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Good one, happy to add my perspective here: DISCLAIMER: I've spent the last 8 month heavily on building a quant-based asset management app (though, still not l

Good one, happy to add my perspective here: DISCLAIMER: I've spent the last 8 month heavily on building a quant-based asset management app (though, still not live, currently in final steps to sync processes with broker) a) I tried to leverage some of this AI-voodoo stuff, though not on the level as in the paper; my findings are clear (at least for me): AI-driven trading does not give you a bigger/better edge than any of the other well-known approaches b) In fact, AI-based approaches are at best on par with traditional approaches, in lot of scenarios not even this; I havent seen any setup from anyone which actually outperformed one of the classic approaches. BUT: The AI-guys have much higher cost, be it Infra, processing time / waiting time in front of screen etc. So you have you to pick carefully, which one you choose. c) I'm doing today only "standard approaches" with volume/statistics/vola/price action, as this approach is super-cost-efficient (i need only one cheap datastream) and a lightweight machine for 10 / 20 USD a month d) It is clearly possible to outperform the market, though these approaches are not scalable unlimited - Ex: depending on the used instruments, there may not be enough liquidity to buy continuously for 100k, but maybe for 10k only. Apply leverage of 5-10 on an asset that moved 5% in last 10 days on a 10k position - is this outperforming? A clear >yes< in my perception? e) People who have built & found a stable approach do not share it or talk about it, there is no real community; you will get details of working approaches only from people whom you are really "friend with"; there is a lot of unshared but working business tactics in the field.

r0b05 1y ago
Are you referring to LLM's when you mention AI here?
- ta12653421 1y ago
  Thanks for the question: Not really in the field of trading, rather prediction-based approaches etc.; Im not sure if LLM could be of any use here? The approaches based on statistical arbitrage are purely math/number models. from my own experience, LLM are absolutely useless when it comes to "trading ideas" (I use them for code generation, instead), this is because they are dicing together values in their output which are not really related, because of their hallucis. Also, for fundamental analysis they are too often incorrect - so running an auto-approach based on LLM-fundamental output would be an "interesting" idea :))
  - throwawaymaths 1y ago
    you could certainly encode data into an transformer using custom tokens and fine-tune but that's not trivial.
    - ta12653421 1y ago
      This is what i referred to as "too high costs" :) Im sure, there are ton of other options by applying whatever "mega-tech", but if the result is only slightly better while having much more costs (and complexity!), for me its not worth (as i'm not a company, but an individual invstor)
      - throwawaymaths 1y ago
        I mean the crazy thing is that I have implemented transformers in another language so I know exactly what needs to be done and I know why it's such a pain in the ass in python, but I just don't have the discipline to sit down and do it with a risky reward (I spend my risk tokens on other things). Now, if I had a patron, I could probably figure it out.
  - r0b05 1y ago
    Interesting, so you're doing more fundamental quantitative analysis and prediction. Yes, the issue with using an LLM is that they are too often incorrect, however, a human in the loop could solve this at the expense of automation. Your project sounds ready cool though. If you ever feel like collaborating, give me a shout at drknyt05@gmail.com.
    - ta12653421 1y ago
      Thats one of the primary edges: That there is _no_ human in the loop in my approach; actually, the system is build around a simple idea (regardless which approach taken): Out of 700+ stocks globally (blue chips only), there is always something in a "robust trend situation", you just have to scan the market automated and pick the ones with the highest probability. To do so, you need an application (otherwise you would die of boringness in front of the screen), and the app is submitting all trades automaticly (sure, you can cancel one, but a manual action is usually not required) What most people dont get: This is not about "predicting" where the price will be, this is about taking momentum and weeding out the bad options - and applied on a large set of stocks, there is always something you can ride with for a couple of days (with applied leverage, there is usually a substantial profit)
gavinray 1y ago
I have zero knowledge of finance and trading, but when I got curious about algorithmic systems, it seemed like sentiment-based trading using current events was a more viable strategy than forecasting/regression-based analysis.
- NotAnOtter 1y ago
  For HFT - yes, and that has been the case since before the .com bubble. Trading on news is what fuels HFT. And value trading is primarily based on insider trading / corruption. What's left over is ETF's or luck.
  - ta12653421 1y ago
    Not really anymore, and for a long time: This article is more than 10 years old and describes what impressive tech & metrics they were using already back then: https://queue.acm.org/detail.cfm?id=2536492 https://queue.acm.org/detail.cfm?id=2536492
pdabbadabba 1y ago
> AI-based approaches are at best on par with traditional approaches This seems like quite the generalization! Wouldn't it completely depend on the approach and model?
- ta12653421 1y ago
  Thats what i meant with "in my experience": For sure, i see & read a lot of content from people who are claiming, that they are using AI for their approaches - though, i havent met one or seeing someone showing some "hard facts". It is very likely possible to find a "stable setup" with this - but it didnt work out for me, maybe i had the wrong perspective :)
NotAnOtter 1y ago
With cutting edge stuff you cannot say statements like "I tried X and found that it is not good at Y". You could not get X to be good at Y, but it's not impossible someone else can.
flessner 1y ago
I have been interested in algorithmic trading for quite a while now. Everything you've said resonates with me as I ran into similar issues. I hope you don't mind that I add my own couple cents here. (a-c) LLMs are especially difficult to use due to their knowledge cutoffs and "unpredictability". A self-trained "old-school" machine learning model can go a long way though. (d) With Crypto the volatility is great for trading, but liquidity can quickly become a problem (even at $1000 non-leveraged positions). For me, the ultimate goal is to find a strategy that is profitable in all market conditions. I personally value consistency and reliability more than absolute profit. (e) There's some chatting about risk management, but absolutely no discussion on profitable strategies. Resources are incredibly scarce - Systematic Trading by Robert Caver is the only book that was actually useful.
- ta12653421 1y ago
  Thanks for your question: Regarding LLM: I do use them to write code in less time, i do not use them to do anything rlated to trade analysis / execution / etc. Regarding Crypto: No, in my location i cant use crypto as Underlying, since the crypto market is open 24/7, but the instruments im using are available only between 0800-2200 on workdays Risk Management is key: If you have a solid hitquote, its mathemathically impossible to ruin the account; most people get to greedy and have no patience, but if you stick strictly to your risk plan, there is not that much that could go wrong.

