ingpoc StockAnalysis .cursorrules file for Python

# Project-Specific Instructions for Cursor AI

## General Guidelines
- Write clear, readable, and well-documented Python code
- Follow PEP 8 style guidelines for code formatting
- Use type hints consistently throughout the codebase
- Implement proper error handling and logging

## Project Structure
- Follow strict directory structure:
  - `src/api/endpoints/` - API endpoint modules
  - `src/services/` - Business logic services
  - `src/models/` - Pydantic models and schemas
  - `src/utils/` - Utility functions
  - `src/config.py` - Configuration settings
  - `tools/` - Development tools

## API Implementation
- Required endpoints:
  - `/api/v1/market-data` - Market overview
  - `/api/v1/stock/{symbol}` - Stock details
  - `/api/v1/stock/{symbol}/analysis-history` - Analysis history
  - `/api/v1/analysis/{analysis_id}` - Analysis content
  - `/api/v1/stock/{symbol}/refresh-analysis` - Generate new analysis

## Database Schema
- Collections:
  - `detailed_financials` - Stock financial data
  - `ai_analysis` - AI-generated analysis results

## AI Integration
- Use xAI's Grok model for analysis
- Required environment variables:
  - XAI_API_KEY
  - XAI_API_URL

## Caching Strategy
- TTL-based caching for:
  - Market data (1 hour)
  - Stock analysis (24 hours)
  - AI insights (6 hours)

## Error Handling
- Use custom exception classes
- Implement proper error logging
- Return appropriate HTTP status codes
- Include detailed error messages

## Performance Requirements
- Maximum response time: 500ms
- Implement connection pooling
- Use proper database indexing
- Cache frequently accessed data

## Security
- Required environment variables:
  - MONGODB_URI
  - MONGODB_DB_NAME
  - API_KEYS
  - ALLOWED_ORIGINS

## Testing
- Implement pytest test cases
- Test coverage requirements:
  - API endpoints: 90%
  - Services: 85%
  - Utils: 80%

## Documentation
- OpenAPI documentation
- Function docstrings
- README.md updates
- API endpoint documentation