074787f067
Features: - FastAPI backend with stocks, news, signals, watchlist, analytics endpoints - React frontend with TailwindCSS dark mode trading dashboard - Celery workers for news fetching, sentiment analysis, pattern detection - TimescaleDB schema for time-series stock data - Docker Compose setup for all services - OpenAI integration for sentiment analysis
37 lines
1.4 KiB
Markdown
37 lines
1.4 KiB
Markdown
# Copilot Instructions for MarketScanner
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## Project Overview
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MarketScanner is a "fear-to-fortune" trading intelligence system that identifies buying opportunities by analyzing how stocks historically respond to panic-inducing news.
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## Tech Stack
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- **Backend**: Python 3.12 + FastAPI + Celery + PostgreSQL/TimescaleDB + Redis
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- **Frontend**: React 18 + TypeScript + TailwindCSS + Vite
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- **Infrastructure**: Docker + Docker Compose
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## Key Concepts
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1. **Panic Detection**: Monitor news sentiment and price drops to identify panic events
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2. **Pattern Matching**: Match current panic against historical recovery patterns
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3. **Confidence Scoring**: Calculate buy signal confidence based on historical success rates
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4. **Real-time Monitoring**: Continuous news and price data fetching via Celery workers
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## Code Style
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- Python: Follow PEP 8, use type hints, async/await for database operations
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- TypeScript: Strict mode, functional components with hooks
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- Use structured logging (structlog)
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- Prefer composition over inheritance
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## Database
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- TimescaleDB hypertables for time-series data (stock_prices)
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- UUID primary keys
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- Soft deletes where appropriate
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## API Design
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- RESTful endpoints under `/api/v1/`
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- Pydantic schemas for validation
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- Pagination with skip/limit
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## Testing
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- pytest for Python
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- Mock external APIs in tests
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- Test critical business logic (pattern matching, confidence scoring)
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