International Journal on Science and Technology

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Machine Learning-based Intraday Trading Strategy using Simple Moving Average Crossovers, Traded Volume and VWAP Confirmations

Author(s) Bidya Bharti, Prashansa Bharti, Kamal Narayan, Arup Kadia
Country India
Abstract Intraday trading involves very fast and precise decision- making under situations of sharp market volatility and noise. This paper introduces an Artificial Neural Network (ANN), based intraday trading strategy that first recognizes the Simple Moving Average (SMA) crossovers and then confirms traded volume and Volume Weighted Average Price (VWAP) to finally increase the strength of the signals dramatically. SMA crossovers are Moving Average crossovers have been adopted to identify changes in short-term trends, which are then validated by the use of trading volume to ascertain the level of market participation and momentum. VWAP serves as a price center of gravity indicator, which helps to eliminate the noise from false breakouts and raises the accuracy of entry and exit points. The ANN framework is capable of understanding complex nonlinear associations among the features, which are price, volume and those generated from VWAP, for the purpose of classifying intraday buy, sell, and hold signals. Its feasibility has been demonstrated on the intraday high-frequency data of the most actively traded stocks, and the criterion for the evaluation included cumulative return, Sharpe ratio, win rate, and maximum drawdown, among others. The test outcomes indicate the superiority of the ANN-based technique, which has been proven to yield higher risk-adjusted returns while also experiencing lower drawdowns, over the traditional SMA crossover as well as the buy, and, hold techniques. The result is that the combination of technical indicators and ANN, based pattern recognition can provide a very strong and flexible tool for intelligent intraday trading systems that operate in volatile financial markets.
Keywords Machine Learning, Algo trading, Artificial Neural Network (ANN), Simple Moving Average Crossover, Average Traded Volume
Field Engineering
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-02-23
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