CNN-GRU-Based Stock Forecasting and VIX Trading Strategy: Supplementary Dataset and Code

This repository contains the supplementary materials for a deep learning study on stock price forecasting and trading strategy enhancement using volatility indicators. The provided dataset and code support a CNN-GRU hybrid model designed to predict stock prices and evaluate trading strategies, with a focus on the Volatility Index (VIX) as an additional feature. Included are two versions of the feature datasets (with and without VIX), preprocessed technical indicators (SMA, EMA, MACD, RSI, etc.), and the full implementation code in a Jupyter Notebook. The code enables reproduction of the experimental results, including model training, forecasting, and trading performance analysis. These materials are shared to support research transparency, reproducibility, and reuse by other researchers in the fields of financial forecasting and applied deep learning. Please refer to the included `README.txt` and `requirements.txt` for usage instructions and software dependencies. **Data sources**:- Historical stock prices: Yahoo Finance- VIX data: Chicago Board Options Exchange (CBOE)