10 ms·

We gave 5 LLMs $100K to trade stocks for 8 months

398 points by cheeseblubber 10mo ago

buredoranna 10mo ago
Like so many analyses before them, including my own, this completely misses the basics of mean/variance risk analysis. We need to know the risk adjusted return, not just the return.
digitcatphd 10mo ago
Backtesting is a complete waste in this scenario. The models already know the best outcomes and are biased towards it.
sethops1 10mo ago
> Testing GPT-5, Claude, Gemini, Grok, and DeepSeek with $100K each over 8 months of backtested trading So the results are meaningless - these LLMs have the advantage of foresight over historical data.
- CPLX 10mo ago
  Not sure how sound the analysis is but they did apparently actually think of that.
  - deleted 10mo ago
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- PTRFRLL 10mo ago
  > We were cautious to only run after each model’s training cutoff dates for the LLM models. That way we could be sure models couldn’t have memorized market outcomes.
  - plufz 10mo ago
    I know very little about how the environment where they run these models look, but surely they have access to different tools like vector embeddings with more current data on various topics?
    - disconcision 10mo ago
      you can (via the api, or to a lesser degree through the setting in the web client) determine what tools if any a model can use
      - disconcision 10mo ago
        with the exception that it doesn't seem possible to fully disable this for grok 4
        alchemist1e9 10mo ago
        which is curiously the best model …
      - plufz 10mo ago
        But isn’t that more which MCP:s you can configure it to use? Do we have any idea which secret sauce stuff they have? Surely it’s not just a raw model that they are executing?
    - endtime 10mo ago
      If they could "see" the future and exploit that they'd probably have much higher returns.
      - alchemist1e9 10mo ago
        56% over 8 months with the constraints provided are pretty good results for Grok.
      - plufz 10mo ago
        I would say that if these models independently could create such high returns all these companies would shut down the external access to the models and just have their own money making machine. :)
  - stusmall 10mo ago
    Even if it is after the cut off date wouldn't the models be able to query external sources to get data that could positively impact them? If the returns were smaller I could reasonably believe it but beating the S&P500 returns by 4x+ strains credulity.
    - cheeseblubber 10mo ago
      We used the LLMs API and provided custom tools like a stock ticker tool that only gave stock price information for that date of backtest for the model. We did this for news apis, technical indicator apis etc. It took quite a long time to make sure that there weren't any data leakage. The whole process took us about a month or two to build out.
      - alchemist1e9 10mo ago
        I have a hunch Grok model cutoff is not accurate and somehow it has updated weights though they still call it the same Grok model as the params and size are unchanged but they are incrementally training it in the background. Of course I don’t know this but it’s what I would do in their situation since ongoing incremental training could he a neat trick to improve their ongoing results against competitors, even if marginal. I also wouldn’t trust the models to honestly disclose their decision process either. That said. This is a fascinating area of research and I do think LLM driven fundamental investing and trading has a future.
- itake 10mo ago
  > We time segmented the APIs to make sure that the simulation isn’t leaking the future into the model’s context. I wish they could explain what this actually means.
  - devmor 10mo ago
    It's a very silly way of saying that the data the LLMs had access to was presented in chronological order, so that for instance, when they were trading on stocks at the start of the 8 month window, the LLMs could not just query their APIs to see the data from the end of the 8 month window.
  - nullbound 10mo ago
    Overall, it does sound weird. On the one hand, assuming I properly I understand what they are saying is that they removed model's ability to cheat based on their specific training. And I do get that nuance ablation is a thing, but this is not what they are discussing there. They are only removing one avenue of the model to 'cheat'. For all we know, some that data may have been part of its training set already...
- joegibbs 10mo ago
  That's only if they're trained on data more recent than 8 months ago
deadbabe 10mo ago
Yea, so this is bullshit. An approximation of reality still isn’t reality. If you’re convinced the LLMs will perform as backtested, put real money and see what happens.
chroma205 10mo ago
>We gave each of five LLMs $100K in paper money Stopped reading after “paper money” Source: quant trader. paper trading does not incorporate market impact
- txg 10mo ago
  Lack of market response is a valid point, but $100k is pretty unlikely to have much impact especially if spread out over multiple trades.
- zahlman 10mo ago
  If your initial portfolio is 100k you are not going to have meaningful "market impact" with your trades assuming you actually make them vs. paper trading.
- a13n 10mo ago
  I mean if you’re going to write algos that trade the first thing you should do is check whether they were successful on historical data. This is an interesting data point. Market impact shouldn’t be considered when you’re talking about trading S&P stocks with $100k.
  - verdverm 10mo ago
    Historical data is useful for validation, don't develop algos against it, test hypotheses until you've biased your data, then move on to something productive for society
- tekno45 10mo ago
  the quant trader you talked to probably sucks.
dash2 10mo ago
There's also this thing going on right now: https://nof1.ai/leaderboard https://nof1.ai/leaderboard Results are... underwhelming. All the AIs are focused on daytrading Mag7 stocks; almost all have lost money with gusto.
- syntaxing 10mo ago
  Let me guess, the mystery model is theirs
  - yahoozoo2 10mo ago
    It says "Undisclosed frontier AI Lab (not Nof1)"
- richardhenry 10mo ago
  If I'm understanding this website correctly, these models can only trade in a handful of tech stocks along with the XYZ100 hyperliquid coin?
  - deleted 9mo ago
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- enlyth 10mo ago
  With the speed of how pricing information propagates, this seems way too dependent on how the agent is built, what information it has access to, and the feedback loop between the LLM and actions it can carry out
- mjk3026 10mo ago
  I also saw the hype on X yesterday and had already checked the https://nof1.ai/leaderboard https://nof1.ai/leaderboard, so I figured this post was about those results — but apparently it’s a completely different arena. I still have no idea how to make sense of the huge gap between the Nof1 arena and the aitradearena results. But honestly, the Nof1 dashboard — with the models posting real-time investment commentary — is way more interesting to watch than the aitradearena results anyway.
chongli 10mo ago
They outperformed the S&P 500 but seem to be fairly well correlated with it. Would like to see a 3X leveraged S&P 500 ETF like SPXL charted against those results.
- 10000truths 10mo ago
  ...over the course of 8.5 months, which is way too short for a meaningful result. If their strategy could outperform the S&P 500's 10-year return, they wouldn't be blogging about it.
- driverdan 10mo ago
  VTI gained over 10% in that time period so it wasn't much better.
bcrosby95 10mo ago
> 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.
- etchalon 10mo ago
  I don't feel like they measured anything. They just confirmed that tech stocks in the US did pretty well.
  - JoeAltmaier 10mo ago
    They measured the investment facility of all those LLMs. That's pretty much what the title says. And they had dramatically different outcomes. So that tells me something.
    - DennisP 10mo ago
      I mean, what it kinda tells me is that people talk about tech stocks the most, so that's what was most prevalent in the training data, so that's what most of the LLMs said to invest in. That's the kind of strategy that works until it really doesn't.
      - ghaff 10mo ago
        Cue 2020 or so. I do have investments in tech stocks but I have a lot more conservative investments too.
    - Libidinalecon 10mo ago
      It shows nothing. This is a bullshit stunt that should be obvious to anyone who has placed a few trades.
      - 10mo ago
parpfish 10mo ago
I wonder if this could be explained as the result of LLMs being trained to have pro-tech/ai opinions while we see massive run ups in tech stock valuations? It’d be great to see how they perform within particular sectors so it’s not just a case of betting big on tech while tech stocks are booming
gwd 10mo ago
The summary to me is here: > 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. If the AI bubble had popped in that window, Gemini would have ended up the leader instead.
- turtletontine 10mo ago
  Yup. This is the fallacy of thinking you’re a genius because you made money on the market. Being lucky at the moment (or even the last 5 years) does not mean you’ll continue to be lucky in the future. “Tech line go up forever” is not a viable model of the economy; you need an explanation of why it’s going up now, and why it might go down in the future. And also models of many other industries, to understand when and why to invest elsewhere. And if your bets pay off in the short term, that doesn’t necessarily mean your model is right. You could have chosen the right stocks for the wrong reasons! Past performance doesn’t guarantee future performance.
  - Vegenoid 10mo ago
    Clearly AI is not a bubble, look how good it is at predicting the stock market!
  - gwd 10mo ago
    What would have been impressive is if the favored industries, or individual companies, experienced a major drop during the target testing window, and the LLMs managed to pull out of those industries before they dropped.
lawlessone 10mo ago
Could they give some random people (i volunteer) 100k for 8 months? ...as a control
- iLoveOncall 10mo ago
  I know this is a joke comment, but there are plenty of websites that simulate the stock market and where you can use paper money to trade. People say it's not equivalent to actually trading though, and you shouldn't use it as a predictor of your actual trading performance, because you have a very different risk tolerance when risking your actual money.
  - ghaff 10mo ago
    Yeah, if you give me $100K I'm almost certainly going to make very different decisions than either a supposedly optimizing computer or myself at different ages.
andirk 10mo ago
Update with Gemini 3. It's far better than its predecessors.
deleted 10mo ago
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apical_dendrite 10mo ago
Looking at the recent holdings for the best models, it looks like it's all tech/semiconductor stocks. So in this time frame they did very well, but if they ended in April, they would have underperformed the S&P500.
halzm 10mo ago
I think these tests are always difficult to gauge how meaningful they actually are. If the S&P500 went up 12% over that period, mainly due to tech stocks, picking a handful of tech stocks is always going to set you higher than the S&P. So really all I think they test is whether the models picked up on the trend. I more surprised that Gemini managed to lose 10%. I wish they actually mentioned what the models invested in and why.
- taylorlapeyre 10mo ago
  Wait — isn't that exactly what good investors do? They look for what stocks are going to beat expectations and invest in them. If a stock broker I hired got this return, I wouldn't be rolling my eyes and saying "that's only because they noticed the trend in tech stocks." That's exactly what I'm paying them to do.
- Marsymars 10mo ago
  > picking a handful of tech stocks is always going to set you higher than the S&P. That's a bold claim.
xnx 10mo ago
Spoiler: They did not use real money or perform any actual trades.
jacktheturtle 10mo ago
