So … you want to be a quant? (2024)

Having worked as a quantitative researcher in systematic trading for about four years, I occasionally find myself trying to answer questions about my job and “the industry” more broadly. Often, these are questions by acquaintances or friends of friends who are considering “moving into finance”. Many of them have Master’s degrees or PhDs in physics, statistics, engineering, mathematics, or similar backgrounds.

Here, I would like to summarise a few thoughts that may be helpful and that aren’t readily accessible via well-known resources. I am not trying to be objective or comprehensive. Instead, I would like to make some points that I think are not frequently discussed, but helpful.

I’m still at an early stage in my career and I’ve mostly worked at hedge funds. Therefore, I would advise everyone who is genuinely interested in the topic to consult other sources, too. However, I’ve got some relevant work experience and a similar background to people typically interested in this kind of career. So, I’m hoping that this post might give a relatively up-to-date view. It will also be more helpful than my disorganised thoughts during a Zoom call.

I’m posting anonymously, because I work in a secretive and litigious industry. This way, I can write slightly more freely. Don’t take my opinionated claims too seriously. Instead, verify everything independently. At the same time, I believe that nothing in this post is incorrect, highly controversial or reveals any real secrets.

Much like “academia” and “data science”, there are many different sub-fields within finance. I know next to nothing about most of them, but I like my particular job. I enjoy the breadth of research and implementation and the quick time from ideas to actual production code.

When applying to jobs, you need to find out what the actual role that you’re applying to is about, and how the specific part of finance works. Even seemingly adjacent or similar jobs can be very different. For example, I can’t say anything interesting about high-frequency market making or derivative pricing. That’s because I’ve only worked in medium-frequency systematic trading. I would highly recommend talking to many people with various types of backgrounds. In particular, this may include sell-side research or structuring, but also quants or data scientists at large asset managers, insurance and reinsurance companies, market makers, and perhaps even gambling or crypto (if you’re interested in it, which I’m not).

To me, it feels more “physicsy” than “mathsy”. It’s more about coming up with a reasonable solution given the data than proving general truths. A lot of my work is more ad hoc and pragmatic than academic research. It’s a lot more fun and fast-paced than a lot of other things I’ve done before. Don’t be discouraged by interview prep material that focuses on probability theory brainteasers.

Hedge funds often have referral schemes. But I would probably only refer someone that I know well and that I’m fairly convinced has a good chance of getting the job. This lowers the risk of me wasting my colleagues’ time. You’re probably better off finding an excellent headhunter that will put you in touch with specific hiring managers. This will save you a lot of work and increase your chances. A good headhunter is a valuable asset because they can help you with material to prepare for an interview. Additionally, they tend to aggregate knowledge about what matters in which interviews. So, ask people about recommendations for headhunters and not for referrals. Also, try to find out which headhunters people have had positive experiences with.

As a general rule, most of them are not good at their job. Aside from getting recommendations from friends, I’d recommend talking to them on the phone for a while. This helps to get a feeling for their knowledge of the industry and the specific job specs. Some of them play a pure “numbers game”, while others are highly focussed on quality. If they are willing to invest some time and build a longer-term relationship, that’s a good sign. If they ask you“whether you are familiar with JP Morgan” … that’s a bad sign.

If they haven’t worked as headhunters for at least one to two years, I would generally avoid them. A bad headhunter might send your CV to too many firms, blindly, or for jobs at good firms that you’re not well suited for. This may reduce your chances in future applications. Mediocre headhunters may also not be aware of all open roles at a given company or not work with the most exciting hiring managers.

A headhunter that appears knowledgeable and reasonable to you is also more likely to work with other people that find them reasonable. In my experience, this is a surprisingly good filter that helps to avoid interviewing hiring managers that you don’t want to work for.

You can consider reducing some of the headhunters’ idiosyncratic risk by diversifying across a few good ones for different applications. Remember to always make sure that they only represent you for a specific role at a time.

I never think about how many hours I work, unless somebody asks me. The time I spend actively working is around 45–50 h a week. However, I am fascinated by systematic trading and think about problems I encounter at work a lot. I don’t “work” on weekends, but occasionally I will read papers or books. I listen to podcasts about finance in my free time and waste time on finance Twitter.

I suspect that if you’re not fascinated by at least one aspect of the job, you will have a hard time. The only way to find out, however, is to try. Trading is competitive, lots of markets are zero-sum games in dollar terms, and the stakes are high.

That’s what efinancialcareers is for, right? A good benchmark for hedge fund salaries is what people at banks make on average for a similar level of experience and qualifications. For good hedge funds, this should roughly be a lower bound. In the US, H1B visa information can also help to understand mid-career base salaries. Headhunters will sometimes tell you about people they recently placed. These tend to be selected to sound good, so discount accordingly.

