# Copilot Instructions for MarketScanner

## Project Overview
MarketScanner is a "fear-to-fortune" trading intelligence system that identifies buying opportunities by analyzing how stocks historically respond to panic-inducing news.

## Tech Stack
- **Backend**: Python 3.12 + FastAPI + Celery + PostgreSQL/TimescaleDB + Redis
- **Frontend**: React 18 + TypeScript + TailwindCSS + Vite
- **Infrastructure**: Docker + Docker Compose

## Key Concepts
1. **Panic Detection**: Monitor news sentiment and price drops to identify panic events
2. **Pattern Matching**: Match current panic against historical recovery patterns
3. **Confidence Scoring**: Calculate buy signal confidence based on historical success rates
4. **Real-time Monitoring**: Continuous news and price data fetching via Celery workers

## Code Style
- Python: Follow PEP 8, use type hints, async/await for database operations
- TypeScript: Strict mode, functional components with hooks
- Use structured logging (structlog)
- Prefer composition over inheritance

## Database
- TimescaleDB hypertables for time-series data (stock_prices)
- UUID primary keys
- Soft deletes where appropriate

## API Design
- RESTful endpoints under `/api/v1/`
- Pydantic schemas for validation
- Pagination with skip/limit

## Testing
- pytest for Python
- Mock external APIs in tests
- Test critical business logic (pattern matching, confidence scoring)