This is really dumb. Because the models themselves, like markets, are indeterministic. They will yield different investment strategies based on prompts and random variance. This is a really dumb measurement.
iLoveOncall 10mo ago
Since it's not included in the main article, here is the prompt: > You are a stock trading agent. Your goal is to maximize returns. > You can research any publicly available information and make trades once per day. > You cannot trade options. > Analyze the market and provide your trading decisions with reasoning. > > Always research and corroborate facts whenever possible. > Always use the web search tool to identify information on all facts and hypotheses. > Always use the stock information tools to get current or past stock information. > > Trading parameters: > - Can hold 5-15 positions > - Minimum position size: $5,000 > - Maximum position size: $25,000 > > Explain your strategy and today's trades. Given the parameters, this definitely is NOT representative of any actual performance. I recommend also looking at the trade history and reasoning for each trade for each model, it's just complete wind. As an example, Deepseek made only 21 trades, which were all buys, which were all because "Companyy X is investing in AI". I doubt anyone believe this to be a viable long-term trading strategy.
- Scubabear68 10mo ago
  Agree. Those parameters are incredibly artificial bullshit.
cheeseblubber 10mo ago
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.
- irishcoffee 10mo ago
  > But wanted to give everyone a way to see how these models think… Think? What exactly did “it” think about?
  - cheeseblubber 10mo ago
    You can click in to the chart and see the conversation as well as for each trade what was the reasoning it gave for it
    - philipwhiuk 10mo ago
      A model can't tell you why it made the decision. What it can do is inspect the decision it made and make up a reason a human might have said when making the decision.
  - stoneyhrm1 10mo ago
    "Pass the salt? You mean pass the sodium chloride?"
- joegibbs 10mo ago
  I think it would be interesting to see how it goes in a scenario where the market declines or where tech companies underperform the rest of the market. In recent history they've outperformed the market and that might bias the choices that the LLMs make - would they continue with these positive biases if they were performing badly?
- apparent 10mo ago
  > Grok ended up performing the best while DeepSeek came close to second. I think you mean "DeepSeek came in a close second".
mlmonkey 10mo ago
> We were cautious to only run after each model’s training cutoff dates for the LLM models Grok is constantly training and/or it has access to websearch internally. You cannot backtest LLMs. You can only "live" test them going forward.
- cheeseblubber 10mo ago
  Via api you can turn off websearch internally. We provided all the models with their own custom tools that only provided data up to the date of the backtest.
  - mlmonkey 10mo ago
    But Grok is internally training on Tweets etc. continuously.
dogmayor 10mo ago
They could only trade once per day and hold 5-15 positions with a position size of $5k-$25k according to the agent prompt. Limited to say the least.
1a527dd5 10mo ago
Time. That has been the best way to get returns. I setup a 212 account when I was looking to buy our first house. I bought in small tiny chunks of industry where I was comfortable and knowledgeable in. Over the years I worked up a nice portfolio. Anyway, long story short. I forgot about the account, we moved in, got a dog, had children. And then I logged in for the first time in ages, and to my shock. My returns were at 110%. I've done nothing. It's bizarre and perplexing.
- lisbbb 10mo ago
  Yeah, uh, all I did was buy BRK.B like a decade ago and it's up 172% or something like that. The only way I have seen people outperform is by having insider information.
- jondwillis 10mo ago
  …did you beat the market? 110% is pretty much what the nasdaq has done over the last 5 years Also N=1
- delijati 10mo ago
  time in the market beats timing the market -> Kenneth Fisher ... i learned it the hard way ;)
theideaofcoffee 10mo ago
“Everyone (including LLMs) is a genius in a bull market.”
- apparent 10mo ago
  Apparently everyone (but Gemini).
  - koakuma-chan 10mo ago
    Could Gemini end up being better over the longer term?
    - scarmig 10mo ago
      Depends on if the market can stay irrational longer than Gemini stays solvent.
- mrweasel 10mo ago
  I was thinking the same thing. A number of coworkers where trading stocks a few years ago and felt pretty good about their skills, until someone pointed out that making good stock picks was easy when everything is going up. Sure enough, when the market started to fail, they all lost money. 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.
tiffani 10mo ago
What was the backtesting method? Was walk-forward testing involved? There are different ways to backtest.
Nevermark 10mo ago
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 credible, meaningful means and standard deviations. This experiment, as is, is a very expensive unbalanced uncharacterizable random number generator.
- cheeseblubber 10mo ago
  Yes definitely we were using our own budget and out of our own pocket and these model runs were getting expensive. Claude costed us around 200-300 dollars a 8 month run for example. We want to scale it and get more statistically significant results but wanted to share something in the interim.
  - Nevermark 10mo ago
    Got it. It is an interesting thing to explore.
- ipnon 10mo ago
  Yes, if these models available for $200/month a making 50% returns reliably, why isn’t Citadel having layoffs?
  - lisbbb 10mo ago
    In my experience, you get a few big winners, but since you have to keep placing new trades (e.g. bets) you eventually blow one and lose most of what you made. This is particularly true with options and futures trades. It's a stupid way to speculate with or without AI help doesn't matter and will never matter.
- energy123 10mo ago
  To their credit, they say in the article that the results aren't statistically significant. It would be better if that disclaimer was more prominently displayed though. The tone of the article is focused on the results when it should be "we know the results are garbage noise, but here is an interesting idea".
Bender 10mo ago
This experiment was also performed with a fish [1] though it was only given $50,000. Spoiler, the fish did great vs wall street bets. [1] - https://www.youtube.com/watch?v=USKD3vPD6ZA https://www.youtube.com/watch?v=USKD3vPD6ZA [video][15 mins]
naet 10mo ago
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 making their own responses. The post did list some potential disadvantages of backtesting, so they clearly aren't totally in the dark on it. Deepseek did not sell anything, but did well with holding a lot of tech stocks. I think that can be a bit of a risky strategy with everything in one sector, but it has been a successful one recently so not surprising that it performed well. Seems like they only get to "trade" once per day, near the market close, so it's not really a real time ingesting of data and making decisions based on that. What would really be interesting is if one of the LLMs switched their strategy to another sector at an appropriate time. Very hard to do but very impressive if done correctly. I didn't see that anywhere but I also didn't look deeply at every single trade.
- bmitc 10mo ago
  I've honestly never understood what backtesting even does because of the things you mention like time it takes to request and close trades (if they even do!), responses to your trades, the continuous and dynamic input of the market into your model, etc. Is there any reference that explains the deep technicalities of backtesting and how it is supposed to actually influence your model development? It seems to me that one could spend a huge amount of effort on backtesting that would distract from building out models and tooling and that that effort might not even pay off given that the backtesting environment is not the real market environment.
  - tim333 10mo ago
    I'm not sure about deep technicalities but backtesting is a useful thing to see how some strategy would have performed at some times in the past but there are quite a lot of limitations to it. Two of the big ones are the market reacting to you and maybe more so a kind of hindsight bias where you devise some strategy that would have worked great on past markets but the real time ones do something different. https://en.wikipedia.org/wiki/Long-Term_Capital_Management https://en.wikipedia.org/wiki/Long-Term_Capital_Management was kind of an example of both of those. They based their predictions on past behaviour which proved incorrect. Also if other market participants figure a large player is in trouble and going to have to sell a load of bonds they all drop their bids to take advantage of that. A lot of deviations from efficient market theory are like that - not deeply technical but about human foolishness.
copypaper 10mo ago
>Each model gets access to market data, news APIs, company financials... The article is very very vague on their methodology (unless I missed it somewhere else?). All I read was, "we gave AI access to market data and forced it to make trades". How often did these models run? Once a day? In a loop continuously? Did it have access to indicators (such as RSI)? Could it do arbitrary calculations with raw data? Etc... I'm in the camp that AI will never be able to successfully trade on its own behalf. I know a couple of successful traders (and many unsuccessful!), and it took them years of learning and understanding before breaking even. I'm not quite sure what the difference is between the successful and non-successful. Some sort of subconscious knowledge from staring at charts all day? A level of intuition? Regardless, it's more than just market data and news. I think AI will be invaluable as an assistant (disclaimer; I'm working on an AI trading assistant), but on its own? Never. Some things simply simply can't be solved with AI and I think this is one of them. I'm open to being wrong, but nothing has convinced me otherwise.
XenophileJKO 10mo ago
So.. I have been using an LLM to make 30 day buy and hold portfolios. And the results are "ok". (Like 8% vs 6% for the S&P 500 over the last 90 days) What you ask the model to do is super important. Just like writing or coding.. the default "behavior" is likely to be "average".. you need to very careful of what you are asking for. For me this is just a fun experiment and very interesting to see the market analysis it does. I started with o3 and now I'm using 5.1 Thinking (set to max). I have it looking for stocks trading below intrinsic value with some caveats because I know it likes to hinge on binary events like drug trial results. I also have it try to have it look at correlation with the positions and make sure they don't have the same macro vulnerability. I just run it once a month and do some trades with one of my "experimental" trading accounts. It certainly has thought of things I hadn't like using an equal weight s&p 500 etf to catch some upside when the S&P seems really top heavy and there may be some movement away from the top components, like last month.
- themafia 10mo ago
  I look for issues with a recent double bottom and high insider buy activity. I've found this to be a highly reliable set of signals.
  - XenophileJKO 10mo ago
    That is interesting. I was trying to not be "very" prescriptive. My initial impression was, if you don't tell it to look at intrinsic value, the model will look at meme or very common stocks too much. Alternatively specifying an investing persona would probably also move it out of that default behavior profile. You have to kind of tell it about what it cares about. This isn't necessarily about trying to maximize a strategy, it was more about learning what kinds of things would it focus on, what kind of analysis.
dismalaf 10mo ago
Back when I was in university we used statistical techniques similar to what LLMs use to predict the stock market. It's not a surprise that LLMs would do well over this time period. The problem is that when the market turns and bucks trends they don't do so well, you need to intervene.
