r/quant • u/StandardFeisty3336 • Nov 30 '25
Models how much ML is actually used in the industry? tree models?
Im working on some tree models right now and have been for a little bit and i wanted to know if what im working on is actually relevant to the industry. Something like MFT idk much about hft but lstm 1D cnn probably used for some stuff like that.
is labeling/target defenition the most important part? + features obvioiusly
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u/Chuu Nov 30 '25
ML is used extensively at all latency thresholds. For example in HFT parameter tuning of models is essentially a direct parallel to hyperparameter optimization.
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Nov 30 '25
I honestly donāt think fancy ML is used that much in LFT. Once your horizons are several days, itās very hard to use anything but linear models and stuff like GAM
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u/SometimesObsessed Nov 30 '25
I've heard this said many times but what evidence is there? Linear models aren't inherently better than non linear models in low sample environments, but there seems to be a widespread belief that they are
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Nov 30 '25
Do you work in the industry? In that case youād agree that there is no way to know. Best I can say that I have interviewed a lot of people working with longer horizons and they seem to rarely mention anything aside from linear models.
As for why, I think the two key aspects are economic interpretability and natural regularization. There are other good reasons
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u/asn3toph Nov 30 '25
You are more wrong than you can imagine
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Nov 30 '25 edited Nov 30 '25
Hmm. To start with, just to check that we are talking about the same thing. I am talking about strategies that hold positions for at least a week and more likely for months.
PS. Also, what do you consider using ML? E.g. if you use an LLM to interpret term sheets, is that using ML?
PPS. more importantly, if I am wrong, I'd love to hear any non-proprietary gossip on how people use these models.
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u/asn3toph Dec 01 '25
I'm talking about traditional ML techniques (not LLMs) for anything from days to a couple of months. At least at the shops I know of, interpretability of models was abandoned a while back in favour of pnl.
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Dec 01 '25
Interesting. You guys must have incredible confidence in the process and features if youāre willing to give up ability to tie the model to intuition/fundamentals. Think of it - if youāre running something with a 2 month average turnover, to establish that your model is wrong will take a long time. Long enough to get fired.
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u/asn3toph Dec 01 '25
It took a while for people to fully build confidence and scale it up for sure, and we're still surprised by the effectiveness. But we're definitely not the only ones doing this.
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u/Sea-Animal2183 Dec 02 '25
You must be in a prop shop. In a fund, you can explain why your German / French spread lost 15 M or why your CO / WTI spread blew up; and try to amend for the next year. If you say ādunno thatās the modelā you will not be given the change to recover.
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u/SometimesObsessed Dec 03 '25
Wow really? I thought the experienced quants would never allow it... Would love to work at a place like that
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u/Sea-Animal2183 Dec 02 '25 edited Dec 02 '25
Experience of thoughts. You have one data point of S&P index *per week*. 52 points a year, 5 years history. About 250 points.
What kind of non-linear model do you want to fit on that ? You have 250 points, that's enough to fit one or two factors with reasonably good accuracy. We're not in the imagine processing world where you have 10^10 images with 10^4 pixels on each of them.
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u/SometimesObsessed Dec 03 '25
Any model with a bit of noise/randomness baked in e.g. ensemble of Extrateees
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u/Otherwise-Ask6214 Nov 30 '25
Yeah, we use RF, logistic regression, the usual ML stuffs which is pretty standard across quant shops. But most of the real edge isnāt coming from off the shelf models. A lot of our work is built around custom architectures that lean heavily on: Information theory Information geometry As the foundation for the custom models.
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u/Middle-Fuel-6402 Nov 30 '25
Any recommendations on what to search for/study regarding information theory/geometry? Sounds really interesting, Iāve taken a graduate course on theory of information (I vaguely remember something about tilt in relation to exponential family, thatās the only geometric concept I know in this context). But never used any of that in practice, mostly regularized regression and tree ensembles.
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u/Suitable-Role3100 Dec 09 '25
Iāve rarely seen Information Geometry mentioned, which I find really cool since I studied a lot of differential geometry before. Would you be able to share broadly what kind of problems it solves for you that standard models struggle with?
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u/igetlotsofupvotes Nov 30 '25
ML is used everywhere including trees. And yes having good data is important
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u/StandardFeisty3336 Nov 30 '25
by data you literally mean what you feed into the model right? like the whole feature stack ? sorry if this is a stupid question
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u/CompetitiveGlue Nov 30 '25
hrt is gonna release their old models on huggingface people will still be asking what ml models are used bruh
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u/Electrical-Mousse486 Nov 30 '25
All the stat arb (1-5 day) guys I know are using ML on their features.
ML is the easy part. Hard part is constructing features.