`tasks/prd-new-feature-for-wine-finder.md`

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# Product Requirements Document: Red Thunder Wine Pricing Tracker

## 1. Introduction/Overview

The Red Thunder Wine Pricing Tracker is a comprehensive wine pricing monitoring system that enables users to track historical price trends, monitor current prices, and receive price predictions for wines. The system will scrape pricing data from multiple sources, provide real-time updates, and offer investment analysis capabilities to help users make informed wine purchasing and investment decisions.

**Project**: wine-finder  
**Feature**: Red Thunder Wine Pricing Tracker  
**Priority**: High  
**Tech Stack**: Node.js + Express, axios  

## 2. Goals

• Enable users to track historical wine price trends across multiple retailers
• Provide real-time price monitoring with hourly data synchronization
• Deliver price predictions and investment analysis for wine portfolios
• Implement robust error handling with graceful degradation when data sources fail
• Create a comprehensive unit testing framework for reliable system operation
• Increase user engagement through valuable pricing insights and analytics

## 3. User Stories

### US-001: Set Up Unit Testing Framework
**Description**: As a developer, I need a unit testing framework configured so that I can write and run tests for the wine pricing tracker functionality.
**Acceptance Criteria**:
- Jest testing framework is installed and configured in package.json
- Test directory structure is created with sample test file
- npm test command runs successfully
- Test coverage reporting is configured
- Typecheck passes

### US-002: Create Wine Price Data Model
**Description**: As a developer, I need a data model for wine pricing information so that I can store and retrieve price data consistently.
**Acceptance Criteria**:
- Wine price model includes fields: wineId, wineName, retailer, price, currency, timestamp, vintage
- Model validation ensures required fields are present
- Unit tests verify model creation and validation
- Typecheck passes

### US-003: Implement Basic Price Scraping Service
**Description**: As a system, I need to scrape wine prices from a retail website so that I can collect current pricing data.
**Acceptance Criteria**:
- Service can fetch HTML content from target wine retailer using axios
- Price extraction logic parses wine name and price from HTML
- Service handles HTTP errors (404, 500, timeout) gracefully
- Unit tests mock axios responses and verify parsing logic
- Typecheck passes

### US-004: Create Price Storage Service
**Description**: As a system, I need to store scraped wine prices in a database so that I can maintain historical price records.
**Acceptance Criteria**:
- Service saves wine price data with timestamp
- Duplicate price entries for same wine/retailer/day are prevented
- Database connection errors are handled with retry logic
- Unit tests verify data storage and duplicate prevention
- Typecheck passes

### US-005: Build Price History Retrieval API
**Description**: As a user, I need an API endpoint to fetch historical prices for a specific wine so that I can view price trends.
**Acceptance Criteria**:
- GET /api/wines/:wineId/prices returns price history in JSON format
- Response includes price, retailer, and timestamp for each entry
- API returns 404 for non-existent wine IDs
- Unit tests verify API response structure and error handling
- Typecheck passes

### US-006: Implement Hourly Price Update Scheduler
**Description**: As a system, I need to automatically update wine prices every hour so that pricing data stays current.
**Acceptance Criteria**:
- Scheduler runs price scraping service every hour using node-cron
- Failed scraping attempts are logged but don't crash the scheduler
- Scheduler can be started/stopped programmatically for testing
- Unit tests verify scheduler timing and error handling
- Typecheck passes

### US-007: Create Price Trend Analysis Service
**Description**: As a user, I need price trend analysis so that I can understand if wine prices are increasing or decreasing.
**Acceptance Criteria**:
- Service calculates 30-day price change percentage
- Service identifies trend direction (increasing, decreasing, stable)
- Analysis handles cases with insufficient historical data
- Unit tests verify calculation accuracy with sample data
- Typecheck passes

### US-008: Build Wine Search and Add API
**Description**: As a user, I need to search for wines and add them to my tracking list so that I can monitor specific wines.
**Acceptance Criteria**:
- GET /api/wines/search?q=:query returns matching wines
- POST /api/wines/track adds wine to user's tracking list
- API validates wine data before adding to tracking
- Unit tests verify search functionality and validation
- Typecheck passes

### US-009: Implement Basic Price Prediction
**Description**: As a user, I need basic price predictions so that I can anticipate future wine price changes.
**Acceptance Criteria**:
- Service calculates simple linear trend projection for next 30 days
- Prediction includes confidence level based on data quality
- Service handles wines with insufficient historical data
- Unit tests verify prediction calculations with known datasets
- Typecheck passes

