Price Intelligence Tracker

Long-term price tracking that teaches strategic thinking about recurring purchases - particularly for chronic condition supplies where necessity meets volatile pricing.

The Challenge

Modern retail conditions us for reactive buying: same-day delivery, "limited time" urgency, algorithmic price changes. For people managing chronic conditions requiring regular supply repurchase, this mindset becomes expensive:

• Hand-to-mouth purchasing: Buy when you run out, at whatever today's price is
• Information gaps: No baseline for what products should cost
• Manufactured urgency: "Sale ends tonight" creates pressure to buy now
• Cognitive load: Managing symptoms leaves little bandwidth for price strategy
• Trust assumptions: Healthcare-adjacent retailers use medical branding to justify markup

Real scenario: Clinic DME suppliers charge $40 for CPAP filters available online for $12. Newly diagnosed patients don't know to question this, and urgency prevents comparison shopping.

What This Does

Instead of reacting to today's price, this tool builds historical intelligence over months and years, revealing patterns that enable strategic purchasing:

• Price baselines: What's normal, what's markup, what's genuinely low
• Seasonal cycles: When do prices reliably drop? When should you stock up?
• Fake sales detection: Was that "40% off" actually a discount, or theater?
• Retailer comparison: Track the same product across multiple sources over time
• Long-term extrapolation: Small strategic decisions compound into significant annual savings

This shifts thinking from "what's cheapest today" to "what's my strategy for the next 6-12 months."

Why This Matters

The Retail Conditioning Problem

Modern e-commerce trains us for immediacy: same-day delivery, flash sales, "buy now" urgency. This works brilliantly for retailers but poorly for consumers managing recurring necessities.

For chronic condition supplies requiring weekly/monthly repurchase:

• Buying reactively (when you run out) means accepting whatever today's price is
• No accumulated knowledge of price patterns or timing
• Strategic purchasing (stocking up during lows) becomes impossible without data
• Small price differences (10-20%) compound into hundreds annually

The Strategic Advantage

Historical data enables thinking in cycles rather than moments:

Scenario: CPAP filters you need monthly

• Reactive buying: $18-28 depending on when you run out = ~$270/year
• Strategic buying: Stock up when they hit $12 every Q3 = ~$144/year
• Savings: $126/year on one product category

Multiply across multiple recurring purchases (eczema cream, contact solution, diabetic strips), and small timing decisions compound significantly.

Growing Market, Growing Relevance

These conditions are increasing at epidemic rates:

• Eczema/Dermatitis: 31.6M Americans, growing due to environmental factors
• Sleep Apnea: 39M diagnosed (up from 25M in 2017)
• Diabetes: 38.4M requiring ongoing supplies

As these populations grow, so does the value of long-term price intelligence.

What Historical Data Enables

Rather than "what's cheapest today," you can ask:

• "When do prices reliably drop for this category?"
• "Should I stock up now or wait two months?"
• "Is this 'sale' actually worth buying, or just marketing?"
• "What's my 12-month supply strategy cost vs. buying hand-to-mouth?"

This isn't about obsessing over pennies. It's about strategic thinking replacing reactive consumption - particularly valuable when managing conditions that already demand significant mental bandwidth.

In an era of algorithmic pricing and manufactured urgency, having your own historical price data is a superpower.

How It Works

Current State:

• Manual price entry with web interface
• SQLite database storing price history with timestamps
• Visual price charts showing trends over time
• Deal indicator based on historical average

Planned Features:

• Automated web scraping for hands-free data collection
• Multi-retailer support (Walmart, Target, Amazon, etc.)
• Smart scheduling (daily/weekly checks per product)
• Price alerts when items hit historical lows
• Export/sharing of trend data

Why Automated Scraping?

