How to Track Yield Farming Partnership Announcements: Complete Catalyst Analysis Guide

# Twitter API monitoring script for partnership announcements
import tweepy
import re
from datetime import datetime

class PartnershipMonitor:
    def __init__(self, api_keys):
        auth = tweepy.OAuthHandler(api_keys['consumer_key'], 
                                 api_keys['consumer_secret'])
        auth.set_access_token(api_keys['access_token'], 
                            api_keys['access_token_secret'])
        self.api = tweepy.API(auth)
    
    def track_partnerships(self, protocols):
        # Monitor specific protocols for partnership keywords
        partnership_keywords = [
            'partnership', 'collaboration', 'integration', 
            'yield farming', 'liquidity mining', 'dual rewards'
        ]
        
        for protocol in protocols:
            try:
                tweets = self.api.user_timeline(screen_name=protocol, 
                                              count=10, 
                                              tweet_mode='extended')
                
                for tweet in tweets:
                    if any(keyword in tweet.full_text.lower() 
                          for keyword in partnership_keywords):
                        self.analyze_announcement(tweet, protocol)
                        
            except Exception as e:
                print(f"Error monitoring {protocol}: {e}")
    
    def analyze_announcement(self, tweet, protocol):
        # Extract partnership details and yield potential
        announcement_data = {
            'protocol': protocol,
            'timestamp': tweet.created_at,
            'text': tweet.full_text,
            'engagement': tweet.retweet_count + tweet.favorite_count
        }
        
        # Score announcement importance (1-10 scale)
        importance_score = self.calculate_importance(announcement_data)
        
        if importance_score >= 7:
            self.send_alert(announcement_data, importance_score)
    
    def calculate_importance(self, data):
        # Scoring algorithm for partnership significance
        score = 5  # Base score
        
        high_impact_terms = ['airdrop', 'exclusive', 'limited time', 
                           'bonus rewards', 'multiplier']
        protocol_tier_1 = ['compound', 'aave', 'uniswap', 'curve', 
                          'sushiswap', 'balancer']
        
        # Boost score for high-impact terms
        for term in high_impact_terms:
            if term in data['text'].lower():
                score += 1.5
        
        # Boost score for tier-1 protocol partnerships
        for protocol in protocol_tier_1:
            if protocol in data['text'].lower():
                score += 1
        
        # Factor in social engagement
        if data['engagement'] > 1000:
            score += 1
        
        return min(score, 10)  # Cap at 10
    
    def send_alert(self, data, score):
        # Send notification for high-priority partnerships
        alert_message = f"""
        HIGH PRIORITY PARTNERSHIP DETECTED
        Protocol: {data['protocol']}
        Importance: {score}/10
        Time: {data['timestamp']}
        Details: {data['text'][:200]}...
        """
        print(alert_message)
        # Add webhook/email notification here

# Usage example
monitor = PartnershipMonitor(api_keys)
protocols_to_watch = ['compoundfinance', 'AaveAave', 'Uniswap', 
                     'CurveFinance', 'SushiSwap']
monitor.track_partnerships(protocols_to_watch)

# Discord partnership monitoring
import discord
from discord.ext import commands

class DiscordPartnershipBot(commands.Bot):
    def __init__(self):
        intents = discord.Intents.default()
        intents.message_content = True
        super().__init__(command_prefix='!', intents=intents)
    
    @commands.Cog.listener()
    async def on_message(self, message):
        # Skip bot messages
        if message.author.bot:
            return
        
        partnership_indicators = [
            'excited to announce', 'partnership with', 
            'collaboration', 'yield farming program',
            'liquidity mining', 'dual rewards'
        ]
        
        message_lower = message.content.lower()
        
        # Check for partnership announcements
        if any(indicator in message_lower for indicator in partnership_indicators):
            # Extract channel and server info
            channel_info = {
                'server': message.guild.name,
                'channel': message.channel.name,
                'author': message.author.name,
                'content': message.content,
                'timestamp': message.created_at
            }
            
            await self.process_partnership_alert(channel_info)
    
    async def process_partnership_alert(self, info):
        # Analyze and score the partnership announcement
        alert_channel = self.get_channel(YOUR_ALERT_CHANNEL_ID)
        
        alert_embed = discord.Embed(
            title="Partnership Alert Detected",
            description=f"Server: {info['server']}\nChannel: {info['channel']}",
            color=0x00ff00,
            timestamp=info['timestamp']
        )
        
        alert_embed.add_field(
            name="Announcement", 
            value=info['content'][:1000], 
            inline=False
        )
        
        await alert_channel.send(embed=alert_embed)

# Run the bot
bot = DiscordPartnershipBot()
bot.run('YOUR_BOT_TOKEN')

# RSS feed monitoring for partnership announcements
import feedparser
import time
from datetime import datetime, timedelta

class RSSPartnershipMonitor:
    def __init__(self):
        self.feeds = [
            'https://compound.finance/feed',
            'https://aave.com/rss',
            'https://blog.uniswap.org/rss',
            'https://curve.fi/rss',
            'https://defipulse.com/rss'
        ]
        self.last_check = datetime.now() - timedelta(hours=1)
    
    def check_feeds(self):
        partnership_posts = []
        
        for feed_url in self.feeds:
            try:
                feed = feedparser.parse(feed_url)
                
                for entry in feed.entries:
                    # Parse publication date
                    pub_date = datetime(*entry.published_parsed[:6])
                    
