International Journal on Science and Technology

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Volume 17 Issue 1
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Swing Trading Strategy using Simple Moving Average Crossovers, Traded Volume and Super Trend Confirmations Integrated in Machine Learning

Author(s) Kamal Narayan, Arup Kadia, Bidya Bharti, Prashansa Bharti
Country India
Abstract Different swing trading strategies emphasize making gains through measuring trend directions and patterns, utilizing technical indicators to achieve the greatest profits, and leveraging market trends. Therefore, swing trading is a highly appealing strategy for buyers and sellers, particularly for those who are financially very active. With regard to overcoming some of the challenges inherent in current rule- based swing trading strategies, such as delayed and false breakout risks, there is a vast opportunity for employing the Artificial Neural Network technique in conjunction with technical indicators making more reliable decisions and providing them more flexibility. The authors of the paper outline a method to use a neural network-based swing trading strategy where buy and sell decisions are based on different parameters, e.g. a simple moving average, volume, flow, as well as a super trend indicator confirmation strategy. Moving averages help fulfill swing trading goals by figuring out the prevailing trend, whereas volume helps make the trading more trust worthy. The super trend indicator also serves as a guide to picking up the right moment for buying and selling, which is especially helpful when you use the output values of the ANN model as swing trading inputs.
Keywords Traded Volume Analysis, Algorithmic Trading Strategy, Simple Moving Average Crossover, Swing Trading, Super Trend Indicator, Technical Analysis
Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-02-23
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