Practical Approaches to Problems in the Financial Industry using Python

02:30 PM - 03:15 PM on August 17, 2014, Room 702

Andy Fundinger

Audience level:
Data Analysis


In this lecture we will show an applied case with Ipython notebook as a quant explores a typical financial problem with the help of various Python libraries and software engineering. The specific case is a small scale market risk platform using historical simulation to calculate value at risk.


In this lecture we'll work through a quantitative problem in general and specifically a market risk platform. We explore the proble and exhibit the set of tools and practices that Python have to offer to modelers and financial quants. Simultaneous and collaborative approaches to the same problem from a data science and engineering perspective will both be shown including analytic and simulation based risk calculations. Supporting exploration will be a basic data access layer retrieving data as needed from Quandl.