2022•Indian Journal of Research in Capital MarketsRequires access

Intelligent Stock Trading Strategy Based on Aroon Indicator

Arjun Singh Saud, Subarna Shakya, Bindu Neupane

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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.

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What this paper is about

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.

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Available 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.

Key concepts: Trading strategy, Sharpe ratio, Algorithmic trading, Technical analysis, Stock trading, Alternative trading system, Pairs trade, Stock (firearms)

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