Quantitative Finance Reading List - Programming | Hacker News Reader

Quantitative Finance Reading List - Programming

quantstart.com

4 pointsshogunmike7 comments

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.

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.

I've actually added a "Post Series" box now on each of the Reading List pages. Hopefully this will improve nav somewhat.

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++.

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?

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?

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.

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