Real-Time Market Momentum & Sentiment Prediction

Project Overview

Designed and implemented a real-time deep learning framework to predict S&P 500 market momentum by combining historical price data with financial news sentiment
Built NLP pipelines to preprocess and encode financial news text for sentiment extraction
Developed LSTM-based neural networks to model temporal dependencies in time-series market data
Integrated multimodal features (numerical price signals + textual sentiment features) to improve predictive capability over single-source models
Evaluated model performance under evolving data conditions, demonstrating adaptability to real-time market updates
Showcased applied AI system design, combining NLP, deep learning, and time-series modeling into a unified predictive workflow

Deep learning framework combining LSTM for time-series analysis with NLP for financial news sentiment to predict market momentum in real-time.

Python NLP Deep Learning LSTM Time Series Analysis Sentiment Analysis Feature Engineering Applied Machine Learning