deleted 10mo ago
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cedws 10mo ago
Backtesting for 8 months is not rigorous enough and also this site has no source code or detailed methodology. Not worth the click.
_alternator_ 10mo ago
Wait, they didn’t give them real money. They simulated the results.
petesergeant 10mo ago
If I'm reading this, almost all of Grok's advantage comes from heavy bets into semi-conductors spiking: ASML, INTC, MU.
mikewarot 10mo ago
They weren't doing it in real time, thus it's possible that the LLMs might have had undisclosed perfect knowledge of the actual history of the market. Only an real time study is going to eliminate this possibility.
itake 10mo ago
Model output is non-deterministic. Did they make 10 calls per decision and then choose the majority? or did they just recreate the monkey picking stocks strategy?
- ta12653421 10mo ago
  ++1 This. Thats also the reason why i still belive in "classic instruments" when configuring my trade app; the model wont give you the same entries on lets say 5 questions.
hoerzu 10mo ago
How many trades? What's the z-score?
hoerzu 10mo ago
For backtesting LLMs on polymarket I built. You can try with live data without sign up at: https://timba.fun https://timba.fun
luccabz 10mo ago
we should: 1. train with a cutoff date at ~2006 2. simulate information flow (financial data, news, earnings, ...) day by day 3. measure if any model predicts the 2008 collapse, how confident they are in the prediction and how far in advance
stuffn 10mo ago
Trading in a nearly 20 year bull market and doing well is not an accomplishment.
dehrmann 10mo ago
Is it just prompting LLMs with "I have $100k to invest. Here are all publicly traded stocks and a few stats on them. Which stocks should I buy?" And repeat daily, rebalancing as needed? This isn't the best use case for LLMs without a lot of prompt engineering and chaining prompts together, and that's probably more insightful than running them LLMs head-to-head.
client4 10mo ago
The obvious next question is: does the AI on cocaine outperform? https://pihk.ai/ https://pihk.ai/
Genego 10mo ago
When I see stuff like this, I feel like rereading the Incerto by Taleb just to refresh and sharpen my bullshit senses.
- bwfan123 10mo ago
  LLM is the fad of the day, and these sort of articles provoke the natural get-rich-quick-greed inherent in all of us, especially the young tech-types. As such they are clickbait, and also a barometer of the silliness that is widespread. I am curious why re-reading incerto sharpens your bullshit sense. I have read a few in that series, but didnt see it as sharpening my bullshit sensor.
dhosek 10mo ago
I wouldn’t trust any backtracking test with these models. Try doing a real-time test over 8 months and see what happens then. I’d also be suspicious of anything that doesn’t take actual costs into account.
- rallies 10mo ago
  We're running some live experiments these days, for both stocks and options. https://rallies.ai/arena https://rallies.ai/arena
  - philipwhiuk 10mo ago
    With actual money? Or still fake money?
    - dhosek 10mo ago
      Fake money is better than nothing, but one hopes that at the very least they’re correctly managing prices with the bid-ask spread, although real money would tend to influence what the actual numbers would be (small dollar amounts likely getting worse pricing, large dollar amounts potentially impacting the movement of the market).
      - Scubabear68 10mo ago
        If they are not actually trading, they are almost certainly getting a lot wrong and I would not trust this one bit.
wowamit 10mo ago
Is finding the right stocks to invest in an LLM problem? Language models aren't the right fit, I would presume. It would also be insightful to compare this with traditional ML models.
XCSme 10mo ago
If it's backtesting on data older than the model, then strategy can have lookahead bias, because the model might already know what big events will happen that can influence the stock markets.
lvspiff 10mo ago
I setup real life accounts with etrade and fidelity using the etrade auto portfolio, fidelity i have an advisor for retirement, and then i did a basket portfolio as well but used ms365 with grok 5 and various articles and strategies to pick a set of 5 etfs that would perform similarly to the exposure of my other two. This year So far all are beating the s&p % wise (only by <1% though) but the ai basket is doing the best or at least on par with my advisor and it’s getting to a point where the auto investment strategy of etrade at least isn’t worth it. Its been an interesting battle to watch as each rebalances at varying times as i put more funds in each and some have solid gains which profits get moved to more stable areas. This is only with a few k in each acct other than retirement but its still fun to see things play out this year. In other words though im not surprised at all by the results. Ai isnt something to day trade with still but it is helpful in doing research for your desired risk exposure long term imo.
- lisbbb 10mo ago
  How much are the expense ratios on those etfs you chose, though? I mean, Vanguard, Fidelity, Blackrock, and others have extremely low cost funds and etfs and it has been shown year after year and decade after decade that you can't beat their average returns over the long term. Indexing works for a reason. Beating something by 1%? It's not even worth it if your costs and taxes are higher than that.
IncreasePosts 10mo ago
Just picking tech stocks and winning isn't interesting unless we know the thesis behind picking the tech sticks. Instead, maybe a better test would he give it 100 medium cap stocks, and it needs to continually balance its portfolio among those 100 stocks, and then test the performance.
refactor_master 10mo ago
Should have done GME stocks only. Now THAT would’ve been interesting to see how much they’d end up losing on that. Just riding a bubble up for 8 months with no consequences is not an indicator of anything.
btbuildem 10mo ago
It turns out DeepSeek only made BUY trades (not a single SELL in the history in their live example) -- so basically, buy & hold strategy wins, again.
- culi 10mo ago
  this study should be replicated during a bear market
  - bmitc 10mo ago
    Buy and hold performs well over long time scales by simply not adjusting based upon sentiment.
    - throwawayffffas 10mo ago
      Operating word is long, historically if you entered the market just before a downturn, it could take years up to a couple of decades to make up. Depending on which downturn we are looking at.
      - bmitc 10mo ago
        I think that requires entering once. I was referring to continuing to enter periodically and holding.
darepublic 10mo ago
So in other words I should have listened to the YouTube brainrot and asked chatgot for my trades. Sigh.
theymademe 10mo ago
prince of zamunda LLM edition or whatever that movie was based on that book was based on the realization how pathetic it all was based on was? .... yeah, some did a good one on ya. just imagine evaluating that offspring one or two generations later ... ffs, this is sooooooooooooooo embarrassing
867-5309 10mo ago
tl;dr https://www.aitradearena.com/blog/llm-performance-chart.png https://www.aitradearena.com/blog/llm-performance-chart.png
867-5309 10mo ago
GPT-5 was released 4 months ago..
regnull 10mo ago
I'm working on a project where you can run your own experiment (or use it for real trading): https://portfoliogenius.ai https://portfoliogenius.ai. Still a bit rough, but most of the main functionality works.
hsuduebc2 10mo ago
In bullish market when few companies are creating a bubble, does this benchmark have any informational value? Wouldn't it be better to run this on seamlessly random intervals in past years?
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mempko 10mo ago
The stats are abysmal. What's the MDD compared to S&P 500. What is the Sortino? What are the confidence intervals for all the stats? Number of trades? So many questions....
energy123 10mo ago
One of the recent NeurIPS best paper recipients is relevant here: https://openreview.net/forum?id=saDOrrnNTz https://openreview.net/forum?id=saDOrrnNTz > an extensive empirical study across more than 70 models, revealing the Artificial Hivemind effect: pronounced intra- and inter-model homogenization So the inter-model variety will be exeptionally low. Users of LLMs will intuitively know this already, of course.
keepamovin 10mo ago
I’d say Grok did best because it has the best access to information. Grok deep search and real time knowledge capabilities due to the X integration and just general being plugged into the pulse of the Internet a really best in class. It’s a great OSINT research tool. Interesting how this research seems to tease out a truth traders have known for eons that picking stocks is all about having information maybe a little bit of asymmetric information due to good research not necessarily about all the analysis that can be done. (that’s important but information is king) because it’s a speculative market that’s collectively reacting to those kind of signals.
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stockresearcher 10mo ago
I appreciate that you’ve made the trade histories downloadable and will be taking a look to see what I can learn. I’ve glanced over some of it and really wonder why they seemed to focus on a small group of stocks.
aperture147 10mo ago
Why is bullshit detector ringing as hell right now??? This sounds like another billion-dollar-Markov-chain-IP that claimed to change the world, opening with a paper with flying colors.
frobisher 10mo ago
lolol Gemini
rallies 10mo ago
This is pretty cool. We're also running a live experiment on both stocks and options. One difference with our experiment is a lot more tools being available to the models (anything you can think of, sec filings, fundamentals, live pricing, options data). We think backtests are meaningless given LLMs have mostly memorized every single thing that happened so it's not a good test. So we're running a forward test. Not enough data for now but pretty interesting initial results https://rallies.ai/arena https://rallies.ai/arena
- touristtam 10mo ago
  How is Qwen so much worse than the rest (for the period accounted)?
- natiman1000 10mo ago
  Is the code/prompts used open source? if not how can we say it's ligit
nurettin 10mo ago
Deepseek and grok together would perform even better.
vpribish 10mo ago
this is so stupid i wish i could flag it twice
- Frieren 10mo ago
  [flagged]
aidenn0 10mo ago
It seems to me that short-term simulations will tend to underprice risk. Imagine a market where you can buy only two stocks: Stock A goes up invariably 1% per month Stock B goes up 1.5% per month with a 99% chance, but loses 99% of its value with a 1% chance. Stock B has a 94% chance of beating stock A on a 6 month simulation, but only a 30% chance of beating stock A on a 10 year simulation.
fortran77 10mo ago
I would love to see this run during an extended bear market period.
- ta12653421 10mo ago
  Cant the model go short in a bear market?
toephu2 10mo ago
Predicting stock prices means you are competing directly against massive hedge funds and professional quant teams with effectively unlimited budgets and large teams of engineers. These professionals are already using and constantly tweaking the latest models to gain an advantage. It is highly unlikely that you guys or any individual, even utilizing the latest LLMs will consistently discover an edge that beats the market over the long run.
pech0rin 10mo ago
8 months of a huge bull market. Not exactly indicative of any real insight.
rcarmo 10mo ago
I spent a while looking at trading algos a few years back (partly because of quant stuff I got involved in, and partly out of curiosity). I found that none of the “slow” trading (i.e., that you could run at home alongside your day trading account) was substantially effective (at least in my sampling), but I never thought an LLM would be any good at it because all the analysis is quantitative, not qualitative or contextual. In short, I don’t think this study proves anything unless they gave the LLMs additional context besides the pure trading data (Bloomberg terminals have news for a reason—there’s typically a lot more context in he market than individual stock values or history).
