Bitcoin Next-Day Price

Forecasting Bitcoin's Price for the Following Day with RNN Models
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
3. Usage
4. Roadmap
5. Contributing
6. License
7. Contact

About The Project

"In this project, our goal is to forecast the closing price of Bitcoin for the following day using data from the yfinance library. To accomplish this, we will implement the 'rolling windows' training technique and leverage two predictive models: SGDRegressor (machine learning) and a recursive neural network, RNN (deep learning), incorporating LSTM (Long Short-Term Memory)."

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Built With

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Getting Started

• images: a folder containing the images used in the Jupyter notebook and README.md.
• Bitcoin_next_day_prediction.ipynb: a Jupyter notebook that includes the entire project, along with markdown comments explaining the entire process.
• README.md: this file.

Prerequisites

It is essential to have the yfinance library correctly installed. You can do so by:

pip install yfinance (using pip)

conda install -c conda-forge yfinance (using conda)

Usage

The Jupyter notebook is ready to be downloaded and executed.

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Roadmap

• Initial Data Distribution Analysis
• Data cleansing
• Feature relation analysis
• Preparation of the final data for training
• Implementation of the regression model
  • SGDRegressor
• Implementation of the recursive neural network
  • LSTM

See the open issues for a full list of proposed features (and known issues).

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Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Javier Requena - GitHub - javier.requena@protonmail.com

Project Link: https://github.com/Javier-Requena/Bitcoin_next_day_price_prediction-RNN-LSTM-python

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