manuhup/LSTM-XGBoost-Hybrid-Forecasting — reverse-engineered prompt

Build me a Python notebook project that predicts Apple stock’s next trading day price using a hybrid LSTM and XGBoost approach.

I want it to feel like a complete walkthrough, not just code. It should load historical AAPL price data, explore the stock behavior with clear charts, show returns, volatility, outliers, and maybe correlations with a few other major stocks. Then create useful time series features, prepare rolling windows, train an LSTM model, combine it with an XGBoost regressor, and show the final prediction for the next day.

Please include simple explanations in the notebook so someone who knows a little Python can follow what’s happening. Add evaluation charts and metrics like mean absolute error, and compare predicted prices against real prices so I can see how well it works. Also make it clear this is for learning and research only, not financial advice.

Use current docs online if needed, and make it run end to end with clean, organized code.