This system was developed as part of a second year Group Software Project. It applies machine learning to weekly S&P 500 price prediction, not as a trading tool, but as an exploration of how historical data can be used to model market behaviour.
The system collects historical S&P 500 index data, trains multiple machine learning models on it, and presents weekly price predictions through this interactive interface. Users can explore forecasts, compare model accuracy, and understand the reasoning behind each prediction.
Predictions cover a 52-week forward horizon, generated from models trained on data spanning back to 1926.
The system has three layers that pass data through a shared MySQL database.
Historical S&P 500 data from 1926 to 2024 is sourced from Wharton Research Data Services (WRDS). Data from 2025 onwards uses the yfinance package via the Yahoo Finance API. Both sources are publicly accessible and permission has been confirmed for academic use.
All predictions are generated for academic and educational purposes only. Markets are affected by geopolitical events, policy changes, and unforeseen shocks that no historical model can anticipate. Do not use this system to make real investment decisions. Model limitations are documented transparently throughout the interface.