Though all eyes are probably on the aftermath of Pycon (which, from all reports, was another great conference), Enthought was happy to sponsor the first New York Quantitative Python User Group Meetup (wow that’s a mouthful) on March 6th. If you are in the New York area, you can sign up for the group here.
The program for the evening featured Marcos Lopez de Prado and our own Kelsey Jordahl (with an assist from yours truly). The meetup focused on the topic of portfolio optimization and some of its foibles. Marcos conducted an in-depth discussion of portfolio optimization in general and outlined his open source implementation of the CLA algorithm. He also discussed why he is such a fan of Python.
Our contribution to the evening focused on the the theme “From Research to Application.” And by “research” we meant both research code (Marcos’ CLA code is one example) and actual investment research. Firms are wrestling with data and trying to marshal all the expertise within the organization to make decisions. Increasingly, software is being used to help synthesize this information. In our thought experiment, we imagined a hypothetical portfolio manager or strategist that is trying to integrate the quantitative and fundamental expertise within the firm. What kind of information would this PM want to see? How could we make the application visually appealing and intuitively interactive?
We chose to use the Black-Litterman model to tie some of these threads together. In a nutshell, Black-Litterman takes a Bayesian approach to portfolio optimization. It assumes that the capital allocations in the market are decent and reverses the classical optimization process to infer expected returns (rather than weights). It also allows modification of these expected returns to reflect analyst views on a particular asset. For those of you not familiar with this subject, you can find an accessible discussion of the approach in He and Litterman (1999). Using the Black-Litterman model as our organizing principle, we put together an application that provides context for historical returns, relative value, and pairwise asset correlations, all wired together to provide full interactivity.
Given the limited time we had to put this together, there are obviously things we would have changed and things we would have liked to include. Nevertheless, we think the demo is a good example of how one can use open source technology to not only take advantage of research code but also integrate quantitative models and fundamental research.
FYI, the libraries used in the app are: Numpy/Pandas, Scipy, Traits, Chaco, and Enaml.
Videos of the talks are below. Tell us what you think!