zlacker

>>cheese+(OP)
> Grok ended up performing the best while DeepSeek came close to second. Almost all the models had a tech-heavy portfolio which led them to do well. Gemini ended up in last place since it was the only one that had a large portfolio of non-tech stocks.

I'm not an investor or researcher, but this triggers my spidey sense... it seems to imply they aren't measuring what they think they are.

>>bcrosb+l2
Yeah I mean if you generally believe the tech sector is going to do well because it has been doing well you will beat the overall market. The problem is that you don’t know if and when there might be a correction. But since there is this one segment of the overall market that has this steady upwards trend and it hasn’t had a large crash, then yeah any pattern seeking system will identify “hey this line keeps going up!” Would it have the nuance to know when a crash is coming if none of the data you test it on has a crash?

It would almost be more interesting to specifically train the model on half the available market data, then test it on another half. But here it’s like they added a big free loot box to the game and then said “oh wow the player found really good gear that is better than the rest!”

Edit: from what I causally remember a hedge fund can beat the market for 2-4 years but at 10 years and up their chances of beating the market go to very close to zero. Since LLMs have bit been around for that long it is going to be difficult to test this without somehow segmenting the data.

>>IgorPa+Z3
> It would almost be more interesting to specifically train the model on half the available market data, then test it on another half.

Yes, ideally you’d have a model trained only on data up to some date, say January 1, 2010, and then start running the agents in a simulation where you give them each day’s new data (news, stock prices, etc.) one day at a time.

>>tshadd+H7
I mean ultimately this is an exercise in frustration because if you do that you will have trained your model on market patterns that might not be in place anymore. For example after the 2008 recession regulations changed. So do market dynamics actually work the same in 2025 as in 2005? I honestly don’t know but intuitively I would say that it is possible that they do not.

I think a potentially better way would be to segment the market up to today but take half or 10% of all the stocks and make only those available to the LLM. Then run the test on the rest. This accounts for rules and external forces changing how markets operate over time. And you can do this over and over picking a different 10% market slice for training data each time.

But then your problem is that if you exclude let’s say Intel from your training data and AMD from your testing data then there ups and downs don’t really make sense since they are direct competitors. If you separate by market segment then does training the model on software tech companies might not actually tell you accurately how it would do for commodities or currency training. Or maybe I am wrong and trading is trading no matter what you are trading.

>>IgorPa+9g

  > I think a potentially better way would be to segment the market up to today but take half or 10% of all the stocks and make only those available to the LLM.

Autocorrelation is going to bite you in the ass.

Those stocks are going to be coupled. Let's take an easy example. Suppose you include Nvidia in the training data and hold out AMD for test. Is there information leakage? Yes. The problem is that each company isn't independent. You have information leakage in both the setting where companies grow together as well as zero sum games (since x + y = 0, if you know x then you know y). But in this example AMD tends with Nvidia. Maybe not as much, but they go in the same direction. They're coupled

Not to mention that in the specific setting the LLMs were given news and other information.