                    # Only check recent posts
                    if pub_date > self.last_check:
                        if self.contains_partnership_content(entry):
                            partnership_posts.append({
                                'title': entry.title,
                                'url': entry.link,
                                'published': pub_date,
                                'source': feed.feed.title,
                                'summary': entry.summary
                            })
                            
            except Exception as e:
                print(f"Error parsing feed {feed_url}: {e}")
        
        self.last_check = datetime.now()
        return partnership_posts
    
    def contains_partnership_content(self, entry):
        # Check title and summary for partnership keywords
        content = f"{entry.title} {entry.summary}".lower()
        
        partnership_keywords = [
            'partnership', 'collaboration', 'integration',
            'yield farming', 'liquidity mining', 'cross-protocol',
            'dual rewards', 'farming program'
        ]
        
        return any(keyword in content for keyword in partnership_keywords)
    
    def run_continuous_monitoring(self, interval_minutes=15):
        while True:
            partnerships = self.check_feeds()
            
            for partnership in partnerships:
                print(f"NEW PARTNERSHIP: {partnership['title']}")
                print(f"Source: {partnership['source']}")
                print(f"URL: {partnership['url']}")
                print(f"Published: {partnership['published']}")
                print("-" * 50)
            
            time.sleep(interval_minutes * 60)

# Start monitoring
monitor = RSSPartnershipMonitor()
monitor.run_continuous_monitoring()

# Partnership catalyst analysis calculator
class CatalystAnalyzer:
    def __init__(self):
        self.protocol_tiers = {
            'tier_1': ['compound', 'aave', 'uniswap', 'makerdao'],
            'tier_2': ['sushiswap', 'curve', 'balancer', 'yearn'],
            'tier_3': ['bancor', '1inch', 'kyber', 'loopring']
        }
        
        self.partnership_multipliers = {
            'integration': 2.0,
            'rewards': 1.5,
            'marketing': 1.2,
            'advisory': 1.0
        }
    
    def analyze_partnership(self, announcement_data):
        # Extract protocols from announcement
        protocols = self.extract_protocols(announcement_data['text'])
        
        # Calculate base credibility score
        credibility_score = self.calculate_credibility(protocols)
        
        # Determine partnership type
        partnership_type = self.identify_partnership_type(announcement_data['text'])
        
        # Calculate yield enhancement potential
        yield_potential = self.assess_yield_potential(announcement_data['text'])
        
        # Compute final catalyst score
        base_score = credibility_score + yield_potential
        final_score = base_score * self.partnership_multipliers[partnership_type]
        
        return {
            'protocols': protocols,
            'credibility_score': credibility_score,
            'partnership_type': partnership_type,
            'yield_potential': yield_potential,
            'catalyst_score': round(final_score, 2),
            'recommendation': self.generate_recommendation(final_score)
        }
    
    def calculate_credibility(self, protocols):
        max_score = 0
        
        for protocol in protocols:
            protocol_lower = protocol.lower()
            
            if any(tier1 in protocol_lower for tier1 in self.protocol_tiers['tier_1']):
                max_score = max(max_score, 9)
            elif any(tier2 in protocol_lower for tier2 in self.protocol_tiers['tier_2']):
                max_score = max(max_score, 7)
            elif any(tier3 in protocol_lower for tier3 in self.protocol_tiers['tier_3']):
                max_score = max(max_score, 5)
            else:
                max_score = max(max_score, 3)
        
        return max_score
    
    def identify_partnership_type(self, text):
        text_lower = text.lower()
        
        if any(term in text_lower for term in ['integration', 'smart contract', 'protocol']):
            return 'integration'
        elif any(term in text_lower for term in ['rewards', 'mining', 'farming', 'airdrop']):
            return 'rewards'
        elif any(term in text_lower for term in ['marketing', 'promotion', 'campaign']):
            return 'marketing'
        else:
            return 'advisory'
    
    def assess_yield_potential(self, text):
        text_lower = text.lower()
        potential = 0
        
        # Check for yield enhancement indicators
        if 'exclusive' in text_lower:
            potential += 3
        if any(term in text_lower for term in ['dual', 'double', 'multiple']):
            potential += 2
        if 'airdrop' in text_lower:
            potential += 2
        if any(term in text_lower for term in ['limited', 'early', 'first']):
            potential += 1
        
        return min(potential, 8)  # Cap at 8 points
    
    def generate_recommendation(self, score):
        if score >= 15:
            return "IMMEDIATE ACTION: High-impact partnership with significant yield potential"
        elif score >= 10:
            return "MONITOR CLOSELY: Promising partnership worth tracking"
        elif score >= 6:
            return "EVALUATE: Moderate partnership potential"
        else:
            return "LOW PRIORITY: Limited immediate impact expected"

# Example usage
analyzer = CatalystAnalyzer()

sample_announcement = {
    'text': 'Excited to announce our integration with Compound Finance for exclusive yield farming pools with dual COMP and PROTOCOL token rewards!',
    'timestamp': datetime.now(),
    'engagement': 1500
}

analysis = analyzer.analyze_partnership(sample_announcement)
print(f"Catalyst Score: {analysis['catalyst_score']}")
print(f"Recommendation: {analysis['recommendation']}")

