Trading Strategies based on Pattern Recognition in Stock Futures Market using Dynamic Time Warping Algorithm

Traditional technical analysis is forecasting the up and down trends in the stock market. However, it is difficult to apply technical analysis directly because it relies on human experience to select optimal strategies for individual stocks. Thus, a stock market trading system has been developed to apply optimal investment strategies by many traders given various market situations. Many pattern recognition methodologies have been used to develop successful strategies in this trading system, and the various strategies were generated using an expanded technical analysis. The aim of this study is to construct a pattern-recognition-based trading system (PRTS) for providing suitable investment strategies in the stock futures market. The PRTS uses a dynamic time warping (DTW) algorithm to recognize various market patterns and provides investment strategies using diverse technical indicators for the market. Through empirical studies, we show that DTW yields an efficient PRTS for various market conditions.

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