C++ Programming for Financial Engineering
Highly recommended by thousands of MFE students. Covers essential C++ topics with applications to financial engineering. Learn more Join!

Python for Finance with Intro to Data Science
Gain practical understanding of Python to read, understand, and write professional Python code for your first day on the job. Learn more Join!

An Intuition-Based Options Primer for FE
Ideal for entry level positions interviews and graduate studies, specializing in options trading arbitrage and options valuation models. Learn more Join!

Reply to thread

Message: <blockquote data-quote="Ian Kaplan" data-source="post: 98851" data-attributes="member: 16310">Great answer, nkabir. Both insightful and well written.I am in the Masters program at the University of Washington, which is a hotbed of R programming. I've been thinking about the issue of R and deployment (not in high frequency trading, however, that's another issue).As you note, R is great for working out the algorithm, back testing and proving the concept (with nice graphics). But then what do you do when you want to deploy? If you write the deployed application in Java or C++ you may have to implement at least some of the math that is in the R libraries. In some cases this can be a daunting task, since some of the R libraries are written by people who pioneered the mathematical technique.I think that a good choice for deployment may be Python (if your application is not time critical in the sense that high frequency trading is). Next to R, Python seems to have the most extensive math libraries and there are quant libraries that are now available.Python can also be linked to Java (Python can be compiled into Java byte code) so in the (increasingly rare) case where there is functionality in Java that doesn't exist in Python, you can build that part of the application in Java.</blockquote>