TradeSense : Predictive Analytics for Stocks

Abstract

The stock market is a complex and rapidly changing environment where accurate predictions can greatly enhance investment decisions. Traditional forecasting methods often struggle to adapt to fluctuating market conditions and diverse economic influences. This study presents a stock prediction and analysis system that uses machine learning and predictive analytics to identify trends and forecast stock movements more effectively. The system processes historical data through techniques such as data cleaning, feature extraction, and model training using algorithms like Linear Regression and Long Short-Term Memory (LSTM) networks. Built with Python for predictive modeling and PHP for backend integration, the platform offers real-time data visualization and performance tracking through an intuitive web interface. By merging AI-powered insights with user-friendly design, the proposed system aims to empower investors and analysts with more reliable, data-driven tools for making informed financial decisions.

Introduction

Overview:
This project presents a stock prediction web application that leverages deep learning to forecast future stock prices. Unlike traditional methods that rely on chart patterns or market sentiment, this system uses real historical data and advanced AI models—Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—to analyze trends and predict next-day closing prices.

Key Features:

• Hybrid AI Model: Combines LSTM (for long-term trends) and CNN (for short-term patterns) to improve prediction accuracy.

• User-Friendly Interface: Allows users to input stock symbols and view real-time charts, historical data, and future price predictions.

• Accessible to All: Designed for both novice and experienced investors.

Literature Review:

• Traditional models like ARIMA fail to capture market volatility.

• LSTM models (Hochreiter & Schmidhuber) significantly improve time-series forecasting.

• Hybrid CNN-LSTM models (Brownlee, Zhang, Chen) offer better accuracy and adaptability.

• There is a need for more interpretable, efficient, and accessible predictive systems.

Research Gap:

• Many AI models act as “black boxes,” limiting user understanding.

• Existing solutions often lack real-time interactivity and usability.

• This project aims to bridge AI precision with user-friendly design and interactive visualization.

Objectives:

• Build a real-time stock forecasting web app using LSTM and CNN.

• Use Python (TensorFlow, Keras) for backend AI model training.

• Create an intuitive frontend with React.js, HTML/CSS, and JavaScript.

• Evaluate model performance using MSE, RMSE, and MAPE.

• Empower users to make informed, data-driven investment decisions.

System Architecture:

1. Data Collection Module: Gathers and pre-processes historical/real-time data from APIs like Yahoo Finance.

2. Database Module (MySQL): Stores stock data, predictions, and user interactions.

3. Prediction Engine: Uses LSTM and CNN models to forecast prices.

4. Visualization Module: Displays predictions using dynamic graphs (Chart.js/Plotly).

5. User Interface: Interactive, responsive dashboard for stock search and visualization.

6. Evaluation Module: Continuously measures prediction performance with error metrics.

Results:

• The application successfully delivers real-time predictions with interactive visuals.

• Demonstrates deep learning’s capability in financial forecasting.

• Future improvements include:

  • Handling highly volatile data

  • Integrating sentiment analysis

  • Adding mobile support and portfolio management

Conclusion

This Stock Prediction Web Application offers a glimpse into the future of financial decision-making by demonstrating the power of machine learning, specifically using advanced CNN-LSTM models, to forecast stock prices based on live market data. The platform significantly enhances the user experience by providing data visualization and trend analysis within an intuitive interface, allowing users to craft better-informed investment strategies. While we recognize the current need to better manage highly volatile market data and continuously refine predictive accuracy, this system establishes a strong foundation for exciting future enhancements, including the integration of sentiment analysis, the development of mobile accessibility, and the addition of comprehensive portfolio management tools, ultimately highlighting the transformative potential of predictive analytics to make stock analysis more accessible and effective in modern finance. As the Stock Prediction Web Application evolves, it is crucial to address existing limitations and expand functionality to better support investors and market enthusiasts. The following initiatives are planned to enhance predictive accuracy, scalability, and user engagement. By integrating advanced algorithms and user- centric features, the platform aims to provide a more reliable and comprehensive financial forecasting tool. Improve accuracy by fine-tuning CNN- LSTM models and exploring hybrid approaches that combine deep learning with traditional financial indicators. Incorporate news and social media sentiment data to capture market psychology and improve forecasting reliability. Allow users to track multiple stocks, manage virtual portfolios, and receive performance insights. Develop a mobile version of the platform for real-time access and convenience, enabling predictions on the go. Expand charting tools with candlestick patterns, technical indicators, and customizable dashboards for deeper market insights.Adapt features based on user preferences, search history, and feedback to deliver more tailored investment suggestions.Extend the system to cover multiple stocks.

References

[1] G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA, USA: Holden-Day, 1976. [2] E. F. Fama, “Efficient capital markets: A review of theory and empirical work,” J. Finance, vol. 25, no. 2, pp. 383–417, 1970. [3] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997. [4] T. Fischer and C. Krauss, “Deep learning with long short-term memory networks for financial market predictions,” Eur. J. Oper. Res., vol. 270, no. 2, pp. 654–669, 2018. [5] J. Brownlee, Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python. San Francisco, CA, USA: Machine Learning Mastery, 2017. [6] G. Zhang, B. E. Patuwo, and M. Y. Hu, “Forecasting with artificial neural networks: The state of the art,” Int. J. Forecast., vol. 14, no. 1, pp. 35–62, 1998. [7] D. M. Nelson, A. C. M. Pereira, and R. A. de Oliveira, “Stock market’s price movement prediction with LSTM neural networks,” in Proc. Int. Joint Conf. Neural Netw. (IJCNN), Anchorage, AK, USA, 2017, pp. 1419–1426. [8] Y. Zhang, H. Zhao, and J. Wang, “Stock Market Prediction Using Machine Learning Algorithms,” IEEE Access, vol. 8, pp. 23456–23466, 2020. [9] A. Singh and P. Gupta, “Design and Implementation of an Inventory Management System,” Proceedings of the IEEE International Conference on Computational Intelligence and Communication Technology (CICT), 2019, pp. 102–107. [10] M. Chen, L. Zhang, and K. Li, “Big Data Analytics for Stock Market Prediction: A Deep Learning Approach,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 6, pp. 703–714, Dec. 2020. [11] Meet Satishbhai Sonani, Atta Badii, Armin Moin, “Stock Price Prediction Using a Hybrid LSTM-GNN Model: Integrating Time-Series and Graph-Based Analysis,” arXiv preprint arXiv:2502.15813, Feb. 2025. [12] Bhanujyothi H. C., I. Jeena Jacob, “A Hybrid CNN-LSTM Attention-Based Deep Learning Model for Stock Price Prediction Using Technical Indicators,” Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 28012-28017, Oct. 2025. [13] Ebiesuwa Seun et al., “Stock Price Prediction: Evaluating the Efficacy of CNN, LSTM, CNN-LSTM, and CNN-BiLSTM Models,” International Journal of Intelligent Systems and Applications in Engineering, 2023-2024. [14] Xinye Sha, “Time Series Stock Price Forecasting Based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) Optimization,” arXiv preprint arXiv:2405.03151, May 2024.

Copyright

Copyright © 2025 Mr. Altamash Khan. 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.