Some of the things that I’d love for people to help with:
- Improve performance of existing code (but not at the cost of readability)
- Add new optimization objectives. For example, if you would like to use something other than the Sharpe ratio, write an optimizer! (or suggest it in Issues and I will have a go).
- Help me write more tests! If you are someone learning about quant finance and/or unit testing in python, what better way to practice than to write some tests on an open-source project! Feel free to check for edge cases, or for uncommon parameter combinations which may cause silent errors.
Seek early feedback¶
Before you start coding your contribution, it may be wise to raise an issue on GitHub to discuss whether the contribution is appropriate for the project.
For this project I have used Black as the formatting standard, with all of the default settings. It would be much appreciated if any PRs follow this standard because if not I will have to format before merging.
Any contributions must be accompanied by unit tests (written with
These are incredibly simple to write, just find the relevant test file (or create
a new one), and write a bunch of
assert statements. The test should be applied
to the dummy dataset I have provided in
tests/stock_prices.csv, and should
cover core functionality, warnings/errors (check that they are raised as expected),
and limiting behaviour or edge cases.
Inline comments are great when needed, but don’t go overboard. Docstring content should follow PEP257 semantically and sphinx syntactically, such that sphinx can automatically document the methods and their arguments. I am personally not a fan of writing long paragraphs in the docstrings: in my view, docstrings should state briefly how an object can be used, while the rest of the explanation and theoretical background should be offloaded to ReadTheDocs.
I would appreciate if changes are accompanied by relevant documentation - it doesn’t have to be pretty, because I will probably try to tidy it up before it goes onto ReadTheDocs, but it’d make things a lot simpler to have the person who wrote the code explain it in their own words.
If you have any questions related to the project, it is probably best to raise an issue and I will tag it as a question.
If you have questions unrelated to the project, drop me an email - contact details can be found on my website.
If you find any bugs or the portfolio optimization is not working as expected, feel free to raise an issue. I would ask that you provide the following information in the issue:
- Descriptive title so that other users can see the existing issues
- Operating system, python version, and python distribution (optional).
- Minimal example for reproducing the issue.
- What you expected to happen
- What actually happened
- A full traceback of the error message (omit personal details as you see fit).