morgengold 10mo ago
Am I right that you let LLMs decide for themselves what to read into their input data (like market data, news APIs, company financials)? While this is worth testing, I think it would be more interesting to give them patterns to look for. I played around with using them for technical analysis and let them make the associations with past stock performances. They can even differentiate on what worked in the last 5 years, what in the last year, in the last 3 month etc. This way they can pick up (hopefully) changes in market behavior. Generally the main strength of this approach is to use their pattern recognition capability and also take out the human factor (emotions) for trading decitions.
Bombthecat 10mo ago
I wouldn't call this a test, I would create a test portfolio of hundred semi random stocks and see what they sell buy or keep. That tells me way more then "YOLO tech stocks"
bitmasher9 10mo ago
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.
- ryandvm 10mo ago
  It seems like back-testing an LLM is going to require significant white-washing of the test data to prevent the LLM from just trading on historical trends it is aware of. 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.
amelius 10mo ago
Nonsense. Title should read $0 because they didn't use actual money. Also, it seems pretty stupid to use commodity tech like LLMs for this.
FrustratedMonky 10mo ago
How much of this is just because the market as a whole is going up. 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.
throwawayffffas 10mo ago
> We also built a way to simulate what an agent would have seen at any point in the past. Each model gets access to market data, news APIs, company financials—but all time filtered: agents see only what would have been available on that specific day during the test period. That's not going to work, these agents especially the larger ones, will have news about the companies embedded in their weights.
- devilsbabe 10mo ago
  Funny how if you kept reading before commenting, they addressed that point specifically > We were cautious to only run after each model’s training cutoff dates for the LLM models. That way we could be sure models couldn’t have memorized market outcomes.
krauses 10mo ago
I'd like to see a variation of the models being fine tuned based on investments of those in congress that seem to consistently outperform the markets.
thedougd 10mo ago
Would be nice to use the logos in the legend. I use these LLMs everyday and didn't know what half these logos on the graph were.
RandomLensman 10mo ago
Could be interesting to see performance distribution for random strategies on that stock universe as a comparison. The reverse could also be interesting: how do the models perform on data that is random?
mvkel 10mo ago
When the market is rising, everyone looks like a genius. Would have been better to have variants of each, locked to specific industries. It also sounds like they were -forced- to make trades every day. Why? deciding not to trade is a good strategy too.
cramcgrab 10mo ago
Yeah I’ve been using grok to manage my yolo fund, it’s been doing great so far, up around 178% ytd, only rebalance once every other month.
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portly 10mo ago
What is the point of this? LLMs are trained to predict the next word in a text. In what way, shape or form does that have anything to do with stock market prediction? Completely ridiculous AI bubble nonsense.
- another_twist 10mo ago
  No it isnt. Next word prediction is what humans do to communicate anyway so the criticism isnt valid. Except you do that for your own sentences (if you do it for others its considered rude :) ). Anyways this criticism is now dated given that modern day LLMs can solve unseen reasoning problems such as those found in the IMO. It does have something to do with the stock market, since its about making hypotheses and trading based off that. However, I'd agree that making a proper trading AI here would require reasoning based fine tuning for stock market trading actions. Sort of like running GRPO taking market feedback as the reward. the article simply cant do that due to not having access to the underlying model weight.
- bwfan123 10mo ago
  shhh. We need more of these as counter-parties to improve alpha.
mvkel 10mo ago
Predicting the stock market will likely never happen because it’s recursive. We can predict the next 10 days of weather, but the weather doesn’t change because it read your forecast. As long as markets continue to react to their own reactions, they will remain unpredictable. If the strategy is long, there might be alpha to be found. But day trading? No way.
- oersted 10mo ago
  If stocks are more of a closed system that are weakly affected by external factors in the short term, now I finally understand why they hire so many physicists for financial modeling! There is of course the fact that physicists tend to be the best applied mathematicians, even if they don’t end up using any of their physics knowledge. And they generally had the reputation of “the smartest” people for the last century. Anyway, such systems are complex and chaotic yes, but there are many ways of predicting aspects of them, like with fluid simulation to give a basic example. And I don’t get your point about weather, it is also recursive in the same way and reacting to its own reactions. Sure it is not reacting to predictions of itself, but that’s just a special kind of reaction, and patterns in others predictions can definitely be predicted accurately, perhaps not individually but in the aggregate.
  - mvkel 10mo ago
    > there are many ways of predicting aspects of them Yes, and it's priced in > but that’s just a special kind of reaction That's just arguing semantics. My point was that weather doesn't react to human predictions, explicitly
- jerf 10mo ago
  "We can predict the next 10 days of weather, but the weather doesn’t change because it read your forecast." Less true than it used to be, with cloud seeding being an off-the-shelf technology now. Still largely true, but not entirely true anymore.
machiaweliczny 10mo ago
> Potential accidental data leakage from the “future” Exactly. Makes no sense with models like grok. DeepSeek also likely has this leak as was trained later.
Glyptodon 10mo ago
Multiple runs of randomized backtesting seem needed for this to mean anything. It's also not clear to me how there's any kind of information update loop. Maybe I didn't read closely enough.
kqr 10mo ago
Extremely similar earlier submission but focused on cryptocurrencies, using real money, and in real time: https://news.ycombinator.com/item?id=45976832 https://news.ycombinator.com/item?id=45976832 I'm extremely skeptical of any attempt to prevent leakage of future results to LLMs evaluated on backtesting. Both because this has beet shown in the literature to be difficult, and because I personally found it very difficult when working with LLMs for forecasting.
kqr 10mo ago
Their annual geometric mean return is 45 %! That's some serious overbetting. In a market that didn't accidentally align with their biases, they would have lost money very quickly.
reactordev 10mo ago
I would love for them to have included a peg position on SPY @ 100k over the course of the same period. Gives a much better benchmark of what an LLM can do (not much above 2-4%). Still, cool to see others in my niche hobby of finding the money printer.
peterbonney 10mo ago
The devil is really in the details on how the orders were executed in the backtest, slippage, etc. Instead of comparing to the S&P 500 I'd love to see it benchmarked against a range of active strategies, including common non-AI approaches (e.g. mean reversion, momentum, basic value focus, basic growth focus, etc.) and some simple predictive (non-generative) AI models. This would help shake out whether there is selection alpha coming out of the models, or whether there is execution alpha coming out of the backtest.
rao-v 10mo ago
I’d rather give an LLM the earnings report for a stock and the next day’s SNP 500 opening and see if it can predict the opening price. Expecting an LLM to magically beat efficient market theory is a bit silly. Much more reasonable to see if it can incorporate information as well as the market does (to start)
- deleted 10mo ago
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natiman1000 10mo ago
If the code and prompts are not open source how can we trust anything yall say?
elzbardico 10mo ago
A rising tide lift all boats.
dudeinhawaii 10mo ago
This is the complete wrong way to do this. I say this as someone who does work in this area of leveraging LLMs to a limited degree in trading. LLMs are naive, easily convinced, and myopic. They're also non-deterministic. We have no way of knowing if you ran this little experiment 10 times whether they'd all pick something else. This is a scattershot + luck. The RIGHT way to do this is to first solve the underlying problem deterministically. That is, you first write your trading algorithm that's been thoroughly tested. THEN you can surface metadata to LLMs and say things along the lines of "given this data + data you pull from the web", make your trade decision for this time period and provide justification. Honestly, adding LLMs directly to any trading pipeline just adds non-useful non-deterministic behavior. The main value is speed of wiring up something like sentiment analysis as a value add or algorithmic supplement. Even this should be done using proper ML but I see the most value in using LLMs to shortcut ML things that would require time/money/compute. Trading value now for value later (the ML algorithm would ultimately run cheaper long-run but take longer to get into prod). This experiment, like most "I used AI to trade" blogs are completely naive in their approach. They're taking the lowest possible hanging fruit. Worst still when those results are the rising tide lifting all boats. Edit (was a bit harsh) This experiment is an example of the kind of embarrassingly obvious things people try with LLMs without understanding the domain and writing it up. To an outsider it can sound exciting. To an insider it's like seeing a new story "LLMs are designing new CPUs!". No they're not. A more useful bit of research would be to control for the various variables (sector exposure etc) and then run it 10_000 times and report back on how LLM A skews towards always buying tech and LLM B skews towards always recommending safe stocks. Alternatively, if they showed the LLM taking a step back and saying "ah, let me design this quant algo to select the best stocks" -- and then succeeding -- I'd be impressed. I'd also know that it was learned from every quant that had AI double check their calculations/models/python.. but that's a different point.
snapdeficit 10mo ago
Anyone who traded tech stocks in the 1990s when AmeriTrade appeared remembers this story. Have the LLMS trade anything BUT tech stocks and see how they do. That’s the real test. EDIT: I remember this is probably before AmeriTrade offered options. I was calling in trades at 6:30AM PST to my broker while he probably laughed at me. But the point is the same: any doofus could make money buying tech stocks and holding for a few weeks. Companies were splitting constantly.
reformd 10mo ago
financial advice of smart refrigerator right before the dump?
johnnienaked 10mo ago
Ok now do risk adjusted returns
diamond559 10mo ago
When you've traded for many, many years, you realize just how little 8 months can mean. Especially during one of the most nonsensical bubble markets of all time.
jbritton 10mo ago
These were paper trades, not actual trades. Sometimes a few seconds can significantly effect the price one gets.
throwaway422432 9mo ago
Have asked LLMs for smallcap trading ideas on the ASX a few times. Grok often suggested shares that jumped significantly within the next few weeks. Wondering if it's access to Twitter gave it an advantage in predicting major upswings based on general sentiment.

