Tokenized Securities vs Traditional Markets: Ollama Performance Comparison

import time
import ollama
import pandas as pd

def benchmark_analysis(data_type, transactions):
    """
    Benchmark Ollama performance on financial data
    
    Args:
        data_type: 'tokenized' or 'traditional'
        transactions: DataFrame with transaction data
    """
    start_time = time.time()
    
    # Prepare prompt for Ollama
    prompt = f"""
    Analyze these {data_type} market transactions:
    {transactions.to_json()}
    
    Provide:
    1. Price trend analysis
    2. Volume patterns
    3. Risk assessment
    4. Trading recommendations
    """
    
    # Run Ollama analysis
    response = ollama.generate(
        model='llama3.1:70b',
        prompt=prompt,
        options={'temperature': 0.1}
    )
    
    end_time = time.time()
    processing_time = end_time - start_time
    
    return {
        'data_type': data_type,
        'processing_time': processing_time,
        'transactions_processed': len(transactions),
        'speed_per_transaction': processing_time / len(transactions)
    }

# Test results
tokenized_results = benchmark_analysis('tokenized', tokenized_data)
traditional_results = benchmark_analysis('traditional', traditional_data)

print(f"Tokenized Securities: {tokenized_results['processing_time']:.2f}s")
print(f"Traditional Markets: {traditional_results['processing_time']:.2f}s")

# Monitor Ollama memory usage
docker stats ollama-container --format "table {{.Name}}\t{{.CPUPerc}}\t{{.MemUsage}}"

# Tokenized securities analysis
ollama-container    45.23%    2.1GB / 8GB

# Traditional markets analysis  
ollama-container    52.87%    2.7GB / 8GB

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

# Pull the best model for financial analysis
ollama pull llama3.1:70b

# Verify installation
ollama list

class MarketDataProcessor:
    def __init__(self, market_type):
        self.market_type = market_type
        self.ollama_client = ollama.Client()
    
    def process_tokenized_data(self, blockchain_data):
        """
        Process tokenized securities from blockchain feeds
        
        Returns: Cleaned DataFrame ready for Ollama analysis
        """
        # Blockchain data is already structured
        df = pd.DataFrame(blockchain_data)
        
        # Standard fields: timestamp, price, volume, token_id
        required_columns = ['timestamp', 'price', 'volume', 'token_id']
        
        if all(col in df.columns for col in required_columns):
            return df[required_columns]
        else:
            raise ValueError("Missing required blockchain data fields")
    
    def process_traditional_data(self, market_feeds):
        """
        Process traditional market data from multiple sources
        
        Returns: Normalized DataFrame for Ollama analysis
        """
        combined_data = []
        
        for feed in market_feeds:
            # Each feed has different format
            if feed['source'] == 'NYSE':
                # CSV format processing
                data = self._parse_nyse_csv(feed['data'])
            elif feed['source'] == 'NASDAQ':
                # JSON format processing
                data = self._parse_nasdaq_json(feed['data'])
            else:
                # Generic processing
                data = self._parse_generic_feed(feed['data'])
            
            combined_data.append(data)
        
        # Merge and normalize timestamps
        df = pd.concat(combined_data, ignore_index=True)
        df['timestamp'] = pd.to_datetime(df['timestamp'])
        
        return df.sort_values('timestamp')

def create_analysis_prompt(data_type, market_data):
    """
    Generate optimized prompts based on market type
    """
    
    if data_type == 'tokenized':
        prompt = f"""
        Analyze these tokenized securities transactions:
        
        Data format: Blockchain-verified transactions
        Total transactions: {len(market_data)}
        Time range: {market_data['timestamp'].min()} to {market_data['timestamp'].max()}
        
        {market_data.to_json(orient='records', date_format='iso')}
        
        Focus on:
        1. Smart contract execution patterns
        2. Token velocity and liquidity
        3. Cross-chain arbitrage opportunities
        4. DeFi yield comparison
        
        Provide actionable trading signals.
        """
    
    else:  # traditional markets
        prompt = f"""
        Analyze these traditional market transactions:
        
        Data sources: Multiple exchanges (normalized)
        Total transactions: {len(market_data)}
        Time range: {market_data['timestamp'].min()} to {market_data['timestamp'].max()}
        
        {market_data.to_json(orient='records', date_format='iso')}
        
        Focus on:
        1. Technical indicator signals
        2. Volume-price relationships
        3. Market maker activity
        4. Institutional flow patterns
        
        Provide actionable trading signals.
        """
    
    return prompt

import asyncio
import aiohttp
from concurrent.futures import ThreadPoolExecutor

class ParallelOllamaAnalyzer:
    def __init__(self, max_workers=4):
        self.max_workers = max_workers
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
    
    async def analyze_market_segments(self, market_data, segment_size=1000):
        """
        Split large datasets into parallel analysis chunks
        """
        segments = [
            market_data[i:i+segment_size] 
            for i in range(0, len(market_data), segment_size)
        ]
        
        tasks = []
        for segment in segments:
            task = asyncio.create_task(
                self._analyze_segment(segment)
            )
            tasks.append(task)
        
        results = await asyncio.gather(*tasks)
        return self._combine_results(results)
    
    async def _analyze_segment(self, segment):
        """
        Analyze individual data segment with Ollama
        """
        loop = asyncio.get_event_loop()
        