# Partnership performance tracking system
import json
from datetime import datetime, timedelta

class PartnershipPerformanceTracker:
    def __init__(self, data_file='partnership_performance.json'):
        self.data_file = data_file
        self.load_historical_data()
    
    def load_historical_data(self):
        try:
            with open(self.data_file, 'r') as f:
                self.performance_data = json.load(f)
        except FileNotFoundError:
            self.performance_data = []
    
    def track_announcement(self, partnership_data, initial_apy, catalyst_score):
        # Record partnership announcement
        tracking_entry = {
            'announcement_date': partnership_data['timestamp'].isoformat(),
            'protocols': partnership_data['protocols'],
            'catalyst_score': catalyst_score,
            'initial_apy': initial_apy,
            'apy_tracking': [],
            'peak_apy': initial_apy,
            'performance_metrics': {}
        }
        
        self.performance_data.append(tracking_entry)
        self.save_data()
        
        return len(self.performance_data) - 1  # Return tracking ID
    
    def update_apy_performance(self, tracking_id, current_apy, days_since_announcement):
        # Update APY performance for existing partnership
        if tracking_id < len(self.performance_data):
            entry = self.performance_data[tracking_id]
            
            entry['apy_tracking'].append({
                'day': days_since_announcement,
                'apy': current_apy,
                'timestamp': datetime.now().isoformat()
            })
            
            # Update peak APY
            entry['peak_apy'] = max(entry['peak_apy'], current_apy)
            
            # Calculate performance metrics
            self.calculate_performance_metrics(tracking_id)
            self.save_data()
    
    def calculate_performance_metrics(self, tracking_id):
        entry = self.performance_data[tracking_id]
        
        if len(entry['apy_tracking']) >= 2:
            initial_apy = entry['initial_apy']
            peak_apy = entry['peak_apy']
            current_apy = entry['apy_tracking'][-1]['apy']
            
            # Calculate key metrics
            peak_increase = ((peak_apy - initial_apy) / initial_apy) * 100
            current_increase = ((current_apy - initial_apy) / initial_apy) * 100
            
            # Calculate time to peak
            peak_day = 0
            for tracking_point in entry['apy_tracking']:
                if tracking_point['apy'] == peak_apy:
                    peak_day = tracking_point['day']
                    break
            
            entry['performance_metrics'] = {
                'peak_apy_increase_percent': round(peak_increase, 2),
                'current_apy_increase_percent': round(current_increase, 2),
                'days_to_peak': peak_day,
                'catalyst_accuracy': self.assess_catalyst_accuracy(entry)
            }
    
    def assess_catalyst_accuracy(self, entry):
        # Evaluate how accurate the catalyst score was
        catalyst_score = entry['catalyst_score']
        peak_increase = entry['performance_metrics'].get('peak_apy_increase_percent', 0)
        
        # Expected performance based on catalyst score
        if catalyst_score >= 15:
            expected_min_increase = 200  # 200%+ increase expected
        elif catalyst_score >= 10:
            expected_min_increase = 100  # 100%+ increase expected
        elif catalyst_score >= 6:
            expected_min_increase = 50   # 50%+ increase expected
        else:
            expected_min_increase = 20   # 20%+ increase expected
        
        # Calculate accuracy score
        if peak_increase >= expected_min_increase:
            return "ACCURATE"
        elif peak_increase >= expected_min_increase * 0.7:
            return "PARTIALLY_ACCURATE"
        else:
            return "INACCURATE"
    
    def generate_performance_report(self):
        # Generate comprehensive performance analysis
        if not self.performance_data:
            return "No partnership data available"
        
        total_partnerships = len(self.performance_data)
        completed_partnerships = [p for p in self.performance_data 
                                if p.get('performance_metrics')]
        
        if not completed_partnerships:
            return "No completed partnership tracking data available"
        
        # Calculate aggregate statistics
        avg_peak_increase = sum(p['performance_metrics']['peak_apy_increase_percent'] 
                              for p in completed_partnerships) / len(completed_partnerships)
        
        avg_time_to_peak = sum(p['performance_metrics']['days_to_peak'] 
                             for p in completed_partnerships) / len(completed_partnerships)
        
        accuracy_distribution = {}
        for partnership in completed_partnerships:
            accuracy = partnership['performance_metrics']['catalyst_accuracy']
            accuracy_distribution[accuracy] = accuracy_distribution.get(accuracy, 0) + 1
        
        # Best performing partnerships
        best_performers = sorted(completed_partnerships, 
                               key=lambda p: p['performance_metrics']['peak_apy_increase_percent'], 
                               reverse=True)[:5]
        
        report = f"""
        PARTNERSHIP PERFORMANCE REPORT
        ================================
        
        Total Partnerships Tracked: {total_partnerships}
        Completed Analysis: {len(completed_partnerships)}
        
        PERFORMANCE METRICS:
        - Average Peak APY Increase: {avg_peak_increase:.1f}%
        - Average Time to Peak: {avg_time_to_peak:.1f} days
        
        CATALYST ACCURACY:
        - Accurate Predictions: {accuracy_distribution.get('ACCURATE', 0)}
        - Partially Accurate: {accuracy_distribution.get('PARTIALLY_ACCURATE', 0)}
        - Inaccurate Predictions: {accuracy_distribution.get('INACCURATE', 0)}
        
        TOP PERFORMING PARTNERSHIPS:
        """
        
        for i, performer in enumerate(best_performers, 1):
            protocols = ', '.join(performer['protocols'])
            increase = performer['performance_metrics']['peak_apy_increase_percent']
            report += f"\n        {i}. {protocols}: +{increase:.1f}% APY increase"
        
        return report
    
    def save_data(self):
        with open(self.data_file, 'w') as f:
            json.dump(self.performance_data, f, indent=2, default=str)

