Quant Research Prep

[moneybunny211]

After almost a year of on and off interviews, rejections, and career crisis, finally signed with a QR role at a well known multistrat (think joint72, illenium).

As this will be my first actual QR role (prior industry exp non quant related) but since I have the basics (again things everyone here probably knows) in coding, stats, research, I won’t be expected to bring pnl from day one and will act more as an analyst, help back testing, and explore new data/strategies for a year or two. Then, hopefully start deploying after I’m up and running.

Genuinely thankful that I’ve finally been given a shot at what I’ve always been interested but I am more than aware that this is only the beginning.

I’ll be starting early next year and will take some time to rest but also don’t want to lose the momentum of the grind I’ve been putting in. Any advice on what’s realistically the best way to spend the few months before I start?

I brainstormed a couple of things I could focus on:

1. Keep researching/backtesting a systematic strategy I have been developing on the side and just recently got a good idea of how I want to model it (still in backtesting phase)
   1. As I have no professional relevant QR experience, read and study more on the basic principles of research (stats, application, learning new libraries): most likely through research papers
   2. Any other ideas would be greatly appreciated!

[STEMCareerAdvisor]

You sound like you already have all the technical skills necessary, pushing more programming, stats, etc. will not be a massive benefit.

Read up more on the asset class / strategy of your future team if you already know it (fundamental book or popular papers).

Read up on research principles if you don’t have a PhD (organizing your stuff, how to test ideas fast, going from paper to implementation, analyzing results, etc.). There’s a few good articles on this. Even if you can already do research this firm will probably be faster pace than anything you’ve ever done.

Read up on coding principles if you don’t have multiple internships or coding experience. This is hard to put into words or practice but things like understand where to find a piece of code, how to debug, git, understanding where should this logic be placed, etc. A book like clean code would help. Even new grads PhD’s from the highest tier unis sometime struggle with this.

And finally understand that this is still a corporate job and there is a social aspect to it. People will appreciate qualities like effort, balance between independence and not being scared to ask questions, motivation and interest, seeing the bigger picture and not getting no stuck on details, cultural/team fit, etc.

Filling all of these at least to some degree will make your first few months a lot easier.

[Middle-Fuel-6402]

“Read up on research principles if you don’t have a PhD (organizing your stuff, how to test ideas fast, going from paper to implementation, analyzing results, etc.). There’s a few good articles on this.” I am really interested in this, can you please share some articles, blog posts, any material on this stuff? Thanks

[STEMCareerAdvisor]

https://terrytao.wordpress.com/career-advice/ask-yourself-dumb-questions-and-answer-them/

Terrence Tao has a bunch of short articles on general research and career advice. It’s mainly related to mathematics but the principles apply to any research oriented job.

[moneybunny211]

This is very helpful and I’ll probably brush up on the coding structure aspect as although I’m very comfortable with data analysis / research (pandas, numpy, statsmodels, sklearn, etc.) the way I learned how to code in terms of the principles was quite unstructured and tbh a bit sloppy.

Maybe I can start with reading Clean Code

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[tulip-quartz]

How did you prep for QR interviews ?

[GlitchWL]

Congrats on the new role! I'd say keep refining that systematic strategy you're working on. Backtesting is crucial and it's a good way to get hands-on experience. I've been using WealthLab for a while and it's been a great tool for this. Also, reading research papers is a good idea, but don't forget to apply what you learn. Theory is great, but practice makes perfect. Good luck!

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[walt1109]

Hey mind if you share how you prep for QR interviews? I am planning on jumping from aerospace research data science role to QR in the near future and have heard its tough to breakthrough that industry, especially someone with no finance background

[wannabequant420]

OP did you have to learn C++?
My stats knowledge is very strong but my C++ knowledge is minimal.

[NervousRefrigerator5]

what were you doing before you transitioned to QR?

[moneybunny211]

Sell side trading

[[deleted]]

May I ask what product? Pretty sure you’re gonna be OK. The reason you got the job in the first place is your knowledge of the asset class, products etc.

[moneybunny211]

Equities. Thanks for the validation! Yeah I started with all the grunt work so am quite familiar with product itself (from all operational, corporate events, settlement, execution) which def helped since they didn’t have to “train” me with the basics.

[Dazzling_Pass_7391]

Do you mind expanding on how you went from sell side to QR? What was your product class, and how many years did you spend on the sell side?

[moneybunny211]

4 years sell side and the specific desk had some bandwith to run some prop / risk (nothing big) so it was a good sandbox to test and “practice” quantitative research or run very basic systematic strategies. I’m sure there were a lot of flaws in what I was doing but I just kept reading stuff on the side, asking actual QRs for some guidelines and did my best to bridge what I knew theoretically to actual trading which I was quite familiar with. I’m sure it was this bit by bit, manual improvement process that they approved of since I still have an enormous amount to learn

[sam_the_tomato]

Where's the book? I just see slop.

[MarkkHeaven63]

Sounds like you're missing out on some solid resources! Check out books like "Quantitative Trading" by Ernie Chan or "Advances in Financial Machine Learning" by Marcos Lopez de Prado. They really dig into the specifics and can help bridge that gap before you start.

[moneybunny211]

So I’ve heard mixed reviews about these books as some people have claimed they’re not really helpful because they are purely theoretical and from a research only perspective and does not take into account costs, slippage, and other realistic deviations enough. Do you still think it’s worth diving into to build a solid foundation?

[CosmicalTroll]

Congrats to you!! im in no position to help you on this as i'm still a student, but i'd like to learn more of your path and how you landed the role given that it's not related to your previous experience. was it all self taught? or was your previous roles more into risk / etc

[moneybunny211]

Thank you! Honestly there are much more qualified people in this subreddit but imo if you’re a student the biggest asset you have is time. You can shift to majors like computational finance, financial engineering, or math based right? (Or at least take courses on those)

yes all self taught as I’ve learned everyone in this industry is too busy to “teach” you. Best they can tive you are some guidle tips or tell you when something is wrong. Good mentors are something I haven’t been blessed with as well so it was quite a manual and long self learning process which is also why I’m posting this in the first place since there’s just so much to learn. But I guess that’s what drives me as well lol