AI Agent Token Analysis with Ollama: Virtuals Protocol and On-Chain Automation

# Install Ollama (macOS)
brew install ollama

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

# Start Ollama service
ollama serve

# Pull a general-purpose model
ollama pull llama3.3:70b

# Pull a crypto-specialized model
ollama pull shreyshah/satoshi-7b-q4_k_m

# Pull the powerful reasoning model
ollama pull deepseek-r1

import requests
import pandas as pd
import json
from datetime import datetime, timedelta

# Ollama API configuration
OLLAMA_URL = "http://localhost:11434/api/generate"
MODEL = "deepseek-r1"

def query_ollama(prompt, model=MODEL):
    """Send query to local Ollama instance"""
    payload = {
        "model": model,
        "prompt": prompt,
        "stream": False
    }
    
    response = requests.post(OLLAMA_URL, json=payload)
    if response.status_code == 200:
        return response.json()['response']
    else:
        raise Exception(f"Ollama API error: {response.status_code}")

# Test connection
test_response = query_ollama("Analyze the AI agent token market in one sentence.")
print(f"Ollama connected: {test_response}")

def get_token_data(token_address, chain="base"):
    """Fetch token data from DeFiLlama API"""
    url = f"https://api.llama.fi/protocol/{token_address}"
    
    try:
        response = requests.get(url)
        data = response.json()
        return data
    except Exception as e:
        print(f"Error fetching data: {e}")
        return None

def get_virtuals_ecosystem_data():
    """Get comprehensive Virtuals Protocol ecosystem data"""
    # This would typically connect to DEX APIs or blockchain data providers
    ecosystem_data = {
        "total_agents": 2200,
        "total_market_cap": 1400000000,  # $1.4B in USD
        "top_agents": [
            {"name": "AIXBT", "market_cap": 300000000, "category": "Analytics"},
            {"name": "LUNA", "market_cap": 130000000, "category": "Entertainment"},
            {"name": "CHAOS", "market_cap": 25000000, "category": "Community"}
        ]
    }
    return ecosystem_data

def analyze_agent_token(token_data, market_context):
    """Comprehensive AI agent token analysis using Ollama"""
    
    analysis_prompt = f"""
    Analyze this AI agent token with the following data:
    
    Token Data: {json.dumps(token_data, indent=2)}
    Market Context: {json.dumps(market_context, indent=2)}
    
    Provide analysis covering:
    1. Agent utility and use case strength
    2. Tokenomics and supply mechanics
    3. Market position relative to competitors
    4. Revenue generation potential
    5. Risk factors and concerns
    6. Price target suggestions (be conservative)
    
    Focus on practical insights for investment decisions.
    """
    
    analysis = query_ollama(analysis_prompt)
    return analysis

# Example analysis
ecosystem_data = get_virtuals_ecosystem_data()
sample_token = {
    "name": "AIXBT",
    "market_cap": 300000000,
    "daily_volume": 15000000,
    "holders": 12000,
    "utility": "Crypto market analysis and insights"
}

analysis_result = analyze_agent_token(sample_token, ecosystem_data)
print("AI Agent Token Analysis:")
print(analysis_result)

def analyze_social_sentiment(agent_name, social_data):
    """Analyze social sentiment around AI agents"""
    
    sentiment_prompt = f"""
    Analyze the social sentiment for AI agent "{agent_name}" based on this data:
    
    {json.dumps(social_data, indent=2)}
    
    Provide:
    1. Overall sentiment score (1-10)
    2. Key sentiment drivers
    3. Community engagement level
    4. Potential red flags
    5. Bullish/bearish indicators
    
    Be objective and highlight both positive and negative aspects.
    """
    
    sentiment = query_ollama(sentiment_prompt)
    return sentiment

# Example social data structure
social_data = {
    "twitter_mentions": 1500,
    "telegram_members": 8000,
    "discord_activity": "high",
    "recent_news": ["Partnership announcement", "New feature release"],
    "community_sentiment": "Generally positive with some concerns about valuations"
}

sentiment_analysis = analyze_social_sentiment("AIXBT", social_data)
print(sentiment_analysis)

def create_trading_strategy(agent_data):
    """Generate trading strategy using Ollama analysis"""
    
    strategy_prompt = f"""
    Create a systematic trading strategy for AI agent tokens based on:
    
    Agent Performance Data: {json.dumps(agent_data, indent=2)}
    
    Include:
    1. Entry criteria (when to buy)
    2. Exit criteria (when to sell)
    3. Position sizing rules
    4. Risk management parameters
    5. Monitoring requirements
    
    Make the strategy actionable and measurable.
    """
    
    strategy = query_ollama(strategy_prompt)
    return strategy

def monitor_agent_performance(agent_address):
    """Monitor AI agent on-chain performance"""
    
