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Major SimFin Update - Jan 2024

January 19, 2024 -
We just deployed a major updated that happened after we completed the biggest data migration so far at SimFin, where we transferred all our data to a brand new data extraction tool that we have been working on for a long time.

Important Changes to Bulk Download / Python API / Web API:

The new backend endpoint is https://prod.simfin.com

instead of https://backend.simfin.com

Python users can simply upgrade to v1.0.0 of the simfin package via pip.

In the course of switching our data extraction tool, we also decided to finally get completely rid of our legacy PHP backend, that was still in parts active, for example for the creation of the files for the bulk download. With this update, the SimFin backend is consolidated and iterations/improvements on the bulk download will be much easier from now on. As such, we plan to add new columns to the bulk download in the next weeks, such as dividend yield to the derived ratios dataset. We have already added a couple of new columns to the "new" bulk files, but only to the "Companies" dataset where we added some more general information like business description and number of employees amongst others.

Transferring almost 300k filings to a new extraction tool hasn't been easy but we think it was an important step as our new tool is much more powerful than the previous iteration. With the new tool we have added many additional checks to further improve data quality and tackle some problems we've had in the past, such as units of shares outstanding where we so far relied on a third party provider but from now on extract the shares outstanding data directly from the reports ourselves. With the new tool we are now also able to extract new data points from the filings, such as Revenues by geography AND sector/industry, which is the next thing we want to release in the near future (data is already being loaded for that right now).

The new tool also enables us to finally extract data from PDF reports seamlessly, so we'll be working on the international data coverage once we have covered the last missing US companies and also fixed all issues with the current dataset.

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