Heard on the street

What Hacker News says

1. KRAKRISMOTTJun 2023
   Physicists are popular because they deal with uncertainty and complex models across the time domain, which is another description of markets. When you zoom out far enough, a fluid dynamicist and a market making quant are not that different.

   If you want books about trading mathematics and interviews in general, the green book (practical guide to quant finance interviews by Zhou) is the usual go-to. Falcon's Heard On the Street is another classic. Note both of these focus on generic interviews (the quant equivalent of leetcoding) usually for recruiting fresh grads directly out of school. You can probably skip some steps if you have the work experience or know the right people at certain funds.

   If you want actual books on trading and modeling, that's another topic entirely.

   Figure out which area specifically you are interested in: https://teddit.net/r/wallstreetbets/comments/kcs4xy/what_qua...

   If you want an autobiography, Emanuel Derman's My Life as a Quant is well-regarded. I have never read it myself but Amazon has often recommended me The Physics of Wall Street.

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2. BostonianAug 2022
   Wilmott.com is a good forum for financial quants. Learn C++ and Python (and OCaml if you have an eye on Jane Street). There are books such as "Heard on The Street: Quantitative Questions from Wall Street Job Interviews".
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3. rmrfstarAug 2020
   For Data Science brain teasers, solving math problems that require clever use of the fundamentals. That means calculus, linear algebra, and basic stats.

   A ninja is not defined by his sword. Reading books gives you new weapons, but you must learn to use them proficiently. Solving problems is therefore critical.

   Some good sources of problems:

   Sanjoy Mahajan (2010) "Street Fighting Mathematics"

   Xingfeng Zhou (2008) "A Practical Guide to Quantitative Finance Interviews"

   Timothy Crack (2019) "Heard on The Street: Quantitative Questions from Wall Street Job Interviews"

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4. throwawaymathSep 2018
   Depending on the hedge fund (I'm thinking of a few in particular), there isn't really a route to practicing these problems just to get better at the interview. You'll be asked to talk about your own research in the domain and you'll work on re-deriving (very small parts of) the research of other members of the team with guidance and from first principles. Regardless of applicability to the job, if you're asked to prove a theorem on the board you either know it (and have known it for a while, and in depth), or you don't. In that sense they're not brainteasers to practice a particular formula for, like solving an algorithms question or probability estimation.

   That being said if you're just talking about mathematics and statistics problems the more mainstream hedge funds like to ask their quant candidates, read Crack's Heard on the Street, Joshi-Denson-Downes' Quant Job Interview Questions and Answers, and Zhou's A Practical Guide to Quantitative Finance Interviews.

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5. dsaccoJul 2017
   It's hard to provide very detailed advice without knowing what exactly you're doing - are you developing market making algorithms? Working on execution algorithms? Implementing strategies developed by researchers? Is this a hedge fund or a prop trading firm? There is a lot that can encompass "quantitative developer" in the front (and back!) office.

   There are three standards that you can think of as the Cracking the Coding Interview of quant finance (insofar as that is a homogenous field, which it isn't):

   1. Heard on the Street

   2. Quant Job Interview Questions and Answers

   3. A Practical Guide to Quantitative Finance Interviews

   You want to also practice algorithms as you would for a Google/Facebook whiteboard interview, but alongside that drill down on statistics. If you have the time review your linear algebra.

   Higher math will be less necessary if you're a developer as opposed to a researcher, but they'll still want it. It's probably in your best interest if you know at least one "serious" language used for performance (C++, Java typically) while also being familiar with the data analysis libraries of Python.

   If you have no experience with finance and you're coming in to that cold, give Options, Futures and Other Derivatives a read. I'm assuming you're in this boat because you're asking the question.

   This is sort of catch-all advice. If you're working at Virtu or Jump, you're going to want to know as much as you can about low level programming with regards to latency, networking and memory, because those are HFT firms. If you're working at a place like Two Sigma, Java will be more applicable and you want to know more about statistics and data analysis on a practical level, because you're be implementing the strategies developed by quantitative researchers.

   Tailor your approach to the type of trading the firm engages in. I've made two assumptions here - 1) you're interviewing with a firm considered "quantitative" as opposed to discretionary and 2) you're interviewing for a front office role, not a back office role (support, farther from money).

   If you give more information about the type of firm and the type of role we can provide much more constructive advice.

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6. dsaccoMar 2017
   >> Broadly speaking I think that you are speaking about two things: derivatives pricing (this is what people think when thy talk about quantitative finance) and algorithmic trading (which can be either pure market making or alpha seeking speculation ;).

   "Quantitative finance" was mostly associated with derivatives pricing in the mid-late aughts, but nowadays I see a pretty loose classification tossed around that essentially makes it shorthand for "we're doing something data science-y to forecast things." It's more of a marketing/cultural term, and has sort of lost a lot of the meaning and precision it used to have. Handy to sort of succinctly sum up where on the Street you vaguely work (or at least which ingroup you'd like to signal therein), but not so handy for trying to e.g. Google around for what to learn. QuantStart is probably helpful there though.

   Not a criticism of your point, just wanted to add some color to it for other readers. I agree with Wilmott forums being a great starting point at the very least, though it's going to be a little esoteric for someone totally new to finance.

   To the OP: pick up Heard on the Street: Quantitative Questions From Wall Street Job Interviews. It's not perfect, but it's a good start. There aren't any textbooks that will take you all of the way in algorithmic trading (from finding alpha, to strategy implementation, to risk management, to portfolio theory, etc). If you're a CS student and looking for quantitative finance, core computer science (not just programming) is what you want to drill down on. You'll want to invest in your math skills quite a bit too, and especially statistics.

   Research firms like Two Sigma, RenTec (good luck), AQR, D.E. Shaw and Bridgewater, which are wholly or in part devoted to what you might call "financial engineering." Learn about their processes and talk to as many people in the industry as you can to learn about what they do and network.

   Finally, chollida1 does this better than I can, but please don't trade with your own money. It's an expensive tuition :)

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