Perl for Equity Analysis?

[sohamdas]

Off late, I have grown really mathematical in my equity and financial analytic decisions. Being a trader, I am trying to develop risk management strategies,my own trading setups, testing them, measuring the analytics etc.

In light of this, does anybody have suggestions, if Perl is a good choice of language for such work. If so, then why do you think so? If no, what would you suggest? What are your experiences? What is your modus operandi? Basically, I am looking for some neat tool to run my own analysis, quick development etc.So what suggestions do you have for me?

Thanks :)

[gtani]

Whatever language you choose, you probably want to bea ble to access the linear alg and statistics/prob. libs mentioned here:

http://quantlib.org/index.shtml

[SwellJoe]

Perl is heavily used on Wall Street and among banks, so, I'd say the answer is yes.

I'm not familiar with the field, but I imagine CPAN has a chunk of functionality to start with. I also recall reading a couple of Perl Journal articles on the topic a while back.

[bavcyc]

As gtani said google the hooks into other programs. Also think about presenting your data in gnuplot, if you are writing the programs to crunch the data.

Perl is a fun language especially for text processing. As long as you have the memory and processor, Perl should work.

Is it the best choice? Depends on the person and how they program along with what preferences they have. If you have the algorithms in your head already, play around and put them into several different languages until you find the language that works best for you.

[hapless]

Bavcyc hit the core of this problem: The central item is YOU.

Are you, the programmer, familiar with perl? That's the question that really matters.

[sohamdas]

Thanks SwellJoes, Thanks gtani

But have you noticed that Perl doesnt really have a great charting/graphing or plotting library? So, I am a bit unsure of developing visualisation tools.

And secondly I would like to ask, will it really have a computation edge while handling MBs of data? Or rather put in this way can it scale?

[gtani]

Based on my experience, I think the important concerns: database drivers, bridges to linear algebra and stat/prob libs i mentioned, C extensions and java libraries.

I work in python and ruby where appropriate, and forgot all the perl i know, except when I maintain other people's code.

The database driver issue, is moot, all three languages have solid packages for mysql, SQLite or any non-obscure RDBMS and probably hook into couchDB, or mnesia, or any of the non-SQL databases.

The hooks to MATLAB, R, gsl or octave or linpack or whatever, not hard to google. Worst case, you pipe / tee flat files between apps (I'm assuming you're using linux or FreeBSD or solaris).

C extensions: Pretty straightforward in python or ruby. In face, ruby-inline, pyrex/cython make it about as straightforward as could be, assuming you know to look for memory leaks and clean up after yourself. And Jruby is produciton-ready, you can be pretty confident you can hook into whatever java libs you need.

I suggest you look at what Jane St Capital is writing about OCaml analytics.

[eru]

You can also have a look at R and K.