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Algoritmic Trading System Based on State Model for Moving Average in a Binary-Temporal Representation

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  • Michał Dominik Stasiak

    (Department of Investment and Real Estate, Poznan University of Economics and Business, al. Niepodleglosci 10, 61-875 Poznan, Poland)

Abstract

One of the most basic methods of technical analysis that is used in the practice of investment is the analysis of moving averages, usually calculated for exchange rates in a candlestick representation. The following paper proposes a new, state model, describing the process of trajectory changes in a binary-temporal representation. This kind of representation carries a significantly higher informative value than the candlestick one. The model is based on a proper definition of the moving average, proposed for a binary-temporal representation. The new model allows for exchange rate trajectory prediction in a short future window and, as a consequence, can be used to construct effective HFT systems. The article provides a concept of this kind of system and its comparison with others based on historical data for AUD/NZD exchange rate from the 2014–2020 period.

Suggested Citation

  • Michał Dominik Stasiak, 2022. "Algoritmic Trading System Based on State Model for Moving Average in a Binary-Temporal Representation," Risks, MDPI, vol. 10(4), pages 1-15, March.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:4:p:69-:d:776397
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    References listed on IDEAS

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    1. Michał Dominik Stasiak, 2018. "Modelling of Currency Exchange Rates Using a Binary-Temporal Representation," Springer Proceedings in Business and Economics, in: Taufiq Choudhry & Jacek Mizerka (ed.), Contemporary Trends in Accounting, Finance and Financial Institutions, pages 97-110, Springer.
    2. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
    3. Michał Dominik Stasiak, 2020. "Candlestick—The Main Mistake of Economy Research in High Frequency Markets," IJFS, MDPI, vol. 8(4), pages 1-15, October.
    4. Bank of England, 2016. "Markets and operations," Bank of England Quarterly Bulletin, Bank of England, vol. 56(4), pages 212-221.
    5. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    6. Michał Dominik Stasiak, 2018. "A study on the influence of the discretisation unit on the effectiveness of modelling currency exchange rates using the binary-temporal representation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 28(2), pages 57-70.
    7. 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.
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    Cited by:

    1. Michał Dominik Stasiak & Żaneta Staszak, 2024. "Modelling and Forecasting Crude Oil Prices Using Trend Analysis in a Binary-Temporal Representation," Energies, MDPI, vol. 17(14), pages 1-13, July.
    2. Michał Dominik Stasiak & Żaneta Staszak & Marcin Stawarz, 2025. "Forecasting Crude Oil Prices Using the Binary RSI (bRSI) Indicator," Energies, MDPI, vol. 18(12), pages 1-14, June.
    3. Michał Dominik Stasiak & Żaneta Staszak & Joanna Siwek & Dawid Wojcieszak, 2025. "Application of State Models in a Binary–Temporal Representation for the Prediction and Modelling of Crude Oil Prices," Energies, MDPI, vol. 18(3), pages 1-14, February.

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