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1

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 ...


2

To identify the number of AR and MA terms you still need to look at the ACF and PACF. To identify the orders of differencing, the easiest way is run an ARIMA model on the data with different orders of differencing (0,1,2) and with only a constant (no AR or MA term). Look at the standard deviation of these models, as well as the ACF plot - the optimal model ...


1

http://www.forbes.com/sites/louiscolumbus/2015/01/24/roundup-of-cloud-computing-forecasts-and-market-estimates-2015/ Does this give you a place to start?


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 ...


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): ...


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 ...


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 ...


1

Try the Avellaneda (2009) paper. The strategy involves some mean-reverting models and some PCAs. Easy to read without getting too technical.


0

I suggest you to start reading E. P. Chan's books; here below you can find the references: Chan, Ernest P. "Quantitative Trading." New Jersey (2008). Chan, Ernest P. "Algorithmic Trading: Winning Strategies and Their Rationale" New Jersey (2013). His books are written down in a readable and simple way, so that a newbie can understand too, and, he ...


1

Start with http://en.wikipedia.org/wiki/Statistical_arbitrage and the references therein. Especially Avellaneda (technical) and Bookstaber (historical, how Bamberger and Thorp got the whole thing started).



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