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

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