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CONTRIBUTING.md

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Contributing Guidelines

Thank you for your interest in contributing to this Financial Time Series Forecasting project!

Getting Started

  1. Fork the repository
  2. Clone your fork: 
    git clone https://github.com/yourusername/ML-Intern.git
cd ML-Intern
  3. Install dependencies: 
    pip install -r requirements.txt
  4. Run tests: 
    python -m pytest tests/

Development Workflow

1. Create a Branch

git checkout -b feature/your-feature-name
# or
git checkout -b fix/bug-description

2. Make Your Changes

  • Follow the existing code style
  • Add docstrings to functions and classes
  • Include type hints where appropriate
  • Write unit tests for new features

3. Test Your Changes

# Run all tests
python -m pytest tests/

# Run specific test
python -m pytest tests/test_models.py

# Check code coverage
python -m pytest tests/ --cov=src

4. Commit Your Changes

git add .
git commit -m "feat: add new feature description"
# or
git commit -m "fix: bug fix description"

Commit Message Format:

  • feat: New feature
  • fix: Bug fix
  • docs: Documentation changes
  • test: Test additions or modifications
  • refactor: Code refactoring
  • style: Code style changes (formatting, etc.)

5. Push and Create Pull Request

git push origin feature/your-feature-name

Then create a Pull Request on GitHub.

Code Style Guidelines

Python Code

  • PEP 8: Follow Python style guide
  • Type Hints: Use type annotations 
    def train_model(model: nn.Module, data: torch.Tensor) -> Dict[str, float]:
    ...
  • Docstrings: Use Google-style docstrings 
    def calculate_rsi(prices: np.ndarray, window: int = 14) -> np.ndarray:
    """
    Calculate Relative Strength Index.
    
    Args:
        prices: Array of closing prices
        window: Lookback period (default: 14)
        
    Returns:
        Array of RSI values
    """

File Organization

  • Keep files under 500 lines
  • One class per file when possible
  • Group related functions

Configuration Files

  • Use YAML for configurations
  • Include comments explaining parameters
  • Provide default values

Testing Guidelines

Writing Tests

  • Test files should mirror source structure (test_models.py for models/)
  • Use descriptive test names: test_lstm_forward_pass_correct_shape()
  • Include edge cases and error conditions

Test Coverage

  • Aim for >80% code coverage
  • Focus on critical paths first
  • Test error handling

Documentation

Code Documentation

  • All functions need docstrings
  • Complex algorithms need inline comments
  • Update README.md if adding features

Report Documentation

When adding features that affect results:

  • Update reports/PROJECT_ANALYSIS_REPORT.md
  • Add figures to reports/figures/
  • Update relevant tables in reports/tables/

Adding New Models

  1. Create model file: src/models/your_model.py
  2. Implement base interface: 
    class YourModel(nn.Module):
    def __init__(self, input_dim: int, ...):
        ...
    
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        ...
  3. Add configuration: configs/your_model.yaml
  4. Write tests: tests/test_your_model.py
  5. Update documentation: Add to README.md and reports

Adding New Features

Technical Indicators

  1. Add to src/data/indicators.py
  2. Update add_technical_indicators() function
  3. Document in reports/tables/TECHNICAL_INDICATORS_TABLE.md
  4. Test in tests/test_preprocess.py

Evaluation Metrics

  1. Add to src/evaluation/metrics.py
  2. Write unit tests
  3. Document purpose and formula

Pull Request Guidelines

PR Checklist

  • Code follows style guidelines
  • Tests added/updated
  • Tests pass locally
  • Documentation updated
  • No unnecessary files included
  • Descriptive PR title and description

PR Description Template

## Description
Brief description of changes

## Type of Change
- [ ] Bug fix
- [ ] New feature
- [ ] Documentation update
- [ ] Performance improvement

## Testing
Describe testing performed

## Screenshots (if applicable)
Add screenshots for UI changes

Code Review Process

  1. Automated Checks: CI/CD runs tests
  2. Code Review: Maintainer reviews code
  3. Feedback: Address review comments
  4. Approval: Once approved, PR is merged

Questions or Issues?

  • Author: Mohansree Vijayakumar
  • Email: mohansreesk14@gmail.com
  • Bug Reports: Use GitHub Issues
  • Feature Requests: Open an issue with [Feature Request] tag
  • Questions: Use GitHub Discussions or issues

Code of Conduct

  • Be respectful and constructive
  • Focus on the code, not the person
  • Welcome newcomers and help them learn
  • Keep discussions professional

License

By contributing, you agree that your contributions will be licensed under the same license as the project.

Thank you for contributing! 🎉