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Optimizing candlesticks patterns for Bitcoin's trading systems

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  • Gil Cohen

    (Western Galilee Academic College)

Abstract

In this research we make the first attempt to construct automated Bitcoin trading systems that are based on classical candlesticks patterns. We than tried to alter the classical formations for better trading results. Our data consists Bitcoins prices from the beginning of 2012 till the end of July 2020. WE found that that out of the tree classical candlesticks pattern only Engulfing has been fertile in predicting Bitcoin's price trends shifts. The classical Engulfing pattern generated Profit Factor (PF) of 3.54 and $38,349 Net Profit (NP). We also found that using a strength proxy of 0.9% may improve the classical pattern results. The classical Harami formation failed to produce positive trading results and therefore does not fit Bitcoin trading platforms. On the other hand, a reversed Harami was proven to be a fertile Bitcoin trading strategy. Our research also finds that classical four bars Kicker signals seldom appears and therefore cannot help Bitcoin traders. On the other hand, a reversed Kicker pattern has found to be a winning strategy with relatively low risk. This strategy fits particularly long positions with 74.36% Percent of Profitable (PP) trades and 6.92 PF. We also find that all the examined candlesticks patterns better predict long trends than short trends.

Suggested Citation

  • Gil Cohen, 2021. "Optimizing candlesticks patterns for Bitcoin's trading systems," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1155-1167, October.
    • Handle: RePEc:kap:rqfnac:v:57:y:2021:i:3:d:10.1007_s11156-021-00973-6
    DOI: 10.1007/s11156-021-00973-6
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gil Cohen, 2021. "Trading Cryptocurrencies Using Second Order Stochastic Dominance," Mathematics, MDPI, vol. 9(22), pages 1-10, November.
    2. Fonseca, Carla L.G. & de Resende, Charlene C. & Fernandes, Danilo H.C. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2021. "Is the choice of the candlestick dimension relevant in econophysics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    3. Kevin Rink, 2023. "The predictive ability of technical trading rules: an empirical analysis of developed and emerging equity markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 403-456, December.
    4. Gil Cohen, 2022. "Algorithmic Trading and Financial Forecasting Using Advanced Artificial Intelligence Methodologies," Mathematics, MDPI, vol. 10(18), pages 1-13, September.
    5. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.

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    More about this item

    Keywords

    Bitcoin; Algorithmic trading; Candlestick; Patterns;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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