This topic is covered by books, blog posts and random pdfs and copypasta sent around by headhunters and similar. I personally like “A Practical Guide To Quantitative Finance Interviews” by Xinfeng Zhou. Material by Wilmott, Joshi and Max Dama is also good and very well-known, and there’s nothing wrong with any of these.

Importantly, stick to the basics and don’t practice too much stochastic calculus and options theory for buy-side jobs. Also, do spend more time on Python (or other languages as required) and algorithms than you would think is needed.

Importantly, there is an overwhelming amount of material out there. I would recommend picking the relevant chapters of one book and sticking with these. Practice a lot and interview a lot, such that you learn about what matters, and find out what your weaknesses are.

If you’re a millennial with a short attention span like myself, use online courses, youtube, and practical exercises in addition to books. Form follows function.

Firstly, ask yourself why. Good quantitative developers enjoy an enviably large and liquid job market. They can switch to adjacent industries more easily than quant researchers and are paid similarly well. Only the very best-case financial outcome is perhaps slightly worse, but the median is probably better. If you’ve got the right background, as a quantitative developer you can probably have a more sustainable career in finance. Over time, you will learn more about the quantitative research part, too. Alternatively, perhaps you can transition into a role with a firm or a team where the boundaries between quant research and quant dev are not rigid.

Hiring managers looking for junior quantitative researchers tend to be sceptical towards developers that want to transition into research. At the very least, you should have a compelling story to tell. Additionally, you should be able to demonstrate the required skills (see interview preparation material below). You don’t need a PhD, but a solid grasp of statistics, basic probability theory and linear algebra are certainly expected.

Learn programming! Over time, you’ll ideally also pick up more abstract software engineering skills.

There are lots of lists on this, so here I will focus more on the curation aspect, which I think is hard. I would highly recommend focussing on some material and going in-depth, instead of emphasising breadth. However, this might be a reflection of my biases, too.

In this vein, I would recommend spending most time using one of the interview prep book(s) from the previous question.

In addition to “A Practical Guide To Quantitative Finance Interviews” and similar, useful references and hints are:

• “The Elements of Statistical Learning”, chapters 1–3 and 7.
• Refresh your linear algebra, calculus and basic probability theory and statistics!
• The first chapters of Grinold and Kahn’s “Active Portfolio Management”.
• Wikipedia articles on standard frequentist statistical tests used in null hypothesis significance testing.
• Understand the full derivation for when and why OLS is BLUE.
• Ben Lambert’s youtube channel is a great resource for statistics and econometrics concepts.
• The introductory chapters by Mark Joshi et al.’s “Quant Job Interview Questions and Answers” (but don’t wear a suit!). Note that this book is somewhat dated.
• I generally tend to recommend that people read this paper by Attilio Meucci, although it may not be 100% accurate.
• Do practice Leetcode, in particular, simple algorithms in Python or C++/Rust for higher frequency jobs and Stat Arb jobs.
• Figure out a way to practice pandas or polars.
• “All of Statistics” by Larry Wasserman is a great reference book for statistics.
• “Options, futures and other derivatives” by Hull is the canonical reference on, well, options futures and…. you get the gist.

Again, pick a small number of books/resources that work for you and go deep instead of focusing on breadth.

Below are some recommendations.

General reading or listening:

• “Financial markets” — introductory online course by Robert Shiller
• “Trading and exchanges” — Larry Harris
• “The rise of carry” — Tim Lee, Jamie Lee, Kevin Coldiron
• “Inside the black box” — Rishi K. Narang
• “Central Banking 101” — Joseph Wang
• “Flirting with Models” podcast hosted by Corey Hoffstein
• One of Nassim Taleb’s books (the earlier the better, avoid the technical one)
• Andrew Gelman’s statistics blog (which is not about trading, but helps to avoid doing statistics badly).
• Rob Carver’s blog and perhaps one of his books
• Standard resources that “people in finance” tend to pay attention to: Matt Levine’s newsletter “Money Stuff”, “Odd Lots” podcast, “Masters in Business” podcast.
• On Quora: Aaron Brown, Vladimir Novakovski
• Twitter: There are some good people, but it’s not worth the time inevitably wasted on Twitter. Sooner or later they will be on “Flirting with Models” anyway.

More technical:

• “Expected Returns” — Antti Ilmanen
• “Quantitative Portfolio Management” — Michael Isichenko
• “Efficiently Inefficient” — Lasse Heje Pedersen
• Books by Jean-Philippe Bouchaud, Marc Potters and co-authors (CFM) — generally interesting, if you’re into it and/or have a physics or random matrix theory background.
• “Active Portfolio Management” — Richard Grinold and Ronald Kahn
• “Convex Optimization” — Stephen Boyd and Lieven Vandenberghe (and related publications on portfolio optimisation via convex optimisation)

Somewhat useful, but probably more in the “entertainment” category:

• “A Man for All Markets” — Ed Thorp
• “My Life as a Quant” — Emanuel Derman
• “The Man Who Solved the Market” — Gregory Zuckerman
• “The Quants” — Scott Patterson
• “Flash Boys” — Michael Lewis
• “Dark Pools” — Scott Patterson
• “Den of Thieves” — James Stewart
• “Black Edge” — Sheelah Kolhatkar
• Films: “The Big Short” and “Margin Call”, mostly because everyone has watched them.

