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Exploring the limits of large language models as quant traders
- jwpapi 11mo agoIsn’t that what Renaissance Technology does?
- chronic740202 11mo ago> Isn’t that what Renaissance Technology does? No.
- ta12653421 11mo ago++1
- kqr 11mo agoSuper interesting! You can click the "live" link in the header to see how they performed over time. The (geometric) average result at the end seems to be that the LLMs are down 35 % from their initial capital – and they got there in just 96 model-days. That's a daily return of -0.6 %, or a yearly return of -81 %, i.e. practically wiping out the starting capital. Although I lack the maths to determine it numerically (depends on volatility etc.), it looks to me as though all six are overbetting and would be ruined in the long run. It would have been interesting to compare against a constant fraction portfolio that maintains 1/6 in each asset, as closely as possible while optimising for fees. (Or even better, Cover's universal portfolio, seeded with joint returns from the recent past.) I couldn't resist starting to look into it. With no costs and no leverage, the hourly rebalanced portfolio just barely outperforms 4/6 coins in the period: https://i.xkqr.org/cfportfolio-vs-6.png https://i.xkqr.org/cfportfolio-vs-6.png. I suspect costs would eat up many of the benefits of rebalancing at this timescale. This is not too surprising, given the similiarity of coin returns. The mean pairwise correlation is 0.8, the lowest is 0.68. Not particularly good for diversification returns. https://i.xkqr.org/coinscatter.png https://i.xkqr.org/coinscatter.png > difficulty executing against self-authored plans as state evolves This is indeed also what I've found trying to make LLMs play text adventures. Even when given a fair bit of help in the prompt, they lose track of the overall goal and find some niche corner to explore very patiently, but ultimately fruitlessly.
- falcor84 11mo agoAgreed, and I'd also love to see a baseline of human performance here, both of experienced quant traders and of fresh grads who know the theory but never did this sort of trading and aren't familiar with the crypto futures market.
- spaceman_2020 11mo agoAs someone who trades crypto semi-professionally, this was one of the toughest trading periods I've ever seen and included a massive liquidation event on 10th of October that wiped out over $20B in capital. Any trader who broke even in this period likely outperformed. I know some very, very good traders who got wiped out on leverage on 10th of October when stop losses didn't trigger and prices plummetted to 2021 levels (still no clarity why). BTC also performed abysmally during this period with a sustained chop down from $126k to $90k.
- kqr 11mo agoNote that 10th of October is before the trading period in this experiment. If anything, autoregression over shorter timescales would suggest entering after 10th of October being a good idea!
- spaceman_2020 11mo agoJust noticed that it was after October 10th. I'll still say that the trading period after October 10th has been brutally choppy. Only now do we have a clear direction (down) where you can at least short with some confidence
- fragmede 11mo ago> find some niche corner to explore very patiently, but ultimately fruitlessly. What, so they're better at my hobbies than me? Someone give Claude a 3d printer!
- michaelcampbell 11mo ago
- XenophileJKO 11mo agoI don't think betting on crypto is really playing to the strengths of the models. I think giving news feeds and setting it on some section of the S&P 500 would be a better evaluation.
- ezekiel68 11mo agoYou don't actually need nanosecond latency to trade effectively in futures markets but it does help to be able to evaluate and make decisions in the single-digit milliseconds range. Almost no generative model is able to perform inference at this latency threshold. A threshold in the single-digit milliseconds range allows the rapid detection of price reversals (signaling the need to exit a position with least loss) in even the most liquid of real futures contracts (not counting rare "flash crash" events).
- vita7777777 11mo agoThis is true for some classes of strategies. At the same time there are strategies that can be profitable on longer timeframes. The two worlds are not mutually exclusive.
- rob_c 11mo agoYes, but LLM can barely cope with following the ordering of complex software tutorials linearly. Why would you reasonably expect them unprompted to understand time any better enough to trade and turn a profit?
