Multi-Agents LLM Financial Trading Framework

ffittingopposite about 5 hours ago 35 commentsRead Article on github.com

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Discussion (35 Comments)Read Original on HackerNews

_pdp_•about 2 hours ago

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

whazor•about 2 hours ago

It would be more interesting to compare trading agents with index tracking ETFs. The better version of an ETF could maybe be a model where you zoom in on the companies and add/remove to your portfolio on the company related news, but keeping a broader portfolio.

Maybe agentic trading still performs worse than ETFs. But alternatively, if it were meaningfully better then it would be okay to opensource, similarly how ETFs are publishing their portfolios.

_pdp_•about 2 hours ago

I think this might work.

When I started working no the trading agent I mentioned above I wanted to see if it can be just a better investor over the long run. The intention was not to do high-frequency trading. As you can see most of the days it is not taking any actions. The losses where down to mistakenly setting the stop losses too close to the top. If it wasn't so careful it might have made some money tbf.

My gut feeling is that AI agents will be able to manage a long-term portfolio much better than a human. Though it is just a gut feeling.

dakolli•about 1 hour ago

Lmao, you llm people have some crazy delusions. You realize markets are zero sum, and if you're using a public model that everyone else also has access too, you llm psychos will destory eachothers "agentic" edge (not that there ever was one). Not to mention all the other obvious flaws with llms, lime having an effective memory of ~200k words and no ability to judge whats actually going on in the real world.

rienbdj•about 2 hours ago

If you like building but have no capital you might release something like this that works.

hacker_9•about 3 hours ago

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.

XenophileJKO•about 1 hour ago

Actually there is a really good reason, it has to do generally with making sure that all the aspects that you want evaluated are actually evaluated.

Now it may be possible with models like Astra that you no longer need to do this, but in earlier models it was beneficial.

So I might want a macro economic read which leads to a market thesis. Then I would hunt for exposure, then evaluate the candidates across different aspects. Breaking the process up at least made sure no steps were missed and the different aspects considered.

looofooo0•about 2 hours ago

Given what a know about the 2008 financial crisis, wouldn't an AI analysis in the years before that crisis of the real state funds helped to understand the risk of them better and avoid the big exposure.

hacker_9•about 2 hours ago

The problem wasn't the analysis, given it was found out before it happened. The problem was politics, and as usual pushing the system to its limits and beyond.

elzbardico•about 2 hours ago

Probably not. On the contrary, it would probably just amplify the mood.

audinalexandre•about 3 hours ago

As local contexts/skills get better at depicting what's to be expected and what an agent can work with and work to get better at, having more and more little specialized agents working as a swarm get you, with a field-skilled human as a supervisor, really good results even in highly niche and technical fields

dsl•about 1 hour ago

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

throw93947309•5 minutes ago

There is also problem with underlying models. There was a study, where they always repest the same investing/management strategy: trust strangers, be open minded/adopt to new unproven ideas, prefer cooperation... Basically they were trained on disney-boomer bull(shit) market of last 15 years.

They have zero guards against market manioulations, and will get wiped without bull market!

embedding-shape•about 1 hour ago

> Sentiment analysis prompt primes the model to be bullish on

Eeh, yeah? At that point I'd stop reading the code and just leave the project behind. How exactly is the prompt doing this right now?

politelemon•about 3 hours ago

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.

testaccount121•about 2 hours ago

103K stars in a few months, smells of purchased stars IMO.

mechazawa•about 2 hours ago

I feel like a lot of the stars for repos like this are from people who are riding the AI hype train and not actually interested in using the software

skanga•about 3 hours ago

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

adyavanapalli•about 1 hour ago

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

shaolinspirit•about 3 hours ago

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?

BenoitP•about 3 hours ago

It helps you spend more token, is more expensive, and thus is obviously more AI. Also novelty and more complexity means less scrutiny of the approach.

These are necessary and perfectly sufficient for an investment firm thesis I believe.

podocarp•about 2 hours ago

If it's a single agent then people will just call it a chatgpt wrapper, can't have that can we

elzbardico•about 2 hours ago

Modularity maybe. Remember, agents are basically workflows that call LLMs in certain nodes.

feverzsj•about 3 hours ago

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

ctdonnet•about 1 hour ago

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.

fedeb95•about 2 hours ago

1) costs? 2) profits/costs?

petesergeant•about 4 hours ago

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

monkeydust•about 3 hours ago

Experimented with multi-llm analysis for problem solving over summer, combined with multi-agent approaches it can tease out interesting angles to problems that I never considered. Expensive but use only for my high value problems.

https://github.com/monkeydust/rightmind

elzbardico•about 2 hours ago

I've built agents that call different LLMs and keep separated memories. Remember, agents are just long-running workflows with some nodes calling LLMs and that sometimes can be started as tool from other "agent".

There are times when I wonder if couldn't just draw then in a BPMN designer that allowed me to write custom code for nodes. Is BPMN still a thing?

reedf1•about 3 hours ago

nightmare horseshit, don't waste your tokens