The real value comes from consistent long-term tracking:

• 1 check per day × 730 days = 2 years of pricing intelligence
• Minimal server impact (respectful, low-frequency requests)
• Builds data while you're not actively shopping
• Reveals patterns you'd never catch manually

Scraping Strategy:

• Low-frequency checks (once daily per product)
• Randomized timing and delays between requests
• Graceful failure handling (retry tomorrow if blocked)
• Respects robots.txt and rate limits
• Focus on public product pages only

Technical Stack

Backend:

• Python 3.x with Flask
• SQLite database for price history
• BeautifulSoup4 + Requests for web scraping
• Optional: Selenium for JavaScript-heavy sites

Frontend:

• HTML/CSS/JavaScript
• Chart.js for price visualization
• Responsive design for mobile tracking

Deployment:

• Raspberry Pi or home server for 24/7 operation
• Optional: Heroku/Railway for cloud hosting
• Scheduled tasks via cron or APScheduler

Project Structure

price-intelligence-tracker/
├── src/
│   ├── app.py              # Flask web server
│   ├── models.py           # Database models (Product, PricePoint)
│   ├── scrapers/
│   │   ├── base.py         # Abstract scraper class
│   │   ├── walmart.py      # Walmart-specific scraper
│   │   ├── target.py       # Target-specific scraper
│   │   └── amazon.py       # Amazon scraper (challenging!)
│   ├── scheduler.py        # Automated scraping jobs
│   └── utils.py            # Helper functions
├── static/
│   ├── css/
│   └── js/
├── templates/
│   ├── index.html          # Dashboard
│   └── product.html        # Individual product view
├── data/
│   └── prices.db           # SQLite database
├── requirements.txt
└── README.md

Setup & Installation

# Clone the repository
git clone https://github.com/yourusername/price-intelligence-tracker.git
cd price-intelligence-tracker

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

# Install dependencies
pip install -r requirements.txt

# Initialize database
python src/models.py

# Run the web interface
python src/app.py

Visit http://localhost:5001 to start tracking prices. Airplay conflicts with 5000.

Usage Examples

Manual Entry (Current):

1. Navigate to product page on retailer site
2. Copy product URL and current price
3. Add to tracker via web interface
4. View historical chart and statistics

Automated Scraping (Planned):

1. Add product URL to tracking list
2. Configure check frequency (daily recommended)
3. Scraper runs automatically in background
4. Review trends and alerts via dashboard

Ethical & Legal Considerations

What's Generally OK:

• Scraping public product pages for personal use
• Respectful, low-frequency automated requests
• Storing publicly available pricing data
• Using data for personal purchase decisions

What to Avoid:

• High-frequency scraping that impacts server load
• Bypassing CAPTCHAs or anti-bot measures aggressively
• Scraping user accounts or protected content
• Reselling or redistributing scraped data commercially

Best Practices:

• Always include descriptive User-Agent headers
• Respect robots.txt directives
• Add random delays between requests (30-120 seconds)
• Handle errors gracefully (don't retry-spam)
• Start with small product lists (5-10 items)

Roadmap

Phase 1: Foundation ✅

• Basic Flask web interface
• SQLite database with price history
• Manual price entry
• Simple chart visualization

Phase 2: Intelligence (Current)

• Automated scraping for 2-3 major retailers
• Scheduled daily price checks
• Price trend analysis (highs, lows, averages)
• Deal quality scoring

Phase 3: Expansion

• Multi-user support
• Price alerts (email/SMS when hitting targets)
• Mobile app or Progressive Web App
• Category-level trend analysis

Phase 4: Advanced

• Machine learning for price prediction
• API for third-party integrations
• Browser extension for instant tracking
• Collaborative pricing database

Known Limitations

• Retailer Changes: Sites frequently update HTML structure, breaking scrapers
• Anti-Bot Measures: Some retailers actively block automated access
• JavaScript Content: Some prices load dynamically, requiring Selenium
• Regional Pricing: Prices vary by location, requires geo-awareness
• Sale Types: Memberships, coupons, and promo codes add complexity

Contributing

This is a personal learning project, but suggestions and improvements are welcome! Areas that need work:

• Scraper implementations for specific retailers
• Better error handling and retry logic
• UI/UX improvements
• Data visualization enhancements
• Testing and documentation

License

MIT License - See LICENSE file for details

Disclaimer

This tool is for personal educational use. Users are responsible for complying with retailers' Terms of Service and applicable laws. The author is not responsible for any consequences of web scraping activities.

---

The Bottom Line: In an era of algorithmic pricing and manufactured urgency, having your own historical price data is a superpower. This tool teaches strategic thinking about recurring purchases - turning reactive consumption into informed long-term planning.