How to Predict Crypto Pump and Dumps Using Ollama Social Media Analysis

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

# Pull the llama2 model for analysis
ollama pull llama2:7b

# Install Python dependencies
pip install pandas requests tweepy praw python-telegram-bot

# config.py - Store your API credentials
import os

TWITTER_CONFIG = {
    'bearer_token': os.getenv('TWITTER_BEARER_TOKEN'),
    'api_key': os.getenv('TWITTER_API_KEY'),
    'api_secret': os.getenv('TWITTER_API_SECRET')
}

REDDIT_CONFIG = {
    'client_id': os.getenv('REDDIT_CLIENT_ID'),
    'client_secret': os.getenv('REDDIT_CLIENT_SECRET'),
    'user_agent': 'CryptoAnalyzer/1.0'
}

# twitter_collector.py
import tweepy
import pandas as pd
from datetime import datetime, timedelta

class TwitterCollector:
    def __init__(self, config):
        self.client = tweepy.Client(bearer_token=config['bearer_token'])
        
    def collect_crypto_mentions(self, coin_symbol, hours_back=24):
        """Collect tweets mentioning specific cryptocurrency"""
        
        # Calculate time range for recent tweets
        end_time = datetime.utcnow()
        start_time = end_time - timedelta(hours=hours_back)
        
        # Search query targeting crypto discussions
        query = f"${coin_symbol} OR #{coin_symbol} -is:retweet lang:en"
        
        tweets = tweepy.Paginator(
            self.client.search_recent_tweets,
            query=query,
            tweet_fields=['created_at', 'author_id', 'public_metrics'],
            start_time=start_time,
            end_time=end_time,
            max_results=100
        ).flatten(limit=1000)
        
        # Convert to structured data
        tweet_data = []
        for tweet in tweets:
            tweet_data.append({
                'id': tweet.id,
                'text': tweet.text,
                'created_at': tweet.created_at,
                'author_id': tweet.author_id,
                'retweet_count': tweet.public_metrics['retweet_count'],
                'like_count': tweet.public_metrics['like_count']
            })
            
        return pd.DataFrame(tweet_data)

# reddit_collector.py
import praw
import pandas as pd

class RedditCollector:
    def __init__(self, config):
        self.reddit = praw.Reddit(
            client_id=config['client_id'],
            client_secret=config['client_secret'],
            user_agent=config['user_agent']
        )
    
    def collect_crypto_posts(self, coin_symbol, subreddits=['CryptoCurrency', 'altcoin']):
        """Collect Reddit posts about specific cryptocurrency"""
        
        posts_data = []
        
        for subreddit_name in subreddits:
            subreddit = self.reddit.subreddit(subreddit_name)
            
            # Search recent posts
            for post in subreddit.search(coin_symbol, time_filter='day', limit=100):
                posts_data.append({
                    'id': post.id,
                    'title': post.title,
                    'text': post.selftext,
                    'score': post.score,
                    'num_comments': post.num_comments,
                    'created_utc': post.created_utc,
                    'author': str(post.author)
                })
                
        return pd.DataFrame(posts_data)

# ollama_analyzer.py
import requests
import json

class OllamaAnalyzer:
    def __init__(self, model_name="llama2:7b"):
        self.model_name = model_name
        self.base_url = "http://localhost:11434"
        
    def analyze_pump_signals(self, text_data):
        """Analyze text for pump and dump indicators"""
        
        prompt = f"""
        Analyze this social media content for cryptocurrency pump and dump signals.
        
        Content: {text_data}
        
        Look for these indicators:
        1. Urgency language ("act now", "limited time")
        2. Unrealistic price predictions
        3. Coordinated messaging patterns
        4. Lack of fundamental analysis
        5. Celebrity or influencer endorsements
        
        Rate the pump risk from 1-10 and explain your reasoning.
        Format: Risk_Score: X, Reasoning: [explanation]
        """
        
        response = requests.post(
            f"{self.base_url}/api/generate",
            json={
                "model": self.model_name,
                "prompt": prompt,
                "stream": False
            }
        )
        
        if response.status_code == 200:
            result = response.json()
            return self._parse_analysis(result['response'])
        else:
            return {"risk_score": 0, "reasoning": "Analysis failed"}
    
    def _parse_analysis(self, response_text):
        """Extract risk score and reasoning from Ollama response"""
        
        lines = response_text.split('\n')
        risk_score = 0
        reasoning = ""
        
        for line in lines:
            if 'Risk_Score:' in line:
                try:
                    risk_score = int(line.split(':')[1].strip())
                except:
                    risk_score = 5  # Default medium risk
            elif 'Reasoning:' in line:
                reasoning = line.split(':', 1)[1].strip()
                
        return {
            "risk_score": risk_score,
            "reasoning": reasoning
        }

