.cursorrules for AI and Financial Computing Project

# .cursorrules for AI and Financial Computing Project

# General Guidelines
1. Prioritize code readability and maintainability.
2. Ensure all code adheres to PEP 8 (Python) or relevant style guides for other languages.
3. Use meaningful variable and function names that reflect their purpose.

# AI-Specific Rules
1. Implement machine learning models using libraries such as TensorFlow or PyTorch.
2. Use Scikit-learn for traditional machine learning algorithms.
3. Ensure all models are properly validated using techniques like cross-validation.
4. Document model performance metrics (e.g., accuracy, precision, recall) clearly.

# Financial Computing Guidelines
1. Use appropriate data types for financial calculations (e.g., Decimal for currency).
2. Implement risk management frameworks in all trading algorithms.
3. Ensure compliance with relevant financial regulations (e.g., GDPR, MiFID II).
4. Use libraries like Pandas for data manipulation and NumPy for numerical computations.
5. Use Plotly

# Data Handling
1. Ensure all data sources are validated and sanitized before use.
2. Handle missing data appropriately, using imputation techniques where necessary.
3. Store sensitive financial data securely, following best practices for encryption.

# Testing and Validation
1. Write unit tests for all functions, especially those handling financial calculations.
2. Use test-driven development (TDD) principles where applicable.
3. Validate models against out-of-sample data to ensure robustness.

# Performance Optimization
1. Optimize code for performance, especially in high-frequency trading scenarios.
2. Profile code to identify bottlenecks and refactor as necessary.
3. Use parallel processing where applicable to speed up computations.

# Documentation
1. Document all functions with clear docstrings explaining parameters and return values.
2. Maintain a README file that outlines project goals, setup instructions, and usage examples.