What are /r/quant's favorite Products & Services?

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What kind of trading interests you the most? You're in a good position because you're so young and have a LOT of time to learn things before trying to apply to positions.

I think you should first focus on foundations. The foundations are programming, data analysis, and math/statistical analysis.

Things like Python (Pandas library), R, C++ (depending on what kind of trading interests you) are all good things to learn from a programming perspective and should round out your toolkit nicely in that regard.

Probably the best book to get you started in math/statistical analysis is The Elements of Statistical Learning.

If you're interest in algorithmic trading this link should be useful. https://quantivity.wordpress.com/2010/01/10/how-to-learn-algorithmic-trading/

I'd also recommend reading all the seminal works in the fields (i.e. The Intelligent Investor by Ben Graham).

Hope that helps a bit.

You know C++ which is a great start as most professional models use it. But these might help:

Effective C++: 55 Specific Ways to Improve Your Programs and Designs by Scot Myers

C++ Primer (4th Edition) by Stanley Lippman

C++ Design Patterns and Derivatives Pricing (2nd edition) by Mark Joshi

Financial Instrument Pricing Using C++ by Daniel Duffy (quite complex, but damn useful)

Then read Flash Boys by Michael Lewis to see whether you guys will actually have an advantage with this.

Then there's these:

A Primer For The Mathematics Of Financial Engineering, Second Edition by Dna Stefanica (Anything by Stefanica will help)

Models Behaving Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life - Emanuel Derman

When Genius Failed: The Rise and Fall of Long-Term Capital Management - Roger Lowenstein

How I Became a Quant: Insights from 25 of Wall Street's Elite - Richard Lindsey, Barry Schachter

My Life as a Quant: Reflections on Physics and Finance - Emanuel Derman

Financial Engineering: The Evolution of a Profession (Robert W. Kolb Series) - Tanya Beder, Cara Marshall

The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It - Scott Patterson

Nerds on Wall Street: Math, Machines and Wired Markets - David Leinweber

Physicists on Wall Street and Other Essays on Science and Society - Jeremey Bernstein

The Complete Guide to Capital Markets for Quantitative Professionals (McGraw-Hill Library of Investment and Finance) - Alex Kuznetsov

Here's a secondary list of resources that you might find useful.

Hope this helps and best of luck.

It depends on your interests. I consider the Econometrics of Financial Markets to be like an encyclopedia (similar to Hamilton's Time Series).

Certain areas, like the section on nonlinearities in financial data are hopelessly out of date. Some of the other stuff, like the chapter on event studies is still relevant.

I like Hasbrouck's Empirical Market Microstructure and Financial Markets and Trading for market microstructure and Cochrane's Asset Pricing for general financial econometrics. I've heard good things about Lopez de Prado's Advances in Financial Machine Learning, but I've yet to read it.

If you're looking for finance specific, and not getting TOO technical, I would recommend www.quantnet.com/cpp/

I didn't do it, but I had a buddy that went through it and I was very impressed with his grasping of the language and his functional knowledge. I mean his ability to just figure out a solution to the problem at hand. I tend to try to find the most elegant or efficient way to do things, but sometimes it just doesn't really matter. He was VERY good at just getting a working solution and moving on down the road. (He was a very bright guy, so I don't know if it was that or the program. But he knew nothing of C++ going in...) It does cost money, but you get a nice little certificate. (Although I don't know if it's worth much...)

For the end-all be-all of knowledge, I like the classic, "The C++ Programming Language" by Bjarne Stroustrup. It is a really good book, and he is the original author of C++. And he is a god. (I went to Texas A&M for my undergrad, and took a class with him once. Super cool...)

If money isn't an issue, I would say buy the Stroustrup book, and take the quantnet class. Best of both worlds.

Agreed. Asking questions is the first step to learning.

OP, if you want to learn more I have some recommendations on textbooks. I think a PDF can be found for this textbook without going to a disreputable site. A Swiss university has a PDF for this textbook as well. Idk if either is violating copyright or not.