# sentiment_analyzer.py
class SentimentAnalyzer:
    def __init__(self, ollama_analyzer):
        self.ollama = ollama_analyzer
    
    def analyze_sentiment_shift(self, texts, timestamps):
        """Detect rapid sentiment changes indicating manipulation"""
        
        sentiments = []
        
        for text in texts:
            prompt = f"""
            Analyze the sentiment of this crypto-related text.
            Text: {text}
            
            Rate sentiment from -5 (very negative) to +5 (very positive).
            Consider artificial enthusiasm vs genuine excitement.
            
            Format: Sentiment_Score: X, Authenticity: [genuine/artificial]
            """
            
            result = self.ollama.analyze_pump_signals(prompt)
            sentiments.append(result)
            
        return self._detect_manipulation_patterns(sentiments, timestamps)
    
    def _detect_manipulation_patterns(self, sentiments, timestamps):
        """Identify coordinated sentiment manipulation"""
        
        # Look for sudden positive sentiment spikes
        # followed by rapid reversals
        
        positive_spikes = []
        for i, sentiment in enumerate(sentiments):
            if sentiment.get('risk_score', 0) > 7:
                positive_spikes.append(i)
                
        return {
            "manipulation_detected": len(positive_spikes) > 5,
            "spike_indices": positive_spikes,
            "pattern_strength": len(positive_spikes) / len(sentiments)
        }

# pump_detector.py
import time
import logging
from datetime import datetime

class PumpDumpDetector:
    def __init__(self, twitter_collector, reddit_collector, ollama_analyzer):
        self.twitter = twitter_collector
        self.reddit = reddit_collector
        self.analyzer = ollama_analyzer
        self.alert_threshold = 7  # Risk score threshold
        
    def monitor_coin(self, coin_symbol, interval_minutes=30):
        """Continuously monitor a cryptocurrency for pump signals"""
        
        logging.info(f"Starting monitoring for ${coin_symbol}")
        
        while True:
            try:
                # Collect recent social media data
                twitter_data = self.twitter.collect_crypto_mentions(coin_symbol, hours_back=2)
                reddit_data = self.reddit.collect_crypto_posts(coin_symbol)
                
                # Analyze for pump signals
                alerts = self._analyze_data(coin_symbol, twitter_data, reddit_data)
                
                # Send alerts if high risk detected
                if alerts:
                    self._send_alerts(coin_symbol, alerts)
                
                # Wait before next analysis
                time.sleep(interval_minutes * 60)
                
            except Exception as e:
                logging.error(f"Monitoring error: {e}")
                time.sleep(300)  # Wait 5 minutes on error
    
    def _analyze_data(self, coin_symbol, twitter_data, reddit_data):
        """Analyze collected data for pump indicators"""
        
        alerts = []
        
        # Analyze Twitter content
        if not twitter_data.empty:
            combined_tweets = ' '.join(twitter_data['text'].tolist())
            twitter_analysis = self.analyzer.analyze_pump_signals(combined_tweets)
            
            if twitter_analysis['risk_score'] >= self.alert_threshold:
                alerts.append({
                    'platform': 'Twitter',
                    'risk_score': twitter_analysis['risk_score'],
                    'reasoning': twitter_analysis['reasoning'],
                    'post_count': len(twitter_data)
                })
        
        # Analyze Reddit content
        if not reddit_data.empty:
            combined_posts = ' '.join(reddit_data['title'].fillna('').tolist())
            reddit_analysis = self.analyzer.analyze_pump_signals(combined_posts)
            
            if reddit_analysis['risk_score'] >= self.alert_threshold:
                alerts.append({
                    'platform': 'Reddit',
                    'risk_score': reddit_analysis['risk_score'],
                    'reasoning': reddit_analysis['reasoning'],
                    'post_count': len(reddit_data)
                })
        
        return alerts
    
    def _send_alerts(self, coin_symbol, alerts):
        """Send pump detection alerts"""
        
        timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        
        for alert in alerts:
            message = f"""
            🚨 PUMP ALERT: ${coin_symbol}
            Platform: {alert['platform']}
            Risk Score: {alert['risk_score']}/10
            Posts Analyzed: {alert['post_count']}
            Time: {timestamp}
            
