A macOS desktop app for tracking sector performance and detecting statistical outliers across all 11 GICS sectors. Supports both the S&P 500 (large-cap) and Russell 2000 (small-cap) universes. Built with Tauri 2, React 19, and Rust.
Switch between S&P 500 (large-cap) and Russell 2000 (small-cap) universes with smooth flip animations and themed UI
- Dual universe support — Toggle between S&P 500 (large-cap) and Russell 2000 (small-cap) with per-universe sector summaries and outlier thresholds
- Sector overview — Live metrics for all 11 GICS sectors (price change, P/E ratio, market cap, beta)
- Outlier detection — Z-score analysis to surface undervalued, overvalued, momentum, and other anomalous stocks (1.5σ threshold for S&P 500, 2.0σ for Russell 2000)
- Real-time refresh — Fetches market data from Yahoo Finance with progress tracking; supports per-sector or full-universe refresh
- Local storage — SQLite database for offline access and historical data
- Dark mode — Follows system appearance
| Tool | Version | Install |
|---|---|---|
| Rust | stable (2021 edition) | rustup.rs |
| Node.js | 18+ | nodejs.org or brew install node |
| npm | 9+ | Included with Node.js |
| Xcode CLT | Latest | xcode-select --install |
Tauri 2 also requires macOS system dependencies. If you haven't built a Tauri app before, install the prerequisites:
xcode-select --install-
Clone the repository
git clone <repo-url> cd sector-view
-
Install frontend dependencies
npm install
-
Run in development mode
npm run tauri dev
This starts the Vite dev server on
localhost:1420and launches the Tauri window with hot-reload. -
Build for production
npm run tauri build
The compiled
.appbundle will be insrc-tauri/target/release/bundle/macos/.
sector-view/
├── src/ # React/TypeScript frontend
│ ├── components/ # UI components (SectorGrid, Header, etc.)
│ ├── hooks/ # useDatabase hook for Tauri commands
│ └── types/ # TypeScript interfaces
├── src-tauri/ # Rust backend
│ ├── src/
│ │ ├── commands.rs # Tauri commands exposed to frontend
│ │ ├── database.rs # SQLite init and migrations
│ │ ├── market_data.rs # Yahoo Finance API integration
│ │ ├── outlier_detection.rs# Z-score outlier analysis
│ │ ├── stock_discovery.rs # S&P 500 stock discovery
│ │ └── russell_discovery.rs# Russell 2000 stock discovery (iShares IWM)
│ └── migrations/ # SQLite migrations
├── package.json
└── vite.config.ts
- Frontend: React 19, TypeScript 5.8, Vite 7
- Backend: Rust, Tauri 2, SQLx (SQLite), Tokio, Reqwest
- Data: Yahoo Finance API, local SQLite database
GICS Intelligence includes comprehensive unit test coverage for both frontend and backend components, with 145 total tests ensuring code quality and reliability.
Run all frontend tests:
npm testRun tests in watch mode (re-runs on file changes):
npm run test:watchRun tests with coverage report:
npm run test:coverageWhat's tested:
- Component rendering and UI behavior (SectorCard, SectorGrid)
- User interactions and event handling
- Snapshot testing for UI consistency
- Props validation and edge cases
Run all backend tests:
cd src-tauri
cargo testRun tests with detailed output:
cd src-tauri
cargo test -- --nocaptureRun specific test modules:
cd src-tauri
cargo test outlier_detection # Z-score calculation tests
cargo test market_data # Yahoo Finance API tests
cargo test stock_discovery # S&P 500 parsing tests
cargo test russell_discovery # Russell 2000 integration testsWhat's tested:
- Mathematical accuracy — Z-score calculations, composite scoring, outlier classification
- Data parsing — Yahoo Finance JSON responses, Wikipedia HTML parsing, IWM CSV processing
- API integration — URL construction, error handling, data validation
- Database operations — SQLite queries, migrations, data consistency
- Edge cases — Missing data handling, malformed responses, boundary conditions
The test suite validates critical functionality across all major modules:
- ✅ Financial calculations — Ensures mathematical accuracy for investment-grade analysis
- ✅ Data integration — Validates API responses and data parsing reliability
- ✅ User interface — Confirms UI components render correctly across scenarios
- ✅ Database operations — Tests data storage and retrieval consistency
- ✅ Error handling — Verifies graceful degradation under failure conditions
All tests pass consistently, ensuring production-ready code quality.
GICS Intelligence is an analytical tool designed to help identify statistical anomalies in financial data. The outlier detection and analysis features are intended as starting points for your own research, not as investment recommendations.
- Not Financial Advice: This app does not provide investment, financial, or trading advice
- Research Tool: Outlier suggestions should prompt further investigation, not immediate investment decisions
- Do Your Own Research: Always conduct thorough fundamental analysis, read financial statements, and understand the business before investing
- Risk Awareness: All investments carry risk, and statistical outliers may indicate both opportunities and risks
- Data Limitations: Market data may be delayed, incomplete, or subject to errors
- Use the app to identify statistically interesting stocks
- Research thoroughly - read annual reports, earnings calls, industry analysis
- Understand the business - products, competitive position, management quality
- Consider multiple factors - not just statistical metrics
- Consult professionals if needed for significant investment decisions
Remember: A stock being a statistical outlier doesn't automatically make it a good or bad investment. Market conditions, company fundamentals, and your personal financial situation should all factor into investment decisions.
Invest responsibly.