

Quantitative Finance Reading List - Programming - shogunmike
http://quantstart.com/articles/Quantitative_Finance_Reading_List_Part_2_-_Programming/

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tzs
Note that the submitted link is to part 2 of a three part (at least so far)
series of reading lists. That particular part seems pretty useless for the HN
crowd, as it is just a list of resources for learning C++. That's something
most HN readers could easily find themselves, on the off chance they don't
already have such resources already in the libraries.

I'd have though part 1 (reading list for financial and math background) or
part 3 (reading list for numerical methods) would have been much better
submissions.

The site is organized a little sub-optimally for navigation, as the parts do
not seem to link to each other. To find parts 1 and 2, click the "articles"
link on the left. That takes you to a list of all articles, but it is a small
list so the reading lists are easy to pick out.

~~~
shogunmike
Actually, in regard to the navigation, this is something I'm working on.
Another visitor had trouble with a finite difference method article I wrote
and so it is high time to make an "Article Series" banner.

In regards to the HN crowd, perhaps an article on how to use C++ for quant
models may be more appropriate? How many of us on HN are interested/practicing
financial analysts?

If you have any more suggestions (particularly about navigation improvement!)
I'd be keen to hear them. I want to make the site as useful as possible.

------
Nick_C
There's not anything there that is specific to quant work, it's just a reading
list for C++.

As an ex-quant, I very much agree with the comment made by Karan (on the
site). Our modelling was developed on statistical software such as SAS and
SPS. The actual production code was simply Powerbuilder or C++.

~~~
shogunmike
It is a similar situation to legacy Fortran code in the academic physics
community. Fortran is used because some guy wrote the core algorithm 30 years
ago in Fortran 77 and never wrote any docs about it. "If it ain't broke don't
fix it."

I don't agree with this mentality at all. In fact, I make use of Python almost
exclusively for the work I do (in a small fund). My NumPy/SciPy is not good
enough though to choose it over C++ when I need to perform some hefty number
crunching.

How did you find using SAS/SPS? Is R not a good candidate for what you're
doing?

~~~
Nick_C
It was over ten years ago and I just don't remember. The development modelling
was done by a couple of other guys.

I recall seeing R around, but I don't remember it being used for my markets,
which were bonds and currencies. The data sets were very large and took hours
to process (pre-Pentium 4); would that have anything to do with it?

~~~
shogunmike
R has certainly gained some traction in recent years, as people have added
more packages. It is interpreted so I'm not sure how this will affect speed. I
haven't had a chance to use it personally so wouldn't be able to comment.

I know a guy who has a dual processor Xeon machine, each with 4 cores, running
MatLab. Once the code was parallelised it zipped along.

