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Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets

Author

Listed:
  • Ning Fu

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Mingu Kang

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Joongi Hong

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

  • Suntae Kim

    (Department of Software Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea)

Abstract

In the dynamic world of finance, the application of Artificial Intelligence (AI) in pair trading strategies is gaining significant interest among scholars. Current AI research largely concentrates on regression analyses of prices or spreads between paired assets for formulating trading strategies. However, AI models typically exhibit less precision in regression tasks compared to classification tasks, presenting a challenge in refining the accuracy of pair trading strategies. In pursuit of high-performance labels to elevate the precision of classification models, this study advanced the Triple Barrier Labeling Method for enhanced compatibility with pair trading strategies. This refinement enables the creation of diverse label sets, each tailored to distinct barrier configurations. Focusing on achieving maximal profit or minimizing the Maximum Drawdown (MDD), Genetic Algorithms (GAs) were employed for the optimization of these labels. After optimization, the labels were classified into two distinct types: High Risk and High Profit (HRHP) and Low Risk and Low Profit (LRLP). These labels then serve as the foundation for training machine learning models, which are designed to predict future trading activities in the cryptocurrency market. Our approach, employing cryptocurrency price data from 9 November 2017 to 31 August 2022 for training and 1 September 2022 to 1 December 2023 for testing, demonstrates a substantial improvement over traditional pair trading strategies. In particular, models trained with HRHP signals realized a 51.42% surge in profitability, while those trained with LRLP signals significantly mitigated risk, marked by a 73.24% reduction in the MDD. This innovative method marks a significant advancement in cryptocurrency pair trading strategies, offering traders a powerful and refined tool for optimizing their trading decisions.

Suggested Citation

  • Ning Fu & Mingu Kang & Joongi Hong & Suntae Kim, 2024. "Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets," Mathematics, MDPI, vol. 12(5), pages 1-21, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:780-:d:1352114
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    References listed on IDEAS

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    Cited by:

    1. Hamid Moradi-Kamali & Mohammad-Hossein Rajabi-Ghozlou & Mahdi Ghazavi & Ali Soltani & Amirreza Sattarzadeh & Reza Entezari-Maleki, 2025. "Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting," Papers 2502.14897, arXiv.org, revised Mar 2025.

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