How to Track Trump Family Crypto Tokens (TRUMP/MELANIA) with Ollama Market Intelligence

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

# Pull specialized crypto analysis models
ollama pull llama3.1:8b
ollama pull 0xroyce/plutus  # Finance-optimized model
ollama pull shreyshah/satoshi-7b-q4_k_m  # Bitcoin/crypto specialist

# trump_tracker.py
import ollama
import requests
import json
import time
from datetime import datetime
import logging

class TrumpTokenTracker:
    def __init__(self):
        self.trump_contract = "Es9vMFrzaCERmJfrF4H2FYD4KCoNkY11McCe8BenwNYB"  # $TRUMP Solana address
        self.melania_contract = "D694J792VTjJeVr6eANvhkFWatFAhBPHRfT9JH5HSC1i"  # $MELANIA Solana address
        self.dexscreener_api = "https://api.dexscreener.com/latest/dex/tokens/"
        
    def get_token_data(self, contract_address):
        """Fetch real-time token data from DEX Screener"""
        try:
            response = requests.get(f"{self.dexscreener_api}{contract_address}")
            return response.json()
        except Exception as e:
            logging.error(f"Error fetching data: {e}")
            return None
    
    def analyze_with_ollama(self, token_data, model="0xroyce/plutus"):
        """Use Ollama to analyze token data and provide insights"""
        
        # Prepare analysis prompt
        analysis_prompt = f"""
        Analyze this Trump family crypto token data:
        
        Token: {token_data['pairs'][0]['baseToken']['symbol']}
        Price: ${token_data['pairs'][0]['priceUsd']}
        24h Change: {token_data['pairs'][0]['priceChange']['h24']}%
        Volume 24h: ${token_data['pairs'][0]['volume']['h24']}
        Market Cap: ${token_data['pairs'][0]['marketCap']}
        
        Provide:
        1. Trend analysis (bullish/bearish/neutral)
        2. Risk level (1-10 scale)
        3. Key support/resistance levels
        4. Trading recommendation
        5. Sentiment assessment
        
        Focus on meme coin volatility patterns and political event impacts.
        """
        
        try:
            response = ollama.chat(
                model=model,
                messages=[{
                    'role': 'user',
                    'content': analysis_prompt
                }]
            )
            return response['message']['content']
        except Exception as e:
            logging.error(f"Ollama analysis failed: {e}")
            return "Analysis unavailable"
    
    def check_alerts(self, token_data):
        """Check for price alerts and significant movements"""
        alerts = []
        
        for pair in token_data['pairs']:
            price_change_24h = float(pair['priceChange']['h24'])
            volume_24h = float(pair['volume']['h24'])
            
            # Alert conditions
            if abs(price_change_24h) > 20:
                alerts.append(f"🚨 {pair['baseToken']['symbol']} moved {price_change_24h:.2f}% in 24h")
            
            if volume_24h > 10000000:  # $10M+ volume
                alerts.append(f"📈 {pair['baseToken']['symbol']} high volume: ${volume_24h:,.0f}")
        
        return alerts
    
    def run_monitoring(self, interval=300):  # 5-minute intervals
        """Main monitoring loop"""
        print("🚀 Trump Token Tracker started...")
        
        while True:
            try:
                # Get data for both tokens
                trump_data = self.get_token_data(self.trump_contract)
                melania_data = self.get_token_data(self.melania_contract)
                
                if trump_data and melania_data:
                    # Analyze with AI
                    trump_analysis = self.analyze_with_ollama(trump_data)
                    melania_analysis = self.analyze_with_ollama(melania_data)
                    
                    # Check alerts
                    trump_alerts = self.check_alerts(trump_data)
                    melania_alerts = self.check_alerts(melania_data)
                    
                    # Display results
                    print(f"\n{'='*50}")
                    print(f"📊 Trump Token Analysis - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
                    print(f"{'='*50}")
                    print(trump_analysis)
                    
                    print(f"\n👑 Melania Token Analysis")
                    print(f"{'='*50}")
                    print(melania_analysis)
                    
                    # Show alerts
                    all_alerts = trump_alerts + melania_alerts
                    if all_alerts:
                        print(f"\n🔔 ALERTS:")
                        for alert in all_alerts:
                            print(alert)
                
                time.sleep(interval)
                
            except KeyboardInterrupt:
                print("\n👋 Tracker stopped by user")
                break
            except Exception as e:
                logging.error(f"Monitoring error: {e}")
                time.sleep(30)  # Wait before retrying

