Development

This project is small enough that development remains lightweight, but it still benefits from a clear workflow for building, testing, and documenting the package.

Recommended environment

Use a virtual environment for local development:

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -r requirements-dev.txt

Then install the project itself in editable mode:

python -m pip install -e .

This allows changes to the Python files and compiled extension to be picked up without reinstalling the package repeatedly.

Running tests

The project uses pytest for regression and validation testing. Run the suite with:

pytest

This checks the pricing functions against known values and ensures that numerical behavior stays consistent after changes.

Building the extension

The package compiles the native C++ and Cython extension as part of installation. If you want to trigger a fresh build manually, use:

python setup.py build_ext --inplace

This builds the extension in place so it can be imported directly from the source checkout during testing and debugging.

Building the docs

The documentation is built with Sphinx. From the project root, run:

python -m sphinx -b html docs/source docs/build/html

This creates a local HTML build that can be reviewed in the browser.

Project conventions

A few conventions help keep the package maintainable:

keep the public API small and deliberate
prefer simple, explicit function signatures
validate common inputs early
add regression tests when changing pricing math
document numerical assumptions and edge cases clearly

When the pricing logic is modified, it is important to validate the change against known examples and the existing test suite.

Contribution workflow

A typical contribution flow looks like this:

create a feature branch
make a focused change
add or update tests
run the relevant test subset
update documentation if behavior or usage changes
submit a pull request with a clear summary

This keeps the project coherent and makes it easier to review numerical changes that may affect pricing outcomes.