r0b05 1y ago

Are you referring to LLM's when you mention AI here?

ta12653421 1y ago
Thanks for the question: Not really in the field of trading, rather prediction-based approaches etc.; Im not sure if LLM could be of any use here? The approaches based on statistical arbitrage are purely math/number models. from my own experience, LLM are absolutely useless when it comes to "trading ideas" (I use them for code generation, instead), this is because they are dicing together values in their output which are not really related, because of their hallucis. Also, for fundamental analysis they are too often incorrect - so running an auto-approach based on LLM-fundamental output would be an "interesting" idea :))
- throwawaymaths 1y ago
  you could certainly encode data into an transformer using custom tokens and fine-tune but that's not trivial.
  - ta12653421 1y ago
    This is what i referred to as "too high costs" :) Im sure, there are ton of other options by applying whatever "mega-tech", but if the result is only slightly better while having much more costs (and complexity!), for me its not worth (as i'm not a company, but an individual invstor)
    - throwawaymaths 1y ago
      I mean the crazy thing is that I have implemented transformers in another language so I know exactly what needs to be done and I know why it's such a pain in the ass in python, but I just don't have the discipline to sit down and do it with a risky reward (I spend my risk tokens on other things). Now, if I had a patron, I could probably figure it out.
- r0b05 1y ago
  Interesting, so you're doing more fundamental quantitative analysis and prediction. Yes, the issue with using an LLM is that they are too often incorrect, however, a human in the loop could solve this at the expense of automation. Your project sounds ready cool though. If you ever feel like collaborating, give me a shout at drknyt05@gmail.com.
  - ta12653421 1y ago
    Thats one of the primary edges: That there is _no_ human in the loop in my approach; actually, the system is build around a simple idea (regardless which approach taken): Out of 700+ stocks globally (blue chips only), there is always something in a "robust trend situation", you just have to scan the market automated and pick the ones with the highest probability. To do so, you need an application (otherwise you would die of boringness in front of the screen), and the app is submitting all trades automaticly (sure, you can cancel one, but a manual action is usually not required) What most people dont get: This is not about "predicting" where the price will be, this is about taking momentum and weeding out the bad options - and applied on a large set of stocks, there is always something you can ride with for a couple of days (with applied leverage, there is usually a substantial profit)

gavinray 1y ago

I have zero knowledge of finance and trading, but when I got curious about algorithmic systems, it seemed like sentiment-based trading using current events was a more viable strategy than forecasting/regression-based analysis.

NotAnOtter 1y ago
For HFT - yes, and that has been the case since before the .com bubble. Trading on news is what fuels HFT. And value trading is primarily based on insider trading / corruption. What's left over is ETF's or luck.
- ta12653421 1y ago
  Not really anymore, and for a long time: This article is more than 10 years old and describes what impressive tech & metrics they were using already back then: https://queue.acm.org/detail.cfm?id=2536492 https://queue.acm.org/detail.cfm?id=2536492

pdabbadabba 1y ago

> AI-based approaches are at best on par with traditional approaches This seems like quite the generalization! Wouldn't it completely depend on the approach and model?

ta12653421 1y ago
Thats what i meant with "in my experience": For sure, i see & read a lot of content from people who are claiming, that they are using AI for their approaches - though, i havent met one or seeing someone showing some "hard facts". It is very likely possible to find a "stable setup" with this - but it didnt work out for me, maybe i had the wrong perspective :)

NotAnOtter 1y ago

With cutting edge stuff you cannot say statements like "I tried X and found that it is not good at Y". You could not get X to be good at Y, but it's not impossible someone else can.

flessner 1y ago

I have been interested in algorithmic trading for quite a while now. Everything you've said resonates with me as I ran into similar issues. I hope you don't mind that I add my own couple cents here. (a-c) LLMs are especially difficult to use due to their knowledge cutoffs and "unpredictability". A self-trained "old-school" machine learning model can go a long way though. (d) With Crypto the volatility is great for trading, but liquidity can quickly become a problem (even at $1000 non-leveraged positions). For me, the ultimate goal is to find a strategy that is profitable in all market conditions. I personally value consistency and reliability more than absolute profit. (e) There's some chatting about risk management, but absolutely no discussion on profitable strategies. Resources are incredibly scarce - Systematic Trading by Robert Caver is the only book that was actually useful.

ta12653421 1y ago
Thanks for your question: Regarding LLM: I do use them to write code in less time, i do not use them to do anything rlated to trade analysis / execution / etc. Regarding Crypto: No, in my location i cant use crypto as Underlying, since the crypto market is open 24/7, but the instruments im using are available only between 0800-2200 on workdays Risk Management is key: If you have a solid hitquote, its mathemathically impossible to ruin the account; most people get to greedy and have no patience, but if you stick strictly to your risk plan, there is not that much that could go wrong.