# Run the tracker
if __name__ == "__main__":
    tracker = TrumpTokenTracker()
    tracker.run_monitoring()

# sentiment_analyzer.py
import ollama
import requests
import re
from textblob import TextBlob

class TrumpCryptoSentiment:
    def __init__(self):
        self.reddit_api = "https://www.reddit.com/r/cryptocurrency/hot.json"
        self.twitter_keywords = ["$TRUMP", "$MELANIA", "Trump crypto", "Melania token"]
    
    def analyze_sentiment_with_ollama(self, text_data):
        """Use Ollama for advanced sentiment analysis"""
        
        sentiment_prompt = f"""
        Analyze the sentiment of this crypto-related text about Trump family tokens:
        
        Text: "{text_data}"
        
        Provide:
        1. Overall sentiment (Bullish/Bearish/Neutral)
        2. Sentiment score (-1 to +1)
        3. Key emotional indicators
        4. Potential market impact
        5. Confidence level (1-10)
        
        Focus on crypto trading psychology and meme coin sentiment patterns.
        """
        
        try:
            response = ollama.chat(
                model="0xroyce/plutus",
                messages=[{
                    'role': 'user',
                    'content': sentiment_prompt
                }]
            )
            return response['message']['content']
        except Exception as e:
            return f"Sentiment analysis failed: {e}"
    
    def get_reddit_sentiment(self):
        """Fetch Trump crypto discussions from Reddit"""
        try:
            response = requests.get(self.reddit_api, headers={'User-Agent': 'TrumpCryptoTracker'})
            posts = response.json()['data']['children']
            
            relevant_posts = []
            for post in posts:
                title = post['data']['title']
                if any(keyword.lower() in title.lower() for keyword in self.twitter_keywords):
                    relevant_posts.append({
                        'title': title,
                        'score': post['data']['score'],
                        'comments': post['data']['num_comments']
                    })
            
            return relevant_posts
        except Exception as e:
            return []
    
    def generate_sentiment_report(self):
        """Create comprehensive sentiment report"""
        reddit_posts = self.get_reddit_sentiment()
        
        if not reddit_posts:
            return "No recent discussions found"
        
        # Combine all text for analysis
        combined_text = " ".join([post['title'] for post in reddit_posts])
        
        # Get AI analysis
        ai_sentiment = self.analyze_sentiment_with_ollama(combined_text)
        
        # Calculate basic metrics
        total_score = sum(post['score'] for post in reddit_posts)
        total_comments = sum(post['comments'] for post in reddit_posts)
        
        report = f"""
        📊 TRUMP CRYPTO SENTIMENT REPORT
        {'='*40}
        
        📈 Basic Metrics:
        - Total discussions: {len(reddit_posts)}
        - Combined upvotes: {total_score}
        - Total comments: {total_comments}
        
        🤖 AI Analysis:
        {ai_sentiment}
        
        💬 Recent Discussions:
        """
        
        for post in reddit_posts[:5]:  # Show top 5
            report += f"- {post['title']} (Score: {post['score']})\n"
        
        return report

# Usage example
sentiment_analyzer = TrumpCryptoSentiment()
print(sentiment_analyzer.generate_sentiment_report())

# smart_alerts.py
import ollama
import smtplib
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

class SmartAlerts:
    def __init__(self, email_config=None):
        self.email_config = email_config
        self.alert_history = []
    
    def analyze_alert_importance(self, price_data, volume_data):
        """Use AI to determine alert priority"""
        
        alert_prompt = f"""
        Analyze this Trump crypto alert data:
        
        Price Change: {price_data['change_percent']}%
        Volume: ${volume_data['volume_24h']:,.0f}
        Current Price: ${price_data['current_price']}
        Previous Price: ${price_data['previous_price']}
        
        Determine:
        1. Alert priority (LOW/MEDIUM/HIGH/CRITICAL)
        2. Recommended action (BUY/SELL/HOLD/WATCH)
        3. Risk assessment
        4. Time sensitivity (minutes/hours/days)
        
