Programming for investment managers

Being able to comprehend and ideally write code has become an important skill in almost every industry over the past 30 years. This is especially true since the breakthrough success of personal computing and eventually the internet in the mid-1990s. More recently, cloud computing and Big Data have boosted companies' reliance on programmers and data analysts to a whole new level.

In the finance profession, for example, programming in a scripting language has become a common task for quantitative researchers, risk managers, asset managers and, yes, even traders. For data analysts, financial markets are a dream-come-true because market participants' trading activities produce a constant stream of information, most of which is instantly available in numeric and machine-readable form. The amount of data produced is so vast that analysing the data manually is practically impossible, hence requiring analysts to be able to code so computers can do the work for them.

Geeks (or "quants", as they were warily called by old-school traders in the 90s) have consequently become a hot commodity in the banking and hedge fund world, despite their involvement in recent market meltdowns, such as the subprime mortgage crisis and the Flash Crash of May 2010. These setbacks notwithstanding, it is undeniable that diligent statistical analysis can add significant value to investment management in the long run, as proven by the real money track record of hedge funds and commodity trading advisors (CTAs) such as AQR, Winton Capital, AHL, the lesser known Millburn Ridgefield or the legendary Renaissance Technologies, although the latter is arguably impossible for outsiders to look into and hence judge objectively based on actual performance data.

Considering the importance of statistics and data analysis for today's asset management profession, I decided to add a "programming" category to this blog. I'm going to write tutorials and code examples for certain programming tasks that you might encounter in the investment profession as well as educational posts about language fundamentals, focussing on MATLAB, Python and R, all of which are commonly used by quants throughout the hedge fund world.