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If this worked it wouldn't have been open source?
Anyway, I have been running my own trading experiment and so far it has lost a bit of money. That being said I have not tried to optimise anything - just let it do whatever it wants. The losses are small and it might be able to recover later this year. Who knows.
The agent writes a blog about its progress here https://trades.chatbotkit.space/
I am thinking to output all the chat logs to HF as well for research.
You can run your own trading agents that communicate over a message buss in your own terms by downloading the CBK platform and running it locally with your own models. I have also shared my trading blueprint if you want to give it a go. https://chatbotkit.com/hub/blueprints/trader
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.
I spent about an hour looking at the code and found some glaring issues that should be fixed before trusting it with real money.
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.
It's always been a joke that stock prediction is a rite of passage for software engineers introduced to ML training. I see the same is true for commodity LLMs.
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?
As the saying goes, those who know don't say and those who say don't know.
Which will make you bankrupt faster, this framework or the its token consumption?
This would make sense only if the LLMs would be constantly updated with a new data set and training phase every day. In that case, I'd could see this approach as having some sense. But, otherwise, these are just stochastic machines trained on static (outdated) data and I don't see how their predictions should be better than any other method around or even better than a human guessing.