The strategy below was completely generated by Claude.ai.
The details have not been fully verified, but they seem likely to be correct and are intended to be followed for this development. Transparency is a key part of this project, and this strategy effectively incorporates it as a core value.
Core Objectives
- Automated data collection from multiple sources
- Transparent, unbiased price and economic indicator tracking
- Simple, clean user interface
- Open-source and community-verifiable data collection
- Technical Architecture
Data Collection Layer
- Python-based data scraping and collection
- Automated scheduled jobs using Airflow or GitHub Actions
- Sources to collect:
- Walmart product prices
- Gasoline prices (per region/state)
- Electricity rates (per region/state)
- Stock market closing prices
- Mortgage rates
- Consumer price indices
- Other economic indicators
Data Storage
- PostgreSQL database
- Schemas designed for:
- Raw data collection timestamps
- Product/metric identifiers
- Price/value data
- Source metadata
- Implemented with SQLAlchemy ORM
- Daily snapshots stored with full historical tracking
Backend Infrastructure
- FastAPI for lightweight, performant API
- Data normalization and cleaning processes
- API endpoints for:
- Raw data retrieval
- Aggregated Statistics
- Time-series data
Frontend Design
- React.js single-page application
- Recharts or D3.js for data visualization
- Key visualizations:
- Line graphs showing price trends
- Comparative charts across different metrics
- Downloadable CSV/Excel exports
- Minimalist, information-focused design
- Responsive mobile and desktop layout
Deployment & Hosting
- Docker containerization
- Hosted on:
- DigitalOcean droplet
- Alternatively: Render or Heroku
- GitHub Actions for CI/CD
- Cloudflare for DNS and basic DDoS protection
Transparency Features
- Full source code on GitHub
- Detailed documentation of:
- Data collection methods
- Source URLs
- Normalization techniques
- Update frequencies
- Public issue tracker for community feedback
- Clear explanation of data sources on website
Potential Future Enhancements
- API access for researchers/developers
- More granular regional data
- Additional economic indicators
- Community-suggested data sources
Let me (this was written by Claude.ai) elaborate on some key design considerations:
Data Collection Philosophy
- The core of DailyTracker.Life is radical transparency. This means:
- Showing exactly how data is collected
- Providing raw data alongside visualizations
- Being completely open about methodologies
- No hidden algorithms or mysterious data transformations
Technical Challenges to Consider
- Handling diverse data sources with different formats
- Creating robust error handling for web scraping
- Ensuring consistent data normalization
- Managing storage of historical data efficiently
Recommended Technology Stack
- Backend: Python (FastAPI, Scrapy, Airflow)
- Database: PostgreSQL
- Frontend: React.js
- Visualization: Recharts
- Deployment: Docker, GitHub Actions
Potential Data Sources
- Walmart: Product price APIs or web scraping
- GasBuddy: Gasoline prices
- EIA.gov: Electricity rates
- Alpha Vantage: Stock market data
- Federal Reserve Economic Data (FRED)
- Bureau of Labor Statistics