Identification and Evaluation of Profitable Technical Trading Rules in the Cryptocurrency Market: A Mixed Method Approach

Document Type : Research Paper

Authors

1 MSc. Student, Department of Industrial Engineering, Meybod University, Meybod, Iran.

2 Assistant Prof., Department of Industrial Engineering, Meybod University, Meybod, Iran.

10.22059/frj.2024.358549.1007462

Abstract

Objective
The purpose of this paper is to identify the most effective technical indicators in the cryptocurrency market, as viewed by market experts, optimize their performance using optimization algorithms, and ultimately compare the performance of the selected trading rules against each other and the buy-and-hold strategy.
 
Methods
In this research, technical trading rules are extracted and optimized for trading two cryptocurrencies, i.e. Bitcoin and Ethereum, using a mixed qualitative-quantitative method. The efficiency of the extracted rules is evaluated and compared with the buy-and-hold strategy. In the qualitative phase, the most important technical indicators are selected and ranked based on the cryptocurrency experts’ view using the fuzzy TOPSIS technique. In the quantitative phase, the selected trading rules are implemented for a certain period and the parameters of the indicators are optimized using the grid search and particle swarm optimization (PSO) algorithm. Finally, the performance of the trading strategies selected by the experts and optimized using metaheuristic algorithms is evaluated and compared.
 
Results
In the qualitative part of this study, 18 technical indicators were selected as candidate indicators in cryptocurrency trading, using the results of related studies. The selected indicators were ranked based on a survey of thirteen cryptocurrency market experts using the snowball sampling method. Finally, four technical indicators were chosen as superior indicators, showing a significant difference compared to the other technical indicators. The selected indicators were the exponential moving average, relative strength index, Ichimoku, and moving average convergence divergence. In the quantitative part of the research, the expert-based trading rules were implemented for the Bitcoin and Ethereum markets from 2018/06/30 to 2020/06/30. According to our numerical results, most of the expert-based trading strategies are more profitable than the buy-and-hold strategy. Then, the parameters of the expert-based trading rules were optimized using two metaheuristic algorithms, namely grid search and particle swarm optimization. The implementation results showed that the trading strategies optimized by these algorithms outperform both the expert-based and the buy-and-hold strategies.
 
Conclusion
Based on the experimental results of this research, exponential moving average, relative strength index, Ichimoku, and moving average convergence-divergence trading rules are commonly used and proposed by cryptocurrency market experts. Moreover, the profitability of the well-known technical trading rules could be significantly improved using the grid search and particle swarm optimization algorithms. Also, trading strategies using the combination of several technical indicators always perform better than trading strategies with a single indicator. However, all technical analysis strategies are not necessarily more profitable compared to the buy-and-hold strategy. Using inappropriate technical strategies may not only be better than the buy and hold strategy, but sometimes it causes losses in cryptocurrency trading.
 

