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Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm

Author

Listed:
  • Sang Hyuk Kim

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 03722, Korea)

  • Han Jun Ko

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Seung Hwan Jeong

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Hyun Woo Byun

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Kyong Joo Oh

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

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 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.

Suggested Citation

  • Sang Hyuk Kim & Hee Soo Lee & Han Jun Ko & Seung Hwan Jeong & Hyun Woo Byun & Kyong Joo Oh, 2018. "Pattern Matching Trading System Based on the Dynamic Time Warping Algorithm," Sustainability, MDPI, vol. 10(12), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4641-:d:188468
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    References listed on IDEAS

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    Cited by:

    1. Seung Hwan Jeong & Hee Soo Lee & Hyun Nam & Kyong Joo Oh, 2021. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    2. Dev Shah & Haruna Isah & Farhana Zulkernine, 2019. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques," IJFS, MDPI, vol. 7(2), pages 1-22, May.
    3. Han, Tian & Peng, Qinke & Zhu, Zhibo & Shen, Yiqing & Huang, Huijun & Abid, Nahiyoon Nabeel, 2020. "A pattern representation of stock time series based on DTW," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

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