Used Car Price Tracker for Ecuador

A Python web scraper that tracks used car prices across major Ecuadorian automotive marketplaces.

Features

• Multi-site Scraping: Collects data from PatioTuerca and OLX Ecuador with expandable architecture for additional sites
• Price Tracking: Monitors price changes over time for all listings
• Data Analysis: Calculates average prices, identifies deals, and tracks market trends
• Automated Notifications: Sends email alerts for price drops and new listings
• Reporting: Generates HTML reports with market insights and significant price changes
• Scheduling: Runs daily jobs automatically to keep data current

Installation

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Setup

1. Clone the repository:

   git clone https://github.com/your-username/ecuador-car-price-tracker.git
   cd ecuador-car-price-tracker

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required packages:

   pip install -r requirements.txt

4. Configure email settings:

   • Open car_price_tracker.py
   • Update the email configuration in the send_email_notification method
   • If using Gmail, create an app password in your Google account security settings

Usage

Basic Usage

Run the script once:

python car_price_tracker.py

This will:

• Scrape car listings from supported websites
• Store data in a SQLite database (car_prices.db)
• Generate an HTML report (car_prices_report.html)
• Send an email notification if configured

Continuous Monitoring

To run the tracker continuously with scheduled jobs:

Linux/Mac:

nohup python car_price_tracker.py > car_tracker.log 2>&1 &

Windows:

Create a batch file run_tracker.bat:

@echo off
cd C:\path\to\script\directory
python car_price_tracker.py

Add this to Windows Task Scheduler to run at system startup.

Customization

Adding New Websites

1. Create a new scraping method following the pattern of existing ones:

   def scrape_new_site(self, max_pages=3):
       # Implementation here
       return new_listings, updated_prices

2. Add a call to your new method in the run_daily_job method

Modifying Email Alerts

Customize the email alert criteria in the run_daily_job method to match your preferences.

Adjusting Scraping Frequency

Modify the scheduling in the main section:

# Change from daily to every 12 hours
schedule.every(12).hours.do(tracker.run_daily_job, email="your_email@example.com")

Database Schema

The script uses SQLite with two main tables:

Cars Table

• id: Primary key
• listing_id: Original ID from the source website
• website: Source website name
• title: Listing title
• make: Car manufacturer
• model: Car model
• year: Manufacturing year
• mileage: Odometer reading
• location: City/region in Ecuador
• url: Original listing URL
• seller_type: Dealer or private
• features: Additional features
• first_seen: Date first discovered

Prices Table

• id: Primary key
• car_id: Foreign key to cars table
• price: Price in USD
• date: Date the price was recorded

Reports

The HTML report includes:

• Total number of listings
• Number of cars with price changes
• Top car makes by popularity
• Recent price drops with links to listings

Logging

The script logs all activities to car_tracker.log, including:

• Scraping starts and completions
• Database operations
• Errors and exceptions
• Email notifications

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Disclaimer

This script is for educational purposes only. Please review and respect the Terms of Service of each website before deploying this scraper.