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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 ...


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 ...


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 ...


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) ...


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

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 ...


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

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.


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.


1

I suppose it will be difficult to provide a precise response as it is a fairly vague question and the reality is quite diverse. From my personal experience, the Quant I used to work with are using techno as R, Matlab combined with Visual Basic. Regarding more sophisticated tool coded in Java or C#, they are most of the cases inhouse frameworks. So the only ...


1

Strata seems like a fairly well designed library, which is an open source library designed by OpenGamma. From their docs Strata allows financial systems developers to build or enhance existing applications with standardized, off-the-shelf market risk components. It provides all the core concepts and market risk functionality at the heart of the ...


1

You can do that with the blotter package. We use it to reconcile our trades. It's only available on R-Forge, so see this stackoverflow question for how to install it. Run the "amzn_test" demo for an example of how to use it: library(blotter) demo(amzn_test)


1

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



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