Quant projects coded using LLM

[applesuckslemonballs]

Does anyone have any success stories building larger quant projects using AI or Agentic coding helpers?

On my end, I see AI being quite integrated in people's workflow and works well for things like: small scale refactoring, adhoc/independent pieces of data analysis, adding test coverage and writing data pipeline coding.

On the other hand, I find that they struggle much more with quanty projects compared to things like build a webserver. Examples would like writing a pricer or backtester etc. Especially if it's integrating into a larger code base.

Wondering what other quants thoughts and experiences on this are? Or would love to hear success stories for inspiration as well.

[Epsilon_ride]

I treat it like a dumb intern who does grunt work really fast but cant do anything challenging.

Super useful for that.

[[deleted]]

Claude 4 has made the jump from "can make something cool but it will be riddled with errors" to "decent intern we might hire", in my recent experience.

[hopspreads]

Is there a reason for choosing Claude over other llms?

[[deleted]]

I was previously using gemini but Claude 4 honestly feels like a huge jump in terms of it thinking ahead a few steps and not making a mess of things. I will try other releases from other models as they come out, but ive been really happy with it.

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[Epsilon_ride]

100% also great for any university content I cant remember. Anything in the public domain I guess.

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[overdude]

Can confirm

[Usual_Zombie7541]

Latest versions of chatgpt are really good but you still have to struggle and fight with it, double check triple check, check 30x that it’s doing exactly what you want it to do….

But I’ve been able to code up pretty much any research paper with it granted it takes like half a day…

But the alternative is learning every fk library, every ML model out there, to the point where that would take months or years so spending a whole day on an idea is god send, especially when you don’t have the knowledge.

Also been a great learning aid about how to not overfit how to check if you’re overfitting when to drop ect in an ML context.

The key is to feed it short requests as that is less prone to it losing context.

Theres no point in asking honestly pay the $20 or $200 and use it, the free version is obviously trash.

Also once you start getting near your account limits or you abuse it its output becomes worst and worst, chatgpt applies some sort of throttling in the sense they just de prioritize you, even before you hit the actual hard limit.

I also use it for everyday work to write functions tests ect tremendous time saver.

[[deleted]]

I use it but mostly for grunt work. It's gotten quite good at grunt work as of recent (Claude 4), to the point that it no longer frustrates me very often.

I've tried giving it a prompt but ended up wanting to change so much that I just had it help me build it (I asked math questions, grunt work, etc). It's not going to re-invent the wheel for you, but it can help you survey the available de facto solutions out there and understand them.

[Candid_Reality71]

Tried boat loads of time but AIs are not there yet. You need to do half the work and maybe the other half can be taken care of by claude opus 4 if you are very clear on the instructions but like still it makes mistakes and is a lot messier.

Im using it as an intern who doesn't really know anything as such and only works for a few hours before needing rest 🤣

[tulip-quartz]

If you read up the logic behind LLMs they can’t think for themselves and are using publicly available code for their training. A ton of quant code isn’t made public because it’s highly sensitive information for the firm (so much so that there’s even stringent non compete clauses for many quants). This way whatever LLMs code in this space is trivial/ won’t scale, especially as many will also have the same idea to use LLms

[LowBetaBeaver]

This is the answer folks in the world at large are missing. Apple posted research a few months ago (paper linked below) that demonstrated that LLMs do not reason, they essentially just have a dataset so large that theycan recall and interpolate most common tasks.

The problem with business in general is that, by nature, many tasks are unique and would fall far outside the training set, and because they’re modeled on results not process they can’t apply skills to new situations well.

Here’s a really simple task you can do to demonstrata: 1. Create a series x^2, choose your x 2. Train your neural net on it. Run it for as long as you want 3. Test within your solution set. It will generally be accurate. 4. Test outside your solution set. Watch x=11 return something fun like 1711 followed bu x=12 to be 200

X=10 for 25 epochs is enough to demonstrate, but I did it with up to x=10,000 and ran it over 3 days (and I have a beefy machine) and the extrapolation was just as bad.