# Usage example
tracker = PartnershipPerformanceTracker()

# Track new partnership
partnership_data = {
    'protocols': ['Compound', 'Balancer'],
    'timestamp': datetime.now()
}

tracking_id = tracker.track_announcement(partnership_data, 5.2, 12.5)

# Update performance over time
tracker.update_apy_performance(tracking_id, 15.8, 1)  # Day 1: 15.8% APY
tracker.update_apy_performance(tracking_id, 22.3, 3)  # Day 3: 22.3% APY
tracker.update_apy_performance(tracking_id, 18.1, 7)  # Day 7: 18.1% APY

# Generate performance report
print(tracker.generate_performance_report())

# Smart contract monitoring for partnership deployments
from web3 import Web3
import json
import time

class ContractPartnershipMonitor:
    def __init__(self, rpc_url, contracts_to_monitor):
        self.w3 = Web3(Web3.HTTPProvider(rpc_url))
        self.contracts = contracts_to_monitor
        self.last_block = self.w3.eth.block_number
    
    def monitor_contract_changes(self):
        current_block = self.w3.eth.block_number
        
        # Check for new events since last check
        for contract_address, contract_info in self.contracts.items():
            try:
                contract = self.w3.eth.contract(
                    address=contract_address,
                    abi=contract_info['abi']
                )
                
                # Monitor for partnership-related events
                partnership_events = [
                    'PoolAdded', 'RewardTokenAdded', 'FarmingProgramLaunched',
                    'MultiTokenRewardEnabled', 'PartnershipActivated'
                ]
                
                for event_name in partnership_events:
                    if hasattr(contract.events, event_name):
                        event_filter = getattr(contract.events, event_name).createFilter(
                            fromBlock=self.last_block + 1,
                            toBlock=current_block
                        )
                        
                        events = event_filter.get_all_entries()
                        
                        for event in events:
                            self.process_partnership_event(
                                contract_address, 
                                event_name, 
                                event, 
                                contract_info['protocol_name']
                            )
                            
            except Exception as e:
                print(f"Error monitoring contract {contract_address}: {e}")
        
        self.last_block = current_block
    
    def process_partnership_event(self, contract_address, event_name, event, protocol):
        # Analyze partnership event for yield opportunities
        event_data = {
            'protocol': protocol,
            'contract': contract_address,
            'event': event_name,
            'block_number': event['blockNumber'],
            'transaction_hash': event['transactionHash'].hex(),
            'event_args': dict(event['args']) if 'args' in event else {}
        }
        
        # Calculate urgency score
        urgency_score = self.calculate_event_urgency(event_name, event_data)
        
        if urgency_score >= 7:
            self.send_immediate_alert(event_data, urgency_score)
        
        return event_data
    
    def calculate_event_urgency(self, event_name, event_data):
        urgency_scores = {
            'PoolAdded': 8,
            'RewardTokenAdded': 9,
            'FarmingProgramLaunched': 10,
            'MultiTokenRewardEnabled': 9,
            'PartnershipActivated': 8
        }
        
        base_score = urgency_scores.get(event_name, 5)
        
        # Boost score for high-value protocols
        tier_1_protocols = ['compound', 'aave', 'uniswap', 'curve']
        if any(protocol in event_data['protocol'].lower() 
               for protocol in tier_1_protocols):
            base_score += 2
        
        return min(base_score, 10)
    
    def send_immediate_alert(self, event_data, urgency):
        alert_message = f"""
        🚨 HIGH URGENCY PARTNERSHIP DEPLOYMENT DETECTED 🚨
        
        Protocol: {event_data['protocol']}
        Event: {event_data['event']}
        Urgency: {urgency}/10
        Contract: {event_data['contract']}
        Block: {event_data['block_number']}
        TX: {event_data['transaction_hash']}
        
        Event Details: {json.dumps(event_data['event_args'], indent=2)}
        
        ⚡ IMMEDIATE ACTION RECOMMENDED ⚡
        """
        
        print(alert_message)
        # Add webhook/notification integration here
    
    def run_continuous_monitoring(self, check_interval=30):
        print("Starting continuous contract monitoring...")
        
        while True:
            try:
                self.monitor_contract_changes()
                time.sleep(check_interval)
            except Exception as e:
                print(f"Monitoring error: {e}")
                time.sleep(check_interval)

# Example configuration
contracts_to_monitor = {
    '0x3d9819210A31b4961b30EF54bE2aeD79B9c9Cd3B': {  # Compound Comptroller
        'protocol_name': 'Compound',
        'abi': compound_comptroller_abi  # Add actual ABI
    },
    '0x7d2768dE32b0b80b7a3454c06BdAc94A69DDc7A9': {  # Aave Lending Pool
        'protocol_name': 'Aave',
        'abi': aave_lending_pool_abi  # Add actual ABI
    }
}

# Start monitoring
monitor = ContractPartnershipMonitor('https://mainnet.infura.io/v3/YOUR_PROJECT_ID', 
                                   contracts_to_monitor)
monitor.run_continuous_monitoring()

