Using Big Data for Competitive Advantage: How 4G/5G Mobile Proxies Enable Advanced Market Intelligence

"""
Strategic Market Intelligence Collection System
---------------------------------------------
This system demonstrates how to implement a robust data collection framework 
using 4G/5G mobile proxies for competitive intelligence gathering.
"""

import requests
import logging
from datetime import datetime
from typing import Dict, List, Optional, Union
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor

# Configure logging for operational monitoring
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger('market_intelligence')

@dataclass
class ProxyConfig:
    """Configuration for a specific mobile proxy endpoint"""
    country: str
    region: str
    carrier: str
    url: str
    username: str
    password: str
    
    @property
    def proxy_url(self) -> str:
        """Format the full proxy URL with authentication"""
        return f"http://{self.username}:{self.password}@{self.url}"

class MobileProxyManager:
    """Manages a pool of mobile proxies for optimal data collection"""
    
    def __init__(self, proxy_configs: List[ProxyConfig], rotation_threshold: int = 10):
        self.proxies = proxy_configs
        self.rotation_threshold = rotation_threshold
        self.usage_count = {proxy.proxy_url: 0 for proxy in self.proxies}
        self.success_rate = {proxy.proxy_url: 1.0 for proxy in self.proxies}
        logger.info(f"Initialized proxy manager with {len(self.proxies)} mobile proxies")
    
    def get_optimal_proxy(self, target_country: Optional[str] = None, 
                         target_region: Optional[str] = None) -> str:
        """
        Select the optimal proxy based on target location, success rate, and usage
        
        Args:
            target_country: Preferred country for data collection
            target_region: Preferred region within country
            
        Returns:
            Best proxy URL for the current request
        """
        # Filter proxies by location if specified
        candidates = self.proxies
        if target_country:
            candidates = [p for p in candidates if p.country == target_country]
            if target_region:
                candidates = [p for p in candidates if p.region == target_region]
        
        # If no location match, fall back to all proxies
        if not candidates:
            candidates = self.proxies
            
        # Select proxy with highest success rate and lowest usage
        best_proxy = max(
            candidates,
            key=lambda p: (self.success_rate[p.proxy_url], -self.usage_count[p.proxy_url])
        )
        
        # Update usage count
        self.usage_count[best_proxy.proxy_url] += 1
        
        # Check if rotation is needed
        if self.usage_count[best_proxy.proxy_url] >= self.rotation_threshold:
            logger.info(f"Rotating proxy {best_proxy.url} after {self.rotation_threshold} uses")
            self.usage_count[best_proxy.proxy_url] = 0
            
        return best_proxy.proxy_url
    
    def update_proxy_performance(self, proxy_url: str, success: bool) -> None:
        """
        Update proxy success rate based on collection results
        
        Args:
            proxy_url: The proxy URL used
            success: Whether the collection was successful
        """
        # Apply exponential smoothing to update success rate
        alpha = 0.3  # Smoothing factor
        current_rate = self.success_rate.get(proxy_url, 1.0)
        new_data_point = 1.0 if success else 0.0
        self.success_rate[proxy_url] = (alpha * new_data_point) + ((1 - alpha) * current_rate)

class MarketIntelligenceCollector:
    """Core engine for collecting competitive market intelligence data"""
    
    def __init__(self, proxy_manager: MobileProxyManager):
        self.proxy_manager = proxy_manager
        self.headers = {
            "User-Agent": "Mozilla/5.0 (iPhone; CPU iPhone OS 15_0 like Mac OS X) AppleWebKit/605.1.15",
            "Accept-Language": "en-US,en;q=0.5",
            "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8"
        }
        
    def collect_regional_data(self, 
                            target_url: str, 
                            target_country: Optional[str] = None,
                            target_region: Optional[str] = None) -> Dict:
        """
        Collect data from a specific URL using regional proxies
        
        Args:
            target_url: The URL to collect data from
            target_country: Preferred country for data collection
            target_region: Preferred region within country
            
