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What is quant trading?


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What is quant trading?

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  #1 (permalink)
Market Wizard
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Does anyone know what quant trading is all about? Is this a new way to analyse the markets?

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  #3 (permalink)
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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: https://www.investopedia.com/terms/q/quantitative-trading.asp

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  #5 (permalink)
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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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  #6 (permalink)
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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.

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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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  #8 (permalink)
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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.

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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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  #10 (permalink)
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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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Jura - you're welcome.

Antisyzygy - Thanks for the link about corn, I enjoyed it. (I eat my corn vertically, while rotating the cob.) I'm glad you enjoyed the read. Kuznetsov (or in fact any other book) does not cover trading strategies, but it is the best introduction to the quant industry. I think it will help with your career decisions more than knowing Hamilton-Jacobi-Bellman approaches to trade execution or filtering of high-frequency data. There are lots of jobs for quants at your qualification level and you should definitely consider. I cannot offer as much as what's available on the Wilmott forums, and seriously recommend that you take a look over there. Seeing your conviction in learning more about this field, I've also sent you a PM with some other reference material with more practical details of quantitative trading strategies.

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artemiso View Post
I've also sent you a PM with some other reference material with more practical details of quantitative trading strategies.

Can you share that reading list with us? More reading suggestions are always helpful for this fascinating field.

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Coursera is offering a free online course starting in February which might be of interest to readers of this thread. The "Financial Engineering and Risk Management" course is probably not as applied as Artemiso's comments in this thread, but it might give a nice overview of quantitative subjects. I'm looking forward to follow it.


Quoting 
Financial Engineering and Risk Management

This course is an introduction to the theory and practice of financial engineering and risk management. We consider the pricing of derivatives, portfolio optimization and risk management and cast a critical eye on how these are used in practice.

(...)

Financial Engineering is a multidisciplinary field involving finance and economics, mathematics, statistics, engineering and computational methods. The emphasis of this course will be on the use of simple stochastic models and optimization for portfolio optimization, derivatives pricing and risk management.

Our examples will draw from many asset classes including equities, fixed income, credit, mortgage-backed securities and structured products. We will also consider the role that some of these asset classes played during the financial crisis. If time permits, we will also discuss other applications including real options, energy and commodities modeling, and algorithmic trading among others.

(...)

We plan to cover the following topics:
  • Introduction to derivative securities and option pricing.
  • The binomial model and martingale pricing.
  • Equity derivatives in practice.
  • Asset allocation and portfolio optimization.
  • The Capital Asset Pricing Model.
  • Statistical biases and portfolio selection.
  • Risk management I: VaR, CVaR and coherent risk measures.
  • Risk management II: scenario analysis and stress testing.
  • Term structure models and fixed income derivatives.
  • Mortgage mathematics and mortgage-backed securities.
  • Credit derivatives, structured products and the Gaussian copula model.
  • Other topics including real options, commodities and energy modeling, and algorithmic trading.

Source: https://www.coursera.org/course/fe

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Jura View Post
Coursera is offering a free online course starting in February which might be of interest to readers of this thread. The "Financial Engineering and Risk Management" course is probably not as applied as Artemiso's comments in this thread, but it might give a nice overview of quantitative subjects. I'm looking forward to follow it.


Source: https://www.coursera.org/course/fe

Thanks. That sounds interesting and does indeed cover the conventional MFE stuff.

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