Algorithmic Trading with MATLAB for Financial Applications
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.
About the Presenter:Stuart Kozola是Mathworks的产品经理,专注于MATLAB®和用于计算融资的附加产品。下载188bet金宝搏在2006年加入Mathworks之前,Stuart在Pratt&Whitney(United Technologies)担任设计工程师,从事燃气轮机发动机燃烧系统的设计工程师。斯图尔特获得了学士学位怀俄明大学的化学工程学博士学位亚利桑那州立大学的化学工程学博士学位伦斯勒理工学院的电气工程学院和卡内基·梅隆大学的硕士学位。
记录:2012年5月8日
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