R language

[Ramanidis]

Hello, everyone!

I have a question concerning R. I have an opportunity to take R language as an extra class in my university. However, after I consulted my quant finance instructor, he told me, that it is somewhat a waste of time, since quants usually use C++. Nevertheless, I heard in "Alchemists of Wall Street" short-film, that it is in fact used. I would like to get your advice on this subject.

Thank you!

 

[Yike Lu]

It's used, although I venture to say that Python is taking over its place in many ways.

 

[Faisal]

It is used quite extensively for initial stage strategy development/ backtesting/ analytics. It is an easy language to learn and the data frames are particularly helpful for data analysis. However, it has obvious drawbacks in speed and amount of data it can crunch. The python package numpy is very similar to R in functionality with added advantages of speed.

 

[danielruc]

It is used quite extensively for initial stage strategy development/ backtesting/ analytics. It is an easy language to learn and the data frames are particularly helpful for data analysis. However, it has obvious drawbacks in speed and amount of data it can crunch. The python package numpy is very similar to R in functionality with added advantages of speed.

May I ask, apart from high frequency trading, why the speed and efficiency would be so important?

 

[Faisal]

May I ask, apart from high frequency trading, why the speed and efficiency would be so important?

Simply because you would probably want to spend as little time as possible on trivial stuffs as computing moving averages on historical data. R (particularly dataframes) involves a lot of multiple copying of data in memory, rendering the actual running time too slow. R is pretty good for running statistical tests on a sampled data but I would probably avoid it for backtesting large data.

 

[alain]

Simply because you would probably want to spend as little time as possible on trivial stuffs as computing moving averages on historical data. R (particularly dataframes) involves a lot of multiple copying of data in memory, rendering the actual running time too slow. R is pretty good for running statistical tests on a sampled data but I would probably avoid it for backtesting large data.

I don't know how much experience you have with R but there are plenty of examples of R in research and production systems. Check the R in finance conference:

http://www.rinfinance.com/

 

[Faisal]

I don't know how much experience you have with R but there are plenty of examples of R in research and production systems. Check the R in finance conference:

http://www.rinfinance.com/

Yes that is true. As you can see above, I have mentioned that R is used extensively for development. I have been using R for quite some time now professionally and is in fact my language of preference for research. However, for most large scale applications, I have to take recourse to add-ons like Revolution to crunch large data faster. I am sure there must be a better way, but I have particular issues using R as an interface to link to existing dll, for instance accessing Bloomberg data through Rbbg.