There is "material". Go read Glassdoor on the failed interviews part. I would expect them to be similar to HRT and Jane Street in style and difficulty.

Their internal recruiter (ex-senior Google talent guy, he has a book but I doubt it's that useful in the TGS context except convincing him to give you a call: https://www.amazon.com/Purple-Squirrel-Interviews-Master-Modern/dp/1467992607) wanted to set up the next round within a couple weeks of the screen, but I told him I would come back when I felt ready. I put it off because I knew I was weak in some areas and given they have like zero turnover, I don't know how many more chances you would ever get.

https://quantnet.com/threads/big-list-of-quant-interview-questions-with-answers.36240/

https://www.amazon.com/Frequently-Questions-Interviews-Pocket-Guides/dp/0979757649

There are some “regular” questions like bayes’ theorem, Bertrand’s related problems. Something that I always kept in mind is that the conceptual (non-numerical) questions usually want to lead down a path that seems obvious. Find that path and find a reason to avoid it. Like the Monty hall you think it’s 50/50 but it’s not.

What do you actually want to do?

It sounds to me you don't really have a decently tested out (https://www.amazon.com/Designing-Your-Life-audiobook/dp/B01K5UD0H4/) the quant idea. Quant jobs are 95% coding and data analysis, more intense and often more people oriented than swe jobs, very narrow market (something like 1% the size of tech jobs market) meaning entry requires a lot of prep. When I say "you haven't tested" I mean (see the book) you haven't at all established basic components of this choice fit you: will you enjoy coding most of the day? do you really like it so much you're gonna go on long preparatory journey and not unlikely an additional MFE degree to break in? would you be able to like finance, or a more technical job like swe is a better fit?

I'd strongly urge you to read "Designing your life"

https://www.amazon.com/Designing-Your-Life-audiobook/dp/B01K5UD0H4

before committing to years of additional education.

I agree with elitelimfish. A PhD in finance/economics is maybe not as helpful as in-depth coding expertise to land a Quant job. Coding is for sure a big plus and a very important skill that will become even more important in the future. So if I personally could choose between a self-taught programmer with a PhD in finance and a self-taught trader with a PhD in CS I would prefer the latter one. So is it worth to get a PhD to proof your coding skills? Maybe not. Research and published papers are not as valuable as examples of what you actually developed. So again, if I could choose between a CS PhD or let’s say three years of hands-on experiences I would prefer the latter case. For example, can you show me algorithms you wrote, evaluated by an independent party? Did you participate in or even win a quantitative trading competition? It’s advantage if you can demonstrate your Quant skills. Maybe you want to check out this post: https://www.linkedin.com/pulse/how-get-foot-quant-door-martin-froehler?trk=prof-post

you're welcome. this sub is understandably upset because frankly the standard of questions asked on here is really poor as most of them easily searchable online

to answer your question, assuming you have some kind of quantitative background, i followed this guide. from experience, i would say follow what this sub calls "green book". figure out what you need to study by failing interview questions

> Baby Rudin for the basics of real analysis.

https://web.math.ucsb.edu/~agboola/teaching/2021/winter/122A/rudin.pdf

is that it?

this the 2nd one?

https://www.amazon.com/Probability-Random-Processes-Geoffrey-Grimmett-dp-0198847599/dp/0198847599/ref=dp_ob_title_bk

Your GPA is meaningfully lower than what is generally accepted in Quant. Get that GPA up asap. If it stays that low by senior year, go to grad school or grab a PhD for the reset button.

You're a sophomore, it's more important that you convert your interview opportunities than hyper focusing on things like networking and excess resume prep. Especially for internship programs, it is abundantly apparent who is smart and prepared. Hard prepare for quant interviews (https://www.amazon.com/Practical-Guide-Quantitative-Finance-Interviews/dp/1438236662), spend time learning all the mental math tricks, and be adept at programming.

> Mark Joshi's The Concepts and Practice of Mathematical Finance

Do you know about Luenberger's one? I was curious what people had to say about it.

https://www.amazon.com/Investment-Science-David-G-Luenberger/dp/0199740089/

Btw, not a quant or financial analyst or anything myself. I just study applied math and am curious about this stuff.

