Leveraging Random Forest Algorithms for Enhanced Bitcoin Price Forecasting 2024 Halving

This project employs the Random Forest algorithm to predict Bitcoin prices, utilising five years of comprehensive Bitcoin market data. It focuses on various market aspects such as volatility, market cycles, and investor sentiment. A significant research element is the 2024 Bitcoin halving event, which significantly impacted Bitcoin's value. The model analyses historical data surrounding previous halvings to identify trends helpful in forecasting future Bitcoin prices.

Features and Benefits

• Random Forest Algorithm: Uses the Random Forest algorithm for robust predictive modeling.
• Halving Events Analysis: Analyzes Bitcoin halving events as significant market indicators.
• Comprehensive Data Analysis: Incorporates volatility, market cycles, and investor sentiment in the model.
• Visualization: Provides intuitive plots for comparing actual and predicted prices.

Methodology

The methodology employs Random Forest algorithm to analyze historical Bitcoin data, focusing on periods around Bitcoin halving events to predict future price movements.

Disclaimer and Warning

• Educational Purpose Only: This research is for educational purposes and represents a self-study project.
• Not Financial Advice: The predictions generated by this code are for educational purposes only and should not be construed as financial or investment advice.
• No Responsibility: The author and contributors to this repository bear no responsibility for any financial losses incurred.
• Accuracy Not Guaranteed: There is no guarantee of the predictive performance of these models.
• Independent Research: Users should conduct their own research and consult with professionals.
• Compliance with Laws: Ensure compliance with all applicable laws and regulations in your jurisdiction.

Prerequisites

• Python 3.11
• Git

Installation

Setting Up a Virtual Environment

First, create and activate a virtual environment:

python -m venv .venv  # Create a virtual environment
source .venv/bin/activate  # Activate on macOS and Linux
.venv\Scripts\activate  # Activate on Windows

Installing Dependencies

Install the required dependencies:

pip install -r requirements.txt  # Install Python dependencies
npm install dukascopy-node --save  # Install Node.js dependencies

Data Collection

chmod +x data-download.sh
./data-download.sh

Quick Start

Windows Specific Notes

• Use PowerShell or Git Bash for shell commands.
• Ensure Docker Desktop for Windows is set to Linux containers.
• Python commands may require using py instead of python or python3.

Clone the Repository

To get started, clone the repository:

git clone git@github.com:SMARTSHEEP-IO/bitcoin-price-prediction-analysis-simulating-growth-near-ath-halving.git

Usage

Execute the main.py script to start the data processing and model training/prediction process:

python main.py

Support and Subscribe

• Farsi Channel: Dr. Samizadeh
• English Channel: Programming in 10 Minutes

Contributing

Contributions to improve the project are welcome. Please adhere to standard open-source contribution guidelines.

Citation

If you use this project in your research or in any project, please cite it using the following:

Samizadeh, Iman. (2024). Leveraging Random Forest Algorithms for Enhanced Bitcoin Price Forecasting 2024 Halving. Version 1.0. SMARTSHEEP-IO. [Online]. Available: https://github.com/SMARTSHEEP-IO/Leveraging-Random-Forest-Algorithms-for-Enhanced-Bitcoin-Price-Forecasting-2024-Halving and https://www.researchgate.net/publication/377591197_Leveraging_Random_Forest_Algorithms_for_Enhanced_Bitcoin_Price_Forecasting_2024_Halving

     @misc{Samizadeh2024BitcoinRF,
        author = {Samizadeh, Iman},
        title = {{Leveraging Random Forest Algorithms for Enhanced Bitcoin Price Forecasting 2024 Halving}},
        year = {2024},
        howpublished = {\url{https://github.com/SMARTSHEEP-IO/Leveraging-Random-Forest-Algorithms-for-Enhanced-Bitcoin-Price-Forecasting-2024-Halving}}
    }

Credit: https://github.com/Leo4815162342/dukascopy-node

Keywords: Bitcoin, Halving, Prediction, Volatility, Growth, ATH, Cryptocurrency, Analysis, Financial Modeling