Trade One: A Prediction System - Stock Market Price Prediction using Deep Learning

Abstract

Because of the market\'s intrinsic volatility, non-stationarity, and non-linear dynamics, accurately and promptly predicting stock prices is a major challenge in financial research. This study offers a comprehensive web-based solution for real-time stock trend forecasting using deep learning. The design combines a dynamic frontend created with HTML, Bootstrap, and JavaScript, a secure MySQL database for user authentication, and a Python Flask backend with a pre-trained Keras-based Long Short-Term Memory (LSTM) time-series model. The system uses a strong data pipeline that comprises sequential data structure using a sliding window technique, normalization using MinMaxScaler to scale features between 0 and 1, and real-time data collecting from Yahoo Finance to guarantee high-quality input for the model. The main features of the tool are presented to users via an interactive dashboard that offers descriptive statistics, technical indicators such as Exponential Moving Averages (EMA), and anticipated price trajectories, indicating the improved perfect angle of the deep learning approach when compared to more conventional forecasting models like ARIMA and Support Vector Regression. The system shows great promise as a decision-support tool for financial market participants, offering clear data visualizations and a historical log of prediction performance to increase interpretability and foster user trust.

Introduction

Predicting stock prices is challenging due to the complexity, volatility, and non-linearity of financial markets. Traditional models like ARIMA and machine learning techniques such as Support Vector Regression (SVR) and Random Forests rely heavily on statistical assumptions or manual feature engineering, which limit their effectiveness in capturing complex market behaviors.

Recent advances in artificial intelligence, particularly deep learning, have improved stock forecasting. Long Short-Term Memory (LSTM) networks, a type of recurrent neural network designed to remember long-term dependencies via specialized memory cells and gating mechanisms, are well-suited for modeling sequential financial data. LSTMs overcome the limitations of traditional RNNs and better capture temporal patterns influencing market movements.

This study develops an end-to-end web application integrating an LSTM-based stock price predictor with real-time data visualization and user management. The system fetches historical stock data from Yahoo Finance, preprocesses it (normalizing and creating sequences), and trains the LSTM model. The backend uses Flask, and predictions alongside technical indicators like moving averages are displayed interactively on the frontend.

Performance evaluation shows the LSTM model significantly outperforms ARIMA and SVR, with lower Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Specifically, the LSTM achieves an RMSE of 6.75 compared to 19.95 (ARIMA) and 17.18 (SVR), indicating more accurate and reliable stock price forecasts. The system also supports live updates every 15 seconds, offering near real-time prediction capabilities for practical financial decision-making.

Conclusion

By incorporating a potent deep learning model into an engaging and intuitive online application, our work offers a reliable and efficient solution for automatic stock price prediction. The solution offers a complete financial analysis tool by integrating a dynamic frontend, a Keras LSTM model, and a Flask backend. The findings unequivocally show that, in terms of predictive correctness, the deep learning method represents noticeably better than conventional statistical and machine learning models.

References

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Copyright

Copyright © 2025 Mrs. Subhashree D C, Dr. Girish Kumar D, Mr. Rakesh Y M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.