# Automated position entry for partnership opportunities
class AutomatedPositionManager:
    def __init__(self, web3_instance, wallet_address, private_key):
        self.w3 = web3_instance
        self.wallet = wallet_address
        self.private_key = private_key
        self.position_rules = {}
        
    def set_position_rules(self, rules):
        """Configure automated positioning rules"""
        self.position_rules = rules
    
    def evaluate_partnership_opportunity(self, partnership_data, catalyst_score):
        # Determine if partnership meets positioning criteria
        if catalyst_score < self.position_rules.get('min_catalyst_score', 8):
            return False, "Catalyst score below threshold"
        
        protocols = partnership_data.get('protocols', [])
        approved_protocols = self.position_rules.get('approved_protocols', [])
        
        if not any(protocol.lower() in [p.lower() for p in approved_protocols] 
                  for protocol in protocols):
            return False, "Protocol not in approved list"
        
        return True, "Partnership meets criteria"
    
    def execute_automated_position(self, partnership_data, position_size_eth):
        """Execute automated position entry"""
        
        # Get current gas price
        gas_price = self.w3.eth.gas_price
        
        # Calculate position allocation
        max_position_size = self.position_rules.get('max_position_size_eth', 5.0)
        actual_position_size = min(position_size_eth, max_position_size)
        
        # Prepare transaction data
        transaction = {
            'from': self.wallet,
            'value': self.w3.toWei(actual_position_size, 'ether'),
            'gas': 200000,
            'gasPrice': gas_price,
            'nonce': self.w3.eth.get_transaction_count(self.wallet)
        }
        
        try:
            # Sign and send transaction
            signed_txn = self.w3.eth.account.sign_transaction(transaction, self.private_key)
            tx_hash = self.w3.eth.send_raw_transaction(signed_txn.rawTransaction)
            
            return {
                'success': True,
                'transaction_hash': tx_hash.hex(),
                'position_size': actual_position_size,
                'gas_used': transaction['gas']
            }
            
        except Exception as e:
            return {
                'success': False,
                'error': str(e),
                'position_size': 0
            }
    
    def calculate_optimal_position_size(self, catalyst_score, available_balance):
        # Calculate position size based on catalyst score and risk tolerance
        risk_tolerance = self.position_rules.get('risk_tolerance', 0.1)  # 10% default
        max_position = available_balance * risk_tolerance
        
        # Scale position size by catalyst score
        score_multiplier = min(catalyst_score / 10, 1.0)
        optimal_size = max_position * score_multiplier
        
        return optimal_size

# Position management rules configuration
position_rules = {
    'min_catalyst_score': 8,
    'max_position_size_eth': 10.0,
    'risk_tolerance': 0.15,  # 15% of portfolio
    'approved_protocols': [
        'Compound', 'Aave', 'Uniswap', 'Curve', 
        'SushiSwap', 'Balancer', 'Yearn'
    ],
    'auto_execute': True,
    'max_daily_positions': 3
}

# Usage example
position_manager = AutomatedPositionManager(w3, wallet_address, private_key)
position_manager.set_position_rules(position_rules)

# Evaluate opportunity
partnership = {
    'protocols': ['Compound', 'Balancer'],
    'type': 'integration',
    'yield_potential': 8
}

meets_criteria, reason = position_manager.evaluate_partnership_opportunity(partnership, 9.2)

if meets_criteria:
    position_size = position_manager.calculate_optimal_position_size(9.2, 50.0)  # 50 ETH available
    result = position_manager.execute_automated_position(partnership, position_size)
    
    if result['success']:
        print(f"Position executed: {result['position_size']} ETH")
        print(f"Transaction: {result['transaction_hash']}")
    else:
        print(f"Position failed: {result['error']}")

# Partnership risk assessment framework
class PartnershipRiskAnalyzer:
    def __init__(self):
        self.risk_factors = {
            'protocol_security': ['audit_status', 'bug_bounty_program', 'previous_exploits'],
            'smart_contract_risk': ['code_complexity', 'upgrade_mechanism', 'admin_keys'],
            'liquidity_risk': ['pool_size', 'token_concentration', 'exit_liquidity'],
            'regulatory_risk': ['compliance_status', 'jurisdiction', 'regulatory_clarity'],
            'market_risk': ['token_volatility', 'correlation_risk', 'liquidity_mining_sustainability']
        }
    
    def assess_partnership_risks(self, partnership_data):
        risk_scores = {}
        
        for risk_category, factors in self.risk_factors.items():
            category_score = self.calculate_category_risk(partnership_data, risk_category, factors)
            risk_scores[risk_category] = category_score
        
        # Calculate overall risk score
        overall_risk = sum(risk_scores.values()) / len(risk_scores)
        
        return {
            'risk_breakdown': risk_scores,
            'overall_risk_score': round(overall_risk, 2),
            'risk_level': self.categorize_risk_level(overall_risk),
            'recommendations': self.generate_risk_recommendations(risk_scores)
        }
    
    def calculate_category_risk(self, partnership_data, category, factors):
        # Risk scoring logic for each category (1-10 scale, 10 = highest risk)
        protocols = partnership_data.get('protocols', [])
        
        if category == 'protocol_security':
            return self.assess_protocol_security(protocols)
        elif category == 'smart_contract_risk':
            return self.assess_smart_contract_risk(partnership_data)
        elif category == 'liquidity_risk':
            return self.assess_liquidity_risk(partnership_data)
        elif category == 'regulatory_risk':
            return self.assess_regulatory_risk(protocols)
        elif category == 'market_risk':
            return self.assess_market_risk(partnership_data)
        
