103K stars, so clearly it's popular. Has anyone here used it, and what are the outcomes like, and importantly, who is the target audience for this?

I can see the intention behind crawling social media and news feeds to determine some 'evidence', but am not sure if that's the best approach or even if an LLM is the best way to get an assessment, or whether having so many input sources is a good idea.

Having worked in hedge funds for the last decade, this seems to miss the mark. Firstly we often reward skillstacking ie a technical person later becoming a trader. The more one person knows the better. These people are rare though hence the reason there is still many seperate job functions, so a person can specialize. But an AI agent? They all have the same brain, so why nerf them by specialising.

Secondly, browsing reddit for sentiment and doing technical analysis is not even a feature in the trading world. At the most basic level, these are lagging indicators. Something on options IV and premiums would have been closer to the mark.

Hedge funds are akin to the maintenance crew for markets, we keep them efficient and liquid. The process is quite scientific, you come up with a theory and validate with real data. Or you go from data to theory.

In case there is interest, I've got a fork with some custom improvements. See section "What this fork adds" in README.md

https://github.com/skanga/TradingAgents

I do not understand the value of multi agent approach? Isn't a single agent with a good harness better than any multi agent env?

1) costs? 2) profits/costs?

Which will make you bankrupt faster, this framework or the its token consumption?

I think multi-agent (eg _different_ underlying LLMs) everything is really the future. Code produced via multi-agent workflows and reviews seems noticeably better. I've been experimenting with a multi-agent message board recently: https://github.com/pjlsergeant/dogpark

nightmare horseshit, don't waste your tokens