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What is quant trading?
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What is quant trading?

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What is quant trading?

Does anyone know what quant trading is all about? Is this a new way to analyse the markets?

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From Invetopedia

Trading strategies based on quantitative analysis which rely on mathematical computations and number crunching to identify trading opportunities.

Read more: http://www.investopedia.com/terms/q/quantitative-trading.asp#ixzz20RC2WOeM

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"Successful trading is one long journey, not a destination" Peter Borish Former Head of Research for Paul Tudor Jones speaking on conversations with John F. Carter
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artemiso View Post
Quant trading involves using mostly mathematical and computational models to analyze and execute trades. (I'll just refer to these as 'trade execution models'.)

The video in GridKing's post covers "quants" in general - which describes a much wider group. To answer your question requires some background context: Most quants are employed in investment banks' back offices, and do not actually carry out any trading; they help with developing the risk management, valuation and sometimes, trading execution models, and programming them into user-friendly interfaces for the traders.

As such, I'd estimate 97% of the quantitative finance material is on valuation and quantitative risk management. Most of it is only useful if you are on the sell-side, e.g. in an exchange, investment bank etc. The techniques have been in existence for a long time, especially since the late 1970s when bonds started to become risky and volatile.

Out of curiosity, how did you get into that field? I've been dabbling here and there since my background is mathematics.

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Thanks for the explanation.

I was in the academic world for a few years and now work in finance services (yep another math guy in finance) and have been trying to learn more about the most interesting parts of my job, i.e. seeing the price time series and wondering about patterns, correlations, etc. I always prefer the mathematical perspective when I look at a time series instead of these traders that use their own built in pattern recognition .

My background is in applied math where I spent most of my graduate studies in signal processing/machine learning. I have my M.S. but no Ph.D yet (working on it part time since I need to pay the bills and not sure if I want to finish at this point). I also worked with a finance professional for awhile and helped him with programming items mainly. It was sort of the beginning of my foray into the finance world which lately I have been finding more and more interesting. Anyway, I've been looking into the field and was just wondering about an insider's perspective. I'll definitely check out those books/links so thanks for that.


Last edited by Antisyzygy; July 15th, 2012 at 12:44 AM. Reason: Added some stuff
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artemiso View Post
For someone with a mathematics background to learn quantitative trading methods, I recommend these books:

Thanks for your insightful posts Artemiso. Do you also happen to know which books on mathematics are helpful for retail traders who don't have a mathematics background but nonetheless still want to incorporate some more quantitative subjects in their trading? In other words, what mathematical concepts should we learn to better understand quantitative models?

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Quoting 
There is only one caveat: why are most quants from physics rather than math backgrounds? It so happens that as a finance practitioner, it is important to be able to get in a problem fast and leave it early. Physicists speciaize in this; we leave an plus-minus uncertainty to our calculation and stop there. Mathematicians, however, are often slow to get into a problem and always slow to get out of it.

Applied mathematicians say the same thing about the theoretical ones sometimes, and the theoretical math and many applied people say that physicists are too "loose" with their calculations. There is also some distinction based on what type of "mathematician" are you--Are you an algebraist or a analyst? Analysts would have the error bounds, etc. whereas algebraists would be focused on definite terms. Of course then there's hybrids of both and those silly topologists, but I digress.

Terence Tao said this "Algebra prizes structure, symmetry, and exact formulae; analysis prizes smoothness, stability, and estimates." Most theoretical mathematics majors would have more curriculum from the algebra side, though you can't really get through undergraduate school without taking some form of basic real analysis and algebra courses (like linear algebra). Most theoretical math curricula would include a serious helping of abstract algebra, whereas applied math would be more along the lines of numerical analysis which is all about error estimates. My thing has always been applied math because my interests are sort of a combination of electrical engineering and mathematics. I just stuck to math because I kept putting off EE courses to take something like Dynamics & Chaos which was offered less frequently. All of my core course work was in applied topics like PDE's for engineering, numerical analysis, etc.

Here is an amusing read about this : Random Observations: Analysis vs Algebra predicts eating corn?

Anyway, thanks for the information. Next pay check I'm picking up that first book.

As far as the PhD thing, its just getting to the point where I am sick of coursework, especially since I have to work full time. I'd rather learn things on my own and I am at the point where that is possible. I keep holding out on it because I am so close to finishing, but I'm still 28 years old and way behind some of my peers who went to work right out of undergrad. I have all the credits I need for my PhD except something called "readings" courses and dissertation credits. Of course I still need to take all the comprehensive examinations as well. I already have a pretty strong programming background but I have been wanting to take some time to really focus on developing more skills in that area, and graduate school right now seems to be getting in the way of that as the work load is quite a lot when one also works full time.


Last edited by Antisyzygy; July 23rd, 2012 at 12:17 PM.
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artemiso View Post
I think that it will be easiest to implement some ideas of the Kelly criterion (e.g. the "Kelly Capital Growth Investment Criterion" book by MacLean, Thorp and Ziemba). Arbitrage is one of the most important ideas; Kuznetsov's book (again, first on my list) is a fun and easy read to anyone that makes it clear early why there are arbitrage-free prices and how people have tried to get around it. It will not hurt to learn some MATLAB/R, and their file exchanges/webinars are rich resources on econometric methods and Monte Carlo. Patrick Burns has a paper (Random Portfolios for Evaluating Trading Strategies by Patrick Burns :: SSRN) on using random portfolios (i.e. Monte Carlo simulations) for evaluating strategies.

Thanks Artemiso, looking forward to read and learn them.

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FYI : Kuznetsov's book "The Complete Guide to Capital Markets for Quantitative Professionals" is great. The historical background of the markets and outline of what the major players do and how they make money at the beginning is worth the purchase of the book IMO. I haven't finished it yet but so far it's been very good to fill in gaps in my knowledge.

Thanks!

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