- vita7777777 11mo agoMy comment makes no such claim. I wrote about different timeframes that trading strategies operate on.
- rob_c 11mo agoExactly. If it can't distinguish between a basic repeat after me ordering how is it going to even get a simple output order correct? Let alone pull apart the strategies themselves
- graemep 11mo agoFrom the article: > The models engage in mid-to-low frequency trading (MLFT) trading, where decisions are spaced by minutes to a few hours, not microseconds. In stark contrast to high-frequency trading, MLFT gets us closer to the question we care about: can a model make good choices with a reasonable amount of time and information?
- bluecalm 11mo ago>>LLMs are achieving technical mastery in problem-solving domains on the order of Chess and Go, solving algorithmic puzzles and math proofs competitively in contests such as the ICPC and IMO. I don't think LLMs are anywhere close to "mastery" in chess or go. Maybe a nitpick but the point is that a NN created to be good at trading is likely to outperform LLMs at this task the same way way NNs created specifically to be good at board games vastly outperform LLMs at those games.
- lukan 11mo ago"Maybe a nitpick but the point is that a NN created to be good at trading is likely to outperform LLMs at this task the same way way NNs created specifically to be good at board games vastly outperform LLMs at those games." Disagree. Go and chess are games with very limited rules. Succesful trading on the other hand is not so much a arbitary numbers game, but involves analyzing events in the news happening right now. Agentic LLMs that do this and accordingly buy and sell might succeed here. (Not what they did here, though "For the first season, they are not given news or access to the leading “narratives” of the market.")
- Havoc 11mo agoAre language models really the best choice for this? Seems to me that the outcome would be near random because they are so poorly suited. Which might manifest as > We also found that the models were highly sensitive to seemingly trivial prompt changes
- baq 11mo agothey're tools. treat them as tools. since they're so general, you need to explore if and how you can use them in your domain. guessing 'they're poorly suited' is just that, guessing. in particular: > We also found that the models were highly sensitive to seemingly trivial prompt changes this is as much as obvious for anyone who seriously looked at deploying these, that's why there are some very successful startups in the evals space.
- rob_c 11mo ago> guessing 'they're poorly suited' is just that, guessing I have a really nice bridge to sell you... This "failure" is just a grab at trying to look "cool" and "innovative" I'd bet. Anyone with a modicum of understanding of the tooling (or hell experience they've been around for a few years now, enough for people to build a feeling for this), knows that this it's not a task for a pre-trained general LLM.
- baq 11mo agoI think you have a different idea of what I'm saying than what I'm actually saying.
- infecto 11mo agoI agree in sentiment but if you spent any amount of time in finance, even outside of equity markets, you would have a pretty quick mental model that LLMs are a weird fit for this space.
- kqr 11mo agoNo, LLMs are not a good choice for this – as the results show! If I had to guess, they're experimenting with LLMs for publicity.
- reedf1 11mo agoyou simply will lose trading directly with an llm. mapping the dislocation by estimating the percentage of llm trading bots is useful though.
- vita7777777 11mo agoThis is very thoughtful and interesting. It's worth noting that this is just a start and in future iterations they're planning to give the LLMs much more to work with (e.g. news feeds). It's somewhat predictable that LLMs did poorly with quantitative data only (prices) but I'm very curious to see how they perform once they can read the news and Twitter sentiment.
- rob_c 11mo agoNot just can i guarantee the models are bad with numbers, unless it's a highly tuned and modified version they're too slow for this arena. Stick to using attention transformers in better model designs which have much lower latencies than pre-trained llms...
- Lapsa 11mo agoI would argue that sentiment classification is where LLMs perform best. folks are already using it for precisely such purpose - have even built a public index out of it
- ritonlajoie 11mo agowhat index ?
