Closed PietropaoloFrisoni closed 12 months ago
Awesome :)
I'll probably have a closer look at it over the weekend.
Just some minor things for now:
you can update the file version
to:
version=0.4.0
release=0.4.0
and with that, you should update the release number references in README.md and README.tex.md as well (but that is now automated, read more about below)
I saw that you took care of the imports manually. You can automate this as well (more below).
To automate the above 2 steps, I recently added some scripts
to the repo.
You can run isort $(git ls-files '*.py')
to sort all imports in all python files that are under version control.
That way, you don't have to worry about my CDO and can only focus on the new features you are interested in.
Btw, thank you once again for your hard work. Hope you are learning more and more about finance that way. :)
Forgot to mention, you'd have to install isort first. I added it to requirements_dev.txt a few days ago. So you can simply install it with pip install -e .[dev]
UPDATE:
About the .git/hooks/pre-commit
script. I just figured out, that it does not apply the black and isort changes to the commit... But, the README updates work. For now, you can however use black and isort manually:
black $(git ls-files '*.py')
isort $(git ls-files '*.py')
Apologies for spamming you with messages and most of them were bugged ;)
Thanks, Frank. I'll go over these steps soon. Yes, I'm learning more about finance this way. Thank you so much!
I'd like to add one more request: Could you also please add this to one of the examples with a bit of text, like you did for the beta parameter?
Thank you for the review and the improvements. Please feel free to ask for any clarification/double-check. It is useful for me as well since I am not a field expert myself : )
I'd like to add one more request: Could you also please add this to one of the examples with a bit of text, like you did for the beta parameter?
Sure, I'll go over it asap.
All the best
Very good, and thank you very much for adding the new feature to the example Example-Analysis. :)
The Value at Risk (VaR) is a way to evaluate the risk of a portfolio. It measures the potential loss that a portfolio can have over a certain period with a specified confidence level. With respect to the beta parameter, variance, etc., VaR uses a probability distribution, and it assumes that returns are normally distributed.
Using the variance-covariance method, we want to compute the portfolio's value at risk (VaR) over the selected period (the 'freq' variable). The Monte Carlo approach is left for further development.