Keywords

Main Subjects

References

 
Adcokh, R. & Gradojevic, N. (2019). Non-fundamental, non-parametric Bitcoin forecasting. Physica A: Statistical Mechanics and its Applications, 531, 121-127.
Afsharirad, E., Alavi, E & Sinaei, H (2018). Developing an Intelling Model to Predict Stock Trend Using the Technical Analysis. Financial Research Journal, 20(2), 249-264.
(in Persian)
Almudhaf, F. (2018). Pricing efficiency of Bitcoin trusts. Applied Economics Letters, 25(7), 504-508.
Al-Yahyaee, Kh., Mensi, W. & Yoon, S.M. (2018). Efficiency, multifractality, and the long-memory property of the Bitcoin market: A comparative analysis with stock, currency, and gold markets. Finance Research Letters, 228-234. DOI:10.1016/j.frl.2018.03.017
Atsalakis, G.S., Atsalaki, I.G., Pasiouras, F. & Zopounidis, C. (2019). Bitcoin price forecasting with neuro-fuzzy techniques. European Journal of Operational Research, 276(2), 770-780. https://doi.org/10.1016/j.ejor.2019.01.040
Azadi, Zh. & Saleh Olia, M. (2021). Development of Topsis model for dynamic ranking with time window approach. Industrial Management Journal, 37(1), 27-35. (in Persian)
Corbet, S., Eraslan, V., Lucey, B. & Sensoy, A. (2019). The effectiveness of technical trading rules in cryptocurrency markets. Finance Research Letters, 31, 32-37.
Day, M., Huang, P., Cheng, Y., Lin, Y. & Ni, Y. (2022). Profitable day trading Bitcoin futures following continuous bullish (bearish) candlesticks. Applied Economics Letters, 29 (10), 947-954.
Detzel, A., Liu, H., Strauss, J., Zhou, G. & Zhu, Y. (2021). Learning and Predictability via Technical Analysis: Evidence from Bitcoin and Stocks with Hard-to-Value Fundamentals. Financial management, 50, 107-137.
Gerritsen, D. F., Bouri, E., Ramezanifar, E. & Roubaud, D. (2020). The profitability of technical trading rules in the Bitcoin market. Finance Research Letters, 34, 101-263.
Gradojevic, N., Kukolj, D., Adcock, R. & Djakovic, D. (2023). Forecasting Bitcoin with technical analysis: A not-so-random forest. International Journal of Forecasting, 39 (1), 1-17.
Grobys, K. & Sapkota N. (2019). Cryptocurrencies and momentum. Economics Letters, 180, 6-10.
Heidari, M. & Saleh Olia, M. (2022). Inspecting the Predictive Power of Artificial Intelligence Model in Predicting the Stock Price Trend in Tehran Stock Exchange. Financial Research Journal, 24(4), 602-623. (in Persian)
Huang, J., Huang, W. & Ni, J. (2019). Predicting bitcoin returns using high-dimensional technical indicators. The Journal of Finance and Data Science, 5(3), 140-155.
Jackson, O. (2018). UK eyes AML rules for bitcoin regulation. International Financial Law Review, London (Jan 4, 2018).
Kani, A. (2005). Advanced technical analysis. Strategic employment research and training center. (in Persian)
Kim, S. W. (2021). Technical Trading Rules for Bitcoin Futures. Journal of Convergence for Information Technology, 11(5), 94-103.
Kristoufek, L. (2018). On Bitcoin markets (in) efficiency and its evolution. Physica A: Statistical Mechanics and its Applications, 503, 257-262.
Kristoufek, L. & Vosvrda, M. (2013). Measuring capital market efficiency: Global and local correlations structure, Physica A: Statistical Mechanics and its Applications, 392 (1), 184-193.
Lee, K., Ulkuatam, S., Beling, P. & Scherer, W. (2018). Generating synthetic Bitcoin transactions and predicting market price movement via inverse reinforcement learning and agent-based modelling. Journal of Artificial Societies and Social Simulation, 21(3), 5.
 Miller, N., Yang, Y., Sun, B. & Zhang, G. (2019). Identification of technical analysis patterns with smoothing splines for bitcoin prices. Journal of Applied Statistics, 46 (12), 2289-2297.
Mohebi, S., Fadaeinejad, M., Osoolian, M. & Hamidzadeh, M. (2022). Feature Selection for the Prediction Model of the Tehran Stock Exchange Index by Dimensionality Reduction Techniques. Financial Research Journal, 24(4), 577-601. (in Persian)
Moradi, B., Bahri Sales, J., Jabbarzadeh Kangarlooi, S. & Ashtab, A. (2022). Explaining and Proposing a Market Liquidity Prediction Model in Tehran Stock Exchange. Financial Research Journal, 24(1), 134-156. (in Persian)
Ortu, M., Uras, N., Conversano, C. Bartolucci, S. & Destefanis, G. (2022). On technical trading and social media indicators for cryptocurrency price classification through deep learning. Expert Systems with Applications, 198, 116804.
Resta, M., Pagnottoni, P. & De Giuli, M. E. (2020). Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall? Risks, 8(2), 44.
Sadeghi Moghadam, M., Alibakhshi, R. & Kalili, E. (2014). An Assessment of Selected Mutual Funds in Iran Stock Market Using a Combined Method of TOPSIS, VIKOR and Similarity-based Approach. Financial Research Journal, 17(2), 259-282. (in Persian)
Seif, S., Jamshidinavid, B., Ghanbari, M. & Esmaeilpour, M. (2021). Predicting Stock Market Trends of Iran Using Elliott Wave Oscillation and Relative Strength Index. Financial Research Journal, 23(1), 134-157. (in Persian)
Sensoy, A. (2019). The inefficiency of Bitcoin revisited a high-frequency analysis with alternative currencies. Finance Research Letters, 28, 68-73
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80-82.
Zhang, W., Wang, P., Li, X. & Shen, D. (2018). The inefficiency of cryptocurrency and its cross-correlation with Dow Jones Industrial Average. Physica A: Statistical Mechanics and its Applications, 510, 658-670.
Financial Research Journal
Volume 26, Issue 3
2024
Pages 509-530

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