Price Tracker

Fee Structure Reference:

• Amazon: ~15% referral fee
• eBay: ~13% final value fee + listing fees
• Walmart: ~8-15% referral fee

Example Request: "Find Nintendo Switch bundles where eBay price is 20%+ higher than Amazon, accounting for all fees and shipping costs."

3. Historical Price Tracking

Price History:

• Track price changes over time (30, 60, 90 days)
• Identify seasonal pricing patterns
• Detect price manipulation or flash sales
• Export historical data for analysis

Example Request: "Show me the price history for AirPods Pro 2 over the last 60 days. Identify the best buying window."

4. Automated Alerts

Alert Configuration:

• Price drop alerts (below threshold)
• Arbitrage opportunity alerts (margin threshold)
• Competitor price alerts (when competitor lowers price)
• Bulk product monitoring

Example Request: "Set up alerts for all Sony TV models. Alert me if any model drops below $400 or has 25%+ arbitrage margin."

Quick Start

Track a Single Product

# Use scripts/track_product.py
python3 scripts/track_product.py \
  --product "Apple iPhone 15 Pro 256GB" \
  --platforms amazon,ebay \
  --alert-below 800 \
  --alert-margin 0.20

Bulk Monitor Products from CSV

# Use scripts/bulk_monitor.py
python3 scripts/bulk_monitor.py \
  --csv products.csv \
  --margin-threshold 0.25 \
  --alert-frequency daily

Price Comparison Report

# Use scripts/compare_prices.py
python3 scripts/compare_prices.py \
  --keyword "Sony WH-1000XM5" \
  --platforms amazon,ebay,walmart,bestbuy \
  --report markdown

Workflow

Arbitrage Opportunity Discovery

1. Search for products in high-demand categories (electronics, gaming, home goods)
2. Compare prices across all platforms using compare_prices.py
3. Calculate net profit after fees/shipping/taxes
4. Filter opportunities with 20%+ margin
5. Verify product condition and seller reliability
6. Execute or set monitoring for price drops

Price Drop Monitoring

1. Identify target products (wishlist, seasonally discounted items)
2. Set alert thresholds using track_product.py
3. Monitor historical patterns to predict optimal buy windows
4. Act when price drops below threshold
5. Repeat for seasonal shopping events (Prime Day, Black Friday)

Scripts

track_product.py

Track a single product across platforms with configurable alerts.

Parameters:

• --product: Product name/keyword
• --platforms: Comma-separated platforms (amazon,ebay,walmart,bestbuy)
• --alert-below: Alert when price drops below this amount
• --alert-margin: Alert when arbitrage margin exceeds this fraction (e.g., 0.20 = 20%)
• --frequency: Check frequency (hourly,daily,weekly)
• --output: Output format (json,csv,markdown)

Example:

python3 scripts/track_product.py \
  --product "Samsung Galaxy S24 Ultra 256GB" \
  --platforms amazon,ebay,walmart \
  --alert-below 900 \
  --alert-margin 0.25 \
  --frequency daily \
  --output markdown

compare_prices.py

Compare prices for a product across all platforms.

Parameters:

• --keyword: Product search keyword
• --platforms: Comma-separated platforms (default: all)
• --report: Report format (markdown,json,csv)
• --sort-by: Sort by price, margin, or rating
• --min-rating: Minimum seller rating

Example:

python3 scripts/compare_prices.py \
  --keyword "PlayStation 5 Slim" \
  --platforms amazon,ebay,walmart,bestbuy \
  --report markdown \
  --sort-by margin \
  --min-rating 4.5

bulk_monitor.py

Monitor multiple products from a CSV file.

CSV Format:

product,platforms,alert_below,alert_margin
"Apple MacBook Air M3 256GB",amazon,ebay,walmart,899,0.20
"Sony PlayStation 5",amazon,ebay,399,0.25
"Dyson V15 Detect",amazon,walmart,bestbuy,500,0.18

Parameters:

• --csv: Path to CSV file
• --margin-threshold: Minimum margin to report
• --alert-frequency: Frequency of alerts
• --output: Output file for alerts

Example:

python3 scripts/bulk_monitor.py \
  --csv products.csv \
  --margin-threshold 0.20 \
  --alert-frequency daily \
  --output alerts.txt

price_history.py

Retrieve and analyze historical price data.

Parameters:

• --product: Product name/keyword
• --days: Number of days of history (default: 30)
• --platform: Specific platform (optional)
• --output: Output format (markdown,json,csv)
• --trend-analysis: Include trend analysis and predictions

Example:

python3 scripts/price_history.py \
  --product "AirPods Pro 2" \
  --days 60 \
  --trend-analysis \
  --output markdown

Best Practices

Arbitrage Profit Calculation

Always calculate net profit:

Net Profit = (Sell Price - Buy Price)
            - Platform Fees
            - Shipping Costs
            - Payment Processing Fees
            - Taxes

Recommended minimum margin: 20-25% to account for:

• Unexpected shipping delays
• Returns/refunds
• Market price fluctuations
• Time value of money

Risk Mitigation

1. Verify seller reliability - Check ratings and reviews
2. Check product condition - New, refurbished, or used
3. Factor in return windows - Platforms have different policies
4. Monitor price stability - Volatile prices increase risk
5. Stay within limits - Don't over-leverage on single opportunities

Seasonal Patterns

• Q4 (Oct-Dec): Holiday sales, best for electronics
• January: Post-holiday clearance
• Prime Day (July): Amazon-specific deals
• Black Friday/Cyber Monday: Cross-platform discounts
• Back-to-School (Aug-Sep): Laptops, tablets, accessories

Automation Integration

Set Up Cron Jobs for Automated Monitoring

# Check prices every 6 hours
0 */6 * * * /path/to/price-tracker/scripts/bulk_monitor.py --csv products.csv --output alerts.txt

# Daily arbitrage scan
0 9 * * * /path/to/price-tracker/scripts/compare_prices.py --keyword "high-demand-products" --report markdown >> /path/to/reports.txt

Integration with Notifications

Combine with notification systems (email, Discord, Telegram) to receive real-time alerts when opportunities are detected.

Limitations

• Platform API rate limits may affect search frequency
• Real-time prices may have slight delays
• Some platforms restrict scraping (comply with ToS)
• Seller inventory changes rapidly

Revenue first. Track smart. Flip fast.