In general, the “signal-to-noise” ratio in finance-related publications is poor, even compared to the dodgier parts of STEM research. Good books/papers are orders of magnitude better than mediocre ones, which is quite different to physics or maths.

• “Heard on the Street” feels somewhat stale at this point, and being asked too many non-maths brainteasers is generally a bad sign.
• For buy-side jobs, don’t spend time on stochastic calculus and options theory, unless you’re sure that these are relevant. That’s not impossible for some roles, e.g. vol RV trading.
• I would avoid courses by authors that are mainly set up to extract money from participants (even reputable ones like Wilmott, and Meucci), although individual books or papers by these authors can be fine. After all, the actual work is to do the exercises yourself and learn how to solve problems.
• I would generally avoid Marcos Lopez de Prado’s books, although his backtest overfitting papers are canonical. The signal-to-noise ratio is just too low.
• Some books are more in the “guilty pleasure category” in that they are entertaining, but their main points can be grasped in a lot shorter time or are perhaps not that deep. This includes Nassim Taleb’s (although I’d read one of them and perhaps listen to a podcast episode with him), the “Market Wizards” series and “Reminiscence of a Stock Operator”. Most of Michael Lewis’ books are also in this category. This doesn’t mean that they are bad. They just aren’t interview preparation.
• In the beginning, avoid very technical math finance papers, e.g. by the rough volatility crowd. They take too much time per useful information.
• Probably avoid newly published papers. The interesting part about these is usually which “classic papers” they cite.
• Original methods papers. Usually, the textbook and/or youtube version will be easier to follow.
• Modern machine learning. Instead, learn the classic stuff that’s in scikit-lean, if you must. If you’re already a deep learning expert, look for specific jobs that require it. If not, you’re not going to become one.
• The “Top Traders Unplugged” podcast is too repetitive, only listen to selected episodes with guests you are interested in.

These are some papers that I particularly like:

• Lo, A.W., 2002. The statistics of Sharpe ratios. Financial analysts journal, 58(4), pp.36–52.
• Kolm, P. and Ritter, G., 2017. On the Bayesian interpretation of Black–Litterman. European Journal of Operational Research, 258(2), pp.564–572.
• Bailey, D.H., Borwein, J., Lopez de Prado, M. and Zhu, Q.J., 2016. The probability of backtest overfitting. Journal of Computational Finance, forthcoming.
• Boyd, S., Busseti, E., Diamond, S., Kahn, R.N., Koh, K., Nystrup, P. and Speth, J., 2017. Multi-period trading via convex optimization. Foundations and Trends in Optimization, 3(1), pp.1–76.
• Muller, P., 2001. Proprietary trading: truth and fiction. Quantitative Finance, 1(1), p.6.
• Asness, C.S., Moskowitz, T.J. and Pedersen, L.H., 2013. Value and momentum everywhere. The Journal of Finance, 68(3), pp.929–985.
• Baz, J., Granger, N., Harvey, C.R., Le Roux, N. and Rattray, S., 2015. Dissecting investment strategies in the cross-section and time series. Available at SSRN 2695101.

I generally find that papers and books published by academics close to practitioners are the most useful. This is compared to both, “pure” practitioners and “pure” academics. These often help to understand ideas and applicable methods.

In addition to the authors above, these are some authors whose papers (and papers with one degree of separation) are generally worth having a look at. This is, of course, a highly subjective and random selection:

• Jean-Philippe Bouchaud et al. (CFM and adjacent)
• Xavier Gabaix
• J. Doyne Farmer
• Lasse Pedersen
• Ralph Kojin
• Andrew Lo
• Craig Pirrong
• Olivier Ledoit, Michael Wolf
• Stephen Boyd
• Campbell Harvey
• Andrew Gelman, Jennifer Hill
• Allan Timmermann
• Andrew Patton
• Darrell Duffie
• Dimitris Politis, Joseph Romano
• Petter Kolm, Gordon Ritter
• Rob Hyndman
• Leo Breiman
• other AQR and adjacent academics
• other AHL and Two Sigma and adjacent academics

This is something that physicists ask me a lot. My theory is that this is partially because they are looking to do something more impactful than theoretical physics in the future. It’s probably also partially driven by “finance” having a bad reputation due to past excesses and crises. I interpret the question to mean something like “What is your estimate of your marginal impact on some form of aggregate social utility by you doing your job compared to other jobs”. Frankly, I don’t know, and I’m not sure it’s knowable. There is a huge uncertainty around what would happen, if I did another job, and it subtly depends on what alternatives you consider.

