I’m building a day-trading system that works exclusively with US-listed stocks and bases every decision on live market sentiment. Price prediction and automated execution can come later; right now I want a solid sentiment-driven engine that issues high-frequency buy/sell signals I can plug into my own trading workflow. Here’s what I need from you: • A sentiment analysis model (NLP) that ingests real-time news headlines, social-media posts, and company filings, converts them into a normalized sentiment score, and refreshes fast enough for intra-day decisions. • A signal-generation layer that combines those sentiment scores with basic technical indicators (e.g., VWAP, momentum) to produce clear entry and exit recommendations for the current trading session. • A back-testing framework so I can replay at least the last three years of intraday data and evaluate win-rate, Sharpe, drawdown, and latency. • Clean, well-commented Python code (PyTorch, scikit-learn, or TensorFlow—your choice) plus setup instructions so I can run everything on my local machine or an AWS instance. The final delivery is a Git repository with reproducible notebooks/scripts, sample configuration files, and a short README outlining how to connect to data feeds (Polygon, Finnhub, Twitter, etc.) and how to trigger the back-test. If you’ve built real-time NLP pipelines for trading before, or have examples of sentiment models beating baseline technical strategies in intraday stock markets, I’d love to see them.