Kim, S.H.; Lee, H.S.; Ko, H.J.; Jeong, S.H.; Byun, H.W.; Oh, K.J. Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm. Sustainability2018, 10, 4641.
Kim, S.H.; Lee, H.S.; Ko, H.J.; Jeong, S.H.; Byun, H.W.; Oh, K.J. Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm. Sustainability 2018, 10, 4641.
Kim, S.H.; Lee, H.S.; Ko, H.J.; Jeong, S.H.; Byun, H.W.; Oh, K.J. Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm. Sustainability2018, 10, 4641.
Kim, S.H.; Lee, H.S.; Ko, H.J.; Jeong, S.H.; Byun, H.W.; Oh, K.J. Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm. Sustainability 2018, 10, 4641.
Abstract
The futures market plays a significant role in hedging and speculating by investors. Although various models and instruments are developed for real-time trading, it is difficult to realize profit by processing and trading a vast amount of real-time data. This study proposes a real-time index futures trading strategy that uses the pattern of KOSPI 200 index futures time series data. We construct a pattern matching trading system (PMTS) based on a dynamic time warping algorithm that recognizes patterns of market data movement in the morning and determines the afternoon's clearing strategy. We adopt 13 and 27 representative patterns and conduct simulations with various ranges of parameters to find optimal ones. Our experimental results show that the PMTS provides stable and effective trading strategies with relatively low trading frequencies. Investor communities that have sustained financial markets are able to make more efficient investments by using the PMTS. In this sense, the system developed in this paper is a sustainable investment technique and helps financial markets achieve efficient sustainability.
Keywords
dynamic time warping; pattern matching trading system; time series data; sliding window
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