            Analysis: {alert['reasoning']}
            """
            
            print(message)  # Replace with actual alert system
            logging.warning(f"Pump alert for {coin_symbol}: {alert}")

# volume_analyzer.py
import requests
import pandas as pd

class VolumeAnalyzer:
    def __init__(self):
        self.coingecko_url = "https://api.coingecko.com/api/v3"
    
    def check_volume_spike(self, coin_id, hours_back=24):
        """Check for unusual trading volume spikes"""
        
        # Get historical volume data
        url = f"{self.coingecko_url}/coins/{coin_id}/market_chart"
        params = {
            'vs_currency': 'usd',
            'days': 7,
            'interval': 'hourly'
        }
        
        response = requests.get(url, params=params)
        if response.status_code != 200:
            return {"volume_spike": False, "multiplier": 0}
        
        data = response.json()
        volumes = [point[1] for point in data['total_volumes']]
        
        # Calculate average volume and recent spike
        recent_volume = volumes[-1]  # Most recent hour
        avg_volume = sum(volumes[:-hours_back]) / len(volumes[:-hours_back])
        
        volume_multiplier = recent_volume / avg_volume if avg_volume > 0 else 0
        
        return {
            "volume_spike": volume_multiplier > 3.0,  # 300% increase threshold
            "multiplier": volume_multiplier,
            "recent_volume": recent_volume,
            "average_volume": avg_volume
        }

# coordination_detector.py
from collections import defaultdict
import networkx as nx

class CoordinationDetector:
    def __init__(self):
        self.posting_patterns = defaultdict(list)
        
    def detect_coordinated_posting(self, social_data):
        """Identify coordinated posting patterns across accounts"""
        
        # Group posts by time windows (15-minute intervals)
        time_groups = defaultdict(list)
        
        for _, post in social_data.iterrows():
            time_window = self._get_time_window(post['created_at'])
            time_groups[time_window].append({
                'author': post.get('author_id', post.get('author')),
                'text': post['text'] if 'text' in post else post.get('title', ''),
                'engagement': post.get('like_count', post.get('score', 0))
            })
        
        coordination_signals = []
        
        for time_window, posts in time_groups.items():
            if len(posts) >= 5:  # Minimum threshold for coordination
                similarity_score = self._calculate_text_similarity(posts)
                author_diversity = len(set(p['author'] for p in posts))
                
                if similarity_score > 0.7 and author_diversity < len(posts) * 0.6:
                    coordination_signals.append({
                        'time_window': time_window,
                        'post_count': len(posts),
                        'similarity_score': similarity_score,
                        'author_diversity': author_diversity / len(posts),
                        'posts': posts
                    })
        
        return coordination_signals
    
    def _get_time_window(self, timestamp):
        """Group timestamps into 15-minute windows"""
        import datetime
        
        if isinstance(timestamp, str):
            timestamp = pd.to_datetime(timestamp)
        
        # Round down to nearest 15 minutes
        minute = (timestamp.minute // 15) * 15
        return timestamp.replace(minute=minute, second=0, microsecond=0)
    
    def _calculate_text_similarity(self, posts):
        """Calculate average text similarity between posts"""
        
        texts = [post['text'].lower() for post in posts]
        similarities = []
        
        for i, text1 in enumerate(texts):
            for text2 in texts[i+1:]:
                # Simple similarity based on common words
                words1 = set(text1.split())
                words2 = set(text2.split())
                
                if len(words1) > 0 and len(words2) > 0:
                    similarity = len(words1.intersection(words2)) / len(words1.union(words2))
                    similarities.append(similarity)
        
        return sum(similarities) / len(similarities) if similarities else 0

# alert_system.py
import smtplib
import requests
from email.mime.text import MIMEText

class AlertSystem:
    def __init__(self, config):
        self.email_config = config.get('email', {})
        self.webhook_urls = config.get('webhooks', [])
        
    def send_pump_alert(self, coin_symbol, analysis_results):
        """Send pump detection alert through multiple channels"""
        
        alert_data = {
            'coin': coin_symbol,
            'timestamp': datetime.now().isoformat(),
            'overall_risk': max(r.get('risk_score', 0) for r in analysis_results),
            'platforms_affected': len(analysis_results),
            'details': analysis_results
        }
        
