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

Anytime I see an investment framework I look for a “Performance” section or at least a backtest

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.

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.

I spent about an hour looking at the code and found some glaring issues that should be fixed before trusting it with real money.

- Yahoo News is introduced twice (sentiment and news analysis) which double weights it

- Sentiment analysis prompt primes the model to be bullish on Nvidia.

- In the self learning loop there is a complex parsing bug that results in hallucinated memories when agents return truncated responses

- You can completely control sentiment analysis of a subreddit by simply maintaining a majority of the 5 most recently posted messages, regardless of any quality metric

- The reflection prompt states the agent must cite alpha, which in a market wide downturn causes it to think correctly placed calls were losses

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

As the saying goes, those who know don't say and those who say don't know.

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?

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

What is the purpose of this repo? Is it to simulate the market so you can reliably backtest trading strategies?

Whatever the stated purpose is, where can I read the test results to show it accurately fulfills that purpose.

Anyone can make a markets simulation that models interactions between market participants. Making a simulation that is accurate enough to be useful for anything is hard.

1) costs? 2) profits/costs?

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

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