# Tag Info

25

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 ...

17

My deal is HFT so what I care about is read/load data from file or DB quickly in memory perform very efficient data-munging operations (group,transform) visualize easily the data I think is is pretty clear that 3. goes to R, graphics and ggplot2 and others allow you to plot anything from scratch with little effort. About 1. and 2. I am amazed reading ...

14

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, ...

13

I've used both R and Python with Pandas in a professional quantitative financial work to do both large and small scale projects. I would strongly recommend Python with Pandas over R for most new projects in the field especially in time series analysis. While I don't dispute vonjd in that you will find more libraries in R with algorithms on the bleeding ...

13

This is interesting because I see another trend: Matlab is being replaced by R, but I guess this is another story. I use R for my academic (I am also teaching this stuff) as well as my consulting work (I am mainly working in the $\mathbb{P}$ area, with some excursions into $\mathbb{Q}$). I tried Python but it didn't work for me. I think the main reasons I ...

11

Instead of wild guesses about R's/python's future in the community, here some facts: The following query on StackExchange Data Explorer counts the number of questions that have <r> or <python> tags. If you scroll down on one of the three webpages provided below, you can see a graph with data on a monthly basis. You can easily run this query on ...

9

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 ...

8

For data analysis, particularly for large data analysis project, pretty much most of the top quant hedge funds and a lot of the banks are using Python (over R) for a couple of reasons but many still have bits and pieces of R for specific packages or functions (I work at a bank and interface with quite a few quant hedge funds on data analysis): Earlier ...

8

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 ...

8

we used fpml all the time in morgan. Of course its not surprising given their involvment in it.

7

@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 ...

7

A Quant is someone who develops mathematical models for financial markets.

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 ...

6

Your questions is unclear but I guess you mean that for the return of stock A you find a model $$r_A = (0.5, 0.75) (r_F^1, r_F^2) + \epsilon_A$$ where $r_F^i$ are the factor returns and $\epsilon_A$ is an uncorrelated error. Let us denote $e_A = (0.5, 0.75)$, the exposure of stock $A$ to the factors. For $B$ you have  r_B = (0.75, 0.5) (r_F^1, r_F^2) ...

5

Quite common - such products as Calypso and Murex use their own derivations of FpML: CalypsoMl and MxML respectively.

5

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 ...

4

MF is linked with physics mostly because it solves the same PDEs (Black-Scholes equation is a certain type of Schrödinger equation for instance). As for the specific links you mentioned : Lie Algebra : Magnus expansion (to build fast approximation of time dependent ODEs like those arising in credit risk) Differential geometry : link with Varadhan ...

4

It is very hard to answer this quiz as people might be good at different at tools. For example, if you are good at VBA, then you can achieve the same effect compared to R in most cases. The following parts are the reasons why I prefer to R based on my own situation. 'package'. This is the most obvious strength of R over Excel in terms of convenience. You ...

4

For the tasks listed, both Python and R perform very well. There are some packages in Python not in R and vice versa. My solution for this is to simply call R from Python. This allows for the best of both worlds. It is also important to note I do not write any R code other than calling an R library from Python. Calling Python from R does not work equally ...

4

People get this problem wrong because they always end up discussing the theoretical advantages of these languages rather than the practical uses of these languages. Theoretically speaking: Haskell is elegant and has many of the theoretical advantages (language conciseness, orthogonality, parametric polymorphism, ADTs, higher-order functions, smart ...

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 ...

3

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 ...

3

You're going to get a pretty broad range of answers with this kind of question, but I'll throw in my two cents. I'm not going to answer your question about the "next big thing" in programming languages, because that's just an opinion survey. Instead, I'm going to describe to you the characteristics of a few popular (and mature and well-supported/documented) ...

3

Some advantages of R over Excel: R is a scripting language, which allows to record a data manipulation script once and reuse it multiple times. R, as a [scripting] programming language is much more flexible than very limited Excel's GUI. In fact, R has become a de facto statistical programming environment, which delivers most recent statistical techniques. ...

3

The right amount of confidence and courage to take risks with other people's money without shading into overconfidence and bad judgement. Especially coping with the emotional pressure of losses without losing your head and doing the wrong thing. It also helps to do mental arithmetic quickly and accurately and have a good short term memory for figures, all in ...

2

Whether to store L1 (trades/BBO only), OHLC and order book depends on your downstream application. I encourage you to start with L1 (easy to store) and then think about what to do as your use cases evolve. If your trading strategy only uses trade prices, then you are fine with L1. And OHLC can be backed out from L1. It is very tempting to store more data ...

2

The factors are the same for both stocks, so there is just one factor covariance matrix for both A and B. Factor models are a way to reduce the dimension of a problem. If every stock had its own set of factors, this would increase the problem dimension.

2

The major advantage of Python (w/ pandas) over R is that Python supports OOP (object-oriented programming). It makes sense to organize a large code base using a hierarchy of classes. Python also supports the notion of polymorphism so that we can use well-known design patterns (e.g., Strategy, Observer, etc.) in our code.

1

Here is a very good online library for econometrics ebooks: http://www.uebook.net/economics/econometrics

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