“Everyone (including LLMs) is a genius in a bull market.”
What could make this a bit more interesting is to tell the LLM to avoid the tech stocks, at least the largest ones. Then give it actual money, because your trades will affect the market.
211–220 of 319 posts
“Everyone (including LLMs) is a genius in a bull market.”
What could make this a bit more interesting is to tell the LLM to avoid the tech stocks, at least the largest ones. Then give it actual money, because your trades will affect the market.
I used to work for a brokerage API geared at algorithmic traders and in my experience anecdotal experience many strategies seem to work well when back-tested on paper but for various reasons can end up flopping when actually executed in the real market. Even testing a strategy in real time paper trading can end up differently than testing on the actual market where other parties are also viewing your trades and makin…
>but for various reasons can end up flopping when actually executed in the real market. 1. Your order can legally be “front run” by the lead or designated market maker who receives priority trade matching, bypassing the normal FIFO queue. Not all exchanges do this. 2. Market impact. Other participants will cancel their order, or increase their order size, based on your new order. And yes, the algos do care about your…
OP here. We realized there are a ton of limitations with backtest and paper money but still wanted to do this experiment and share the results. By no means is this statistically significant on whether or not these models can beat the market in the long term. But wanted to give everyone a way to see how these models think about and interact with the financial markets.
If you really wanted to do this, you would have to train specialist models - not LLMs - for trading, which is what firms are doing, but those are strictly proprietary.
The only other option would be to train an LLM on actually correct information and then see if it can design the specialist model itself, but most of the information you would need for that purpose is effectively hidden and not found in public sources. It is also entirely possible that these trading firms have already been trying this: using their proprietary knowledge and data to attempt to train a model that can act as a quant researcher.
Also, it seems pretty stupid to use commodity tech like LLMs for this.
Earlier quoted context omitted.
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…
> 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 In that case the winning strategy would be to switch hedge funds every 3 years.
Just one run per model? That isn't backtesting. I mean technically it is, but "testing" implies producing meaningful measures. Also just one time interval? Something as trivial as "buy AI" could do well in one interval, and given models are going to be pumped about AI, ... 100 independent runs on each model over 10 very different market behavior time intervals would producing meaningful results. Like actually credibl…
This same kind of mentality happened pre-2008. People thought they were great at being day-traders, and had all kinds of algorithms that were 'beating the market'.
But it was just that the entire market was going up. They weren't doing anything special.
Once the market turned downward, that was when it took talent to stay even.
Show me these things beating a downward market.Earlier quoted context omitted.
Grok would likely have an advantage there, as well - it's got better coupling to X/Twitter, a better web search index, fewer safety guardrails in pretraining and system prompt modification that distort reality. It's easy to envision random market realities that would trigger ChatGPT or Claude into adjusting the output to be more politically correct. DeepSeek would be subject to the most pretraining distortion, but ha…
I know that Musk deserving a lifetime achievement award at the Adult Video Network awards over Riley Reid is definitely an indication of minimal "system prompt modification that distort[s] reality."
That's not going to work, these agents especially the larger ones, will have news about the companies embedded in their weights.
1. Backtesting doesn’t mean very much. For lots of reasons real trading is different than backtesting. 2. 8 months is an incredibly short trading window. I care where the market will be in 8 years way more then 8 months.
Scrubbing symbol names wouldn't even be enough because I suspect some of these LLMs could "figure out" which stock is, say NVDA, based on the topology of its performance graph.