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Identification of technical analysis patterns with smoothing splines for bitcoin prices

Author

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  • Nikolay Miller
  • Yiming Yang
  • Bruce Sun
  • Guoyi Zhang

Abstract

This research studies automatic price pattern search procedure for bitcoin cryptocurrency based on 1-min price data. To achieve this, search algorithm is proposed based on nonparametric regression method of smoothing splines. We investigate some well-known technical analysis patterns and construct algorithmic trading strategy to evaluate the effectiveness of the patterns. We found that method of smoothing splines for identifying the technical analysis patterns and that strategies based on certain technical analysis patterns yield returns that significantly exceed results of unconditional trading strategies.

Suggested Citation

  • Nikolay Miller & Yiming Yang & Bruce Sun & Guoyi Zhang, 2019. "Identification of technical analysis patterns with smoothing splines for bitcoin prices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(12), pages 2289-2297, September.
    • Handle: RePEc:taf:japsta:v:46:y:2019:i:12:p:2289-2297
    DOI: 10.1080/02664763.2019.1580251
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    Cited by:

    1. Artur Sokolovsky & Luca Arnaboldi, 2020. "A Generic Methodology for the Statistically Uniform & Comparable Evaluation of Automated Trading Platform Components," Papers 2009.09993, arXiv.org, revised Jun 2022.
    2. Sapkota, Niranjan & Grobys, Klaus, 2021. "Asset market equilibria in cryptocurrency markets: Evidence from a study of privacy and non-privacy coins," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    3. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    4. Grobys, Klaus & Junttila, Juha, 2021. "Speculation and lottery-like demand in cryptocurrency markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    5. Hakan Pabuccu & Serdar Ongan & Ayse Ongan, 2023. "Forecasting the movements of Bitcoin prices: an application of machine learning algorithms," Papers 2303.04642, arXiv.org.
    6. Svogun, Daniel & Bazán-Palomino, Walter, 2022. "Technical analysis in cryptocurrency markets: Do transaction costs and bubbles matter?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    7. Łęt Blanka & Sobański Konrad & Świder Wojciech & Włosik Katarzyna, 2022. "Is the cryptocurrency market efficient? Evidence from an analysis of fundamental factors for Bitcoin and Ethereum," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, vol. 58(4), pages 351-370, December.
    8. Nicolás Magner & Nicolás Hardy, 2022. "Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle," Mathematics, MDPI, vol. 10(13), pages 1-27, July.
    9. Deprez, Niek & Frömmel, Michael, 2024. "Are simple technical trading rules profitable in bitcoin markets?," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 858-874.
    10. Ahmed, Shaker & Grobys, Klaus & Sapkota, Niranjan, 2020. "Profitability of technical trading rules among cryptocurrencies with privacy function," Finance Research Letters, Elsevier, vol. 35(C).
    11. Grobys, Klaus & Ahmed, Shaker & Sapkota, Niranjan, 2020. "Technical trading rules in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 32(C).
    12. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.

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