        return 5  # Default moderate risk
    
    def assess_protocol_security(self, protocols):
        # Security assessment based on protocol reputation
        high_security_protocols = ['compound', 'aave', 'uniswap', 'makerdao']
        medium_security_protocols = ['curve', 'sushiswap', 'balancer', 'yearn']
        
        min_risk = 10  # Start with highest risk
        
        for protocol in protocols:
            protocol_lower = protocol.lower()
            
            if any(secure in protocol_lower for secure in high_security_protocols):
                min_risk = min(min_risk, 2)  # Very low security risk
            elif any(medium in protocol_lower for medium in medium_security_protocols):
                min_risk = min(min_risk, 4)  # Low-medium security risk
            else:
                min_risk = min(min_risk, 7)  # Higher security risk for unknown protocols
        
        return min_risk
    
    def assess_smart_contract_risk(self, partnership_data):
        # Assess smart contract complexity and risk factors
        partnership_type = partnership_data.get('type', 'unknown')
        
        contract_risk_scores = {
            'integration': 6,  # Higher complexity
            'rewards': 4,      # Medium complexity
            'marketing': 2,    # Low complexity
            'advisory': 1      # Minimal complexity
        }
        
        return contract_risk_scores.get(partnership_type, 5)
    
    def assess_liquidity_risk(self, partnership_data):
        # Assess liquidity and exit risk
        protocols = partnership_data.get('protocols', [])
        
        # High liquidity protocols have lower liquidity risk
        high_liquidity_protocols = ['uniswap', 'curve', 'balancer', 'sushiswap']
        
        if any(protocol.lower() in high_liquidity_protocols for protocol in protocols):
            return 3  # Low liquidity risk
        else:
            return 6  # Medium-high liquidity risk
    
    def assess_regulatory_risk(self, protocols):
        # Assess regulatory compliance risk
        established_protocols = ['compound', 'aave', 'uniswap', 'makerdao']
        
        if any(protocol.lower() in established_protocols for protocol in protocols):
            return 3  # Lower regulatory risk for established protocols
        else:
            return 6  # Higher regulatory uncertainty
    
    def assess_market_risk(self, partnership_data):
        # Assess market and volatility risk
        yield_potential = partnership_data.get('yield_potential', 5)
        
        # Higher yield potential often means higher market risk
        if yield_potential >= 8:
            return 7  # High market risk
        elif yield_potential >= 6:
            return 5  # Medium market risk
        else:
            return 3  # Lower market risk
    
    def categorize_risk_level(self, overall_risk):
        if overall_risk <= 3:
            return "LOW"
        elif overall_risk <= 5:
            return "MEDIUM"
        elif overall_risk <= 7:
            return "HIGH"
        else:
            return "VERY_HIGH"
    
    def generate_risk_recommendations(self, risk_scores):
        recommendations = []
        
        for category, score in risk_scores.items():
            if score >= 7:
                if category == 'protocol_security':
                    recommendations.append("⚠️  Verify protocol security audits before positioning")
                elif category == 'smart_contract_risk':
                    recommendations.append("⚠️  Start with smaller position size due to contract complexity")
                elif category == 'liquidity_risk':
                    recommendations.append("⚠️  Plan exit strategy due to potential liquidity constraints")
                elif category == 'regulatory_risk':
                    recommendations.append("⚠️  Monitor regulatory developments closely")
                elif category == 'market_risk':
                    recommendations.append("⚠️  Use stop-losses due to high volatility risk")
        
        if not recommendations:
            recommendations.append("✅ Risk levels acceptable for standard position sizing")
        
        return recommendations

# Example risk assessment
risk_analyzer = PartnershipRiskAnalyzer()

partnership = {
    'protocols': ['NewProtocol', 'ExperimentalDeFi'],
    'type': 'integration',
    'yield_potential': 9,
    'announcement_source': 'twitter'
}

risk_assessment = risk_analyzer.assess_partnership_risks(partnership)

print(f"Overall Risk: {risk_assessment['risk_level']} ({risk_assessment['overall_risk_score']}/10)")
print(f"Risk Breakdown: {risk_assessment['risk_breakdown']}")
print("Recommendations:")
for rec in risk_assessment['recommendations']:
    print(f"  {rec}")

# Dynamic position sizing based on risk assessment
class RiskAdjustedPositionCalculator:
    def __init__(self, portfolio_balance, base_risk_tolerance=0.1):
        self.portfolio_balance = portfolio_balance
        self.base_risk_tolerance = base_risk_tolerance
        self.position_history = []
    
    def calculate_position_size(self, catalyst_score, risk_assessment, current_exposure=0):
        # Base position calculation
        base_position = self.portfolio_balance * self.base_risk_tolerance
        
        # Catalyst score multiplier (1-10 scale)
        catalyst_multiplier = min(catalyst_score / 10, 1.0)
        
        # Risk adjustment factor (higher risk = smaller position)
        risk_score = risk_assessment['overall_risk_score']
        risk_adjustment = max(0.2, 1 - (risk_score - 1) / 9)  # Scale from 0.2 to 1.0
        
        # Portfolio concentration limit
        max_single_position = self.portfolio_balance * 0.25  # Max 25% in single opportunity
        
