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CONTRIBUTING.md

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Contributing to ProjectX Python SDK

Thank you for considering contributing to the ProjectX Python SDK! This document provides guidelines and instructions to help you contribute effectively.

Table of Contents

Development Setup

Prerequisites

  • Python 3.12 or higher
  • UV package manager (recommended)

Local Development Environment

  1. Clone the repository

    git clone https://github.com/yourusername/project-x-py.git
cd project-x-py

  2. Set up the development environment with UV (recommended)

    # Install UV if you haven't already
curl -sSf https://install.determinate.systems/uv | python3 -

# Install development dependencies
uv sync

  3. Alternative setup with pip

    pip install -e ".[dev]"

  4. Verify installation

    uv run pytest -xvs tests/

Code Style and Conventions

This project follows strict code style guidelines to maintain consistency and quality:

Python Version

  • Use Python 3.12+ features and syntax
  • Use modern typing features (e.g., int | None instead of Optional[int])

Formatting and Linting

  • We use Ruff for both formatting and linting
  • Always run these commands before submitting code: 
    # Format code
uv run ruff format .

# Lint code (with safe auto-fix)
uv run ruff check --fix .

Type Hints

  • All code MUST include comprehensive type hints
  • Use Python 3.10+ union syntax: int | None instead of Optional[int]
  • Use isinstance(x, (A | B)) instead of isinstance(x, (A, B))
  • Use dict[str, Any] instead of Dict[str, Any]

Async/Await

  • This project uses an async-first architecture
  • All I/O operations must be async
  • Use async/await consistently
  • Use appropriate locking mechanisms for thread safety

Data Processing

  • Use Polars exclusively for DataFrame operations
  • Never include Pandas fallbacks or compatibility code
  • Use vectorized operations where possible
  • Validate DataFrame schemas before operations

Error Handling

  • Wrap ProjectX API calls in try-catch blocks
  • Log errors with context: self.logger.error(f"Error in {method_name}: {e}")
  • Return meaningful error responses instead of raising exceptions
  • Validate input parameters and API data

Pull Request Process

  1. Create a feature branch from main

    git checkout -b feature/your-feature-name

  2. Implement your changes following the code style guidelines

  3. Add/update tests to cover your changes

  4. Ensure all tests pass

    uv run pytest -xvs tests/

  5. Format and lint your code

    uv run ruff format .
uv run ruff check --fix .

  6. Update documentation to reflect your changes

  7. Submit a pull request with:

    • Clear description of the changes
    • Reference to any related issues
    • Explanation of how to test the changes

  8. Address review feedback until your PR is approved

Testing Guidelines

All code contributions should include appropriate tests:

Test Coverage

  • Maintain or improve test coverage with each PR
  • Write both unit and integration tests

Test Structure

  • Follow the existing test pattern in the tests/ directory
  • Use descriptive test names (test_should_validate_market_data)
  • Include tests for both success and failure scenarios

Test Organization

  • Unit tests: Focus on testing individual functions/methods
  • Integration tests: Test the interaction between components
  • Comprehensive tests: Test full workflows and realistic scenarios

Test Execution

# Run all tests
uv run pytest

# Run specific test file
uv run pytest tests/test_async_client.py

# Run tests with coverage report
uv run pytest --cov=src/project_x_py

Documentation Requirements

Good documentation is essential for this project:

Code Documentation

  • All public classes, methods, and functions MUST have docstrings
  • Follow the established docstring format (Google style)
  • Include Args and Returns sections in docstrings
  • Document expected parameter types and return values
  • Include examples for complex methods

README and Example Updates

  • Update the README.md when adding new features
  • Add examples to the examples/ directory for significant features
  • Keep the documentation synchronized with the code

API Reference Documentation

  • Add/update Sphinx documentation for public APIs
  • Build and verify documentation changes: 
    cd docs
uv run sphinx-build -b html . _build/html

Architecture Guidelines

Project Structure

The SDK uses a modular architecture where large components are split into multi-file packages:

  • Client Module (client/): Core async client functionality

    • auth.py: Authentication and token management
    • http.py: HTTP client and request handling
    • cache.py: Caching for instruments and market data
    • market_data.py: Market data operations
    • trading.py: Trading operations
    • base.py: Base class combining mixins

  • Trading Modules:

    • order_manager/: Order lifecycle management (10 modules)
    • position_manager/: Portfolio and risk management (12 modules)

  • Real-time Modules:

    • realtime/: WebSocket client functionality (8 modules)
    • realtime_data_manager/: Real-time OHLCV data (9 modules)

  • Utilities (utils/): Shared utilities (10 modules)

    • Trading calculations, portfolio analytics, pattern detection
    • Market microstructure, formatting, environment handling

  • Indicators (indicators/): 58+ technical indicators

    • Organized by category (momentum, overlap, volatility, etc.)

Adding New Features

  • Place new functionality in the appropriate existing module
  • For large features, consider creating a new sub-module
  • Maintain backward compatibility for all public APIs
  • Follow the established mixin pattern for client extensions

Performance Considerations

  • Implement time window filtering for analysis methods
  • Filter data BEFORE processing to reduce memory usage
  • Implement appropriate data cleanup for old entries
  • Use appropriate data types (int vs float vs str)
  • Consider memory management in all components

API Design

  • Follow the established API patterns
  • Use async context managers for resource management
  • Implement proper error handling and validation
  • Provide clear feedback on API errors

ProjectX Platform Integration

  • Follow the ProjectX API documentation
  • Use configuration objects for URL management
  • Never hardcode platform URLs
  • Handle all required/optional fields in API responses
  • Support both TopStepX endpoints and custom endpoints

Reporting Issues

If you encounter any bugs or have feature requests:

  1. Check if the issue already exists in the GitHub issue tracker
  2. If not, create a new issue with:
    • Detailed description of the issue
    • Steps to reproduce
    • Expected vs actual behavior
    • Environment information (Python version, OS, etc.)

License

By contributing to this project, you agree that your contributions will be licensed under the project's MIT license.

Questions?

If you have any questions or need help, please open an issue or contact the project maintainers.

Thank you for contributing to the ProjectX Python SDK!