                'date': test_data.iloc[i]['timestamp'],
                'predicted': prediction['predicted_volume'],
                'actual': actual,
                'confidence_lower': prediction['confidence_interval'][0],
                'confidence_upper': prediction['confidence_interval'][1],
                'error': abs(prediction['predicted_volume'] - actual),
                'error_pct': abs(prediction['predicted_volume'] - actual) / actual * 100
            })
        
        return self.calculate_metrics(predictions, actuals)
    
    def calculate_metrics(self, predictions, actuals):
        """Calculate prediction accuracy metrics"""
        errors = np.array(predictions) - np.array(actuals)
        
        metrics = {
            'mae': np.mean(np.abs(errors)),
            'rmse': np.sqrt(np.mean(errors**2)),
            'mape': np.mean(np.abs(errors) / np.array(actuals)) * 100,
            'accuracy': 100 - np.mean(np.abs(errors) / np.array(actuals)) * 100
        }
        
        return metrics

# Run backtest
backtester = VolumeBacktester(predictor)
metrics = backtester.backtest_predictions(featured_data)
print(f"Prediction Accuracy: {metrics['accuracy']:.2f}%")

class VolumeAlertSystem:
    def __init__(self, thresholds):
        self.thresholds = thresholds
        self.alerts = []
    
    def check_volume_alerts(self, current_data, prediction):
        """Check for volume alert conditions"""
        current_volume = current_data['volume_24h_sum'].iloc[-1]
        predicted_volume = prediction['predicted_volume']
        
        # Volume spike alert
        if current_volume > self.thresholds['spike_threshold']:
            self.create_alert(
                'VOLUME_SPIKE',
                f"Current volume ${current_volume:,.0f} exceeds threshold",
                'HIGH'
            )
        
        # Prediction deviation alert
        volume_change = (predicted_volume - current_volume) / current_volume
        if abs(volume_change) > self.thresholds['prediction_deviation']:
            self.create_alert(
                'PREDICTION_DEVIATION',
                f"Predicted volume change: {volume_change:.2%}",
                'MEDIUM'
            )
        
        # Cross-chain imbalance alert
        self.check_cross_chain_imbalance(current_data)
        
        return self.alerts
    
    def create_alert(self, alert_type, message, severity):
        """Create new alert"""
        alert = {
            'type': alert_type,
            'message': message,
            'severity': severity,
            'timestamp': datetime.now(),
            'resolved': False
        }
        
        self.alerts.append(alert)
        return alert

# Configure alerts
alert_system = VolumeAlertSystem({
    'spike_threshold': 50000000000,  # $50B
    'prediction_deviation': 0.25,    # 25% change
    'cross_chain_threshold': 0.3     # 30% imbalance
})

# Check for alerts
alerts = alert_system.check_volume_alerts(featured_data, prediction)

class VolumeSignalGenerator:
    def __init__(self):
        self.signals = []
    
    def generate_trading_signals(self, data, prediction):
        """Generate trading signals based on volume analysis"""
        current_volume = data['volume_24h_sum'].iloc[-1]
        predicted_volume = prediction['predicted_volume']
        
        # Volume momentum signal
        volume_momentum = self.calculate_volume_momentum(data)
        
        # Cross-chain flow signal
        chain_signal = self.analyze_chain_flows(data)
        
        # Combined signal
        combined_signal = self.combine_signals(
            volume_momentum, chain_signal, prediction
        )
        
        signal = {
            'timestamp': datetime.now(),
            'signal_type': combined_signal['type'],
            'strength': combined_signal['strength'],
            'confidence': combined_signal['confidence'],
            'recommendations': combined_signal['recommendations'],
            'risk_level': combined_signal['risk_level']
        }
        
        self.signals.append(signal)
        return signal
    
    def calculate_volume_momentum(self, data):
        """Calculate volume momentum indicator"""
        recent_avg = data['volume_24h_sum'].tail(3).mean()
        historical_avg = data['volume_24h_sum'].tail(30).mean()
        
        momentum = (recent_avg - historical_avg) / historical_avg
        
        if momentum > 0.2:
            return {'type': 'bullish', 'strength': min(momentum * 5, 1.0)}
        elif momentum < -0.2:
            return {'type': 'bearish', 'strength': min(abs(momentum) * 5, 1.0)}
        else:
            return {'type': 'neutral', 'strength': 0.5}

# Generate signals
signal_generator = VolumeSignalGenerator()
trading_signal = signal_generator.generate_trading_signals(featured_data, prediction)

class OptimizedVolumeProcessor:
    def __init__(self):
        self.cache = {}
        self.batch_size = 1000
    
    def process_large_datasets(self, data):
        """Process large volume datasets efficiently"""
        # Use chunking for large datasets
        chunks = [data[i:i+self.batch_size] 
                 for i in range(0, len(data), self.batch_size)]
        
        processed_chunks = []
        for chunk in chunks:
            processed_chunk = self.process_chunk(chunk)
            processed_chunks.append(processed_chunk)
        
        # Combine results
        return pd.concat(processed_chunks, ignore_index=True)
    
    def process_chunk(self, chunk):
        """Process individual data chunk"""
        # Apply optimized transformations
        chunk['volume_normalized'] = chunk['volume_24h'] / chunk['volume_24h'].max()
        chunk['volume_rank'] = chunk['volume_24h'].rank(method='min')
        
        return chunk
    
    def cache_results(self, key, result):
        """Cache computation results"""
        self.cache[key] = {
            'result': result,
            'timestamp': datetime.now()
        }
    
    def get_cached_result(self, key, max_age_minutes=10):
        """Retrieve cached results if fresh"""
        if key in self.cache:
            cache_entry = self.cache[key]
            age = datetime.now() - cache_entry['timestamp']
            
            if age.total_seconds() < max_age_minutes * 60:
                return cache_entry['result']
        
        return None

# Optimize processing
optimizer = OptimizedVolumeProcessor()
optimized_data = optimizer.process_large_datasets(featured_data)

class TradingPlatformIntegrator:
    def __init__(self, api_key, api_secret):
        self.api_key = api_key
        self.api_secret = api_secret
        self.base_url = "https://api.exchange.com"
    
    def send_volume_alert(self, alert_data):
        """Send volume alerts to trading platform"""
        headers = {
            'Authorization': f'Bearer {self.api_key}',
            'Content-Type': 'application/json'
        }
        
        payload = {
            'alert_type': 'volume_prediction',
            'data': alert_data,
            'timestamp': datetime.now().isoformat()
        }
        
        try:
            response = requests.post(
                f"{self.base_url}/alerts",
                headers=headers,
                json=payload
            )
            
            return response.status_code == 200
            
        except Exception as e:
            print(f"Failed to send alert: {e}")
            return False
    
    def execute_automated_trade(self, signal):
        """Execute trades based on volume signals"""
        if signal['confidence'] > 0.8 and signal['strength'] > 0.7:
            trade_payload = {
                'symbol': 'USDT',
                'side': signal['signal_type'],
                'type': 'market',
                'quantity': self.calculate_position_size(signal),
                'metadata': {
                    'signal_source': 'volume_prediction',
                    'confidence': signal['confidence']
                }
            }
            
            return self.place_order(trade_payload)
        
        return False

# Integration example
# integrator = TradingPlatformIntegrator(api_key, api_secret)
# integrator.send_volume_alert(trading_signal)