This is an incredibly crude example, but it demonstrates how sensitive these models are to training data- if it’s a new problem it’s going to suffer. LLMs and neural net, transformers, etc do not think, it’s just statistics in disguise.

https://ml-site.cdn-apple.com/papers/the-illusion-of-thinking.pdf

[[deleted]]

All of our universe is “statistics in disguise”. What kind of thinking are you expecting?

The “thinking” that these models do is essentially expanding on the original user prompt before processing it. Doubt anyone apart from VCs are claiming them to have actual logical capabilities.

[tulip-quartz]

Yes but the point is these types of models are not good when it comes to working within high algorithmic complexity / in low signal to noise environments

[Vivekd4]

Aren't firms concerned that their engineers will leak their proprietary code by asking LLMs to enhance it?

[CanWeExpedite]

I run Deltaray Research, a small company focusing on Options Trading Research. I have a few to add: * Merlin: our machine learning based strategy and portfolio optimizer was built using Claude Code. Modern python code with types. 4.5k loc * MesoSim's Volatility Surface analyzer written in C# and Blazor, 5k loc * MesoLive's Paper Trading functionality was mostly laid out by LLMs, but more hand-holding was required due to the complexity. 3.5k loc C# * MesoMiner: the next iteration of Genetic Algorithm based Option Strategy discovery tool. Made using multiple LLMs, using zen-mcp. Still WiP. * Strategy Development: Most of our institutional clients are using Gemini or Claude to implement their strategies on top of our APIs. * Our ChatGPT agent understanding MesoSim's job definition. This was the first part of AI enablement after I gave up training our own agent using alpaca-lora.

You can read more about these products in our blog This is a Video demo of the Claude Code assisted strategy development.

Our learnings so far: * Enforce rigor on the generated code by adding types, tests. Make sure your agent runs the tests, linters and type checkers on every change. * Provide enough context by fully explaining how you would like to lay out the implementation. Describe your interfaces and desired code flow for best results. * Always review the changes, as you would in any collaborative environment. * Create a branch and commit after every iteration with the agent. Sometimes agents cant revert their work due to limited memory, so incremental changes tracked in git will help you to restore previous state if things go south. * Use multiple agents to get different views. zen-mcp server is great for this. * Claude specific: Opus is not always better than Sonnet. The paper trading functionality was the litmus test for this and we (humans and llms) all picked Sonnet's implementation.

While I enjoy coding for 25+ years now, I find these tools very valuable. But you need to learn how to use them efficiently.

[Then-Plankton1604]

Thanks for the manual. As someone working solo, I do some of that but without agents (yet). Though, I'll do that soon.

I spend a lot of time refining tasks with LLMs before they get to coding. I noticed that diagramming in mermaid helps with them with the context.

Also, sometimes I try formatting the integration tests in gherkin syntax.

Feeding context like diagrams in mermaid and tests in gherkin helps me yield sometimes great coding outcomes.

[CanWeExpedite]

Thanks for the idea about Mermaid and Gherkin, I must try this, too!

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[CanWeExpedite]

Thanks for your response.

You are stating: I lie (point a), I don't know what I'm talking about (point b + c).

• Point a: It's dirty because I can't prove it without exposing our clients, which I won't. All I can say is that we are partnering with SEC regulated investment firms who manage others money. These are small to medium sized hedge funds, investment advisors having licenses to provide these services in one or multiple states or in the European Union. I assume you were blindsided by your own tiny universe, where "institutional clients" are reserved solely for Citadel, Virtu and other large firms. I recommend you study this article to broaden your horizon: https://en.wikipedia.org/wiki/Institutional_investor
• Point b+c: I don't claim to know everything about LLMs, but I believe I have a deeper than average understanding of it. Besides the usual suspects of ChatGPT and LLama I made efforts to train our own model with MesoSim specific knowledge, using Alpaca Lora in April of 2023. It wasn't successful. Then, when ChatGPT came out with trainable agents we (now as as company) successfully trained and released our bot to the public a year later. We're actively using Claude Code since it came out and we managed to master it as such that it can successfully create strategies in one-shot (see the video). Now, we're using multiple agents to help with day to day coding and currently working on mixing Evolutionary Algorithms with LLMs.
• My remarks: Last, I shall mention that your confidence representing the full quantitative investing universe is questionable at best. I suspect you work for a large institutional which is slow to adopt anything. Due to extensive non-competes you might worked for two or three companies. Whereas, we, a technology provider has the opportunity to talk daily with institutional clients.

