IPython Notebooks for Statistics and Data Analysis for Financial Engineering

David Ruppert's book "Statistics and Data Analysis for Financial Engineering" is frequently used in graduate computational finance and financial engineering courses. If you are considering a career in finance as a quant, this would be a great book to go through.

Warning: this book assumes a high baseline knowledge of the underlying mathematics and finance and focuses more on applying this knowledge.

Unfortunately, the worked examples are all done in R. So as an exercise to solidify my own knowledge of the subject, I have begun creating python solution guide that include replications of every lab, most visualizations in the text, and select exercises. I will try to get at least one chapter out per week.

Here is the link to the github repository. I have just posted the the solution to the second chapter.

If you are going through this book and have any questions- don't hesitate to ask!

What Interested Me Today 05/26/16

Two journal papers interested me today. The first is "101 Formulaic Alphas" that was published some members of WorldQuant team. They explicitly list 101 "real-life trading alphas used in production-" that they have discovered through data mining.

Flipping through them, what jumps out at me is the ubiquity of cross-sectional rank.- maybe it turns out to be a good proxy for momentum and that's why it turns up all over the place.

Anyhow, I'm going to recreate these alphas, probably on Quantopian so anyone can play with them.

The prevalence of cross-sectional rank spurred some google searches, which led me to "Jumps in Cross-Sectional Rank and Expected Returns: a Mixture Model." The TL;DR for that is that they first modeled the likelihood of a jump in the next timestep and then modeled the conditional return distributions and used the two to create a nonlinear model of returns which was used in a trading rule with promising results. I'll have to come back to this one when I'm done with 101 alphas.