The part people are missing here is that if the trading firms are all doing something, that in itself influences the market.

If they are all giving the LLMs money to invest and the AIs generally buy the same group of stocks, those stocks will go up. As more people attempt the strategy it infuses fresh capital and more importantly signaling to the trading firms there are inflows to these stocks. I think its probably a reflexive loop at this point.

"includes a vector embedding of a selection of key social and news media accounts or other information sources that have historically been difficult to encode until LLMs."

Not really. Sentiment analysis in social networks has been around for years. It's probably cheaper to by that analysis and feed it to LLMs than to have LLMs do it.

  > 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.

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.

> you will have trained your model on market patterns that might not be in place anymore

My working definition of technical analysis [0]

[0]: https://en.wikipedia.org/wiki/Technical_analysis

Just to name a different but related approach, as a hobby project I built a (non LLM) model that trained mainly on data from stocks that didn't move much over the past decade, seeking ways to beat the performance of those particular stocks. I put it into practice for a couple of years, and came out roughly even by constantly rebalancing a basket of stocks that, as a whole, dropped by about 20%. I considered that to be a success, although it would've been nicer to make money.

> you will have trained your model on market patterns that might not be in place anymore

How is that relevant to what was proposed? If it's trading and training on 2010 data, what relevance does todays market dynamics and regulations have?

Which further begs the question, what's the point of this exercise?

Is it to develop a model than compete effectively in today's market? If so then yeah, the 2010 trading/training idea probably isn't the best idea for the reasons you've outlined.

Or is it to determine the capacity of an AI to learn and compete effectively within any given arbitrary market/era? If so, then today's dynamics/constraints are irrelevant unless you're explicitly trying to train/trade on todays markets (which isn't what the person you're replying to proposed, but is obviously a valid desire and test case to evaluate in it's own right)

Or is it evaluating its ability to identify what those constraints/limitations are and then build strategies based on it? In which case it doesn't matter when you're training/trading so much as your ability to feed it accurate and complete data for that time period be it today, or 15 years ago or whenever, which is no small ask.

full throttle until AGI is achieved, then we will see