LLM's can do language but not much else, not poker, not trading and definitely no intelligence
Language is powerful.
Language can do poker, trading, and other intelligent activities.
Exploring the limits of large language models as quant traders
61–70 of 103 posts
Re: Exploring the limits of large language models as quant traders
#62LLMs 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.
Re: Exploring the limits of large language models as quant traders
#63LLMs 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 a…
>>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.
Re: Exploring the limits of large language models as quant traders
#64I 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…
You 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…
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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.
do you want to have a chat by Whatsapp then I can show you quite the opposite! :-)
And in my case: Nobody knows, only one friend who is also deep in the stuff; people doing this are usually more quiet, since nobody is interested at all.
I have some contacts in academia and shared my ideas with them - none of them said: "this wont work"
(Disclaimer: 25+y IT experience, 15 of them in finance)
Re: Exploring the limits of large language models as quant traders
#65The 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.
In general, I agree - but there is one exception, I think: However you put AI into an stat arb context, I think it may help for trading on a daily base like "tell me where i should enter this morning and exit this evening".
(not daytrading throughout the whole day)
But, I havent tested it so far since I do not believe it either :D
Re: Exploring the limits of large language models as quant traders
#66Earlier quoted context omitted.
20 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…
What 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.
Good one!
The thing is, you are assuming "perfect/symmetric distribution" of all known/available technologies across all market participants - this far off the reality.
Sure: Jane Street et al are on the same level, but the next big buckets are a huge variety of trading shops doing whatever proprietary stuff to get their cut; most of them may be aware of the latest buzz, but just dont deploy it et.
Re: Exploring the limits of large language models as quant traders
#67LLMs 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 a…
>>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 y…
You seem to be debating a point which was never made by holding on to one word - simple. I didn't say trading code is simple neither I did say that your trading code setup is simple.
Still let me clarify - the trading logic as you say is simple and just 300 lines. That is what LLMs seem to be doing in part in the post. The point I made is that doesn't seem to be a good use case for LLMs given that everything costs token. IMO, you could run this in your complex application without spending that much money on tokens.
If you can explain why original opinion of wasting tokens on something which can "simply" be done in python is wrong, I am all ears.
Re: Exploring the limits of large language models as quant traders
#68Today 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…
..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
Re: Exploring the limits of large language models as quant traders
#69Re: Exploring the limits of large language models as quant traders
#70I 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…
You 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…
> 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.
Well I'm in the space, but I've come across more than one guy who discovered a money making algo, all on their own, with all the right ideas but without the industry standard terms for them.
All logic would suggest this shouldn't be possible, but what I've seen is what I've seen.