Machine Learning–Driven Stock Price Breakout Identification with Simple Moving Average and Traded Volume Confirmation

Precisely locating stock price breakouts has been a key problem in algorithmic trading for a long time because of price fluctuations and the nonlinear nature of price behavior. In this paper, we develop an Artificial Neural Network (ANN), based system to spot stock price breakouts, which also uses the integration of Simple Moving Average (SMA) trend signals and traded volume confirmation for higher predictability. The model relies on historical price and volume data, in which SMA crossover situations are used to determine the trend while extraordinary volume changes are used as a measure of the breakout strength. These calculated parameters are input to a multilayer feed, forward ANN is trained with backpropagation to grasp the complex, and nonlinear interplay between the stock price changes and stock traded volume increases. The proposed automation system issues probabilistic breakout stock price alerts that can separate real breakouts from fake and noise breakout in price movements. The experimental test is based on standard equity market data sets and the results are compared with conventional rule, based strategies using only SMA and volume. Various metrics, such as accuracy, precision, recall, cumulative returns, and drawdown, are used to evaluate the performance. According to the findings, the ANN, based system is able to greatly enhance the accuracy of breakout detection and consequently the profitability of trading, whereas at the same time it decreases the number of false signals. The suggested hybrid method is a powerful and versatile tool for developing smart breakout, based trading strategies in highly volatile financial markets.