- Lapsa 11mo agosorry dude. tried going down the rabbit hole but I'm too lazy and uninterested in it. read about it month ago or so. perhaps Daily News Sentiment Index uses LLMs, not sure. if you go long enough through https://quantocracy.com/ https://quantocracy.com/ you should be able to find it
- Lapsa 11mo agofound one such index https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5763042 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5763042 called Populism Index (POP) built from Wall Street Journal articles (not sure how publicly accessible it is)
- callamdelaney 11mo agoThe limits of LLM's for systematic trading were and are extremely obvious to anybody with a basic understanding of either field. You may as well be flipping a coin.
- rob_c 11mo agoAt least a coin is faster and more reliable.
- kqr 11mo agoI agree. Plus it's way too short a timeframe to evaluate any trading activity seriously. But I still think the experiment is interesting because it gives us insight into how LLMs approach risk management, and what effects on that we can have with prompting.
- falcor84 11mo ago20 years ago NNs were considered toys and it was "extremely obvious" to CS professors that AI can't be made to reliably distinguish between arbitrary photos of cats and dogs. But then in 2007 Microsoft released Asirra as a captcha problem [0], which prompted research, and we had an AI solving it not that long after. Edit - additional detail: The original Asirra paper from October 2007 claimed "Barring a major advance in machine vision, we expect computers will have no better than a 1/54,000 chance of solving it" [0]. It took Philippe Golle from Palo Alto a bit under a year to get "a classifier which is 82.7% accurate in telling apart the images of cats and dogs used in Asirra" and "solve a 12-image Asirra challenge automatically with probability 10.3%" [1]. Edit 2: History is chock-full of examples of human ingenuity solving problems for very little external gain. And here we have a problem where the incentive is almost literally a money printing machine. I expect progress to be very rapid. [0] https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/ https://www.microsoft.com/en-us/research/publication/asirra-... [1] https://xenon.stanford.edu/~pgolle/papers/dogcat.pdf https://xenon.stanford.edu/~pgolle/papers/dogcat.pdf
- lambdaone 11mo agoWhat makes trading such a special case is that as you use new technology to increase the capability of your trading system, other market participants you are trading against will be doing the same; it's a never-ending arms race.
- aswegs8 11mo agoGiven that LLMs can't even finish Pokemon Red, how would you expect they are able to trade futures?
- falcor84 11mo ago(Unless you're a marketer) It makes a lot more sense to build a benchmark before the capabilities are there.
- wild_pointer 11mo agoHey! That wasn't easy!
- agentifysh 11mo agoi always felt that emotions, instincts, fear, greed, courage, pain are elements of a self-aware conscious loop system that can't be replicated accurately in a digital system and that a seasoned successful traders realize and utilize that the activity is largely is a psychological one. I'm not talking about neutral plays where you can absorb market fluctuations in the short term to extract 1~2% a week but directional trades that almost all traders play (regardless of how what exotic option strategies they are employing). also the other curious nature of the markets is its ability to destroy any persistent trading system by reverting to its core stochastic properties and its constant ebb and flow from stability to instability that crescendos into systematic instability that rewrite the rules all over again. ive tried all sorts of ways to do this and without being a large institution and being able to absorb the noise for neutral or legal quasi insider trading via proximity, for the average joe the emotional/psychological hardness you need to survive and be in the <1% of traders is simply too much, its not unlike any other sports or arts, many dream the dream but only few get interviewed and written about. rather i think to myself the best trade is the simplest one: buy shares or invest in a business with money or time (strongly recommend against using this unless you have no other means) and sell it at a higher price or maintain a long term DCF from a business you own as leverage/collateral to arbitrage whatever rate your central bank sets on assets in demand or will be in demand. to me its clear where LLM fits and doesn't but ultimately it cannot, will not, must not replace your own agency.
- Saline9515 11mo ago
- Edvinyo 11mo agoCool experiment, but it’s nothing more than a random walk.
- p1dda 11mo agoLLM's can do language but not much else, not poker, not trading and definitely no intelligence
- Drakim 11mo agoLanguage is powerful. Language can do poker, trading, and other intelligent activities.
