***Quant is not a Coder as.. Pilot is not a Captain ...

The key differences between qual and quant and coder ..

Pilot is not the Captain as Coder is not a Quant ...

Engineers are hired to create business value, not to program things: Businesses do things for irrational and political reasons all the time (see below), but in the main they converge on doing things which increase revenue or reduce costs. Status in well-run businesses generally is awarded to people who successfully take credit for doing one of these things. (That can, but does not necessarily, entail actually doing them.) The person who has decided to bring on one more Quant is not doing it because they love having a geek around the room, they are doing it because adding the geek allows them to complete a project (or projects) which will add revenue or decrease costs or find the EDGE . Producing beautiful software is not a goal. Solving complex technical problems is not a goal. Writing bug-free code is not a goal. Using sexy programming languages is not a goal. Add revenue. Reduce costs. Those are your only goals.

Quant research is based on measurements, an assumption that phenomena can be discovered, and predictions made by using the differences between people. 

Qual research is based on understanding what is happening within cases and using that understanding to make generalizable findings. The researcher is creating (not discovering) an explanation. The focus of the research is more about producing useful insight, rather than a ‘correct’ representation.

In theory, different quant researchers who use the same algorithms on the same data should produce the same results. In theory, qualitative researchers who have different backgrounds, different life experiences, working on the same data might produce different stories.

Do the differences matter?

Quite often when the topic of methodology crops up somebody will say that ‘clients don’t care about methodology’ – implying that the topic is not important for discussion. Whilst it is clearly the case that many clients are not particularly interested in methodology, it is important for somebody to be interested. This is because clients are interested in the end result, the impact on the business, and the results are impacted by the method. This is like the difference to the taxi customer about the type of fuel used. The customer is not, generally, interested in the type of fuel, but we want the driver to be aware of the difference so that they fill it with the right type of fuel and we want whoever is in charge of servicing the car to use the right schedule and the right tools.

In terms of the difference between qual and quant the key issue is that the researcher knows the power and limitation of the technique they are using and should be aware of the benefits and drawbacks of the main alternatives. In my experience it is not uncommon for quant researchers to offer quant research in ignorance of the epistemological problems of quant and being equally unaware of the benefits of qual (and vice versa).

AI and the end of Quant Research?

Artificial Intelligence (AI) could spell the end of the dichotomy of qual and quant. One interesting AI development is the creation of software that can interpret material in an intuitive way. This opens the door to conducting qualitative data at scale with AI providing large-scale, intuitive, consistent research.

At that point we might we see an end to the need to have quant research. Quant research, as discussed above, needs to trade-off a large amount of sensitivity and the ability to deal with within case information in order to produce measurements. With large-scale AI, the current simplifications of quant research may be redundant. We might see a world where AI intelligently selects information, engages in discourses, and constructs experiments with the need to structured data and without any explicitly process of operationalizing.

However, although the advocates of AI often claim we are on the verge of creating machines that will interpret qualitative information in an intuitive way, my feeling is that we are still a few years away from this.

The most common route into quantitative development is via an academic background in scientific computing. This is because the core skills necessary for a "quant dev" are advanced programming skills and numerical algorithm implementation. These skills are developed as a matter of course within a grad school research environment for the physical sciences or engineering. If this is your background then your task will be to get to grips with the specific products and numerical algorithms used within quantitative finance, as your general implementation and programming skills are likely to be sufficiently developed.

However, if your background is not in scientific computing, there are still plenty of opportunities to become a quantitative developer leveraging a background in programming. At the very least though you will need to be familiar with implementing algorithms, the practice of which I will discuss below.

Programming Skills

First and foremost a quantitative developer IS a software developer. Thus the role will almost exclusively be 100% programming based. You will find yourself optimizing trading prototypes or developing trading infrastructure from scratch. If you're targeting bank roles, then you will likely need to be using C++, Java or C# in a Microsoft/Windows environment. If you are targeting hedge funds then you will likely be translating MatLab or R into C++ and/or Python. Funds tend to use Java and C# less, since they're often in a UNIX environment where C++ and Python make more sense. If you have a background in either of these programming environments, it makes sense to develop your strengths and stick with software you know well. Thus if you know Java, for instance, it would be wise to target investment banking roles. I've written an article on programming languages for quant developer roles if you want more detail.

If you are applying for C++ jobs directly, you will probably want to go beyond these two works. Scott Meyers has also written More Effective C++ and Effective STL. You will then need to consider the Boost library, multithreaded programming and Linux operating system fundamentals to become a true expert.

For those who are definitely keen on the quantitative trading side of the industry, it will be necessary to learn how to carry out data analysis within Python. This is a skill often picked-up while in grad school, but Python for Data Analysis.

After following the above plan you should have a good chance at any C++ or Python interview. However, in order to solidify your developer skills it is necessary to be aware of some of the recent innovations in software engineering, which only tend to be figured out "on the job", but can certainly be studied and practiced at home in your spare time.

Since a quantitative developer works in the financial markets, it is useful to have a relatively good understanding of the products that banks produce or the instruments that funds will be trading. Thus it will be necessary to familiarise yourself (broadly) with the equities, forex, fixed income, commodities and related derivatives markets. In particular you want to be continually thinking about how this data is represented, stored and accessed as a big part of a quant dev's job is to provide storage and access to financial data. Once in the job you will almost certainly concentrate on one particular area in depth, so make sure your initial research is quite broad.

Of more relevance are the algorithms used in quantitative finance to carry out both instrument pricing and algorithmic trading. The investment bank derivatives pricing techniques will almost certainly concentrate on Monte Carlo Methods and Finite Difference Methods, both of which rely on knowledge of probability, statistics, numerical analysis and partial differential equations. These are all topics which a good student will be familiar with in grad school, but for those considering a career change, you will need to gain a good understanding of these methods if you wish to become an options pricing quant developer in a bank.

For hedge funds, you will likely be implementing trading infrastructure - either low or high frequency. This will involve taking an algorithm already coded up in MatLab, R or Python (or even C++) and then optimising it in a faster language, such as C++, as well as hooking up this algorithm to a prime brokerage application programming interface (API) and executing trades. The skills required here are quite disparate. You will need to be able to pull together data from various sources, put it into the correct context, iterate over it rapidly and then generate on-demand reports either in fixed-format (PDF), over the web or as an API itself. These skills are hard to learn from books directly and require a few years of software development experience in the technology industry.

Questions before I hire a quant /qual/coder .. or a trader ..

1.       Tell me something about yourself that is not listed on your resume.

2.       What was is your knowledge in trading? / What was your achievements as trader?

3.       Do you think that a quant need to know to trade?

4.       Have you ever trade your own live account? (size/if)

5.       What is the riskiest thing you’ve ever done?

6.       Are you more risk-averse or risk-seeking? Give me an example.

7.       What is your greatest weakness?

8.       How do you feel about waking up early or staying up late?

9.       What are your interests outside the office?

10.     Why should we hire you?

11.     What is your favorite trading style that you know?

12.     Who has personally influenced you the most? Name someone whom you’ve never met who’s been a strong influence.

13.     Tell me about your biggest failure or a period of adversity.

14.     Discuss a difficult ethical decision you recently faced.

15.     What’s the last book you read?

16.     What you don’t like to do in your profession?

17.     How many hours you are usually working a day / week /month?

Thanks

AI