University project about predicting Bitcoin (BTC) returns using ensemble machine learning models. Includes extensive fine-tuning code.
The project uses two datasets:
- BTC:USDT_price_1dfreq.csv: This file contains the daily frequency price of BTC to USDT.
- full_df.csv: This file contains the full dataset used for the project.
- preprocessing_ML.py: This script is used for preprocessing the data for machine learning algorithms.
- ML_algo.ipynb: This Jupyter notebook contains the machine learning algorithms used for predicting BTC returns.
To run this project, follow these steps:
- Run the preprocessing script to prepare the data:
python preprocessing_ML.py
jupyter notebook ML_algo.ipynb