398 points by cheeseblubber 10mo ago

Like so many analyses before them, including my own, this completely misses the basics of mean/variance risk analysis. We need to know the risk adjusted return, not just the return.

digitcatphd 10mo ago

Backtesting is a complete waste in this scenario. The models already know the best outcomes and are biased towards it.

sethops1 10mo ago

> Testing GPT-5, Claude, Gemini, Grok, and DeepSeek with $100K each over 8 months of backtested trading So the results are meaningless - these LLMs have the advantage of foresight over historical data.

CPLX 10mo ago
Not sure how sound the analysis is but they did apparently actually think of that.
- deleted 10mo ago
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PTRFRLL 10mo ago
> We were cautious to only run after each model’s training cutoff dates for the LLM models. That way we could be sure models couldn’t have memorized market outcomes.
- plufz 10mo ago
  I know very little about how the environment where they run these models look, but surely they have access to different tools like vector embeddings with more current data on various topics?
  - disconcision 10mo ago
    you can (via the api, or to a lesser degree through the setting in the web client) determine what tools if any a model can use
    - disconcision 10mo ago
      with the exception that it doesn't seem possible to fully disable this for grok 4
      - alchemist1e9 10mo ago
        which is curiously the best model …
    - plufz 10mo ago
      But isn’t that more which MCP:s you can configure it to use? Do we have any idea which secret sauce stuff they have? Surely it’s not just a raw model that they are executing?
  - endtime 10mo ago
    If they could "see" the future and exploit that they'd probably have much higher returns.
    - alchemist1e9 10mo ago
      56% over 8 months with the constraints provided are pretty good results for Grok.
    - plufz 10mo ago
      I would say that if these models independently could create such high returns all these companies would shut down the external access to the models and just have their own money making machine. :)
- stusmall 10mo ago
  Even if it is after the cut off date wouldn't the models be able to query external sources to get data that could positively impact them? If the returns were smaller I could reasonably believe it but beating the S&P500 returns by 4x+ strains credulity.
  - cheeseblubber 10mo ago
    We used the LLMs API and provided custom tools like a stock ticker tool that only gave stock price information for that date of backtest for the model. We did this for news apis, technical indicator apis etc. It took quite a long time to make sure that there weren't any data leakage. The whole process took us about a month or two to build out.
    - alchemist1e9 10mo ago
      I have a hunch Grok model cutoff is not accurate and somehow it has updated weights though they still call it the same Grok model as the params and size are unchanged but they are incrementally training it in the background. Of course I don’t know this but it’s what I would do in their situation since ongoing incremental training could he a neat trick to improve their ongoing results against competitors, even if marginal. I also wouldn’t trust the models to honestly disclose their decision process either. That said. This is a fascinating area of research and I do think LLM driven fundamental investing and trading has a future.
itake 10mo ago
> We time segmented the APIs to make sure that the simulation isn’t leaking the future into the model’s context. I wish they could explain what this actually means.
- devmor 10mo ago
  It's a very silly way of saying that the data the LLMs had access to was presented in chronological order, so that for instance, when they were trading on stocks at the start of the 8 month window, the LLMs could not just query their APIs to see the data from the end of the 8 month window.
- nullbound 10mo ago
  Overall, it does sound weird. On the one hand, assuming I properly I understand what they are saying is that they removed model's ability to cheat based on their specific training. And I do get that nuance ablation is a thing, but this is not what they are discussing there. They are only removing one avenue of the model to 'cheat'. For all we know, some that data may have been part of its training set already...
joegibbs 10mo ago
That's only if they're trained on data more recent than 8 months ago

deadbabe 10mo ago

Yea, so this is bullshit. An approximation of reality still isn’t reality. If you’re convinced the LLMs will perform as backtested, put real money and see what happens.

chroma205 10mo ago

>We gave each of five LLMs $100K in paper money Stopped reading after “paper money” Source: quant trader. paper trading does not incorporate market impact

txg 10mo ago
Lack of market response is a valid point, but $100k is pretty unlikely to have much impact especially if spread out over multiple trades.
zahlman 10mo ago
If your initial portfolio is 100k you are not going to have meaningful "market impact" with your trades assuming you actually make them vs. paper trading.
a13n 10mo ago
I mean if you’re going to write algos that trade the first thing you should do is check whether they were successful on historical data. This is an interesting data point. Market impact shouldn’t be considered when you’re talking about trading S&P stocks with $100k.
- verdverm 10mo ago
  Historical data is useful for validation, don't develop algos against it, test hypotheses until you've biased your data, then move on to something productive for society
tekno45 10mo ago
the quant trader you talked to probably sucks.

dash2 10mo ago

There's also this thing going on right now: https://nof1.ai/leaderboard https://nof1.ai/leaderboard Results are... underwhelming. All the AIs are focused on daytrading Mag7 stocks; almost all have lost money with gusto.

syntaxing 10mo ago
Let me guess, the mystery model is theirs
- yahoozoo2 10mo ago
  It says "Undisclosed frontier AI Lab (not Nof1)"
richardhenry 10mo ago
If I'm understanding this website correctly, these models can only trade in a handful of tech stocks along with the XYZ100 hyperliquid coin?
- deleted 9mo ago
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enlyth 10mo ago
With the speed of how pricing information propagates, this seems way too dependent on how the agent is built, what information it has access to, and the feedback loop between the LLM and actions it can carry out
mjk3026 10mo ago
I also saw the hype on X yesterday and had already checked the https://nof1.ai/leaderboard https://nof1.ai/leaderboard, so I figured this post was about those results — but apparently it’s a completely different arena. I still have no idea how to make sense of the huge gap between the Nof1 arena and the aitradearena results. But honestly, the Nof1 dashboard — with the models posting real-time investment commentary — is way more interesting to watch than the aitradearena results anyway.

chongli 10mo ago

They outperformed the S&P 500 but seem to be fairly well correlated with it. Would like to see a 3X leveraged S&P 500 ETF like SPXL charted against those results.

10000truths 10mo ago
...over the course of 8.5 months, which is way too short for a meaningful result. If their strategy could outperform the S&P 500's 10-year return, they wouldn't be blogging about it.
driverdan 10mo ago
VTI gained over 10% in that time period so it wasn't much better.

bcrosby95 10mo ago

> 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.

etchalon 10mo ago
I don't feel like they measured anything. They just confirmed that tech stocks in the US did pretty well.
- JoeAltmaier 10mo ago
  They measured the investment facility of all those LLMs. That's pretty much what the title says. And they had dramatically different outcomes. So that tells me something.
  - DennisP 10mo ago
    I mean, what it kinda tells me is that people talk about tech stocks the most, so that's what was most prevalent in the training data, so that's what most of the LLMs said to invest in. That's the kind of strategy that works until it really doesn't.
    - ghaff 10mo ago
      Cue 2020 or so. I do have investments in tech stocks but I have a lot more conservative investments too.
  - Libidinalecon 10mo ago
    It shows nothing. This is a bullshit stunt that should be obvious to anyone who has placed a few trades.
    - 10mo ago

parpfish 10mo ago

I wonder if this could be explained as the result of LLMs being trained to have pro-tech/ai opinions while we see massive run ups in tech stock valuations? It’d be great to see how they perform within particular sectors so it’s not just a case of betting big on tech while tech stocks are booming

gwd 10mo ago

The summary to me is here: > 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. If the AI bubble had popped in that window, Gemini would have ended up the leader instead.

turtletontine 10mo ago
Yup. This is the fallacy of thinking you’re a genius because you made money on the market. Being lucky at the moment (or even the last 5 years) does not mean you’ll continue to be lucky in the future. “Tech line go up forever” is not a viable model of the economy; you need an explanation of why it’s going up now, and why it might go down in the future. And also models of many other industries, to understand when and why to invest elsewhere. And if your bets pay off in the short term, that doesn’t necessarily mean your model is right. You could have chosen the right stocks for the wrong reasons! Past performance doesn’t guarantee future performance.
- Vegenoid 10mo ago
  Clearly AI is not a bubble, look how good it is at predicting the stock market!
- gwd 10mo ago
  What would have been impressive is if the favored industries, or individual companies, experienced a major drop during the target testing window, and the LLMs managed to pull out of those industries before they dropped.

lawlessone 10mo ago

Could they give some random people (i volunteer) 100k for 8 months? ...as a control

iLoveOncall 10mo ago
I know this is a joke comment, but there are plenty of websites that simulate the stock market and where you can use paper money to trade. People say it's not equivalent to actually trading though, and you shouldn't use it as a predictor of your actual trading performance, because you have a very different risk tolerance when risking your actual money.
- ghaff 10mo ago
  Yeah, if you give me $100K I'm almost certainly going to make very different decisions than either a supposedly optimizing computer or myself at different ages.

andirk 10mo ago

Update with Gemini 3. It's far better than its predecessors.

deleted 10mo ago

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apical_dendrite 10mo ago

Looking at the recent holdings for the best models, it looks like it's all tech/semiconductor stocks. So in this time frame they did very well, but if they ended in April, they would have underperformed the S&P500.

halzm 10mo ago

I think these tests are always difficult to gauge how meaningful they actually are. If the S&P500 went up 12% over that period, mainly due to tech stocks, picking a handful of tech stocks is always going to set you higher than the S&P. So really all I think they test is whether the models picked up on the trend. I more surprised that Gemini managed to lose 10%. I wish they actually mentioned what the models invested in and why.

taylorlapeyre 10mo ago
Wait — isn't that exactly what good investors do? They look for what stocks are going to beat expectations and invest in them. If a stock broker I hired got this return, I wouldn't be rolling my eyes and saying "that's only because they noticed the trend in tech stocks." That's exactly what I'm paying them to do.
Marsymars 10mo ago
> picking a handful of tech stocks is always going to set you higher than the S&P. That's a bold claim.

xnx 10mo ago

Spoiler: They did not use real money or perform any actual trades.

jacktheturtle 10mo ago

This is really dumb. Because the models themselves, like markets, are indeterministic. They will yield different investment strategies based on prompts and random variance. This is a really dumb measurement.

iLoveOncall 10mo ago

Since it's not included in the main article, here is the prompt: > You are a stock trading agent. Your goal is to maximize returns. > You can research any publicly available information and make trades once per day. > You cannot trade options. > Analyze the market and provide your trading decisions with reasoning. > > Always research and corroborate facts whenever possible. > Always use the web search tool to identify information on all facts and hypotheses. > Always use the stock information tools to get current or past stock information. > > Trading parameters: > - Can hold 5-15 positions > - Minimum position size: $5,000 > - Maximum position size: $25,000 > > Explain your strategy and today's trades. Given the parameters, this definitely is NOT representative of any actual performance. I recommend also looking at the trade history and reasoning for each trade for each model, it's just complete wind. As an example, Deepseek made only 21 trades, which were all buys, which were all because "Companyy X is investing in AI". I doubt anyone believe this to be a viable long-term trading strategy.

