Dexy seems to offer an amazing solution to documentation that is Platform Independent. It’s a tool created by a Ana Nelson, an open source software developer, who happens to have a PhD in economics. Obviously a better output that star gazing and making predictions in economics , that anyway go wrong . Dexy’s USP in Ana’s words: There are many excellent tools for specific types of documentation, in particular many object-oriented languages have built-in tools for writing API reference documentation (e.

In the Mar 2012 issue of “Advanced Trading” magazine , I found these points worth noting down : Gone are the days when you could take a prime brokerage account from Goldman/big Prime brokerage and start your hedge fund. In Today’s world, launching a hedge fund is becoming harder than ever. You have to have the tech platform that talks to multiple primes , an ops team , at least two counter parties.

I like books that explain things visually and this book falls in that category. I am reading this book after reading and understanding the basics of Python from “Think Python” and “Learn Python the Hard Way”. This book serves a nice visual recap of Python 101. I have listed down some of the points in various chapters mainly to ruminate over the learning’s from the previous two books. Chapter 1- Getting Started

Prof. Hadley Wickham, the creator of ggplot2 and other useful packages like plyr, reshape etc. has one strong advice to R programmers – “Read other’s code”. This comes from a person who has developed 30 packages till date. We all have an immense urge to program, code up something, view the results, tweak our code to make it work etc. However pausing to read somebody else’s source code requires a certain amount of hard work, willingness to learn from others .

If there is a lot of data parsing and cleaning that needs to done before modeling, I tend to follow one of the three paths : Path 1: Use Python to clean the data, export the data structure in to a file/database. Leave Python environment and move in to R to do the modeling. Path 2 : Use Python to clean the data, Stay in Python environment and invoke R to do the modeling.