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18

I can only talk about quantitative trading. As a rule of thumb, the lower frequency you work in, the more econometrics is important, whereas for a higher frequency, the more econometrics becomes useless. (I would still recommend a top econometrician for HFT since they have what it takes to succeed, it's just the models aren't out-of-the-box applicable.) But ...


12

Mark Joshi briefly describes the roles of six types of quants in his advice for wannabe quants: Front office or desk quant Model validating quant Research quant Quant developer Statistical arbitrage quant Capital quant His classification agrees more or less with the taxonomy contained in the Wikipedia article. "For the past twenty years, ...


7

"Extreme programming" is a buzzword that has received a lot of hype in the past few years. However it's important to note that it's only one item in the long list of SW development philosophies and that it's not - contrary to its proponents' claims - a panacea. On the other side it's very beneficial to follow a few simple rules while writing even small ...


7

I have only seen one framework that works in a research oriented development environment which is the spiral model. Using try agile methodologies is impossible because the frontier of tasks is not known. Agile is very useful for building/maintaining known applications with known functionality and problem spaces. It is not useful for research oriented ...


6

A simple google search should get your started: I like this one the best because it compares different packages: http://stat-www.berkeley.edu/~brill/Stat248/kalmanfiltering.pdf and here couple more: http://www.r-bloggers.com/the-kalman-filter-for-financial-time-series/ http://cran.r-project.org/web/packages/dlm/index.html ...


6

@user2763361 has a very thorough list of useful econometric topics for quantitative finance. I would add missing, mixed frequency, and irregular data as major issues that I'm either constantly dealing with or begrudgingly ignoring. Seasonal adjustment is important too for some data (like electricity futures), though the subject is also related to his ...


4

There are some Agile benefits that you will reap, even if you are the sole programmer. You may feel silly doing a scrum by yourself in the morning. But you may find it to be a benefit to plan what you would like to work on that day, and to think about what you might need that day (especially if you need to read about solving a quant problem). Planning out ...


3

As an agile developer and quant finance programmer, I think that unit testing is invaluable. Because you really never know if your code is doing what it is supposed to do without tests. How do you know that your code is calculating your proprietary indicators correctly? You probably ran your new code and checked the result against some other code or system ...


2

A great example of kalman filtering is in the Kyle Model. I have attached a presentation on the application of R to the kalman filter in the Kyle Model. http://www.rinfinance.com/RinFinance2009/presentations/microstructure-tutorial.pdf Basically in the Kyle Model, a market maker finds the likelihood an asset is ending up at a certain price given that a ...


1

As an overview, Expected Returns, by Antti Ilmanen, was recommended to me. He has a preference for data over theory, so it will appeal to quants. The book is longish, and got a bit heavy at times, but he covers all the investment products and all styles of investing. The biggest problem might be that it is now 3 years old, and was heavily influenced by ...


1

An important part of research is reproducibility of results. There is no point of drawing conclusions, if you can't reproduce the data to back them up. For this you need at least an organized way of storing your code, so that you can find what was the algorithm you used to produce that graph. A source code repositore is an ideal way to do it. By the same ...



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