Open Source in Quantitative Finance

Python for Quant Finance
Python for Quant Finance
Public group
Location image of event venue


Dear Python Quants,

we are going open! For the first time, there will be a meetup which does not only focus on Python but on a variety of Open Source technologies useful for and used in Quantitative Finance. Expect a series of shorter talks on the following topics:

• Dr. Malcolm Sherrington (Amis Consulting): "Interoperability of Python, Julia & R" -- The Julia programming language is renowned for being very easy to work with and fast. In this talk Dr Malcolm Sherrington discusses a feature which is less well known, how easy it is to utilise modules from other languages, including C, Fortran, C++, Java, R and Python, from within Julia. To show that the cooperation is not all one-way, Malcolm will indicate how Python can use Julia to overcome "two-language" problems.

• Antonio Roldao, PhD: "Scala in Finance" -- In recent years, Scala has gained significant traction in a number of Financial Institutions. This talk describes the Pros and Cons of this language for large Big Data systems applied to Finance.

• Carole Griffiths (Plotly): "Interactive D3.js Visualizations with Python & R" -- Using Plotly on a a central notebook engine and introducing the widget's.

• Yves Hilpisch ( "Open Source Deployment via the Browser" -- It is complex, costly, risky to deploy heterogeneous open source components across an organization. Web-based technologies allow for a central, unified deployment with end users only needing a (current) browser. Such a strategy facilitates introduction and maintenance of OS components for Quant Finance.

• Ian Huston (Pivotal): "Bring your Code to Your Data: Embedding Python (& R) in a Distributed Database" -- When working with large volumes of data it makes sense to minimise data movement by doing your analysis close to where your data is stored. I will show how to write Python (and R, Java, C etc) code that runs inside PostgreSQL compatible databases, including the distributed Pivotal Greenplum and HAWQ databases.

The beauty of Open Source technology these days is that there is hardly ever an "either/or" decision to be made -- a best of breed approach and technology blending strategies are in general straightforward to implement. In that sense, Quantitative Finance can tremendously benefit from the right use of Open Source.

Looking forward to another great meetup hosted by Thomson Reuters.



P.S. As announced in the last meetup, from now on RSVPs will only be valid if you provide your correct First Name and Last Name in your Meetup profile. This is due to security policies that are in place for most of our sponsors' venues.