        # Calculate adjusted position size
        adjusted_position = base_position * catalyst_multiplier * risk_adjustment
        
        # Apply concentration limits
        final_position = min(adjusted_position, max_single_position)
        
        # Account for current exposure
        available_allocation = max_single_position - current_exposure
        final_position = min(final_position, available_allocation)
        
        return {
            'recommended_position': round(final_position, 4),
            'base_position': round(base_position, 4),
            'catalyst_multiplier': round(catalyst_multiplier, 3),
            'risk_adjustment': round(risk_adjustment, 3),
            'position_percentage': round((final_position / self.portfolio_balance) * 100, 2),
            'reasoning': self.generate_sizing_reasoning(catalyst_score, risk_score, final_position)
        }
    
    def generate_sizing_reasoning(self, catalyst_score, risk_score, position_size):
        reasoning = []
        
        if catalyst_score >= 8:
            reasoning.append(f"High catalyst score ({catalyst_score}/10) supports larger position")
        elif catalyst_score <= 5:
            reasoning.append(f"Moderate catalyst score ({catalyst_score}/10) suggests conservative sizing")
        
        if risk_score >= 7:
            reasoning.append(f"High risk score ({risk_score}/10) requires position reduction")
        elif risk_score <= 3:
            reasoning.append(f"Low risk score ({risk_score}/10) allows standard sizing")
        
        position_pct = (position_size / self.portfolio_balance) * 100
        
        if position_pct >= 15:
            reasoning.append("Large allocation due to strong opportunity profile")
        elif position_pct <= 5:
            reasoning.append("Conservative allocation due to risk factors")
        else:
            reasoning.append("Standard allocation within risk parameters")
        
        return reasoning
    
    def track_position_performance(self, position_data, current_value):
        # Track position performance for portfolio optimization
        position_entry = {
            'entry_date': datetime.now(),
            'catalyst_score': position_data['catalyst_score'],
            'risk_score': position_data['risk_score'],
            'position_size': position_data['position_size'],
            'entry_value': position_data['position_size'],
            'current_value': current_value,
            'performance': ((current_value - position_data['position_size']) / position_data['position_size']) * 100
        }
        
        self.position_history.append(position_entry)
        
        # Analyze performance patterns
        return self.analyze_performance_patterns()
    
    def analyze_performance_patterns(self):
        if len(self.position_history) < 5:
            return "Insufficient data for pattern analysis"
        
        # Analyze correlation between catalyst scores and performance
        high_catalyst_performance = []
        low_catalyst_performance = []
        
        for position in self.position_history:
            if position['catalyst_score'] >= 8:
                high_catalyst_performance.append(position['performance'])
            else:
                low_catalyst_performance.append(position['performance'])
        
        if high_catalyst_performance and low_catalyst_performance:
            avg_high_catalyst = sum(high_catalyst_performance) / len(high_catalyst_performance)
            avg_low_catalyst = sum(low_catalyst_performance) / len(low_catalyst_performance)
            
            return {
                'high_catalyst_avg_performance': round(avg_high_catalyst, 2),
                'low_catalyst_avg_performance': round(avg_low_catalyst, 2),
                'catalyst_effectiveness': avg_high_catalyst > avg_low_catalyst,
                'total_positions': len(self.position_history),
                'recommendation': self.generate_strategy_recommendation(avg_high_catalyst, avg_low_catalyst)
            }
        
        return "Need more diverse position data for analysis"
    
    def generate_strategy_recommendation(self, high_catalyst_perf, low_catalyst_perf):
        performance_diff = high_catalyst_perf - low_catalyst_perf
        
        if performance_diff > 50:
            return "Strong catalyst correlation - increase allocation to high-score opportunities"
        elif performance_diff > 20:
            return "Moderate catalyst correlation - maintain current strategy"
        elif performance_diff < -20:
            return "Weak catalyst correlation - consider reducing catalyst weight"
        else:
            return "Mixed results - continue data collection"

# Example position sizing calculation
calculator = RiskAdjustedPositionCalculator(portfolio_balance=100.0, base_risk_tolerance=0.12)

# Sample opportunity data
catalyst_score = 8.5
risk_assessment = {
    'overall_risk_score': 4.2,
    'risk_level': 'MEDIUM'
}

position_recommendation = calculator.calculate_position_size(catalyst_score, risk_assessment)

print(f"Recommended Position: {position_recommendation['recommended_position']} ETH")
print(f"Position Percentage: {position_recommendation['position_percentage']}%")
print(f"Catalyst Multiplier: {position_recommendation['catalyst_multiplier']}")
print(f"Risk Adjustment: {position_recommendation['risk_adjustment']}")
print("\nReasoning:")
for reason in position_recommendation['reasoning']:
    print(f"  • {reason}")

# Multi-platform partnership monitoring system
import asyncio
import aiohttp
from datetime import datetime

class MultiPlatformPartnershipMonitor:
    def __init__(self):
        self.platforms = {
            'twitter': TwitterMonitor(),
            'discord': DiscordMonitor(),
            'telegram': TelegramMonitor(),
            'github': GitHubMonitor(),
            'medium': MediumMonitor()
        }
        self.partnership_cache = {}
    
    async def monitor_all_platforms(self):
        """Run monitoring across all platforms simultaneously"""
        tasks = []
        
        for platform_name, monitor in self.platforms.items():
            task = asyncio.create_task(
                self.monitor_platform(platform_name, monitor)
            )
            tasks.append(task)
        
