Smart Money Detection with Simple Moving Average and Traded Volume Confirmation Integrated in Machine Learning

Anshu Kumar; Golu Kumar; Md. Kaif Alam; ARUP KADIA

Smart Money Detection with Simple Moving Average and Traded Volume Confirmation Integrated in Machine Learning

Author(s)	Mr. Anshu Kumar, Mr. Golu Kumar, Mr. Md. Kaif Alam, Mr. ARUP KADIA
Country	India
Abstract	The identification of smart money activity represents a mainstay challenge for traders in financial markets. After all, various studies have shown that institutional moves usually come ahead of large price rises as well as trend reversals. This paper presents a smart money detection framework based on an Artificial Neural Network (ANN). This method has been developed to identify the movements of smart money through a combined framework of trend and volume analysis. Trend analysis is done by using the simple moving average (SMA), while the confirmation is done through volume changes in the market. In essence, this approach is a price-based trend signal from the SMA, which is then combined with a volume pattern indicating abnormal accumulation or distribution. First, the final signals get to a feed-forward ANN, which is a very powerful type of neural network that is able to recognize even very subtle, nonlinear interactions between price trends and market participation. Actually, this is a model that is able to sift through the noise, i.e. retail traders and focus on the smart money segment. The empirical analysis uses stock market data for a time period during which the market was changing from one condition to another. The model performance is measured by classification accuracy, precision, recall, and trading-related metrics. The result reveals that the merging of the ANN, enhanced SMA, and volume is by far more capable of detecting the smart money segment than the traditional heuristic methods. Thus, this method not only reduces the number of false signals but also increases the model's robustness over different market phases and provides an efficient solution for the identification of the intelligent market phases that can be scaled up. The solution thus designed is expected to be a major component of the core of highly sophisticated algorithmic trading systems and a great aid to the decision-making process.
Keywords	Artificial Neural Network; Smart Money Detection; Financial Time Series; Algorithmic Trading; Accumulation–Distribution Phase
Field	Computer > Automation / Robotics
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-02-13

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Smart Money Detection with Simple Moving Average and Traded Volume Confirmation Integrated in Machine Learning

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