    # This would connect to blockchain APIs to track:
    performance_metrics = {
        "transactions_24h": 150,
        "revenue_generated": 1200.50,
        "success_rate": 0.73,
        "gas_efficiency": 0.85,
        "active_strategies": 3
    }
    
    return performance_metrics

# Example usage
agent_performance = monitor_agent_performance("0x123...")
trading_strategy = create_trading_strategy(agent_performance)
print("Generated Trading Strategy:")
print(trading_strategy)

def optimize_agent_portfolio(current_holdings, market_data):
    """Optimize AI agent token portfolio using Ollama"""
    
    optimization_prompt = f"""
    Optimize this AI agent token portfolio:
    
    Current Holdings: {json.dumps(current_holdings, indent=2)}
    Market Data: {json.dumps(market_data, indent=2)}
    
    Recommend:
    1. Rebalancing actions
    2. New agent tokens to consider
    3. Tokens to reduce/exit
    4. Target allocation percentages
    5. Timeline for implementation
    
    Consider risk diversification across different agent categories.
    """
    
    optimization = query_ollama(optimization_prompt)
    return optimization

# Example portfolio optimization
current_portfolio = {
    "VIRTUAL": {"allocation": 40, "value": 10000},
    "AIXBT": {"allocation": 30, "value": 7500},
    "LUNA": {"allocation": 20, "value": 5000},
    "Others": {"allocation": 10, "value": 2500}
}

market_context = {
    "trend": "bullish",
    "new_agents_launching": 15,
    "average_daily_volume": 50000000
}

portfolio_optimization = optimize_agent_portfolio(current_portfolio, market_context)
print("Portfolio Optimization Recommendations:")
print(portfolio_optimization)

def comprehensive_aixbt_analysis():
    """Complete AIXBT token analysis example"""
    
    aixbt_data = {
        "launch_date": "2024-11-01",
        "market_cap": 300000000,
        "daily_active_users": 5000,
        "revenue_streams": ["Trading insights", "Market analysis", "Premium alerts"],
        "competitive_advantage": "Real-time social media monitoring",
        "team_background": "Experienced crypto analysts"
    }
    
    analysis_prompt = f"""
    Perform a comprehensive investment analysis of AIXBT token:
    
    {json.dumps(aixbt_data, indent=2)}
    
    Structure your analysis as:
    1. Executive Summary (2-3 sentences)
    2. Business Model Strength (1-10 score with reasoning)
    3. Token Utility Assessment
    4. Competitive Positioning
    5. Financial Projections (conservative estimates)
    6. Risk Assessment
    7. Investment Recommendation (Buy/Hold/Sell with target price)
    
    Be specific and quantitative where possible.
    """
    
    return query_ollama(analysis_prompt)

aixbt_analysis = comprehensive_aixbt_analysis()
print("AIXBT Comprehensive Analysis:")
print(aixbt_analysis)

def track_agent_kpis(agent_list):
    """Track key performance indicators for multiple agents"""
    
    for agent in agent_list:
        kpi_prompt = f"""
        Calculate and interpret key performance indicators for AI agent {agent['name']}:
        
        Raw Data: {json.dumps(agent, indent=2)}
        
        Calculate:
        1. Revenue per token holder
        2. Growth rate (weekly/monthly)
        3. Market share within category
        4. Efficiency ratios
        5. User engagement metrics
        
        Highlight the most important KPI and explain why.
        """
        
        kpis = query_ollama(kpi_prompt)
        print(f"\n{agent['name']} KPIs:")
        print(kpis)
        print("-" * 50)

# Example agent list
agents_to_track = [
    {"name": "AIXBT", "category": "Analytics", "users": 5000, "revenue": 50000},
    {"name": "LUNA", "category": "Entertainment", "users": 12000, "revenue": 30000},
    {"name": "CHAOS", "category": "Community", "users": 8000, "revenue": 15000}
]

track_agent_kpis(agents_to_track)

def ensemble_analysis(token_data):
    """Use multiple Ollama models for robust analysis"""
    
    models = ["llama3.3:70b", "deepseek-r1", "shreyshah/satoshi-7b-q4_k_m"]
    analyses = {}
    
    for model in models:
        try:
            prompt = f"""
            Analyze this AI agent token data and provide a score from 1-100 
            with brief reasoning:
            
            {json.dumps(token_data, indent=2)}
            """
            
            analysis = query_ollama(prompt, model=model)
            analyses[model] = analysis
            
        except Exception as e:
            print(f"Error with model {model}: {e}")
    