Bit out of date but: https://www.amazon.co.uk/Patterns-Derivatives-Pricing-Mathematics-Finance/dp/B01JQH7SIY

Luigi Ballabio's book about QuantLib is a bore but gives some insight into what's good and what's bad in the project.

Hopefully someone else has some HFT recs.

unrelated to its accuracy but if you were interested in just reading general quant stuff then you might like My Life as a Quant

there's a lot of math stuff that can go over your head but it's still an approachable read and more credible than some manhwa lol

OK, that sinks it. For 15 years I've resisted reading the book, but you've convinced me. I'll pick up a copy from Amazon and have a read. Thank you for convincing me!

Yeah -- dynamic replication is pretty much how everyone introduces the BS PDE. I've seen someone compile a list of .. I dunno .. 13? 11? 15? ways to derive BS, but this is the way most introductory texts approach the topic.

Hey. I'll trade you recommendations. I work in the RMBS / CMBS / ABS space. One of my favorite books is this one:

https://www.amazon.com/Liars-Poker-Norton-Paperback-Michael/dp/039333869X

I did my PhD in theoretical physics, so I'm pretty well acquainted with physics books. This book is the quant's analogy with Surely, You're Joking, Mister Feynman. Tremendously funny, and he slips in a HECK of a lot of very cool history from his point of view, and he was right in the middle of it. So much insight, and so funny. I don't usually read non-tech books, but this one is a gem!

Parts from Shreve's book and https://www.amazon.com/Stochastic-Financial-Applications-Modelling-Probability/dp/1441928626

​

I was familiar with measure theory and some measure theoretic prob.

It depends.

If you build a run-of-the-mill, over-fitted, and under-scrutinized trading bot, then no, that will do nothing to help your chances of getting such a job (it'd probably hurt your chances if it demonstrates your ignorance of the field). If you instead build something that actually resembles something a quant dev would work on, then it could help your chances. You'll know the difference when you can statistically prove that your application provides some sort of real value (i.e., following some YouTube tutorials and swing trading crypto isn't gonna cut it).

Odds are if you're asking this question on here, then you have some work to do before you should set out to build anything worth putting on a resume for quant dev positions. Quant devs don't really build "algotrading bots" in the same sense that you may be familiar with, so it's important to recognize what kind of job, exactly, you're looking for. It'd probably be worth reading a few books on the actual work that occurs in the industry so that you can know what to focus on.

Shreve's stochastic calc for finance I and II (Baby Shreve? Feels like the nomenclature should've caught on at some point...) texts were actually designed for an audience (MFE students) who aren't familiar with measure theory. I think they're excellent at what they do.

With that said, I'd imagine the probability theorists in the room went through Durrett/Billingsley/Resnick --> Oskendal/Bjork --> Shreve+Karatzas (this one) or similar.

[https://www.amazon.co.uk/Financial-Hacking-Derivatives-Structure-Intuition/dp/9814322555](Financial Hacking) is a good book that goes over writing basic simulations and how to think about different option structures. Its worth a read even if you won't be trading options

You can try graphing them online. The link below is a graphed that’s helped me visualize these functions well.

https://www.desmos.com/calculator

It is only a 2D grapher so you can only see the relationship between 2 variables at a time.

Not quite, it's a combination of markdown and my own bespoke HTML/CSS/JS. The site is built on Jekyll which will generate static HTML (according to your styling, layout, config options, etc) from common markdown but admits inline HTML. See https://jekyllrb.com/

Hi OP,

You may enjoy Derman’s book My Life as a Quant: Reflections on Physics and Finance https://www.amazon.com/dp/0470192739/ref=cm_sw_r_awdo_navT_a_ZX120NS2PS77ZQD5CAJ7

Also, his research notes from Goldman may be a very fun read as they are wonderfully written and insightful.

Here's the book my school uses for portfolio optimization. You could probably find copies on Libgen, if you want to check it out.

If you post their documentation I’ll look.

If you want to do trend following the first step is to read what other trend followers do or have done.

