Next Article in Journal
Participatory Guarantee Systems in Peru: Two Case Studies in Lima and Apurímac and the Role of Capacity Building in the Food Chain
Next Article in Special Issue
Enabling Effective Social Impact: Towards a Model for Impact Scaling Agreements
Previous Article in Journal
Effects of Tree Root Density on Soil Total Porosity and Non-Capillary Porosity Using a Ground-Penetrating Tree Radar Unit in Shanghai, China
Previous Article in Special Issue
Enhancing Bank Loyalty through Sustainable Banking Practices: The Mediating Effect of Corporate Image
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sustainability 2018, 10(12), 4641;

Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
Department of Business Administration, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 03722, Korea
Author to whom correspondence should be addressed.
Received: 27 October 2018 / Revised: 22 November 2018 / Accepted: 1 December 2018 / Published: 6 December 2018
(This article belongs to the Special Issue Sustainable Financial Markets)
Full-Text   |   PDF [2981 KB, uploaded 6 December 2018]   |  


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 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. Financial market investors are able to make more efficient investment strategies by using the PMTS. In this sense, the system developed in this paper contributes the efficiency of the financial markets and helps to achieve sustained economic growth. View Full-Text
Keywords: dynamic time warping; pattern matching trading system; time series data; sliding window dynamic time warping; pattern matching trading system; time series data; sliding window

Figure 1

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 (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top