        # Send email alert
        if self.email_config:
            self._send_email_alert(alert_data)
        
        # Send webhook notifications
        for webhook_url in self.webhook_urls:
            self._send_webhook_alert(webhook_url, alert_data)
    
    def _send_email_alert(self, alert_data):
        """Send email notification"""
        
        subject = f"🚨 Pump Alert: ${alert_data['coin']}"
        
        body = f"""
        Cryptocurrency Pump Detection Alert
        
        Coin: ${alert_data['coin']}
        Overall Risk Score: {alert_data['overall_risk']}/10
        Platforms Affected: {alert_data['platforms_affected']}
        Time: {alert_data['timestamp']}
        
        Analysis Details:
        """
        
        for detail in alert_data['details']:
            body += f"\n{detail['platform']}: Risk {detail['risk_score']}/10"
            body += f"\n  Reasoning: {detail['reasoning']}\n"
        
        msg = MIMEText(body)
        msg['Subject'] = subject
        msg['From'] = self.email_config['from']
        msg['To'] = self.email_config['to']
        
        try:
            with smtplib.SMTP(self.email_config['smtp_server'], self.email_config['port']) as server:
                server.starttls()
                server.login(self.email_config['username'], self.email_config['password'])
                server.send_message(msg)
        except Exception as e:
            print(f"Email alert failed: {e}")
    
    def _send_webhook_alert(self, webhook_url, alert_data):
        """Send webhook notification (Discord, Slack, etc.)"""
        
        payload = {
            "content": f"🚨 **Pump Alert: ${alert_data['coin']}**",
            "embeds": [{
                "title": f"Cryptocurrency Pump Detection",
                "color": 16711680,  # Red color
                "fields": [
                    {"name": "Coin", "value": f"${alert_data['coin']}", "inline": True},
                    {"name": "Risk Score", "value": f"{alert_data['overall_risk']}/10", "inline": True},
                    {"name": "Platforms", "value": str(alert_data['platforms_affected']), "inline": True}
                ],
                "timestamp": alert_data['timestamp']
            }]
        }
        
        try:
            requests.post(webhook_url, json=payload)
        except Exception as e:
            print(f"Webhook alert failed: {e}")

# main.py
import logging
from config import TWITTER_CONFIG, REDDIT_CONFIG
from twitter_collector import TwitterCollector
from reddit_collector import RedditCollector
from ollama_analyzer import OllamaAnalyzer
from pump_detector import PumpDumpDetector

def main():
    # Configure logging
    logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s - %(levelname)s - %(message)s',
        handlers=[
            logging.FileHandler('pump_detector.log'),
            logging.StreamHandler()
        ]
    )
    
    # Initialize components
    twitter_collector = TwitterCollector(TWITTER_CONFIG)
    reddit_collector = RedditCollector(REDDIT_CONFIG)
    ollama_analyzer = OllamaAnalyzer()
    
    # Create detector
    detector = PumpDumpDetector(
        twitter_collector,
        reddit_collector, 
        ollama_analyzer
    )
    
    # List of coins to monitor
    watch_list = ['SHIB', 'DOGE', 'PEPE', 'FLOKI']  # Add your coins
    
    # Start monitoring (run each coin in separate thread for production)
    for coin in watch_list:
        print(f"Monitoring ${coin} for pump and dump activity...")
        detector.monitor_coin(coin, interval_minutes=15)

if __name__ == "__main__":
    main()

# ml_enhancement.py
from sklearn.ensemble import RandomForestClassifier
import numpy as np

class MLPumpDetector:
    def __init__(self):
        self.model = RandomForestClassifier(n_estimators=100)
        self.features = [
            'sentiment_score', 'volume_spike', 'post_frequency',
            'account_age_avg', 'coordination_score', 'price_volatility'
        ]
        
    def train_model(self, historical_data):
        """Train ML model on historical pump patterns"""
        
        X = historical_data[self.features]
        y = historical_data['is_pump']  # Binary classification
        
        self.model.fit(X, y)
        
    def predict_pump_probability(self, current_features):
        """Predict probability of ongoing pump"""
        
        feature_array = np.array([current_features[f] for f in self.features]).reshape(1, -1)
        probability = self.model.predict_proba(feature_array)[0][1]
        
        return {
            'pump_probability': probability,
            'confidence': max(self.model.predict_proba(feature_array)[0]) 
        }