- lvl155 11mo agoAt the end of the day it all comes down to input data. There are a lot of things you can do to collect proprietary data to give you an edge.
- GaryNumanVevo 11mo agoThat's funny because that advice is _directly_ counter to what most HFT quants say
- lvl155 11mo agoRight, because they will tell you exactly how they generate alpha for all the world to see. It’s worth mentioning quant is not all HFT.
- deleted 11mo ago[deleted]
- lordnacho 11mo agoI was chatting to a friend in the space. This guy is both experienced in trading and LLMs, and has gone all-in on using LLMs to get his day-to-day coding done. Now he's working on the model to end all models, which is a fairly ambitious way to put it, but it throws off some interesting conversations. You need domain knowledge to get this to work. Things like "we fed the model the market data" are actually non-obvious. There might be more than one way to pre-process the data, and what the model sees will greatly affect what actions it comes up with. You also have to think about corner cases, eg when AlphaZero was applied to StarCraft, they had to give it some restrictions on the action rate, that kind of thing. Otherwise the model gets stuck in an imaginary money fountain. But yeah, the AI thing hasn't passed by the quant trading community. A lot of things going on with AI trading teams being hired in various shops.
- JumpCrisscross 11mo ago> There might be more than one way to pre-process the data I'm honestly more hopeful about AI replacing this process than the core algorithmic component, at least directly. (AI could help write the latter. But it's immediately useful for the former.)
- poopiokaka 11mo ago[dead]
- Libidinalecon 11mo agoYou can vibe code in this space as an individual because practically everything you are going to write is already in the training data. The big Quant hedge funds have been using machine learning for decades. I took the coursera RL in finance class years ago. The idea you are going to beat Two Sigma at their own game with tokens is just an absurdity. Personally, I think any individual on their own that claims they are doing anything in the algorithmic / ML high frequency space is full of shit. I could talk like I am too and sound really impressive to someone outside the space. That is much different though than actually making money on what you claim you are doing. It reminds me of an artist friend when I was younger. She was an artist and I quite liked her paintings. She would tell everyone she is an artist. She was also an encyclopedia when it came to anything art related. She wasn't actually selling much art though. She lived off the $10k a month allowance her rich father gave her. She wasn't even being dishonest but when you didn't know the full picture a person would just assume she was living off her art sales.
- chronic740202 11mo agoEven ChatGPT knows why LLMs for quant trading would never work.
- IAmGraydon 11mo agoCrazy how people continue to treat LLMs like they’re anything more than a record of past human knowledge and are then surprised when they can’t predict the future.
- EMM_386 11mo agoHumans don't trade on future knowledge either. Well, most of them - that can be illegal.
- binsquare 11mo agoToday it's clear that there are limitations to LLM's. But I also see this incredible growth curve to LLM's improvement. 2 years ago, I wouldn't expect llm's to one shot a web application or help me debug obscure bugs and 2 years later I've been proven wrong. I completely believe that trading is going to be saturated with ai traders in the future. And being able to predict and detect ai trading patterns is going to be an important leverage for human traders if they'll still exist
- thunky 11mo ago> I completely believe that trading is going to be saturated with ai traders in the future That's probably good news for us index fund investors. We need people to believe they're going to beat the market.
- ta12653421 11mo ago..though even until lately, none of them could tell me how to fix the Azure bug I have with my account: It does not allow me to spin up new machines and shouts an obscure error message :-D
- DivingForGold 11mo ago. . . "The (geometric) average result at the end seems to be that the LLMs are down 35 % from their initial capital – and they got there in just 96 model-days. That's a daily return of -0.6 %, or a yearly return of -81 %, i.e. practically wiping out the starting capital." Proves that LLM's are nowhere near close to AGI.
