I’m developing a strategy that uses the trade data from members of congress. Here’s what I need:

Use data from the Quiver Quantitative dataset: https://www.quantconnect.com/datasets/quiver-quantitative-congress-trading/documentation

Plus fundamental data, like market cap, p/e ratio, ect… (I need help defining exactly what to pull from here)

I want you to take data from both of these datasets, and create me an algorithm that creates objects in the object store. These objects should each be a single transaction, contain all the data from each congressional transaction. I have uploaded a sample of this data in the form of an excel spreadsheet. I want to also add some fundamental data to each of these transactions, but I need help figuring out exactly what data to add.

I want these objects to be setup in a very flexible way, so I’ll be able to create strategies to query them based on any of the fields and are also setup to be able to do machine learning in the future.

This algorithm is going to need to setup all the objects for all the congress trades historically (data starts in Jan 2016) as a one time exercise and then also have a function to update daily, as new transactions come in, so that I constantly have an up to date object store, with the latest data.

If you look at my requirements and have a different idea on how to setup these objects, I’m very open to your ideas as well.

I’m happy to get on the phone to discuss as well!

Posted On: March 24, 2024 01:38 UTC
Category: Back-End Development
Skills:Python, API

Country: United States

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