        # Wait for all monitoring tasks to complete
        results = await asyncio.gather(*tasks, return_exceptions=True)
        
        # Consolidate results
        return self.consolidate_partnership_signals(results)
    
    async def monitor_platform(self, platform_name, monitor):
        """Monitor individual platform for partnership signals"""
        try:
            signals = await monitor.scan_for_partnerships()
            return {
                'platform': platform_name,
                'signals': signals,
                'timestamp': datetime.now(),
                'status': 'success'
            }
        except Exception as e:
            return {
                'platform': platform_name,
                'signals': [],
                'timestamp': datetime.now(),
                'status': 'error',
                'error': str(e)
            }
    
    def consolidate_partnership_signals(self, platform_results):
        """Consolidate and deduplicate partnership signals"""
        all_signals = []
        platform_coverage = {}
        
        for result in platform_results:
            if isinstance(result, dict) and result['status'] == 'success':
                platform_coverage[result['platform']] = len(result['signals'])
                
                for signal in result['signals']:
                    # Add platform context
                    signal['source_platform'] = result['platform']
                    signal['detection_time'] = result['timestamp']
                    
                    # Check for duplicates across platforms
                    if not self.is_duplicate_signal(signal, all_signals):
                        all_signals.append(signal)
        
        # Sort by priority and recency
        sorted_signals = sorted(all_signals, 
                              key=lambda x: (x.get('priority_score', 0), x['detection_time']), 
                              reverse=True)
        
        return {
            'total_signals': len(sorted_signals),
            'platform_coverage': platform_coverage,
            'consolidated_signals': sorted_signals,
            'top_opportunities': sorted_signals[:5]
        }
    
    def is_duplicate_signal(self, new_signal, existing_signals):
        """Check if signal is duplicate based on content similarity"""
        new_protocols = set(p.lower() for p in new_signal.get('protocols', []))
        new_keywords = set(new_signal.get('content', '').lower().split())
        
        for existing in existing_signals:
            existing_protocols = set(p.lower() for p in existing.get('protocols', []))
            existing_keywords = set(existing.get('content', '').lower().split())
            
            # Check protocol overlap
            protocol_overlap = len(new_protocols.intersection(existing_protocols))
            keyword_overlap = len(new_keywords.intersection(existing_keywords))
            
            # Consider duplicate if significant overlap
            if (protocol_overlap >= 1 and keyword_overlap > 5) or protocol_overlap >= 2:
                return True
        
        return False

class TwitterMonitor:
    async def scan_for_partnerships(self):
        # Simulated Twitter monitoring (replace with actual API calls)
        await asyncio.sleep(1)  # Simulate API delay
        
        return [
            {
                'protocols': ['Compound', 'Aave'],
                'content': 'Excited to announce partnership with Aave for cross-protocol lending',
                'engagement_score': 85,
                'priority_score': 9,
                'url': 'https://twitter.com/compound/status/123456789'
            }
        ]

class DiscordMonitor:
    async def scan_for_partnerships(self):
        await asyncio.sleep(0.5)
        
        return [
            {
                'protocols': ['SushiSwap', 'Polygon'],
                'content': 'New farming opportunity launching with Polygon integration',
                'engagement_score': 65,
                'priority_score': 7,
                'url': 'discord://channel/123456789'
            }
        ]

class TelegramMonitor:
    async def scan_for_partnerships(self):
        await asyncio.sleep(0.8)
        
        return [
            {
                'protocols': ['Curve', 'Balancer'],
                'content': 'Curve Finance partners with Balancer for liquidity optimization',
                'engagement_score': 92,
                'priority_score': 8,
                'url': 'https://t.me/curvefi/12345'
            }
        ]

class GitHubMonitor:
    async def scan_for_partnerships(self):
        await asyncio.sleep(1.2)
        
        return [
            {
                'protocols': ['Yearn', 'Convex'],
                'content': 'Integration commit: Add Convex strategy support',
                'engagement_score': 45,
                'priority_score': 9,
                'url': 'https://github.com/yearn/yearn-vaults/commit/abc123'
            }
        ]

class MediumMonitor:
    async def scan_for_partnerships(self):
        await asyncio.sleep(1.5)
        
        return [
            {
                'protocols': ['1inch', 'ParaSwap'],
                'content': 'Strategic partnership announcement for DEX aggregation',
                'engagement_score': 78,
                'priority_score': 6,
                'url': 'https://medium.com/@1inch/partnership-announcement-abc123'
            }
        ]

# Usage example
async def run_monitoring():
    monitor = MultiPlatformPartnershipMonitor()
    
    print("Starting multi-platform partnership monitoring...")
    
    while True:
        results = await monitor.monitor_all_platforms()
        
        print(f"\n🔍 Monitoring Cycle Complete")
        print(f"Total Signals: {results['total_signals']}")
        print(f"Platform Coverage: {results['platform_coverage']}")
        
        if results['top_opportunities']:
            print("\n🚀 Top Partnership Opportunities:")
            
            for i, opportunity in enumerate(results['top_opportunities'], 1):
                protocols = ', '.join(opportunity.get('protocols', []))
                priority = opportunity.get('priority_score', 0)
                platform = opportunity.get('source_platform', 'Unknown')
                
                print(f"  {i}. {protocols} (Priority: {priority}/10, Platform: {platform})")
                print(f"     {opportunity.get('content', '')[:100]}...")
                print(f"     {opportunity.get('url', '')}")
                print()
        
        # Wait before next monitoring cycle
        await asyncio.sleep(300)  # 5 minutes

# Run the monitoring system
# asyncio.run(run_monitoring())