Scubabear68 10mo ago
Agree. Those parameters are incredibly artificial bullshit.

cheeseblubber 10mo ago

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.

irishcoffee 10mo ago
> But wanted to give everyone a way to see how these models think… Think? What exactly did “it” think about?
- cheeseblubber 10mo ago
  You can click in to the chart and see the conversation as well as for each trade what was the reasoning it gave for it
  - philipwhiuk 10mo ago
    A model can't tell you why it made the decision. What it can do is inspect the decision it made and make up a reason a human might have said when making the decision.
- stoneyhrm1 10mo ago
  "Pass the salt? You mean pass the sodium chloride?"
joegibbs 10mo ago
I think it would be interesting to see how it goes in a scenario where the market declines or where tech companies underperform the rest of the market. In recent history they've outperformed the market and that might bias the choices that the LLMs make - would they continue with these positive biases if they were performing badly?
apparent 10mo ago
> Grok ended up performing the best while DeepSeek came close to second. I think you mean "DeepSeek came in a close second".

mlmonkey 10mo ago

> We were cautious to only run after each model’s training cutoff dates for the LLM models Grok is constantly training and/or it has access to websearch internally. You cannot backtest LLMs. You can only "live" test them going forward.

cheeseblubber 10mo ago
Via api you can turn off websearch internally. We provided all the models with their own custom tools that only provided data up to the date of the backtest.
- mlmonkey 10mo ago
  But Grok is internally training on Tweets etc. continuously.

dogmayor 10mo ago

They could only trade once per day and hold 5-15 positions with a position size of $5k-$25k according to the agent prompt. Limited to say the least.

1a527dd5 10mo ago

Time. That has been the best way to get returns. I setup a 212 account when I was looking to buy our first house. I bought in small tiny chunks of industry where I was comfortable and knowledgeable in. Over the years I worked up a nice portfolio. Anyway, long story short. I forgot about the account, we moved in, got a dog, had children. And then I logged in for the first time in ages, and to my shock. My returns were at 110%. I've done nothing. It's bizarre and perplexing.

lisbbb 10mo ago
Yeah, uh, all I did was buy BRK.B like a decade ago and it's up 172% or something like that. The only way I have seen people outperform is by having insider information.
jondwillis 10mo ago
…did you beat the market? 110% is pretty much what the nasdaq has done over the last 5 years Also N=1
delijati 10mo ago
time in the market beats timing the market -> Kenneth Fisher ... i learned it the hard way ;)

theideaofcoffee 10mo ago

“Everyone (including LLMs) is a genius in a bull market.”

apparent 10mo ago
Apparently everyone (but Gemini).
- koakuma-chan 10mo ago
  Could Gemini end up being better over the longer term?
  - scarmig 10mo ago
    Depends on if the market can stay irrational longer than Gemini stays solvent.
mrweasel 10mo ago
I was thinking the same thing. A number of coworkers where trading stocks a few years ago and felt pretty good about their skills, until someone pointed out that making good stock picks was easy when everything is going up. Sure enough, when the market started to fail, they all lost money. 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.

tiffani 10mo ago

What was the backtesting method? Was walk-forward testing involved? There are different ways to backtest.

Nevermark 10mo ago

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 credible, meaningful means and standard deviations. This experiment, as is, is a very expensive unbalanced uncharacterizable random number generator.

cheeseblubber 10mo ago
Yes definitely we were using our own budget and out of our own pocket and these model runs were getting expensive. Claude costed us around 200-300 dollars a 8 month run for example. We want to scale it and get more statistically significant results but wanted to share something in the interim.
- Nevermark 10mo ago
  Got it. It is an interesting thing to explore.
ipnon 10mo ago
Yes, if these models available for $200/month a making 50% returns reliably, why isn’t Citadel having layoffs?
- lisbbb 10mo ago
  In my experience, you get a few big winners, but since you have to keep placing new trades (e.g. bets) you eventually blow one and lose most of what you made. This is particularly true with options and futures trades. It's a stupid way to speculate with or without AI help doesn't matter and will never matter.
energy123 10mo ago
To their credit, they say in the article that the results aren't statistically significant. It would be better if that disclaimer was more prominently displayed though. The tone of the article is focused on the results when it should be "we know the results are garbage noise, but here is an interesting idea".

Bender 10mo ago

This experiment was also performed with a fish [1] though it was only given $50,000. Spoiler, the fish did great vs wall street bets. [1] - https://www.youtube.com/watch?v=USKD3vPD6ZA https://www.youtube.com/watch?v=USKD3vPD6ZA [video][15 mins]

naet 10mo ago

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 making their own responses. The post did list some potential disadvantages of backtesting, so they clearly aren't totally in the dark on it. Deepseek did not sell anything, but did well with holding a lot of tech stocks. I think that can be a bit of a risky strategy with everything in one sector, but it has been a successful one recently so not surprising that it performed well. Seems like they only get to "trade" once per day, near the market close, so it's not really a real time ingesting of data and making decisions based on that. What would really be interesting is if one of the LLMs switched their strategy to another sector at an appropriate time. Very hard to do but very impressive if done correctly. I didn't see that anywhere but I also didn't look deeply at every single trade.

bmitc 10mo ago
I've honestly never understood what backtesting even does because of the things you mention like time it takes to request and close trades (if they even do!), responses to your trades, the continuous and dynamic input of the market into your model, etc. Is there any reference that explains the deep technicalities of backtesting and how it is supposed to actually influence your model development? It seems to me that one could spend a huge amount of effort on backtesting that would distract from building out models and tooling and that that effort might not even pay off given that the backtesting environment is not the real market environment.
- tim333 10mo ago
  I'm not sure about deep technicalities but backtesting is a useful thing to see how some strategy would have performed at some times in the past but there are quite a lot of limitations to it. Two of the big ones are the market reacting to you and maybe more so a kind of hindsight bias where you devise some strategy that would have worked great on past markets but the real time ones do something different. https://en.wikipedia.org/wiki/Long-Term_Capital_Management https://en.wikipedia.org/wiki/Long-Term_Capital_Management was kind of an example of both of those. They based their predictions on past behaviour which proved incorrect. Also if other market participants figure a large player is in trouble and going to have to sell a load of bonds they all drop their bids to take advantage of that. A lot of deviations from efficient market theory are like that - not deeply technical but about human foolishness.

copypaper 10mo ago

>Each model gets access to market data, news APIs, company financials... The article is very very vague on their methodology (unless I missed it somewhere else?). All I read was, "we gave AI access to market data and forced it to make trades". How often did these models run? Once a day? In a loop continuously? Did it have access to indicators (such as RSI)? Could it do arbitrary calculations with raw data? Etc... I'm in the camp that AI will never be able to successfully trade on its own behalf. I know a couple of successful traders (and many unsuccessful!), and it took them years of learning and understanding before breaking even. I'm not quite sure what the difference is between the successful and non-successful. Some sort of subconscious knowledge from staring at charts all day? A level of intuition? Regardless, it's more than just market data and news. I think AI will be invaluable as an assistant (disclaimer; I'm working on an AI trading assistant), but on its own? Never. Some things simply simply can't be solved with AI and I think this is one of them. I'm open to being wrong, but nothing has convinced me otherwise.

XenophileJKO 10mo ago

So.. I have been using an LLM to make 30 day buy and hold portfolios. And the results are "ok". (Like 8% vs 6% for the S&P 500 over the last 90 days) What you ask the model to do is super important. Just like writing or coding.. the default "behavior" is likely to be "average".. you need to very careful of what you are asking for. For me this is just a fun experiment and very interesting to see the market analysis it does. I started with o3 and now I'm using 5.1 Thinking (set to max). I have it looking for stocks trading below intrinsic value with some caveats because I know it likes to hinge on binary events like drug trial results. I also have it try to have it look at correlation with the positions and make sure they don't have the same macro vulnerability. I just run it once a month and do some trades with one of my "experimental" trading accounts. It certainly has thought of things I hadn't like using an equal weight s&p 500 etf to catch some upside when the S&P seems really top heavy and there may be some movement away from the top components, like last month.

themafia 10mo ago
I look for issues with a recent double bottom and high insider buy activity. I've found this to be a highly reliable set of signals.
- XenophileJKO 10mo ago
  That is interesting. I was trying to not be "very" prescriptive. My initial impression was, if you don't tell it to look at intrinsic value, the model will look at meme or very common stocks too much. Alternatively specifying an investing persona would probably also move it out of that default behavior profile. You have to kind of tell it about what it cares about. This isn't necessarily about trying to maximize a strategy, it was more about learning what kinds of things would it focus on, what kind of analysis.

dismalaf 10mo ago

Back when I was in university we used statistical techniques similar to what LLMs use to predict the stock market. It's not a surprise that LLMs would do well over this time period. The problem is that when the market turns and bucks trends they don't do so well, you need to intervene.

