Market Trend Prediction Model

Overview

This project develops a logistic regression model to predict market trend reversals, specifically identifying potential "buy" or "sell" opportunities in financial markets. The model analyzes technical indicators and price data to make predictions.

Features

• Utilizes Logistic Regression for binary classification of market trends.
• Processes and analyzes data using technical indicators like EMAs, RSI, MACD, and StochRSI.
• Uses data from Yahoo Finance for the period from January 2020 to January 2024.

Prerequisites

Before running this project, ensure you have the following installed:

• Python 3.8+
• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn

Installation

Clone this repository to your local machine:

git clone https://github.com/Basith-Ahmed/MTRP-Butcher.git
cd mtrp-butcher

Usage

To run the model training and prediction script, navigate to the project directory and run:

python Butcher_UI.py

Directory Structure

mtrp-butcher/
│
├── Butcher_Model.sav
├── Butcher_UI.py
├── requirements.txt
└── README.md

Data

The data used in this project is sourced from Yahoo Finance, covering daily price movements of Bitcoin (BTC-USD) from January 1, 2020, to January 1, 2024 by default which you can change as required.

Configurations

Edit the config.py file to modify the parameters of the logistic regression model, including the choice of technical indicators and the thresholds for "buy" and "sell" predictions.

Contributing

Contributions to this project are welcome. To contribute:

• Fork the repository.
  
• Create a new branch (git checkout -b feature-branch).
  
• Make your changes.
  
• Commit your changes (git commit -am 'Add some feature').
  
• Push to the branch (git push origin feature-branch).
  
• Submit a new Pull Request.
  

License

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

Contact

Basith Ahmed - Link
Project - Link

Acknowledgements

Yahoo Finance for providing the data.
Contributors who have participated in this project.