        Consider meme coin volatility patterns and political event impacts.
        """
        
        try:
            response = ollama.chat(
                model="0xroyce/plutus",
                messages=[{
                    'role': 'user',
                    'content': alert_prompt
                }]
            )
            return response['message']['content']
        except Exception as e:
            return "Alert analysis failed"
    
    def send_email_alert(self, subject, body):
        """Send email alerts for critical movements"""
        if not self.email_config:
            print(f"EMAIL ALERT: {subject}")
            print(body)
            return
        
        try:
            msg = MIMEMultipart()
            msg['From'] = self.email_config['from_email']
            msg['To'] = self.email_config['to_email']
            msg['Subject'] = subject
            
            msg.attach(MIMEText(body, 'plain'))
            
            server = smtplib.SMTP('smtp.gmail.com', 587)
            server.starttls()
            server.login(self.email_config['from_email'], self.email_config['password'])
            
            server.send_message(msg)
            server.quit()
            
            print(f"✅ Alert sent: {subject}")
        except Exception as e:
            print(f"❌ Email failed: {e}")
    
    def process_alert(self, token_symbol, price_data, volume_data):
        """Process and send intelligent alerts"""
        
        # Get AI analysis
        ai_analysis = self.analyze_alert_importance(price_data, volume_data)
        
        # Create alert message
        alert_message = f"""
        🚨 {token_symbol} ALERT
        
        📊 Current Data:
        - Price: ${price_data['current_price']}
        - Change: {price_data['change_percent']}%
        - Volume: ${volume_data['volume_24h']:,.0f}
        
        🤖 AI Analysis:
        {ai_analysis}
        
        ⏰ Alert Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
        """
        
        # Determine if email is needed
        if "CRITICAL" in ai_analysis.upper() or abs(price_data['change_percent']) > 50:
            self.send_email_alert(
                f"🚨 CRITICAL: {token_symbol} moved {price_data['change_percent']}%",
                alert_message
            )
        else:
            print(alert_message)
        
        # Store alert history
        self.alert_history.append({
            'timestamp': datetime.now(),
            'token': token_symbol,
            'price_change': price_data['change_percent'],
            'analysis': ai_analysis
        })

# performance_tracker.py
import json
from datetime import datetime, timedelta

class PerformanceTracker:
    def __init__(self):
        self.tracking_data = []
        self.predictions = []
    
    def log_prediction(self, token, current_price, predicted_direction, confidence):
        """Log AI predictions for accuracy tracking"""
        self.predictions.append({
            'timestamp': datetime.now(),
            'token': token,
            'price': current_price,
            'prediction': predicted_direction,
            'confidence': confidence
        })
    
    def calculate_accuracy(self, hours_ahead=24):
        """Calculate prediction accuracy over time"""
        accurate_predictions = 0
        total_predictions = 0
        
        cutoff_time = datetime.now() - timedelta(hours=hours_ahead)
        
        for prediction in self.predictions:
            if prediction['timestamp'] < cutoff_time:
                # Check if prediction was accurate
                # (This would require actual price data comparison)
                total_predictions += 1
                # Placeholder for actual accuracy calculation
                
        return {
            'accuracy_rate': accurate_predictions / total_predictions if total_predictions > 0 else 0,
            'total_predictions': total_predictions,
            'time_period': f"{hours_ahead} hours"
        }
    
    def generate_performance_report(self):
        """Create detailed performance report"""
        accuracy_24h = self.calculate_accuracy(24)
        accuracy_7d = self.calculate_accuracy(168)  # 7 days
        
        report = f"""
        📊 OLLAMA TRACKING PERFORMANCE REPORT
        {'='*45}
        
        🎯 Prediction Accuracy:
        - 24 Hour: {accuracy_24h['accuracy_rate']:.1%}
        - 7 Day: {accuracy_7d['accuracy_rate']:.1%}
        
        📈 Total Predictions: {len(self.predictions)}
        
        🔍 Recent Activity:
        """
        
        # Show recent predictions
        for pred in self.predictions[-5:]:
            report += f"- {pred['token']}: {pred['prediction']} (Confidence: {pred['confidence']})\n"
        
        return report

# model_optimizer.py
import ollama
import json

class ModelOptimizer:
    def __init__(self):
        self.training_data = []
        self.model_performance = {}
    
    def collect_training_data(self, token_data, actual_outcome):
        """Collect data for model fine-tuning"""
        self.training_data.append({
            'input': token_data,
            'output': actual_outcome,
            'timestamp': datetime.now()
        })
    
    def test_model_performance(self, models=['0xroyce/plutus', 'llama3.1:8b']):
        """Compare performance across different models"""
        
        test_prompt = """
        Based on this Trump crypto data, predict the next 4-hour price direction:
        
