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Support Resistance Levels towards Profitability in Intelligent Algorithmic Trading Models

Author

Listed:
  • Jireh Yi-Le Chan

    (Institute for Advanced Studies, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Seuk Wai Phoong

    (Department of Management, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Wai Khuen Cheng

    (Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia)

  • Yen-Lin Chen

    (Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei 106344, Taiwan)

Abstract

Past studies showed that more advanced model architectures and techniques are being developed for intelligent algorithm trading, but the input features of the models across these studies are very similar. This justifies the increasing need for new meaningful input features to better explain price movements. This study shows that the inclusion of Support Resistance input features engineered from the proposed novel methodology increased the machine learning model’s aggregate profitability performance by 65% across eight currency pairs when compared to an identical machine learning model without the Support Resistance input features. Moreover, the results also showed that the profitability distribution is statistically significantly different between two identical intelligent models with and without the Support Resistance input features, respectively. Therefore, the objective of this study is 3-fold: (1) to propose a novel methodology to automate meaningful Support Resistance price levels identification; (2) to propose a methodology to engineer Support Resistance features for Machine Learning Models to improve algorithmic trading profitability; (3) to provide empirical evidence towards the significant incremental contribution of Support Resistance (Psychological Price Levels) input features towards profitability in algorithmic trading models.

Suggested Citation

  • Jireh Yi-Le Chan & Seuk Wai Phoong & Wai Khuen Cheng & Yen-Lin Chen, 2022. "Support Resistance Levels towards Profitability in Intelligent Algorithmic Trading Models," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3888-:d:947666
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    References listed on IDEAS

    as
    1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    2. Ken Chung & Anthony Bellotti, 2021. "Evidence and Behaviour of Support and Resistance Levels in Financial Time Series," Papers 2101.07410, arXiv.org.
    3. Ibrahim Chowdhury & Lucio Sarno, 2004. "Time‐Varying Volatility in the Foreign Exchange Market: New Evidence on its Persistence and on Currency Spillovers," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 759-793, June.
    4. Jireh Yi-Le Chan & Steven Mun Hong Leow & Khean Thye Bea & Wai Khuen Cheng & Seuk Wai Phoong & Zeng-Wei Hong & Jim-Min Lin & Yen-Lin Chen, 2022. "A Correlation-Embedded Attention Module to Mitigate Multicollinearity: An Algorithmic Trading Application," Mathematics, MDPI, vol. 10(8), pages 1-13, April.
    5. Carol L. Osler, 2000. "Support for resistance: technical analysis and intraday exchange rates," Economic Policy Review, Federal Reserve Bank of New York, issue Jul, pages 53-68.
    6. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
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