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cedws 10mo ago

Backtesting for 8 months is not rigorous enough and also this site has no source code or detailed methodology. Not worth the click.

_alternator_ 10mo ago

Wait, they didn’t give them real money. They simulated the results.

petesergeant 10mo ago

If I'm reading this, almost all of Grok's advantage comes from heavy bets into semi-conductors spiking: ASML, INTC, MU.

mikewarot 10mo ago

They weren't doing it in real time, thus it's possible that the LLMs might have had undisclosed perfect knowledge of the actual history of the market. Only an real time study is going to eliminate this possibility.

itake 10mo ago

Model output is non-deterministic. Did they make 10 calls per decision and then choose the majority? or did they just recreate the monkey picking stocks strategy?

ta12653421 10mo ago
++1 This. Thats also the reason why i still belive in "classic instruments" when configuring my trade app; the model wont give you the same entries on lets say 5 questions.

hoerzu 10mo ago

How many trades? What's the z-score?

hoerzu 10mo ago

For backtesting LLMs on polymarket I built. You can try with live data without sign up at: https://timba.fun https://timba.fun

luccabz 10mo ago

we should: 1. train with a cutoff date at ~2006 2. simulate information flow (financial data, news, earnings, ...) day by day 3. measure if any model predicts the 2008 collapse, how confident they are in the prediction and how far in advance

stuffn 10mo ago

Trading in a nearly 20 year bull market and doing well is not an accomplishment.

dehrmann 10mo ago

Is it just prompting LLMs with "I have $100k to invest. Here are all publicly traded stocks and a few stats on them. Which stocks should I buy?" And repeat daily, rebalancing as needed? This isn't the best use case for LLMs without a lot of prompt engineering and chaining prompts together, and that's probably more insightful than running them LLMs head-to-head.

client4 10mo ago

The obvious next question is: does the AI on cocaine outperform? https://pihk.ai/ https://pihk.ai/

Genego 10mo ago

When I see stuff like this, I feel like rereading the Incerto by Taleb just to refresh and sharpen my bullshit senses.

bwfan123 10mo ago
LLM is the fad of the day, and these sort of articles provoke the natural get-rich-quick-greed inherent in all of us, especially the young tech-types. As such they are clickbait, and also a barometer of the silliness that is widespread. I am curious why re-reading incerto sharpens your bullshit sense. I have read a few in that series, but didnt see it as sharpening my bullshit sensor.

dhosek 10mo ago

I wouldn’t trust any backtracking test with these models. Try doing a real-time test over 8 months and see what happens then. I’d also be suspicious of anything that doesn’t take actual costs into account.

rallies 10mo ago
We're running some live experiments these days, for both stocks and options. https://rallies.ai/arena https://rallies.ai/arena
- philipwhiuk 10mo ago
  With actual money? Or still fake money?
  - dhosek 10mo ago
    Fake money is better than nothing, but one hopes that at the very least they’re correctly managing prices with the bid-ask spread, although real money would tend to influence what the actual numbers would be (small dollar amounts likely getting worse pricing, large dollar amounts potentially impacting the movement of the market).
    - Scubabear68 10mo ago
      If they are not actually trading, they are almost certainly getting a lot wrong and I would not trust this one bit.

wowamit 10mo ago

Is finding the right stocks to invest in an LLM problem? Language models aren't the right fit, I would presume. It would also be insightful to compare this with traditional ML models.

XCSme 10mo ago

If it's backtesting on data older than the model, then strategy can have lookahead bias, because the model might already know what big events will happen that can influence the stock markets.

lvspiff 10mo ago

I setup real life accounts with etrade and fidelity using the etrade auto portfolio, fidelity i have an advisor for retirement, and then i did a basket portfolio as well but used ms365 with grok 5 and various articles and strategies to pick a set of 5 etfs that would perform similarly to the exposure of my other two. This year So far all are beating the s&p % wise (only by <1% though) but the ai basket is doing the best or at least on par with my advisor and it’s getting to a point where the auto investment strategy of etrade at least isn’t worth it. Its been an interesting battle to watch as each rebalances at varying times as i put more funds in each and some have solid gains which profits get moved to more stable areas. This is only with a few k in each acct other than retirement but its still fun to see things play out this year. In other words though im not surprised at all by the results. Ai isnt something to day trade with still but it is helpful in doing research for your desired risk exposure long term imo.

lisbbb 10mo ago
How much are the expense ratios on those etfs you chose, though? I mean, Vanguard, Fidelity, Blackrock, and others have extremely low cost funds and etfs and it has been shown year after year and decade after decade that you can't beat their average returns over the long term. Indexing works for a reason. Beating something by 1%? It's not even worth it if your costs and taxes are higher than that.

IncreasePosts 10mo ago

Just picking tech stocks and winning isn't interesting unless we know the thesis behind picking the tech sticks. Instead, maybe a better test would he give it 100 medium cap stocks, and it needs to continually balance its portfolio among those 100 stocks, and then test the performance.

refactor_master 10mo ago

Should have done GME stocks only. Now THAT would’ve been interesting to see how much they’d end up losing on that. Just riding a bubble up for 8 months with no consequences is not an indicator of anything.

btbuildem 10mo ago

It turns out DeepSeek only made BUY trades (not a single SELL in the history in their live example) -- so basically, buy & hold strategy wins, again.

culi 10mo ago
this study should be replicated during a bear market
- bmitc 10mo ago
  Buy and hold performs well over long time scales by simply not adjusting based upon sentiment.
  - throwawayffffas 10mo ago
    Operating word is long, historically if you entered the market just before a downturn, it could take years up to a couple of decades to make up. Depending on which downturn we are looking at.
    - bmitc 10mo ago
      I think that requires entering once. I was referring to continuing to enter periodically and holding.

darepublic 10mo ago

So in other words I should have listened to the YouTube brainrot and asked chatgot for my trades. Sigh.

theymademe 10mo ago

prince of zamunda LLM edition or whatever that movie was based on that book was based on the realization how pathetic it all was based on was? .... yeah, some did a good one on ya. just imagine evaluating that offspring one or two generations later ... ffs, this is sooooooooooooooo embarrassing

867-5309 10mo ago

tl;dr https://www.aitradearena.com/blog/llm-performance-chart.png https://www.aitradearena.com/blog/llm-performance-chart.png

867-5309 10mo ago

GPT-5 was released 4 months ago..

regnull 10mo ago

I'm working on a project where you can run your own experiment (or use it for real trading): https://portfoliogenius.ai https://portfoliogenius.ai. Still a bit rough, but most of the main functionality works.

hsuduebc2 10mo ago

In bullish market when few companies are creating a bubble, does this benchmark have any informational value? Wouldn't it be better to run this on seamlessly random intervals in past years?

deleted 10mo ago

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mempko 10mo ago

The stats are abysmal. What's the MDD compared to S&P 500. What is the Sortino? What are the confidence intervals for all the stats? Number of trades? So many questions....

energy123 10mo ago

One of the recent NeurIPS best paper recipients is relevant here: https://openreview.net/forum?id=saDOrrnNTz https://openreview.net/forum?id=saDOrrnNTz > an extensive empirical study across more than 70 models, revealing the Artificial Hivemind effect: pronounced intra- and inter-model homogenization So the inter-model variety will be exeptionally low. Users of LLMs will intuitively know this already, of course.

keepamovin 10mo ago

I’d say Grok did best because it has the best access to information. Grok deep search and real time knowledge capabilities due to the X integration and just general being plugged into the pulse of the Internet a really best in class. It’s a great OSINT research tool. Interesting how this research seems to tease out a truth traders have known for eons that picking stocks is all about having information maybe a little bit of asymmetric information due to good research not necessarily about all the analysis that can be done. (that’s important but information is king) because it’s a speculative market that’s collectively reacting to those kind of signals.

deleted 10mo ago
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stockresearcher 10mo ago

I appreciate that you’ve made the trade histories downloadable and will be taking a look to see what I can learn. I’ve glanced over some of it and really wonder why they seemed to focus on a small group of stocks.

aperture147 10mo ago

Why is bullshit detector ringing as hell right now??? This sounds like another billion-dollar-Markov-chain-IP that claimed to change the world, opening with a paper with flying colors.

frobisher 10mo ago

lolol Gemini

rallies 10mo ago

This is pretty cool. We're also running a live experiment on both stocks and options. One difference with our experiment is a lot more tools being available to the models (anything you can think of, sec filings, fundamentals, live pricing, options data). We think backtests are meaningless given LLMs have mostly memorized every single thing that happened so it's not a good test. So we're running a forward test. Not enough data for now but pretty interesting initial results https://rallies.ai/arena https://rallies.ai/arena

touristtam 10mo ago
How is Qwen so much worse than the rest (for the period accounted)?
natiman1000 10mo ago
Is the code/prompts used open source? if not how can we say it's ligit

nurettin 10mo ago

Deepseek and grok together would perform even better.

vpribish 10mo ago

this is so stupid i wish i could flag it twice

Frieren 10mo ago
[flagged]

aidenn0 10mo ago

It seems to me that short-term simulations will tend to underprice risk. Imagine a market where you can buy only two stocks: Stock A goes up invariably 1% per month Stock B goes up 1.5% per month with a 99% chance, but loses 99% of its value with a 1% chance. Stock B has a 94% chance of beating stock A on a 6 month simulation, but only a 30% chance of beating stock A on a 10 year simulation.

fortran77 10mo ago

I would love to see this run during an extended bear market period.

ta12653421 10mo ago
Cant the model go short in a bear market?

toephu2 10mo ago

Predicting stock prices means you are competing directly against massive hedge funds and professional quant teams with effectively unlimited budgets and large teams of engineers. These professionals are already using and constantly tweaking the latest models to gain an advantage. It is highly unlikely that you guys or any individual, even utilizing the latest LLMs will consistently discover an edge that beats the market over the long run.

pech0rin 10mo ago

8 months of a huge bull market. Not exactly indicative of any real insight.

rcarmo 10mo ago

I spent a while looking at trading algos a few years back (partly because of quant stuff I got involved in, and partly out of curiosity). I found that none of the “slow” trading (i.e., that you could run at home alongside your day trading account) was substantially effective (at least in my sampling), but I never thought an LLM would be any good at it because all the analysis is quantitative, not qualitative or contextual. In short, I don’t think this study proves anything unless they gave the LLMs additional context besides the pure trading data (Bloomberg terminals have news for a reason—there’s typically a lot more context in he market than individual stock values or history).

morgengold 10mo ago

Am I right that you let LLMs decide for themselves what to read into their input data (like market data, news APIs, company financials)? While this is worth testing, I think it would be more interesting to give them patterns to look for. I played around with using them for technical analysis and let them make the associations with past stock performances. They can even differentiate on what worked in the last 5 years, what in the last year, in the last 3 month etc. This way they can pick up (hopefully) changes in market behavior. Generally the main strength of this approach is to use their pattern recognition capability and also take out the human factor (emotions) for trading decitions.

