Market-Plus-Pulse: LSTM-Based Equity Price Prediction Framework with Real-Time Web Interface

Abstract

Financial markets exhibit high volatility driven by economic indicators, global events, and investor sentiment, making accurate price prediction crucial for portfolio management and risk assessment. This paper presents a comprehensive framework for equity price prediction using Long Short-Term Memory (LSTM) networks integrated with a Django-based web application. The system incorporates historical market data extraction via APIs, advanced preprocessing techniques including normalization and feature engineering, and a bidirectional LSTM architecture for temporal pattern recognition. Performance evaluation on S&P 500 data demonstrates significant improvements over traditional ARIMA models, achieving 23.7% reduction in RMSE and 19.2% reduction in MAE. The web interface enables real-time prediction, comparative analysis, and risk assessment tools for both institutional and retail investors. This integrated approach bridges the gap between academic research and practical financial technology deployment.

Introduction

1. Background & Motivation

Financial markets are complex and non-linear, making traditional econometric models like ARIMA and GARCH insufficient for capturing long-term dependencies. Deep learning, particularly LSTM networks, has shown superior performance in modeling such complexities.

2. Research Contribution

The paper introduces Market-Plus-Pulse, a scalable, production-ready framework that:

• Integrates Bidirectional LSTM models for time-series financial prediction,

• Incorporates multiple data sources (technical indicators, historical prices),

• Offers a user-friendly web application for real-time financial decision-making.

3. Literature Review Highlights

• Traditional models lack the ability to model temporal dependencies and nonlinearities.

• Deep learning models (LSTM, CNN-LSTM, Bi-LSTM, Graph-based) outperform classical methods.

• Existing gaps: lack of deployment-ready tools, limited data integration, poor usability, and lack of scalability.

4. Methodology

• Architecture: Four components—data acquisition, preprocessing, LSTM modeling, web interface.

• Data: OHLCV data (2001–2024) from 20 S&P 500 companies across sectors.

• Model: Bidirectional LSTM with two LSTM layers, ReLU activations, and optimized with Adam.

• Training: 100 epochs, early stopping, MSE loss function, trained on NVIDIA RTX 4080.

5. Results

• Performance: Bi-LSTM outperforms ARIMA, SVR, Random Forest, and standard LSTM across RMSE, MAE, MAPE, and directional accuracy (DA).

• Best Predictions: AAPL and MSFT showed the highest Sharpe Ratios and prediction accuracy.

• Significance: Improvements statistically validated using t-tests and Diebold-Mariano tests.

6. Web Application

• Built with Django (backend) and Bootstrap/JavaScript (frontend).

• Features include:

  • Real-time price prediction dashboard

  • Portfolio analysis and optimization

  • Risk management tools (VaR, CVaR)

  • Sector/stock comparisons

  • Alerts and interactive charts

7. Practical Applications

• For Traders & Analysts:

  • Supports trend-following, mean-reversion strategies, and portfolio optimization.

• For Different Users:

  • Tailored for retail investors, financial advisors, institutional traders, and researchers.

8. Limitations

• Struggles with unprecedented market events (e.g., black swans).

• Heavy computational demand and dependency on data quality.

• Limited interpretability and exclusion of human-like fundamental analysis.

9. Future Work

• Implement Transformer models, Graph Neural Networks, and GANs for better forecasting and scenario simulation.

• Incorporate alternative data (sentiment, macroeconomic indicators, satellite data).

• Expand to mobile and local device versions.

• Explore blockchain for transparency and smart contract integration.

• Improve explainability and fairness of AI decisions to meet regulatory expectations.

Conclusion

This research presents Market-Plus-Pulse, a comprehensive LSTM-based framework for equity price prediction that successfully bridges academic research and practical application. The bidirectional LSTM architecture demonstrates significant improvements over traditional forecasting methods, achieving 23.7% reduction in RMSE compared to ARIMA models and 61.2% directional accuracy.The integrated Django web application provides institutional-grade functionality through an intuitive interface, supporting real-time prediction, portfolio optimization, and risk management. The system\'s modular architecture ensures scalability and adaptability to evolving market conditions and user requirements.Key contributions include: (1) comprehensive comparison of deep learning architectures for financial prediction, (2) production-ready implementation with robust web interface, (3) integration of multiple technical indicators and risk metrics, and (4) statistical validation of performance improvements across diverse market conditions.The framework\'s practical impact extends beyond academic metrics, providing actionable insights for investment decision-making, risk assessment, and portfolio management. Future enhancements incorporating transformer architectures, alternative data sources, and edge computing capabilities position this research at the forefront of financial technology innovation.The success of this implementation demonstrates the potential for AI-driven financial analysis tools to democratize sophisticated investment strategies while maintaining the rigor required for institutional applications.

References

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Copyright

Copyright © 2025 Gagandeep H P, Yashaswini Y. 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.