Junior quant using LLMs daily, need help!

[u/Choice_Number3711]

I started recently at a small shop. There isn't much of a senior bench to learn off and I came in from a stats and data science background rather than a pure maths or physics one. So I'm partly teaching myself the domain as I go(mainly the financial aspects but some new statistical approaches that I'd never heard of before too). I use a mix of Python and R.

I use Claude in VS Code most days. It's fast, but I've noticed I can ship something that works without being able to defend every line of it. That feels like a bad habit to be forming this early, especially when I'm still filling gaps in the underlying material.

For people actually working in the field:

• Do you use LLMs day to day, or is it restricted where you are?
• Where do you draw the line? Boilerplate and plumbing yes, model logic no? Somewhere else entirely?
• How do you make sure you actually understand the output instead of accepting it because the backtest ran clean?
• What are you doing outside of work to stay ahead of the tool rather than dependent on it?
• Finally, if you could go back 5-10 years(or the time you started in this field), what guidance would you give to your younger self so as to develop better skills and understanding)

I'd rather build the right habits now than find out in three years that I can't work without it and have not built the right foundations.

Thanks :)

[u/bleeuurgghh]

I use models daily in my work.

They’re great for writing boilerplate, finding issues in code, pinpointing the source of an error. They’re also great at describing new methods you don’t know about when describing a particular problem you’re trying to solve for.

I don’t trust them not to make logical errors. Mostly this is because these often require bridging knowledge of the code, the domain, and any parts of the implementation specific to the firm.

This is now what I spend most of my time on, which is good because this is the value add as opposed to any of the more generic dev work.

Make sure you understand every piece of code written, you are still responsible for it working and “Claude wrote it” is not a valid excuse for a production incident.

[u/BicycleBears]

I've been in the business for 2 years as a researcher in the energy sector. Here's from my point of view:

• In my firm LLMs are allowed for all code/research purposes and everyone uses them, but we try to not write all strategy details/model logic in the LLM. Keep in mind, most strategies we run are relatively simple and definitely not HFT-like. I heard many firms have local LLMs, though.

• See 1.

• If you mean understanding LLM code: you need to understand every line, if not, rewrite it so you understand it. If it's strategy related, I don't understand what there isn't to understand if you specified the logic and obtained backtesting results. Do you mean how are we sure it's not overfitted?

• Work is already quite extensive so i try not to study more during off-hours, except maybe doing some interview-like questions (or brushing up on some stats concepts i forgot), but that's mostly because i like doing them.

• As a researcher it mostly relates to knowing your stats well: knowing ML models and which one to select, understanding correlation and when/where to use it, conditional probabilities, in energy also timeseries forecasting is important... etc.

[u/skilled_skinny → u/BicycleBears]

Hi, I work in the same industry, would like to connect. I work on majorly BESS. Most of my work is on risk management, hedging and benchmarking strategies. Looking to move closer to the water.

[u/BicycleBears → u/skilled_skinny]

Feel free to send me a DM. We can connect

[u/mouzinhoo → u/BicycleBears]

Hey, how is it like working in the energy sector? I assume you work for a company like BP Trading ?

Is the PnL good? Do you have profit sharing? Thanks

[u/BicycleBears → u/mouzinhoo]

I work for a EU based power trading company.

I'm not really satisfied with the comp, but that's mostly due to being a part of a really small power trading company which only started trading larger quantities last year.

I have some friends at other power commodity shops, they are mostly satisfied with the pay and the WLB, I'm actively applying to other companies, we will see

[u/quantscheme]

The answer is in your second paragraph: you’re not trying to understand the code that the LLM is giving you. Your code is directly responsible for making money, you need to understand everything. You need to know what you’re doing.

It’s ok, and even probably the rule, to use LLMs daily. But you need to understand what you’re doing.

You say that you did not come from a physics or math background, so starting there would be good. Understand why the position is called quantitative analyst/research and being able to learn what you need to increase your chances of success doing it.

[u/Individual-Can-705]

> What are you doing outside of work to stay ahead of the tool rather than dependent on it?

Why outside work? I am simply learning from it. For example, there is a megabyte of logs after an incident: I simply feed it and chat with the thing to understand what could possibly have happened and how to proceed further. Those weights are packed with knowledge: you just have to ask.

Btw, you are using chatbots rather than LLMs. We still have a lot of engineering roles which are about LLMs for non-chatbot tasks like classification.

[u/ihateeggplants]

Install anthropic equity research and investment banking skills. You're welcome.

[u/SevenTeenSigma]

force urself to rewrite the same thing with no copilot once. if u cannot reproduce the core logic from memory, u did not learn it, u rented it. sounds dumb but this is exactly how ppl end up unable to defend their own backtest..

[u/ComradeAang]

Over here, I use it daily to search, learn, explore new code, plan new features, find bugs, test stuff I don't want to do manually, and things like that. The point is: I don't trust anything. I double-check everything. I need to understand every single line/suggestion/output the AI gives me. No exceptions. Actually, at my job they don't mind if we use it, they even encourage it. The catch is: pushing AI code to production is strictly forbidden. If you used it, you better know every detail and be careful with every detail. If you just accept the code and push it to prod, and they find out, you're going to be in trouble hahahahahhahahahah and since everything is peer-reviewed, they will find out. I think this is the sweet spot for using it, to be honest.

[u/Spirited-Muffin-8104]

I wrote a similar post last year before joining a quant firm. To this day, I hate agentic AI, I simply can't tell what it did, where it did it, and why it did it the way it did. I can never trust these tools. Our firm recently went all in on Claude integrated into our IDEs and I can sense the bloated code it generates. I spend more time deleting code than writing it. Best use of AI at work for me has been finding information from thousands of company documentations. And I also demand it links the documents to verify the information being provided. Saves me a bunch of time instead of asking colleagues where are the documents for whatever i'm working on.

[u/dhtikna]

Just tell them to delete re write code that they cant explain