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9

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 ...


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): ...


3

It doesn't matter if you use *100 or just pct_change, as long as you are consistent. However, in practice, due to underlying floating point numerical instabilities in the underlying optimization algorithms/default tolerances used in scipy/arch, having the returns expressed in %, i.e. multiplied by 100, will have a better chance of converging during the ...


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 ...


1

Just checked in my python script for daily futures data from Interactive Brokers. Maybe it will be useful for you: https://github.com/busygin/ib_data_loader



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