        Returns:
            Dictionary containing collected data and metadata
        """
        proxy_url = self.proxy_manager.get_optimal_proxy(target_country, target_region)
        proxies = {"http": proxy_url, "https": proxy_url}
        
        try:
            logger.info(f"Collecting data from {target_url} via {proxy_url}")
            response = requests.get(
                target_url,
                proxies=proxies,
                headers=self.headers,
                timeout=30
            )
            
            success = response.status_code == 200
            self.proxy_manager.update_proxy_performance(proxy_url, success)
            
            if success:
                # In a real implementation, you would extract structured data here
                # using libraries like BeautifulSoup, lxml, etc.
                return {
                    "success": True,
                    "url": target_url,
                    "status_code": response.status_code,
                    "proxy_used": proxy_url.split('@')[-1],  # Hide credentials
                    "data": response.text[:500] + "...",  # Truncated for example
                    "timestamp": datetime.now().isoformat(),
                    "region": target_region,
                    "country": target_country
                }
            else:
                logger.warning(f"Failed to collect data: HTTP {response.status_code}")
                return {
                    "success": False,
                    "url": target_url,
                    "status_code": response.status_code,
                    "error": f"HTTP error: {response.status_code}",
                    "timestamp": datetime.now().isoformat()
                }
                
        except Exception as e:
            logger.error(f"Error collecting data: {str(e)}")
            self.proxy_manager.update_proxy_performance(proxy_url, False)
            return {
                "success": False,
                "url": target_url,
                "error": str(e),
                "timestamp": datetime.now().isoformat()
            }
    
    def batch_collect(self, 
                     targets: List[Dict[str, str]], 
                     max_workers: int = 5) -> List[Dict]:
        """
        Collect data from multiple targets in parallel
        
        Args:
            targets: List of dictionaries with 'url', optional 'country' and 'region'
            max_workers: Maximum number of parallel collection threads
            
        Returns:
            List of collection results
        """
        results = []
        
        with ThreadPoolExecutor(max_workers=max_workers) as executor:
            futures = []
            for target in targets:
                futures.append(
                    executor.submit(
                        self.collect_regional_data,
                        target_url=target['url'],
                        target_country=target.get('country'),
                        target_region=target.get('region')
                    )
                )
            
            for future in futures:
                results.append(future.result())
                
        # Log collection statistics
        success_count = sum(1 for r in results if r['success'])
        logger.info(f"Batch collection complete: {success_count}/{len(results)} successful")
        
        return results


# Example usage of the framework
def main():
    # Define proxy configurations (in production, load from secure configuration)
    proxy_configs = [
        ProxyConfig(country="US", region="New York", carrier="T-Mobile", 
                  url="us-ny.4g.coronium.io:7000", username="user", password="pass"),
        ProxyConfig(country="US", region="Texas", carrier="AT&T", 
                  url="us-tx.4g.coronium.io:7000", username="user", password="pass"),
        ProxyConfig(country="DE", region="Berlin", carrier="Deutsche Telekom", 
                  url="de.4g.coronium.io:7000", username="user", password="pass"),
        ProxyConfig(country="UK", region="London", carrier="Vodafone", 
                  url="uk.4g.coronium.io:7000", username="user", password="pass"),
        ProxyConfig(country="FR", region="Paris", carrier="Orange", 
                  url="fr.4g.coronium.io:7000", username="user", password="pass"),
    ]
    
    # Initialize the proxy manager and intelligence collector
    proxy_manager = MobileProxyManager(proxy_configs, rotation_threshold=10)
    collector = MarketIntelligenceCollector(proxy_manager)
    
    # Define target URLs for collection
    collection_targets = [
        {"url": "https://example-marketplace.com/products/electronics", "country": "US", "region": "New York"},
        {"url": "https://example-retailer.com/offers/weekly", "country": "DE", "region": "Berlin"},
        {"url": "https://example-review-site.com/top-rated", "country": "UK", "region": "London"},
        {"url": "https://example-marketplace.com/products/electronics", "country": "FR", "region": "Paris"},
    ]
    
    # Perform batch collection
    results = collector.batch_collect(collection_targets, max_workers=4)
    
    # In a real implementation, you would now:
    # 1. Process and validate the collected data
    # 2. Transform it into a standardized format
    # 3. Store it in your data warehouse or database
    # 4. Trigger analytics pipelines for insight generation
    
    # Example of simple results summarization
    for result in results:
        status = "✅ Success" if result.get('success') else "❌ Failed"
        print(f"{status}: {result.get('url')} via {result.get('proxy_used', 'N/A')}")


if __name__ == "__main__":
    main()