Global Commerce Stablecoin Adoption: Ollama Cross-Border Payment Tracker

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

# Pull specialized models for financial analysis
ollama pull llama3.1:70b
ollama pull qwen2.5:32b
ollama pull codellama:13b

# Verify installation
ollama list

# Create Modelfile for payment analytics
cat > StablecoinAnalyzer << 'EOF'
FROM llama3.1:70b

# Set parameters for financial Data Analysis
PARAMETER temperature 0.1
PARAMETER top_p 0.9
PARAMETER num_ctx 4096

# System prompt for stablecoin analysis
SYSTEM """You are a specialized financial analyst focused on stablecoin adoption and cross-border payments. 

Your expertise includes:
- Analyzing blockchain transaction data
- Identifying payment corridor trends
- Calculating cost savings versus traditional methods
- Monitoring regulatory impacts on adoption
- Generating actionable business insights

Always provide specific, data-driven recommendations and cite relevant metrics in your analysis."""

EOF

# Build the custom model
ollama create stablecoin-analyzer -f StablecoinAnalyzer

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

class StablecoinTracker:
    def __init__(self):
        self.api_endpoints = {
            'ethereum': 'https://api.etherscan.io/api',
            'polygon': 'https://api.polygonscan.com/api',
            'solana': 'https://api.solana.fm',
            'tron': 'https://api.trongrid.io'
        }
        self.stablecoins = {
            'USDC': '0xa0b86a33e6c42ad6f2f43d318b2e1f6e0e6a50a3',
            'USDT': '0xdac17f958d2ee523a2206206994597c13d831ec7',
            'DAI': '0x6b175474e89094c44da98b954eedeac495271d0f'
        }
        
    def fetch_transaction_data(self, stablecoin, timeframe='24h'):
        """Fetch stablecoin transaction data for analysis"""
        try:
            # Example API call (replace with actual endpoints)
            params = {
                'module': 'account',
                'action': 'tokentx',
                'contractaddress': self.stablecoins[stablecoin],
                'startblock': '0',
                'endblock': '99999999',
                'sort': 'desc',
                'apikey': 'YOUR_API_KEY'
            }
            
            response = requests.get(self.api_endpoints['ethereum'], params=params)
            return response.json()
            
        except Exception as e:
            print(f"Error fetching data: {e}")
            return None
    
    def analyze_payment_corridors(self, transaction_data):
        """Identify top payment corridors and volumes"""
        corridors = {}
        
        for tx in transaction_data.get('result', []):
            # Extract geographic indicators from transaction patterns
            value = float(tx['value']) / 10**6  # Convert to readable units
            
            # Analyze transaction patterns (simplified example)
            if value > 10000:  # Large transactions likely B2B
                corridor_key = f"B2B_Corridor_{tx['from'][:6]}"
            else:  # Smaller transactions likely remittances
                corridor_key = f"Remittance_Corridor_{tx['from'][:6]}"
                
            corridors[corridor_key] = corridors.get(corridor_key, 0) + value
            
        return sorted(corridors.items(), key=lambda x: x[1], reverse=True)

def analyze_with_ollama(self, data, analysis_type="trend"):
    """Use Ollama to analyze stablecoin adoption patterns"""
    
    # Prepare data summary for AI analysis
    data_summary = {
        'total_volume': sum([tx['volume'] for tx in data]),
        'transaction_count': len(data),
        'average_transaction_size': sum([tx['volume'] for tx in data]) / len(data),
        'time_period': '24h',
        'top_corridors': data[:10]
    }
    
    # Create analysis prompt
    prompt = f"""
    Analyze this stablecoin payment data and provide insights:
    
    Data Summary:
    - Total Volume: ${data_summary['total_volume']:,.2f}
    - Transaction Count: {data_summary['transaction_count']:,}
    - Average Size: ${data_summary['average_transaction_size']:,.2f}
    - Top Corridors: {data_summary['top_corridors']}
    
