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Intelligent Stock Trading Strategy Based on Aroon Indicator


Affiliations
1 Assistant Professor, Central Department of Computer Science and IT, Tribhuvan University Kathmandu, Bagmati - 44613, Nepal
2 Professor, Department of Electronics and Computer Engineering, IOE, Tribhuvan University, Lalitpur, Bagmati - 44700, Nepal
3 Lecturer, Nagarjuna College of IT, Tribhuvan University, Lalitpur, Bagmati - 44700, Nepal

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The study of stock trading signal forecasting has piqued the interest of machine learning and technical analysis specialists. One of the popular tools for anticipating buy and sell signals is the Aroon indicator, but it is not utilized by machine learning researchers to predict stock trading signals. This study proposed an intelligent stock trading strategy based on the association between Aroon indicators. The performance of the proposed stock trading strategy was compared to that of a classical Aroon indicator based trading strategy in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of gain/loss trades. In terms of all three measures, it was discovered that the intelligent trading strategy outperformed the classical trading method. The intelligent trading method generated 3.91% to 40.07% greater ARR than the classical strategy, and it did so with a positive SR for all 10 stocks studied. In addition, the intelligent approach executed a higher percentage of profitable transactions than the traditional strategy. Thus, it was established that the proposed intelligent trading method is a better and safer trading technique than the classical strategy.



Keywords

Intelligent stock trading, Aroon indicator, trading signals, algorithmic stock trading.

Paper Submission Date : October 21, 2021 ; Paper sent back for Revision : February 9, 2022 ; Paper Acceptance Date : February 25, 2022.

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  • Intelligent Stock Trading Strategy Based on Aroon Indicator

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Authors

Arjun Sing Saud
Assistant Professor, Central Department of Computer Science and IT, Tribhuvan University Kathmandu, Bagmati - 44613, Nepal
Subarna Shakya
Professor, Department of Electronics and Computer Engineering, IOE, Tribhuvan University, Lalitpur, Bagmati - 44700, Nepal
Bindu Neupane
Lecturer, Nagarjuna College of IT, Tribhuvan University, Lalitpur, Bagmati - 44700, Nepal

Abstract


The study of stock trading signal forecasting has piqued the interest of machine learning and technical analysis specialists. One of the popular tools for anticipating buy and sell signals is the Aroon indicator, but it is not utilized by machine learning researchers to predict stock trading signals. This study proposed an intelligent stock trading strategy based on the association between Aroon indicators. The performance of the proposed stock trading strategy was compared to that of a classical Aroon indicator based trading strategy in terms of annual rate of return (ARR), Sharpe ratio (SR), and percentage of gain/loss trades. In terms of all three measures, it was discovered that the intelligent trading strategy outperformed the classical trading method. The intelligent trading method generated 3.91% to 40.07% greater ARR than the classical strategy, and it did so with a positive SR for all 10 stocks studied. In addition, the intelligent approach executed a higher percentage of profitable transactions than the traditional strategy. Thus, it was established that the proposed intelligent trading method is a better and safer trading technique than the classical strategy.



Keywords


Intelligent stock trading, Aroon indicator, trading signals, algorithmic stock trading.

Paper Submission Date : October 21, 2021 ; Paper sent back for Revision : February 9, 2022 ; Paper Acceptance Date : February 25, 2022.




DOI: https://doi.org/10.17010/ijrcm%2F2022%2Fv9i1%2F170401