# Cursor Rules for QuantX Project
# Project Context
This is a cryptocurrency quantitative trading system built on the Jesse framework.
The project focuses on developing and implementing efficient and scalable trading strategies for the crypto market.
All code should follow best practices for financial applications with emphasis on reliability and risk management.
# Code Style
- Follow PEP 8 style guide for Python code
- Use type hints for all function parameters and return values
- Add docstrings to all classes and methods using Google style
- Maximum line length: 100 characters
- Use 4 spaces for indentation
# Naming Conventions
- Class names: PascalCase (e.g., SimpleMovingAverage)
- Function/method names: snake_case (e.g., calculate_rsi)
- Variables: snake_case (e.g., closing_prices)
- Constants: UPPER_CASE (e.g., MAX_LEVERAGE)
- Strategy classes should end with 'Strategy' (e.g., TrendFollowingStrategy)
# File Organization
- Strategy files should be in strategies/ directory
- Each strategy should be in its own file
- Utility functions should be in utils/ directory
- Configuration files should be in config/ directory
# Documentation Requirements
- Each strategy class must include:
- Strategy description
- Required parameters
- Risk management rules
- Expected behavior in different market conditions
- All trading functions must document:
- Entry/exit conditions
- Position sizing rules
- Risk management parameters
# Code Structure
- Strategies should inherit from jesse.strategies.Strategy
- Keep strategies modular and single-responsibility
- Implement proper error handling for API calls
- Include logging for important trading events
# Testing
- All strategies must have unit tests
- Include backtesting results in strategy documentation
- Test edge cases and error conditions
- Validate risk management rules
# Security
- Never commit API keys or sensitive data
- Use environment variables for credentials
- Implement proper error handling for API failures
- Validate all input data before processing
# Performance
- Optimize calculations for time-critical operations
- Cache frequently used data when possible
- Minimize API calls in live trading
- Profile code for potential bottlenecks
# Risk Management
- Always implement position sizing rules
- Include stop-loss mechanisms
- Monitor and log risk metrics
- Implement circuit breakers for extreme conditions
golang
python
