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Algorithmic Trading with MATLAB for Financial Applications

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Stuart Kozola, MathWorks

Learn how MATLAB can support the prototyping and development of algorithmic trading in your organization.

Algorithmic trading is a complex and multi-dimensional problem; there are a large number of different challenges that need to be addressed and solved. At its heart one needs to be able to develop, build and test a robust trading algorithm, but this process requires one to solve a range of surrounding issues including data gathering, preparation and visualization, model development, backtesting, calibration, integration with existing systems and ultimately deployment. We look at each of the parts in this process and see how MATLAB provides a single platform that allows the efficient solution of all parts of this problem.

Specific topics include:

  •  Data gathering options, including daily historic, intraday, and real-time data
  •  Model building and prototyping in MATLAB
  •  Backtesting and calibrating a model
  •  Interacting with existing libraries and software
  •  Deployment of the final application in a number of environments, including .NET, JAVA, and Excel
  •  Tools for high frequency trading, including parallel computing, GPUs, and C code generation from MATLAB

View MATLAB example code from this webinar here.

About the Presenter: Stuart Kozola is a product manager at MathWorks and focuses on MATLAB® and add-on products for computational finance. Prior to joining MathWorks in 2006, Stuart worked at Pratt & Whitney (United Technologies) as a design engineer working on combustion systems for gas turbine engines. Stuart earned a B.S. in Chemical Engineering from the University of Wyoming, M.S. in Chemical Engineering from Arizona State University, M.S. in Electrical Engineering from Rensselaer Polytechnic Institute, and an M.B.A. from Carnegie Mellon University.

Product Focus

  • Parallel Computing Toolbox

Recorded: 8 May 2012