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A machine learning approach for trading in financial markets using dynamic threshold breakout labeling

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Abstract

Researchers often use machine learning and deep learning to predict price trends in the financial markets, aiming to achieve high returns. However, accurately predicting market prices is challenging due to their nonlinear and seemingly random nature. Improving the accuracy of the prediction model is the common focus of researchers, yet it is crucial to also consider the data used in training. Traditional labeling methods used in most price trend prediction studies are not robust as they are sensitive to small price changes, leading to inefficient model training. To address this issue, this study introduces a Dynamic Threshold Breakout (DTB) labeling system that labels data based on the price percentage change during a specific period. This proposed labeling system was then integrated into an automated trading system using LightGBM and evaluated using three different markets. The results showed that the DTB labeling method is effective for trading in financial markets in terms of winning ratio, payoff ratio, profit factor, accuracy and ROI in trading performance.

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No datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

    Erfan Saberi, Jamshid Pirgazi & Ali Ghanbari sorkhi

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  1. Erfan Saberi
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  2. Jamshid Pirgazi
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Contributions

E. Saberi conceptualized and designed the study, collected and analyzed the data, and wrote the manuscript. J. Pirgazi assisted with data analysis and interpretation and provided critical revisions to the manuscript. A. Ghanbari contributed to the study design. All authors read and approved the final version of the manuscript.

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Correspondence to Jamshid Pirgazi.

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Saberi, E., Pirgazi, J. & Ghanbari sorkhi, A. A machine learning approach for trading in financial markets using dynamic threshold breakout labeling. J Supercomput 80, 25188–25221 (2024). https://doi.org/10.1007/s11227-024-06403-3

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