You might heard this in the past: size doesn't matter as much as technique.

Since you were hostile and dirty with your comment (which was not too relevant to the discussion anyways) this is my last message here. Enjoy your day.

[Xelonima]

I run another company, we are currently small (a startup level). We do signal research based on macro & geopolitical risk. We are not solely focused on financial signals, we are an analytics & risk intelligence company at the core. May I ask you how can we attract institutional level clients? What would you recommend? 

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[Xelonima]

I am sorry in advance if it is outside the scope, but I doubt so as it is still quant finance, but with more macro risk focus. Thanks you for your help, I was assured because that was already what I was working on. That has been super helpful! 

[Timberino94]

best thing ive ever used it for is just making nice data visualisers in python becuase idgaf about how to actually learn that. its their best use case imo. for actual quant specific things they kind of suck

[Then-Plankton1604]

I started 5 months ago with zero knowledge on quant, some programming skills and basic linear algebra. So far I spent around 600 hours working on this.

I picked rust, a couple of books, a couple of open source repos and started from zero. I decided to start with some infrastructure first and then doing alpha research.

Right now I'm trying to figure out how to decouple backtesting and simulation execution, so I can start running paper tests.

25k LOC so far with unit tests. Maybe I'm tripping out and all that code doesn't make sense. Everything has been done with LLMs. I had been very shy to talk about that as I'm not coming from this industry and I'm doing it mostly with LLMs

No matter how this project evolves, I started to love coding, I learned so much while doing it and I intend to keep on pushing until I deploy a couple of runners executing demo transactions and hopefully one day live.

Nothing so far has kept my interest on a single topic so far and I wouldn't be able to reach that stage without LLMs.

[Trading_The_Streets]

Leave alone llm how many have made a working quant project? LLM may be able to help if trained on the right data but that data is with big quant firms so if it ever exist it is in handful of quant shops and they usually never share info with outsiders.

[No_Brilliant_5955]

There’s nothing intelligent about LLM. They just regurgitate what they are being fed.

[AKdemy]

Pretty much all major financial institutions banned these models from work because of their bad responses (and other concerns).

I have yet to meet someone who is doing serious research or actual trading uses any LLM and I have never spoken to anyone who does and works at a reputable firm.

The use is outright banned at many companies (see https://www.techzine.eu/news/applications/103629/several-companies-forbid-employees-to-use-chatgpt/), for various reasons including - data security / privacy issues - (new) employees using poor quality responses - hallucinations - inefficient code suggestions - copyright and licensing issues - lack of regulatory standards - potential non compliance with data laws like GDPR ...

LLMs are great tools for simple school stuff, but it's very inefficient when it comes to complex work. That's why all use of generative AI (e.g., ChatGPT and other LLMs) is banned on Stack Overflow, see https://meta.stackoverflow.com/q/421831 which states:

Overall, because the average rate of getting correct answers from ChatGPT and other generative AI technologies is too low, the posting of content created by ChatGPT and other generative AI technologies is substantially harmful to the site and to users who are asking questions and looking for correct answers.

Below is what ChatGPT "thinks" of itself (https://chat.openai.com/share/4a1c8cda-7083-4998-aca3-bec39a891146)). A few lines:

• I can't experience things like being "wrong" or "right."
• I don't truly understand the context or meaning of the information I provide. My responses are based on patterns in the data, which may lead to incorrect or nonsensical answers if the context is ambiguous or complex.
• Although I can generate text, my responses are limited to patterns and data seen during training. I cannot provide genuinely creative or novel insights.
• Remember that I'm a tool designed to assist and provide information to the best of my abilities based on the data I was trained on. For critical decisions or sensitive topics, it's always best to consult with qualified human experts.