### US-010: Create User Dashboard API
**Description**: As a user, I need a dashboard view of my tracked wines so that I can see current prices and trends at a glance.
**Acceptance Criteria**:
- GET /api/dashboard returns user's tracked wines with current prices
- Response includes price change indicators and trend information
- API handles cases where current price data is unavailable
- Unit tests verify dashboard data structure and content
- Typecheck passes

### US-011: Build Error Handling and Fallback System
**Description**: As a system, I need comprehensive error handling so that the service remains functional when external data sources fail.
**Acceptance Criteria**:
- Service falls back to cached data when scraping fails
- Users receive "last known price" indicators for stale data
- System logs all errors with appropriate severity levels
- Service continues operating even when some retailers are unavailable
- Unit tests verify fallback behavior and error logging
- Typecheck passes

### US-012: Implement Investment Analysis Features
**Description**: As a user, I need investment analysis tools so that I can evaluate wine purchases as investments.
**Acceptance Criteria**:
- Service calculates annualized return rates for wine holdings
- Analysis compares individual wine performance to market trends
- Service identifies top performing wine categories
- Unit tests verify ROI calculations with sample portfolios
- Typecheck passes

## 4. Functional Requirements

**FR-1**: System shall scrape wine pricing data from at least 2 major wine retailers  
**FR-2**: System shall update price data every hour during business hours  
**FR-3**: System shall store minimum 1 year of historical pricing data  
**FR-4**: System shall provide price trend analysis for tracked wines  
**FR-5**: System shall generate basic price predictions using historical data  
**FR-6**: System shall handle retailer website unavailability with graceful degradation  
**FR-7**: System shall maintain 99% uptime for price tracking functionality  
**FR-8**: System shall support tracking unlimited wines per user  
**FR-9**: System shall provide investment analysis including ROI calculations  
**FR-10**: System shall maintain comprehensive unit test coverage (>80%)

## 5. Non-Goals

• Real-time price alerts or notifications
• Integration with wine purchasing/e-commerce platforms
• Wine recommendation engine based on pricing
• User authentication and personal accounts (Phase 1)
• Mobile application development
• Wine inventory management features
• Social features or wine community integration

## 6. Design Considerations

• **Data Freshness**: Implement clear indicators when price data is stale or unavailable
• **Scalability**: Design data models to handle multiple retailers and thousands of wines
• **Error Recovery**: Graceful degradation when external services fail
• **Performance**: Efficient database queries for historical price data
• **Maintainability**: Modular service architecture for easy testing and updates

## 7. Technical Considerations

• **Rate Limiting**: Implement respectful scraping with delays to avoid being blocked
• **Data Storage**: Consider time-series database for efficient price history storage
• **Caching**: Implement Redis or in-memory caching for frequently accessed price data
• **Monitoring**: Add logging and metrics for scraping success rates and API performance
• **Testing**: Mock external HTTP requests for reliable unit testing
• **Error Handling**: Implement circuit breaker pattern for external service calls

## 8. Success Metrics

**Primary Metrics**:
• Daily Active Users (target: establish baseline, then +20% month-over-month)
• Average Session Duration (target: establish baseline, then +15% month-over-month)
• User Retention Rate (target: 60% weekly retention)

**Secondary Metrics**:
• Price Data Accuracy (target: 95% successful hourly updates)
• API Response Time (target: <500ms for 95th percentile)
• System Uptime (target: 99.5%)
• Number of Wines Tracked per User (target: average 10+ wines per active user)

## 9. Open Questions

1. **Data Sources**: Which specific wine retailers should be prioritized for initial scraping implementation?

2. **Legal Compliance**: What are the terms of service requirements for scraping target wine retailer websites?

3. **Data Retention**: How long should historical price data be retained before archiving?

4. **Prediction Accuracy**: What minimum accuracy threshold should price predictions meet before being displayed to users?

5. **Scalability Planning**: At what point should we consider migrating from file-based storage to a dedicated time-series database?

6. **User Feedback**: How should users report incorrect price data or request new wines to be tracked?

7. **Integration Timeline**: When should wine purchasing integration be considered for Phase 2?

8. **Performance Monitoring**: What third-party monitoring tools should be integrated for production deployment?