- sd9 11mo agoThe vast majority of intelligent humans cannot profitably trade on intraday timeframes
- spaceman_2020 11mo agoHyperliquid now has select tokenized equities as well. Would love to see how these models perform when trading equities I've been following these for a while and many of the trades taken by DeepSeek and Qwen were really solid
- thisisit 11mo agoLLMs are very good at NLP/classification tasks and weak at calculations and numbers. So, I doubt feeding it numerical data is a good idea. And if you feeding or harnessing as the blog post puts it in a way that where it reasons things like: > RSI 7-period: 62.5 (neutral-bullish) Then it is no better than normal automated trading where the program logic is something along the lines if RSI > 80 then exit. And looking at the reasoning trace that is what the model is doing. > BTC breaking above consolidation zone with strong momentum. RSI at 62.5 shows room to run, MACD positive at 116.5, price well above EMA20. 4H timeframe showing recovery from oversold (RSI 45.4). Targeting retest of $110k-111k zone. Stop below $106,361 protects against false breakout. My understanding is that technical trading using EMA/timeframes/RSI/MACD etc is big in crypto community. But to automate it you can simply write python code. I don't know if this is a good use of LLMs. Seems like an overkill. Better use case might have been to see if it can read sentiments from Twitter or something.
- ta12653421 11mo ago>>But to automate it you can simply write python code. haha, if it would be that easy, most of them would do this? :-D The thing is - its fucking complicated and most people will give up far before they enter any level of operational capability. I've developed such a system for myself and Im running it in production (though, not with crypto): And whilte most people will see the complexity in "whatever trading magic you apply", its QUITE the opposite: - the trading logic itself is simple, its ~ 300 lines - whats not simple is the part of everything else in the context of "asset management", you need position tracking, state management (orders and positions and account etc.), you need to be able to pour in whatever new quotedata for whatever new assete you identify, the system needs to be stable to work in "mass mode" and be super robust as data provider quality is volatile; you need some type of accounting logic on your side; you need a very capable reporting engine (imagine managing 200 positions simultaneously), I could enlength this list more or less unlimited. There is MUCH MORE in such an application than the question of "when and how do I trade" - my systems raw source is around 2 MB by today, 3rd party libs and OSS libs not included.
- thisisit 11mo ago
- infecto 11mo agoThis might be the dumbest thing I have ever seen but I am happy to be corrected and told why it’s not. I use LLMs a lot and I work in finance and I don’t see how a LLM benefits in this space. Also it looks like none of their data uses any kind of benchmarking. It’s purely a which model did better which I don’t think tells you much.
- pinkmuffinere 11mo ago> Ordering bias. Early prompts listed market data newest→oldest. Even with explicit notes, several models still read it as oldest → newest, inferring the wrong state. Switching to oldest → newest fixed the immediate error and suggests a formatting prior in current LLMs. This kind of error just feels comical to me, and really makes it hard for me to believe that AGI is anywhere near. LLM's struggle to understand the order of datasets, when explicitly told. This is like showing a coin trick to a child, except perhaps even simpler.
- p1necone 11mo agoI've noticed similar issues to this with rendering related code. Most models had a strong preference for z-up over z-down (I think, it might have been the other way around), and correcting them only fixed it for maybe the next response then the model would go back to using the wrong coordinates and getting confused by the outcome. No amount of added context or instructions seems to fix these kind of issues in a way that doesn't still feel pretty hobbled. The only way to get the full power out of the model is to conform your problem to the expectations that seem to be baked in - i.e. just change your rendering coordinate system to be z-up.
- deleted 11mo ago[deleted]
- devin 11mo agoWhen I saw this I rolled my eyes. It is well-understood that purpose-built models perform better than general models on tasks like this, and yet it would seem one of the main purposes of running this experiment according to the website is to figure out if general models are enough. In addition, I cannot imagine how the selection of securities was chosen. Is XRP seriously part of the proposed asset mix here? It's hard not to look at this and view it as a marketing stunt. Nothing about the results are surprising and the setup does not seem to make any sense to me to begin with.
- jeisc 11mo agofour days later 24/11 they are all in the negative with grok having lost nearly half of its starting sum