    Please provide:
    1. Key adoption trends
    2. Growth opportunities
    3. Risk factors
    4. Recommendations for businesses
    5. Competitive advantages versus traditional banking
    """
    
    # Send to Ollama for analysis
    response = ollama.chat(
        model='stablecoin-analyzer',
        messages=[{
            'role': 'user',
            'content': prompt
        }]
    )
    
    return response['message']['content']

def generate_market_report(self, timeframe='weekly'):
    """Generate comprehensive market adoption report"""
    
    # Collect data from multiple chains
    all_data = []
    for stablecoin in self.stablecoins.keys():
        data = self.fetch_transaction_data(stablecoin, timeframe)
        if data:
            all_data.extend(data.get('result', []))
    
    # Process with Ollama
    analysis = self.analyze_with_ollama(all_data, "comprehensive")
    
    # Generate structured report
    report = {
        'timestamp': datetime.now().isoformat(),
        'timeframe': timeframe,
        'executive_summary': analysis,
        'raw_data_points': len(all_data),
        'recommendations': self.extract_recommendations(analysis)
    }
    
    return report

class RealTimeMonitor:
    def __init__(self, tracker):
        self.tracker = tracker
        self.baseline_metrics = {}
        
    def calculate_adoption_score(self, current_data, historical_data):
        """Calculate stablecoin adoption score based on multiple factors"""
        
        adoption_prompt = f"""
        Calculate a stablecoin adoption score (0-100) based on these metrics:
        
        Current Period:
        - Volume: ${current_data['volume']:,.2f}
        - Transactions: {current_data['tx_count']:,}
        - Unique Addresses: {current_data['addresses']:,}
        - Average Size: ${current_data['avg_size']:,.2f}
        
        Historical Baseline:
        - Volume Growth: {((current_data['volume'] / historical_data['volume']) - 1) * 100:.1f}%
        - Transaction Growth: {((current_data['tx_count'] / historical_data['tx_count']) - 1) * 100:.1f}%
        - Address Growth: {((current_data['addresses'] / historical_data['addresses']) - 1) * 100:.1f}%
        
        Consider:
        1. Volume growth trends
        2. User adoption (address growth)
        3. Transaction frequency
        4. Market penetration indicators
        
        Provide score and explanation.
        """
        
        response = ollama.chat(
            model='stablecoin-analyzer',
            messages=[{'role': 'user', 'content': adoption_prompt}]
        )
        
        return response['message']['content']
    
    def detect_market_shifts(self, data_stream):
        """Identify significant changes in adoption patterns"""
        
        shift_analysis = f"""
        Analyze this payment data for market shifts or anomalies:
        
        Recent 7-day patterns:
        {json.dumps(data_stream, indent=2)}
        
        Identify:
        1. Unusual volume spikes or drops
        2. New geographic adoption patterns
        3. Changes in transaction size distribution
        4. Regulatory impact indicators
        5. Competitive threats or opportunities
        
        Flag any significant changes requiring immediate attention.
        """
        
        response = ollama.chat(
            model='stablecoin-analyzer',
            messages=[{'role': 'user', 'content': shift_analysis}]
        )
        
        return response['message']['content']

def create_competitive_analysis(self, competitor_data):
    """Generate competitive intelligence report"""
    
    competitive_prompt = f"""
    Analyze competitive positioning in cross-border stablecoin payments:
    
    Market Data:
    - Our adoption metrics: {competitor_data['our_metrics']}
    - Competitor metrics: {competitor_data['competitor_metrics']}
    - Industry benchmarks: {competitor_data['benchmarks']}
    
    Traditional banking comparison:
    - Average fees: 3-6%
    - Settlement time: 2-5 days
    - Transparency: Limited
    
    Provide:
    1. Competitive advantages
    2. Market gaps to exploit
    3. Strategic recommendations
    4. Threat assessment
    5. Partnership opportunities
    """
    
    response = ollama.chat(
        model='stablecoin-analyzer',
        messages=[{'role': 'user', 'content': competitive_prompt}]
    )
    
    return response['message']['content']