Love the long rant.

re: metadata table - it's constructed from the SEC's submissions.zip, which they update daily. What my script does is download the zip, decompress just the bytes where the information (ticker, sic code, etc) is stored, then convert into a csv.

And yep! Agree with most of this. Currently, I'd say my data is in the stage where it's useful for startups / phd research and some hedge funds / quant stuff (at least that's who is using it so far!)

I've seen the trillion dollar trades, and they're hilarious! You see it every so often in Form 3,4,5 disclosures.

re: LLMs, this is something I'm planning to move into in a month or two. I'm mostly planning to use older NLP methods which are cheaper and faster, while using LLMs for specific stuff like structured output. e.g. wrds boardex data can be constructed from 8-k item 5.02s.

I think the biggest difficulty wrt to data is just the raw data ingest is annoying AF. My approach has been to make each step easy -> use it to build the next step.