hvass-labs / financeops Goto Github PK

Hi @Hvass-Labs !!

I hope your well.

I am interested in using different signals for the optimization.
Ie. if I have a model that forecasts data for the next quarter. Ie 60 data points ahead.

Can I use this for the optimisation.

Kind regards and thanks,
Andrew

Hi @Hvass-Labs,
Can I add simple docker instructions so users can get up and running quickly? With Docker container, users don't have to install required software such as pythonand jupyter notebook since it is already available in docker container.

It is something like this but I can simplify it and update the readme with a short section for running the notebooks from the docker container.
docker run --rm -p 8888:8888 -e JUPYTER_ENABLE_LAB=yes -v "$PWD":/home/jovyan/work jupyter/datascience-notebook

great work by the way! 💯

In the video you mentioned about further exploring the outliers.

In [26] plot 10. The outliers in the range 15% to 20% revenue and 1.5 to 3 P/B ratio could be explained by software companies.

Software companies have very little capital intensive assets compared to heavy manufacturing and energy industries. It makes sense that they have a high revenue while maintaining a low P/B ratio.

However I cannot confirm my hypothesis as the ticker symbols are not included in the dataset.

In [24] plot 8. The outliers in the range 10%-20% revenue and P/S ratio 1-1.5 might also be the same tech corporations for reasons unknown. (Maybe they are lesser known startups, who are making big bucks but flying under the radar. This hypothesis would be hard to test.)

Could you add the ticker symbol data in the given dataset so that I can proceed with my hypothesis testing?