University Honors

Accurate short-term equity forecasting is challenging due to nonstationarity and noisy market signals. This project develops an end-to-end forecasting prototype that predicts short-horizon price movement (future return over a user-selected horizon) for a selected stock. Historical openñhighñlowñcloseñvolume (OHLCV) data are retrieved using yahoo finance API and transformed into a compact set of core technical indicators, including daily returns, moving averages, RSI, Bollinger percent-B, average true range (ATR), and volume/range-based measures. To incorporate qualitative market information that may not be fully captured by prices alone, the system also retrieves recent news headlines for the chosen ticker and computes VADER sentiment scores, producing a sentiment signal used to generate sentiment-adjusted forecasts for demonstration. The forecasting model is a Long Short-Term Memory (LSTM) network, a recurrent neural architecture designed to learn patterns and dependencies in sequential time-series data, and it is used because it can model temporal structure in engineered financial features across fixed-length historical windows. Model uncertainty is summarized using holdout residual variability to provide a confidence interval for predicted prices. The trained model, scaler, and metadata deployed through an interactive Streamlit application that displays results including predicted returns, uncertainty range, sentiment summary, and optional backtest comparisons.