Compared to continuing to work in the field that I’ve got a PhD in, I’d say the impact of either is fairly neutral for the next few years. However, don’t forget to integrate social utility with some discount factor over my lifetime. Perhaps people in our generation are a little bit too concerned with impact early on in their careers. Instead, they should care more about growth and learning opportunities.

In my view, you should ask these sorts of questions in terms of “marginal impact”. This requires comparing realistic career paths given where you’re currently at any point in time. It’s also a function of what you think other people will do as a function of your decision. For example, consider quantitative trading, academia, “data science” and a tech startup. The world will probably be in a slightly better state in 20 years with me sticking with trading. Part of this is because I think others can make a more positive difference in the part of academia or the startup ecosystems that I could work in. Additionally, I believe that I behave generously and ethically, but who doesn’t?

Do internships before you graduate, consider roles adjacent to the ones that you think you like, and work hard. Change jobs relatively quickly, if you aren’t happy. In particular, when you’re young, take some career risk. The specific people you work with are more important than one would first think, in particular, compared to the company’s reputation. Finding managers that are good mentors is hard because mentoring requires high levels of several uncorrelated skills. Be nice, most people are, and it makes everyone’s life easier!

Also, even if you’re a brilliant scientist don’t assume that you can just walk into a hedge fund and people will throw money at you. It’s a competitive field, and you will also spend time doing boring work.

Working for reputable banks in quantitative roles can be very helpful. It’s a classic route into hedge funds and prop trading. It helps you learn about relevant parts of finance, improve your financial maths and programming skills, and gain street cred.

Working in asset management, real money (e.g. pension funds, sovereign wealth funds) or central banking seems similarly useful.

I would also consider specialising in C++ and/or Rust and becoming a quantitative developer if you’ve got a suitable background. It’s a different job, but it could be similarly exciting, with higher expected compensation if you get into top prop trading firms.

Probably not. To a large extent, my job is “learning how to predict specific markets and their relationships”. People do this by any (implementable, legal) means available. This might include more modern machine learning in the future. So, new developments in machine learning may influence my job. They might make some parts easier and others harder. It might also open up new data sources. That’s exciting!

Like, I kind of do all the things involved in machine learning. Occasionally I even use a machine learning algorithm. But on the whole, the pieces are not configured in the way they would be in machine learning research. I do a mixture of thinking, statistics, programming and learning about concepts. However, the reason why I hope this has an edge is not that I can implement a better machine-learning algorithm than other market participants. It’s valuable to know things about machine learning.

Be sceptical of papers along the lines of “using machine learning algorithm or statistical technique X to predict market Y”. What matters more are usually the features, not the techniques. Robustness across techniques given features is highly desirable compared to robustness across features given technique. The latter probably means that something has gone wrong.

You work a lot and it can be stressful. Some might not find the work as academically satisfying as pure research. You can lose your job quickly, and part of your compensation is compensation for this type of risk. You make well-off people or countries richer if you do a good job.

Make a choice and set your priorities early on during the application process. Don’t try to maximise different objective functions at the same time. If you’ve decided to focus on one area, get good at it and deprioritise optimising for other outcomes (e.g. interview prep can be more important than writing the world’s best thesis, choose not to prepare for specific types of jobs and focus on others).

Here are some (overlapping, incomplete) lists of well-known hedge funds and trading firms. It’s generally interesting to read up on them. For example, stories about their founders can be an indicator of their culture.

Multistrategy hedge funds:

• DE Shaw
• Citadel
• Millennium
• Point72 (includes Cubist)
• Balyasny
• Schonfeld
• Exodus Point
• Verition
• Man GLG
• Marshall Wace
• GSA (now mostly prop?)
• Bluecrest (now mostly prop?)
• Tudor
• Capstone

Macro hedge funds with “quanty” features:

• Brevan Howard
• Bridgewater
• Element
• Capula
• Caxton
• Rokos

Quant hedge funds (broadly):

• Two Sigma
• PDT
• AQR
• Renaissance
• various Man Group, e.g. Man AHL and Man Numeric
• Squarepoint
• Qube RT
• CFM
• Cubist (part of Point72)
• OxAM (now prop?)

Quant hedge funds (CTA and adjacent):

• Man AHL
• Aspect Capital
• Alpha Simplex
• Systematica
• Winton
• GSA (now mostly prop?)
• Graham
• Florin Court
• Gresham Quant

Prop trading, high frequency, market making:

• Jump Trading
• Hudson River Trading
• DRW
• XTX
• Quadrature
• G-research
• Jane street
• Flow Traders
• Tower Research
• Optiver
• IMC
• Virtu
• Susquehanna