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.

- 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

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.

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.

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?

I’ve been running a day trading bot for the last 4-5 months, and it’s a lot of work. It never comes down to agent skills or abilities, more so the data you can receive and how quickly you can receive it. 1m bars, MFE calculations, rvol, executable bid/ask, spread, volume, market/sector context, and then making sure none of it has look ahead leakage

I’ve been doing a fairly similar experiment, but I ended up moving in almost the opposite direction than what this framework purports. deterministic code decides what is actually legal to trade, handles sizing/risk/execution, and an LLM (nanobot architecture) only gets to rank the already valid candidate set. If the model fails or times out, deterministic ordering takes over, so only the -nth degree of data actually makes it to the non-deterministic part (haha).

The hard part hasn’t been making the agents smarter/skillset but getting clean, fast data, preserving exact order/fill lineage (Postgres) and separating bad selection from bad execution or exits without leaking future information into the analysis

The multi agent debate stuff is interesting, but if every agent is reasoning over the same stale or incomplete inputs, I’m not convinced you gain much. I’ve built PoCs for my same project, and a round robin of LLMs is just hallucination and self approval city. Better data and tighter decision boundaries seem to matter more

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.

Instead of going after money printing schemes like this (which I feel are unlikely to actually work and carry a ton of risk). I (thoughtfully) vibed up tooling and skills to just help LLMs manage my investments long-term. While not flashy, tax loss harvesting and direct indexing are areas where LLMs can reliably save you on fees that you'd otherwise have to pay. It's been very helpful, the LLM automatically deploys new cash based on my priorities, implements tax harvesting for me, and provides customized exposure guidance while keeping my goals in mind (ESG/sustainability focus, no FF rules) . Way better than any financial planner has ever done for me. https://github.com/mshafir/investbot

Using a token slot machine to beat a financial slot machine, what could go wrong?

I vibe coded a little stock market sim game then i wrote an agent who’s job is to win the game. A couple friends and family members play the game too. If it works I’ll just follow along and hold the same portfolio the bot does. The bot, named stonker, makes its first trades in about an hour actually. Assuming it works I mean hah

https://stonks.jettdigital.app

edit: i just talked to my little sister ("Ginger" on the leaderboad) who is doing a good job beating the sp500. She said she just asks her ai what she should invest in and then executes those trades.

I helped someone day trade 25 years ago and we made 40% in 4 months, mostly on AAPL, and it was easy. Way easier than working the 2 years moving furniture that it would have taken to earn that money, which I was foolishly doing to make rent for my defunct shareware business.

I've heard that high frequency trading eats market opportunities within milliseconds of spotting them now though, basically making the market even more random. The catch being that human nature isn't random, it's stochastic, so there will always be more money to be made on trades (or else trading firms wouldn't exist).

I'm thinking about getting back into trading because AI represents the end of buying software and we'll all be out of work soon, even if we're in denial about it. But everything I invest my time and energy into turns to crap. In a very real sense, as soon as I start trading, then karmically that could trigger recession, market reforms which ban what I'm trying, or even the end of money. I'm kind of joking and kind of not.

I've worked on a lot of really hard stuff over my brief but stupid career, and am basically used up mentally and physically, at least for now. Quant stuff is easy with AI. Should I try it?

LLMs are notoriously bad with finance. I prefer deterministic rules for Agents like this one https://github.com/AlexBThomsen/vaultcharts-trading-engine, which I literally shared a few days ago on Hacker News, but no interest :D

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.

I'd be curious as to how correlated development on these frameworks (ai or otherwise) is correlated with the market cycle. It seems during bull runs would be traders think they have some edge - whereas they're probably all just buying the trend.

I am sure it won't be long until some rando from /r/wallstreetbets/ turns something like this loose without considering the ramifications and loses $250k.

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

serious traders working on this type of project would have run extensive back testing on both real as well as synthetic analogues

not my agentic trading strategy but luck

I might contribute to this, my public github is looking stale for headhunters

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

> The process is quite scientific

This part is lost on many. The value of data is in the theories it confirms or more importantly disconfirms, and defining the trading edge is not easy after accounting for costs. I am wary of black-boxes that produce an edge - not only because I dont know how it works, but also because regimes shift unpredictably, and what works today may stop working tomorrow. That being said, AI can be useful to help automate many routine processes just like any other software.

Reddit sentiment is mostly noise for a while now.

but...

options premiums imply volatility.

Lagging indicator would still be useful if it was accurate before the event. After the event, its just lagging history.

technical analysis is using historical data to make future predictions. No professional trader would trade anything without looking at price history.

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.

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

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.

The obvious and dumb theory for trading on LLM recommendations is that lots of people are doing it so the price of stocks LLM recommend should go up. (ie: following the herd).