        Price: $45.23
        Volume: $12.5M
        24h Change: +8.5%
        Social mentions: 1,247 (up 23%)
        
        Provide: Direction (UP/DOWN), Confidence (1-10), Reasoning
        """
        
        results = {}
        
        for model in models:
            try:
                response = ollama.chat(
                    model=model,
                    messages=[{
                        'role': 'user',
                        'content': test_prompt
                    }]
                )
                
                results[model] = {
                    'response': response['message']['content'],
                    'response_time': response.get('eval_duration', 0),
                    'tokens_per_second': response.get('eval_count', 0) / (response.get('eval_duration', 1) / 1000000000)
                }
            except Exception as e:
                results[model] = {'error': str(e)}
        
        return results
    
    def recommend_best_model(self):
        """Recommend the best model based on performance data"""
        
        performance_data = self.test_model_performance()
        
        recommendation = """
        🎯 MODEL RECOMMENDATION REPORT
        {'='*35}
        
        Based on performance testing:
        """
        
        for model, data in performance_data.items():
            if 'error' not in data:
                recommendation += f"""
        
        📊 {model}:
        - Response Quality: {self.rate_response_quality(data['response'])}/10
        - Speed: {data['tokens_per_second']:.1f} tokens/sec
        - Reliability: ✅
        """
            else:
                recommendation += f"""
        
        ❌ {model}: {data['error']}
        """
        
        return recommendation
    
    def rate_response_quality(self, response):
        """Rate response quality (simplified)"""
        # Basic quality scoring
        if 'confidence' in response.lower() and 'reasoning' in response.lower():
            return 8
        elif 'up' in response.lower() or 'down' in response.lower():
            return 6
        else:
            return 4

# portfolio_integration.py
class PortfolioManager:
    def __init__(self):
        self.holdings = {}
        self.transactions = []
    
    def calculate_trump_exposure(self):
        """Calculate total exposure to Trump family tokens"""
        trump_value = self.holdings.get('TRUMP', 0)
        melania_value = self.holdings.get('MELANIA', 0)
        total_portfolio = sum(self.holdings.values())
        
        exposure_percent = (trump_value + melania_value) / total_portfolio * 100
        
        return {
            'trump_value': trump_value,
            'melania_value': melania_value,
            'total_exposure': trump_value + melania_value,
            'exposure_percent': exposure_percent
        }
    
    def ai_portfolio_analysis(self):
        """Get AI recommendations for portfolio balance"""
        exposure = self.calculate_trump_exposure()
        
        analysis_prompt = f"""
        Analyze this Trump crypto portfolio exposure:
        
        Trump Token Value: ${exposure['trump_value']:,.2f}
        Melania Token Value: ${exposure['melania_value']:,.2f}
        Total Exposure: {exposure['exposure_percent']:.1f}%
        
        Provide:
        1. Risk assessment for current exposure
        2. Rebalancing recommendations
        3. Risk management strategies
        4. Diversification suggestions
        """
        
        response = ollama.chat(
            model="0xroyce/plutus",
            messages=[{
                'role': 'user',
                'content': analysis_prompt
            }]
        )
        
        return response['message']['content']

# web_dashboard.py
from flask import Flask, render_template, jsonify
import json
from datetime import datetime

app = Flask(__name__)

@app.route('/')
def dashboard():
    return render_template('dashboard.html')

@app.route('/api/trump-data')
def get_trump_data():
    """API endpoint for current Trump token data"""
    # This would integrate with your tracker
    return jsonify({
        'trump': {
            'price': 45.23,
            'change_24h': 8.5,
            'volume': 12500000,
            'market_cap': 9000000000
        },
        'melania': {
            'price': 8.91,
            'change_24h': -2.3,
            'volume': 5800000,
            'market_cap': 1800000000
        },
        'last_updated': datetime.now().isoformat()
    })

@app.route('/api/ai-analysis')
def get_ai_analysis():
    """API endpoint for latest AI analysis"""
    # This would call your Ollama analysis
    return jsonify({
        'trump_analysis': 'Bullish trend with strong volume support...',
        'melania_analysis': 'Consolidating after recent correction...',
        'overall_sentiment': 'Neutral to slightly bullish',
        'risk_level': 7
    })

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