This repository contains a Jupyter notebook that demonstrates how to use Long Short-Term Memory (LSTM) neural networks to predict stock prices for the S&P 500 index. The notebook covers the following steps:
- Importing libraries and data
- Exploratory data analysis and visualization
- Data preprocessing and feature engineering
- Building and training the LSTM model
- Evaluating the model performance and making predictions
- Conclusion and future work
The data used in this project is obtained from Yahoo Finance and contains the daily closing prices of the S&P 500 index from January 1st, 2010 to December 31st, 2020. The LSTM model is implemented using TensorFlow and Keras.
The main goal of this project is to showcase how LSTM networks can capture the temporal dependencies and patterns in time series data, such as stock prices, and provide accurate forecasts for future values. However, this project is not intended to provide any financial advice or recommendations for trading or investing in stocks.
To run this notebook, you will need to run the requirements.txt
To run this notebook, simply clone this repository and open it in Jupyter Notebook or Google Colab. You can also view it on GitHub.
This project is licensed under the MIT License - see the LICENSE file for details.