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10

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 ...


8

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 ...


8

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 ...


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) ...


6

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, although 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): ...


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

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 ...


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) ...


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

For the tasks listed, both Python and R preform very well. There are some packages in Python not in R and visa-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 ...



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