Automated Trading with MATLAB

In this webinar we will present an example workflow for researching, implementing and testing an automated trading strategy. You will learn how MATLAB^® and add-on products can be used for 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.

As of R2021a, Trading Toolbox has been merged into Datafeed Toolbox. As a part of this merge, a subset of the functionality is moving to File Exchange. Find more details in the release notes.

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 for trade execution
• 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 example code from this webinar.

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.