Using Volume Weighted Support Vector Machines with walk forward testing and feature selection for the purpose of creating stock trading strategy

This study aims to verify whether modified Support Vector Machine classifier can be successfully applied for the purpose of forecasting short-term trends on the stock market. As the input, several technical indicators and statistical measures are selected. In order to conduct appropriate verification dedicated system with the ability to proceed walk-forward testing was designed and developed. In conjunction with modified SVM classifier, we use Fishers method for feature selection. The outcome shows that using the example weighting combined with feature selection significantly improves sample trading strategy results in terms of the overall rate of return, as well as maximum drawdown during a trading period.