Walk-Forward Analysis: Using MATLAB to Backtest Your Trading Strategy
Many traders, fund managers, or investors may find that they run into limitation to backtest their trading ideas. Or the existing backtesting frameworks cannot be used to fully test their trading ideas. An increasing complexity in market data, trading strategies, and backtesting frameworks is a challenging issue. In this webinar, you will learn how MATLAB can support the prototyping and development of walk-forward analysis in order to backtest your trading ideas, starting from getting market data, implement trading strategy, testing framework, and performance analytics. You will see how MATLAB provides a single platform that allows the efficient solution of walk-forward analysis.
With MATLAB, you can efficiently explore, analyze, and visualize your data. Through this webinars, you will learn:
- The challenging issues on developing trading strategies
- Different types of backtesting framework
- Different types of optimization method that can be used for optimizing trading parameters
- The basic pair trading strategy based on Bollinger Band
- The calculation of technical indicators and performance metric
- The important of parallel computing for scalability
This webinar is for financial professionals, quantitative researchers and analysts, traders, and portfolio managers whose focus is quantitative analysis, trading strategy development, or equity research.
About the Presenter: Kawee Numpacharoen is a computational finance product manager at MathWorks. Prior to joining MathWorks, Kawee worked at Phatra Securities as a senior vice president in Equity and Derivatives Trading department. Kawee earned a B.S. in Electrical Engineering from King Mongkut’s Institute of Technology Ladkrabang, M.S. in Financial Engineering from University of Michigan, Ann Arbor, and a Ph.D. in Mathematics from Mahidol University.
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