    # Synthesize results
    synthesis_prompt = f"""
    Synthesize these multiple AI analyses into a final recommendation:
    
    {json.dumps(analyses, indent=2)}
    
    Provide:
    1. Consensus score (1-100)
    2. Areas of agreement
    3. Areas of disagreement
    4. Final investment thesis
    """
    
    final_analysis = query_ollama(synthesis_prompt)
    return final_analysis, analyses

def assess_agent_risks(agent_data, market_conditions):
    """Comprehensive risk assessment for AI agent tokens"""
    
    risk_prompt = f"""
    Assess risks for this AI agent investment:
    
    Agent Data: {json.dumps(agent_data, indent=2)}
    Market Conditions: {json.dumps(market_conditions, indent=2)}
    
    Evaluate and score (1-10, 10 being highest risk):
    1. Technology risk (agent reliability)
    2. Market risk (token volatility)
    3. Regulatory risk (potential compliance issues)
    4. Competition risk (market saturation)
    5. Liquidity risk (ability to exit)
    6. Team risk (developer competence)
    
    Provide specific mitigation strategies for each risk category.
    """
    
    risk_assessment = query_ollama(risk_prompt)
    return risk_assessment

# Example risk assessment
agent_risk_data = {
    "name": "NewAgent",
    "age_days": 30,
    "team_size": 3,
    "code_audited": False,
    "daily_volume": 500000
}

market_conditions = {
    "overall_sentiment": "bullish",
    "regulatory_clarity": "low",
    "competition_level": "high"
}

risk_report = assess_agent_risks(agent_risk_data, market_conditions)
print("Risk Assessment Report:")
print(risk_report)

def monitor_ollama_performance():
    """Track Ollama performance metrics"""
    
    start_time = datetime.now()
    
    test_prompt = "Analyze the current AI agent market trends in 100 words."
    response = query_ollama(test_prompt)
    
    end_time = datetime.now()
    response_time = (end_time - start_time).total_seconds()
    
    # Estimate tokens (rough approximation)
    estimated_tokens = len(response.split())
    tokens_per_second = estimated_tokens / response_time
    
    print(f"Response time: {response_time:.2f} seconds")
    print(f"Estimated tokens/second: {tokens_per_second:.2f}")
    
    return {
        "response_time": response_time,
        "tokens_per_second": tokens_per_second,
        "response_length": len(response)
    }

performance_metrics = monitor_ollama_performance()

def validate_analysis_quality(analysis_text, expected_criteria):
    """Validate the quality of Ollama analysis output"""
    
    validation_prompt = f"""
    Evaluate this analysis against quality criteria:
    
    Analysis: {analysis_text}
    
    Required Criteria: {json.dumps(expected_criteria, indent=2)}
    
    Score each criterion (1-10) and provide overall quality score:
    1. Factual accuracy
    2. Logical reasoning
    3. Actionable insights
    4. Risk consideration
    5. Clarity and structure
    
    Highlight any red flags or missing elements.
    """
    
    validation = query_ollama(validation_prompt)
    return validation

# Example validation
quality_criteria = [
    "Includes quantitative metrics",
    "Considers market context",
    "Addresses risk factors",
    "Provides specific recommendations",
    "Uses logical reasoning"
]

sample_analysis = "AIXBT shows strong fundamentals with growing user base..."
quality_check = validate_analysis_quality(sample_analysis, quality_criteria)
print("Quality Validation:")
print(quality_check)

import time
from functools import wraps

def rate_limit(calls_per_minute=30):
    """Rate limiting decorator for API calls"""
    def decorator(func):
        last_called = [0.0]
        
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            left_to_wait = 60.0 / calls_per_minute - elapsed
            if left_to_wait > 0:
                time.sleep(left_to_wait)
            ret = func(*args, **kwargs)
            last_called[0] = time.time()
            return ret
        return wrapper
    return decorator

@rate_limit(calls_per_minute=20)
def safe_ollama_query(prompt):
    """Rate-limited Ollama queries"""
    return query_ollama(prompt)

def check_system_resources():
    """Check if system can handle chosen models efficiently"""
    
    import psutil
    
    cpu_percent = psutil.cpu_percent(interval=1)
    memory = psutil.virtual_memory()
    disk = psutil.disk_usage('/')
    
    requirements = {
        "cpu_usage": cpu_percent,
        "memory_total_gb": memory.total / (1024**3),
        "memory_available_gb": memory.available / (1024**3),
        "memory_percent": memory.percent,
        "disk_free_gb": disk.free / (1024**3)
    }
    
    print("System Resources:")
    for key, value in requirements.items():
        print(f"{key}: {value:.2f}")
    
    # Recommendations based on resources
    if memory.available / (1024**3) < 8:
        print("Warning: Consider using smaller models (7B parameters or less)")
    elif memory.available / (1024**3) > 32:
        print("Good: System can handle large models (70B+ parameters)")
    
    return requirements

system_check = check_system_resources()