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An internal library containing functions and classes that can be used across Quant models.

Project description

DET Quant Library

Table of contents

General overview

The DET Quant Library is an internal library containing functions and classes that can be used across Quant models.

Configuration

Dependencies

Dependency manager

Project dependencies are managed by Poetry.

The project follows the standard Poetry structure:

detquantlib
├── pyproject.toml
├── README.md
├── detquantlib
│   └── __init__.py
└── tests
    └── __init__.py

Dependabot

Automated dependency updates are executed with Dependabot.

GitHub actions

The project's CI/CD pipeline is enforced with GitHub actions workflows.

Workflow: Continuous integration (CI)

The continuous integration (CI) workflow runs tests to check the integrity of the codebase's content, and linters to check the consistency of its format.

The workflow was inspired by the following preconfigured templates:

  • Python package: A general workflow template for Python packages.
  • Poetry action: A GitHub action for installing and configuring Poetry.
Invoke tasks

The workflow's checks and linters are specified with Invoke tasks, defined in a tasks.py file.

Invoke tasks can be executed directly from the terminal, using the inv (or invoke) command line tool.

For guidance on the available Invoke tasks, execute the following command in the terminal:

inv --list

Use the -h (or --help) argument for help about a particular Invoke task. For example:

inv lint -h
CI check: Testing

Code changes are tested with the Pytest package.

The CI check is executed with the following Invoke task:

inv test -c
CI check: Code formatting

Linters are used to check that the code is properly formatted:

The CI check is executed with the following Invoke task:

inv lint -c

If the CI check fails, execute the following command in the terminal:

inv lint

This command fixes the parts of the code that should be reformatted. Adding the -c (or --check) optional argument instructs the command to only check if parts of the code should be reformatted, without applying any actual changes.

Workflow: Package publisher

The package publisher workflow checks the validity of package version updates, creates version tags, and publishes package updates to PyPI.

Checking version updates

The workflow enforces version control:

  • The version number is specified via the version field in the pyproject.toml file.
  • The version number needs to be updated with every new master commit. If the version is not updated, the GitHub workflow will fail.
  • Version numbers should follow semantic versioning (i.e. X.Y.Z). That is:
    • X increments represent major, non-backward compatible updates.
    • Y increments represent minor, backward compatible functionality updates.
    • Z increments represent patch/bugfix, backward compatible updates.
Creating version tags

If the new package version is valid, the workflow automatically creates a new tag for every new master commit.

Publishing package updates to PyPI

The workflow automatically publishes every new master commit to PyPI.

Release notes

When deemed necessary (especially in case of major updates), developers can document code changes in dedicated GitHub release notes.

Release notes can be created via https://github.com/Dynamic-Energy-Trading/detquantlib/releases.

In any case, all codes changes should always be properly described/documented in GitHub issues and/or pull requests.

Project details


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