Have you read books like the turtle trader, Jegadeesh and Titman (1993) or Wes Gray’s book on momentum? quant momentum

What do you think of Harvard's CS50: Introduction to Computer Science?

>This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50 teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. As of Fall 2014, the on-campus version of CS50 was Harvard's largest course.

If it's the runtime that's tying up your computer, you could always launch a small Linux instance and have it run with a CRON job every day. Start at 5pm Eastern. By morning you can download your distilled-down data.

https://www.linode.com/

$5/mo - 1 CPU, 25GB Storage, 1GB RAM

$10/mo - 1 CPU, 50GB Storage, 2GB RAM

Clojure. Incanter is something to start with and Clojure has access to the whole java ecosystem. Also Racket has some nice solid ground to build things on it, but lacks libraries likes pandas, scikit....

This one is also good https://www.amazon.com/Fortunes-Formula-Scientific-Betting-Casinos/dp/0809045990/ref=mp_s_a_1_1?crid=2XK6EEZ8GNXUV&dchild=1&keywords=fortunes+formula&qid=1633340816&sprefix=fortunes&sr=8-1

The best way is to do a shit ton of problems. Why?

(1) You will start to see common patterns and tricks.

(2) Sometimes, identical problems will come up in interviews. Yes, they recycle problems.

Here's a decent book to get you started by a Millennium/Ex-Point72 quant portfolio manager: https://www.amazon.com/Practical-Guide-Quantitative-Finance-Interviews/dp/1438236662

I second this recommendation - it's a great treatment of the core topics. If you want to get more into the credit aspect, Moorad Chaudry's text is good too. https://www.amazon.com/Fixed-Income-Securities-Derivatives-Handbook-Valuation/dp/1576603342

oh, congrats, it took me till halfway thru my pure math phd to regain my freedom.

oh, you're saying you didn't have a community around to tell you the basics re how one actually gets those jobs.

you pry know now, but green zhou

https://www.amazon.com/Practical-Guide-Quantitative-Finance-Interviews/dp/1438236662/ref=sr_1_3?dchild=1&keywords=zhou+quant+interviews&qid=1603376475&sr=8-3

is good, Joshi's one ok as well.

though with your background it might be basic coding and data analysis and maybe some slight familiarity with finance that is more relevant.

you're actly in a pretty good position if you're able to get interviews, and seeing what they ask and what you don't know should be pretty straightforward learning signal which should allow you to get there pretty efficiently.

> I still want do a math PHD at some point
why?:)

I believe Grinold and Kahn have released an Advances in Active Portfolio Management within the last year or so that may be worth looking into.

https://www.amazon.com/Advances-Active-Portfolio-Management-Econometrics/dp/1260453715

It may also be interesting to check out Machine Learning for Asset Managers (Elements in Quantitative Finance) - new from Marcos Lopez de Prado, the author of Advances in Financial Machine Learning.

https://www.amazon.com/gp/product/B08461XP7R/

Congrats on grad school! I'm starting an finance masters program in the fall and am also interested in following this thread. They have me reading "A Random Walk Down Wall Street" as summer reading, which also may be interesting to you. I am also looking at the CFA exam curriculum, which you may find cool or useful. Good luck!

Thanks a lot. I did search for Stochastic Calculus on Coursera and eDx but nothing turned up. Where would you recommend learning about this?

Also, is this the Paul Wilmott book you're talking about? https://www.amazon.com/Mathematics-Financial-Derivatives-Student-Introduction/dp/B00AHTN44S/ref=la_B000AQ78LI_1_1?s=books&ie=UTF8&qid=1471902853&sr=1-1#nav-subnav

Thanks :-).

Edit: Article with several books on c++

not free but heres a book.

Wow I totally thought this subreddit was dead, glad to see another soul. Shreve's texts are kind of the de facto standard, aside from that it really depends on what specific areas you're looking for. I personally liked this one as a primer for time series. Have you read Basic Black Scholes by Timothy Falcon-Crack? It's perhaps not as formal as other academic works but I found it to be quite insightful.