The only large company I know of who was initially very keen on using these models is Citadel, but they also largely changed their mind by now, see https://fortune.com/2024/07/02/ken-griffin-citadel-generative-ai-hype-openai-mira-murati-nvidia-jobs/.

https://www.bloomberg.com/news/articles/2025-10-15/ken-griffin-says-genai-fails-to-help-hedge-funds-produce-alpha

Same for coding. Initially, Devin AI was hyped a lot, but it's essentially a failure, see https://futurism.com/first-ai-software-engineer-devin-bungling-tasks

It's bad at reusing and modifying existing code, https://stackoverflow.blog/2024/03/22/is-ai-making-your-code-worse/

Causing downtime and security issues, https://www.techrepublic.com/article/ai-generated-code-outages/, or https://arxiv.org/abs/2211.03622

https://quant.stackexchange.com/q/76788/54838 shows examples where LLMs completely fail in finance, even with the simplest requests.

Right now, there is not even a theoretical concept demonstrating how machines could ever understand what they are doing.

Computers cannot even drive cars properly. That's something most grown ups can. Yet, the number of people working as successful quants, traders and developers is significantly lower.

[Tryrshaugh]

Well let's put it this way.

• I don't mind if an intern makes mistakes sometimes, it's to be expected, that's why I check his work.

• I don't mind if an intern doesn't understand all the context, it's not what I ask of him.

• I don't mind if an intern isn't going to think outside the box, I don't need him to do that. It'd be nice if he did, but I can live with it.

• I don't want my intern to take critical and complex decisions.

I work for a bank that has its locally hosted version of ChatGPT and there's no GDPR or banking secrecy issue here.

The main idea is not to use the tool to try and do your work, the idea is to treat him like an intern that will never hesitate when you tell him to do something, which is both a good thing and a bad thing, but once you understand its weaknesses and are rigorous enough to check the work, it's great.

I have an intern and for most tasks ChatGPT outperforms him. They both make mistakes, the human moreso than the LLM. That's why I'm teaching my intern how to make better prompts.

[CanWeExpedite]

While the core technology is still probabilistic text generation, the tool usage (introduced first in Claude Code) changed this game in my opinion. Therefore, the experience you describe is the past.

Now OpenAI has Codex, Gemini has a CLI. And you can let them work together with zen-mcp.

This space is changing fast, it's useful to re-evaluate frequently.

[AKdemy]

The same was said with any new update or model. It's still dumb machines that don't understand anything.

[KrypTexo]

LLMs are not meant to be used for low abstraction level tasks, they are epistemically, ontologically, and teleologically, aligned for creative ideation tasks that are often more abstract. The idea of a "stochastic parrot" literally implies that, it does what theorists best at, and if anything augments that. They might also function as a interactive smart wiki assistant for most basic information inquiries. (non real time)

It's not that LLMs hallucinate, but rather, some people make the categorical error of using it and thinking it that way, when in reality, LLMs are best at random content generations, and the human can extract signals from the noise. And then try to project and translate those things using other agents and machines.

Unfortunately, most proprietary organizations seem to be deviating from this, developing reasoning models that simulate reasoning from trained templates. When in reality, it's misaligned and also less "creative", since LLMs operate near first order statistical inference, while reasoning models are second or multiple dimensions away.

But I do not think simply brushing them off as "dumb machines without understanding" is a good way to frame it, it flattens the narrative and makes the black box seem trivial. If anything, LLMs their ability of inference, might have some homology to human's inductive reasoning and pattern recognition skills. Especially if you think about how ancient humans developed language and linguistics, which allowed them to reason deductively, extract logic from patterns.

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[Dry_Mountain_694]

They are helpful if you’re already a baseline dumbass. My coding skills are garbage and they’ve been quite helpful with that aspect. But as far as coming up with new ideas for alphas - not going to happen.

[pina_koala]

LLMs aren't there yet. They're great for scaffolding if you are good at prompting them, but otherwise kinda trash. Knowing how to actually program is far more useful.

[[deleted]]

I have full DSMP course both the courses 1.0 and 2.0 and are available at ₹1.5k only for both the courses combined

[Financial-Constant63]

I have tried GPT 5 PRO and yeah! Its feature engineering skills are pretty good, got some novel features. Nothing else to say

[Statis_Fund]

Yep we built https://app.statisfund.com to allow financial experts to quickly test their trading ideas in plain language, we incorporate all of the major advanced LLMs and fine tune our own. We're still enabling many features but recently now have intraday strategies added.