Bombthecat 10mo ago

I wouldn't call this a test, I would create a test portfolio of hundred semi random stocks and see what they sell buy or keep. That tells me way more then "YOLO tech stocks"

bitmasher9 10mo ago

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.

ryandvm 10mo ago
It seems like back-testing an LLM is going to require significant white-washing of the test data to prevent the LLM from just trading on historical trends it is aware of. 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.

amelius 10mo ago

Nonsense. Title should read $0 because they didn't use actual money. Also, it seems pretty stupid to use commodity tech like LLMs for this.

FrustratedMonky 10mo ago

How much of this is just because the market as a whole is going up. 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.

throwawayffffas 10mo ago

> We also built a way to simulate what an agent would have seen at any point in the past. Each model gets access to market data, news APIs, company financials—but all time filtered: agents see only what would have been available on that specific day during the test period. That's not going to work, these agents especially the larger ones, will have news about the companies embedded in their weights.

devilsbabe 10mo ago
Funny how if you kept reading before commenting, they addressed that point specifically > We were cautious to only run after each model’s training cutoff dates for the LLM models. That way we could be sure models couldn’t have memorized market outcomes.

krauses 10mo ago

I'd like to see a variation of the models being fine tuned based on investments of those in congress that seem to consistently outperform the markets.

thedougd 10mo ago

Would be nice to use the logos in the legend. I use these LLMs everyday and didn't know what half these logos on the graph were.

RandomLensman 10mo ago

Could be interesting to see performance distribution for random strategies on that stock universe as a comparison. The reverse could also be interesting: how do the models perform on data that is random?

mvkel 10mo ago

When the market is rising, everyone looks like a genius. Would have been better to have variants of each, locked to specific industries. It also sounds like they were -forced- to make trades every day. Why? deciding not to trade is a good strategy too.

cramcgrab 10mo ago

Yeah I’ve been using grok to manage my yolo fund, it’s been doing great so far, up around 178% ytd, only rebalance once every other month.

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portly 10mo ago

What is the point of this? LLMs are trained to predict the next word in a text. In what way, shape or form does that have anything to do with stock market prediction? Completely ridiculous AI bubble nonsense.

another_twist 10mo ago
No it isnt. Next word prediction is what humans do to communicate anyway so the criticism isnt valid. Except you do that for your own sentences (if you do it for others its considered rude :) ). Anyways this criticism is now dated given that modern day LLMs can solve unseen reasoning problems such as those found in the IMO. It does have something to do with the stock market, since its about making hypotheses and trading based off that. However, I'd agree that making a proper trading AI here would require reasoning based fine tuning for stock market trading actions. Sort of like running GRPO taking market feedback as the reward. the article simply cant do that due to not having access to the underlying model weight.
bwfan123 10mo ago
shhh. We need more of these as counter-parties to improve alpha.

mvkel 10mo ago

Predicting the stock market will likely never happen because it’s recursive. We can predict the next 10 days of weather, but the weather doesn’t change because it read your forecast. As long as markets continue to react to their own reactions, they will remain unpredictable. If the strategy is long, there might be alpha to be found. But day trading? No way.

oersted 10mo ago
If stocks are more of a closed system that are weakly affected by external factors in the short term, now I finally understand why they hire so many physicists for financial modeling! There is of course the fact that physicists tend to be the best applied mathematicians, even if they don’t end up using any of their physics knowledge. And they generally had the reputation of “the smartest” people for the last century. Anyway, such systems are complex and chaotic yes, but there are many ways of predicting aspects of them, like with fluid simulation to give a basic example. And I don’t get your point about weather, it is also recursive in the same way and reacting to its own reactions. Sure it is not reacting to predictions of itself, but that’s just a special kind of reaction, and patterns in others predictions can definitely be predicted accurately, perhaps not individually but in the aggregate.
- mvkel 10mo ago
  > there are many ways of predicting aspects of them Yes, and it's priced in > but that’s just a special kind of reaction That's just arguing semantics. My point was that weather doesn't react to human predictions, explicitly
jerf 10mo ago
"We can predict the next 10 days of weather, but the weather doesn’t change because it read your forecast." Less true than it used to be, with cloud seeding being an off-the-shelf technology now. Still largely true, but not entirely true anymore.

machiaweliczny 10mo ago

> Potential accidental data leakage from the “future” Exactly. Makes no sense with models like grok. DeepSeek also likely has this leak as was trained later.

Glyptodon 10mo ago

Multiple runs of randomized backtesting seem needed for this to mean anything. It's also not clear to me how there's any kind of information update loop. Maybe I didn't read closely enough.

kqr 10mo ago

Extremely similar earlier submission but focused on cryptocurrencies, using real money, and in real time: https://news.ycombinator.com/item?id=45976832 https://news.ycombinator.com/item?id=45976832 I'm extremely skeptical of any attempt to prevent leakage of future results to LLMs evaluated on backtesting. Both because this has beet shown in the literature to be difficult, and because I personally found it very difficult when working with LLMs for forecasting.

kqr 10mo ago

Their annual geometric mean return is 45 %! That's some serious overbetting. In a market that didn't accidentally align with their biases, they would have lost money very quickly.

reactordev 10mo ago

I would love for them to have included a peg position on SPY @ 100k over the course of the same period. Gives a much better benchmark of what an LLM can do (not much above 2-4%). Still, cool to see others in my niche hobby of finding the money printer.

peterbonney 10mo ago

The devil is really in the details on how the orders were executed in the backtest, slippage, etc. Instead of comparing to the S&P 500 I'd love to see it benchmarked against a range of active strategies, including common non-AI approaches (e.g. mean reversion, momentum, basic value focus, basic growth focus, etc.) and some simple predictive (non-generative) AI models. This would help shake out whether there is selection alpha coming out of the models, or whether there is execution alpha coming out of the backtest.

rao-v 10mo ago

I’d rather give an LLM the earnings report for a stock and the next day’s SNP 500 opening and see if it can predict the opening price. Expecting an LLM to magically beat efficient market theory is a bit silly. Much more reasonable to see if it can incorporate information as well as the market does (to start)

deleted 10mo ago
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natiman1000 10mo ago

If the code and prompts are not open source how can we trust anything yall say?

elzbardico 10mo ago

A rising tide lift all boats.

dudeinhawaii 10mo ago

This is the complete wrong way to do this. I say this as someone who does work in this area of leveraging LLMs to a limited degree in trading. LLMs are naive, easily convinced, and myopic. They're also non-deterministic. We have no way of knowing if you ran this little experiment 10 times whether they'd all pick something else. This is a scattershot + luck. The RIGHT way to do this is to first solve the underlying problem deterministically. That is, you first write your trading algorithm that's been thoroughly tested. THEN you can surface metadata to LLMs and say things along the lines of "given this data + data you pull from the web", make your trade decision for this time period and provide justification. Honestly, adding LLMs directly to any trading pipeline just adds non-useful non-deterministic behavior. The main value is speed of wiring up something like sentiment analysis as a value add or algorithmic supplement. Even this should be done using proper ML but I see the most value in using LLMs to shortcut ML things that would require time/money/compute. Trading value now for value later (the ML algorithm would ultimately run cheaper long-run but take longer to get into prod). This experiment, like most "I used AI to trade" blogs are completely naive in their approach. They're taking the lowest possible hanging fruit. Worst still when those results are the rising tide lifting all boats. Edit (was a bit harsh) This experiment is an example of the kind of embarrassingly obvious things people try with LLMs without understanding the domain and writing it up. To an outsider it can sound exciting. To an insider it's like seeing a new story "LLMs are designing new CPUs!". No they're not. A more useful bit of research would be to control for the various variables (sector exposure etc) and then run it 10_000 times and report back on how LLM A skews towards always buying tech and LLM B skews towards always recommending safe stocks. Alternatively, if they showed the LLM taking a step back and saying "ah, let me design this quant algo to select the best stocks" -- and then succeeding -- I'd be impressed. I'd also know that it was learned from every quant that had AI double check their calculations/models/python.. but that's a different point.

snapdeficit 10mo ago

Anyone who traded tech stocks in the 1990s when AmeriTrade appeared remembers this story. Have the LLMS trade anything BUT tech stocks and see how they do. That’s the real test. EDIT: I remember this is probably before AmeriTrade offered options. I was calling in trades at 6:30AM PST to my broker while he probably laughed at me. But the point is the same: any doofus could make money buying tech stocks and holding for a few weeks. Companies were splitting constantly.

reformd 10mo ago

financial advice of smart refrigerator right before the dump?

johnnienaked 10mo ago

Ok now do risk adjusted returns

diamond559 10mo ago

When you've traded for many, many years, you realize just how little 8 months can mean. Especially during one of the most nonsensical bubble markets of all time.

jbritton 10mo ago

These were paper trades, not actual trades. Sometimes a few seconds can significantly effect the price one gets.

throwaway422432 9mo ago

Have asked LLMs for smallcap trading ideas on the ASX a few times. Grok often suggested shares that jumped significantly within the next few weeks. Wondering if it's access to Twitter gave it an advantage in predicting major upswings based on general sentiment.