        # Run Ollama analysis in thread pool
        result = await loop.run_in_executor(
            self.executor,
            self._ollama_analysis,
            segment
        )
        
        return result

# Usage example
analyzer = ParallelOllamaAnalyzer(max_workers=8)
results = await analyzer.analyze_market_segments(large_dataset)

import hashlib
import json
from functools import lru_cache

class CachedOllamaAnalyzer:
    def __init__(self):
        self.cache = {}
    
    def _generate_cache_key(self, data, prompt_type):
        """
        Create unique cache key for data + analysis type
        """
        data_hash = hashlib.md5(
            json.dumps(data, sort_keys=True).encode()
        ).hexdigest()
        
        return f"{prompt_type}_{data_hash[:16]}"
    
    def analyze_with_cache(self, market_data, analysis_type):
        """
        Check cache before running Ollama analysis
        """
        cache_key = self._generate_cache_key(
            market_data.to_dict(), 
            analysis_type
        )
        
        if cache_key in self.cache:
            print(f"Cache hit for {analysis_type} analysis")
            return self.cache[cache_key]
        
        # Run fresh analysis
        result = self._run_ollama_analysis(market_data, analysis_type)
        
        # Store in cache
        self.cache[cache_key] = result
        
        return result

# Calculate cost savings from faster processing
def calculate_roi(analysis_volume, hourly_rate=150):
    """
    Calculate ROI from improved Ollama performance
    
    Args:
        analysis_volume: Number of analyses per month
        hourly_rate: Cost per hour for analyst time
    """
    
    # Traditional processing time per analysis (minutes)
    traditional_time = 45
    
    # Tokenized processing time per analysis (minutes)  
    tokenized_time = 28
    
    time_saved_per_analysis = traditional_time - tokenized_time
    monthly_time_saved = (analysis_volume * time_saved_per_analysis) / 60
    
    monthly_savings = monthly_time_saved * hourly_rate
    
    return {
        'time_saved_hours': monthly_time_saved,
        'cost_savings': monthly_savings,
        'efficiency_gain': (time_saved_per_analysis / traditional_time) * 100
    }

# Example: 100 analyses per month
roi_data = calculate_roi(100)
print(f"Monthly time savings: {roi_data['time_saved_hours']:.1f} hours")
print(f"Monthly cost savings: ${roi_data['cost_savings']:,.2f}")
print(f"Efficiency improvement: {roi_data['efficiency_gain']:.1f}%")

def safe_ollama_analysis(large_dataset, chunk_size=500):
    """
    Prevent memory errors with automatic chunking
    """
    results = []
    
    for i in range(0, len(large_dataset), chunk_size):
        chunk = large_dataset[i:i+chunk_size]
        
        try:
            # Process chunk with memory monitoring
            result = ollama.generate(
                model='llama3.1:70b',
                prompt=create_analysis_prompt('tokenized', chunk),
                options={
                    'num_predict': 512,  # Limit response length
                    'temperature': 0.1
                }
            )
            results.append(result)
            
        except Exception as e:
            print(f"Error processing chunk {i//chunk_size + 1}: {e}")
            # Reduce chunk size and retry
            smaller_chunks = [
                chunk[j:j+chunk_size//2] 
                for j in range(0, len(chunk), chunk_size//2)
            ]
            
            for small_chunk in smaller_chunks:
                result = ollama.generate(
                    model='llama3.1:70b',
                    prompt=create_analysis_prompt('tokenized', small_chunk),
                    options={'num_predict': 256}
                )
                results.append(result)
    
    return results

def validate_market_data(df, market_type):
    """
    Ensure data quality before Ollama analysis
    """
    required_fields = {
        'tokenized': ['timestamp', 'price', 'volume', 'token_id'],
        'traditional': ['timestamp', 'price', 'volume', 'symbol']
    }
    
    # Check required columns
    missing_fields = set(required_fields[market_type]) - set(df.columns)
    if missing_fields:
        raise ValueError(f"Missing fields: {missing_fields}")
    
    # Validate data types
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df['price'] = pd.to_numeric(df['price'], errors='coerce')
    df['volume'] = pd.to_numeric(df['volume'], errors='coerce')
    
    # Remove invalid rows
    df = df.dropna(subset=['price', 'volume'])
    
    # Sort by timestamp
    df = df.sort_values('timestamp')
    
    return df

class VersionCompatibleAnalyzer:
    def __init__(self):
        self.ollama_version = self._get_ollama_version()
        self.prompt_templates = self._load_version_specific_prompts()
    
    def _get_ollama_version(self):
        """
        Detect current Ollama version for compatibility
        """
        import subprocess
        
        try:
            result = subprocess.run(
                ['ollama', '--version'], 
                capture_output=True, 
                text=True
            )
            return result.stdout.strip()
        except:
            return "unknown"
    
    def analyze_with_compatibility(self, market_data, analysis_type):
        """
        Use version-appropriate prompts and parameters
        """
        if "v0.3" in self.ollama_version:
            # Use legacy prompt format
            prompt = self._legacy_prompt_format(market_data, analysis_type)
        else:
            # Use modern prompt format
            prompt = self._modern_prompt_format(market_data, analysis_type)
        
        return ollama.generate(
            model=self._get_best_model(),
            prompt=prompt,
            options=self._get_optimal_parameters()
        )

class EnterpriseOllamaCluster:
    def __init__(self, node_count=4):
        self.nodes = self._initialize_nodes(node_count)
        self.load_balancer = self._setup_load_balancer()
    
    def distribute_analysis(self, large_dataset):
        """
        Distribute analysis across multiple Ollama instances
        """
        node_assignments = self._assign_data_to_nodes(large_dataset)
        
        futures = []
        for node, data_chunk in node_assignments.items():
            future = self._submit_to_node(node, data_chunk)
            futures.append(future)
        
        # Collect results from all nodes
        results = self._gather_results(futures)
        
        return self._merge_analysis_results(results)