Stock Market Analysis using Machine Learning to Predict Profit & Loss

Authors: Vikas Chavan, Sakshi Mandwade, Sarvesh Parkhe, Rohini Aher, Yogesh Bhalerao

Country: India

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Abstract: The stock market is a highly dynamic and unpredictable environment influenced by various economic, social, and global factors. Accurate prediction of stock price movements is essential for investors to make informed decisions and minimize financial risks. This project presents a Stock Market Analysis System using Machine Learning that aims to predict profit and loss by analyzing historical data and monitoring real-time market trends. The system integrates machine learning models to generate buy or sell predictions based on patterns identified in past stock data. Additionally, it incorporates the Exponential Moving Average (EMA) crossover strategy for live market analysis, enabling timely identification of trading signals. A real-time notification mechanism is implemented to alert users whenever significant market events occur, ensuring quick response to market changes. The system is developed using technologies such as Python, Django, and SQLite, providing a user-friendly web interface for seamless interaction. By combining predictive analytics with real-time monitoring, the proposed system enhances decision-making accuracy, reduces manual effort, and supports efficient investment strategies for both beginners and experienced traders.

Keywords: Stock Market Prediction, Machine Learning, Data Analysis, EMA Crossover, Buy/Sell Signal, Real-Time Monitoring, Django, Financial Forecasting, Algorithmic Trading, Data Analytics

Paper Id: 233114

Published On: 2026-05-11

Published In: Volume 14, Issue 3, May-June 2026

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