Predicting the price of cryptocurrencies like Bitcoin is an ongoing challenge due to the volatility and unpredictability of the market. In this article, we explore a Python-based price prediction pipeline that combines machine learning techniques and deep learning algorithms to forecast Bitcoin's closing price. The code for this pipeline can be found on GitHub at https://github.com/amar-muratovic/bitcoin-price-prediction-pipeline.
Key Components
Data Acquisition and Preprocessing: The pipeline uses the CCXT library to fetch historical price data for Bitcoin (BTC/USD) from the CryptoCompare API. The data is then preprocessed, resampled, and saved into a CSV file for further analysis.
Feature Engineering: The pipeline uses three input features - High, Low, and Open prices - and the target variable, which is the Close price.
Model Ensemble: The pipeline trains an ensemble of four models: Linear Regression, Bayesian Ridge, Support Vector Regression, and Random Forest Regressor. The predictions from these models are averaged to produce the final forecast.
Deep Learning: The pipeline also incorporates a neural network with two hidden layers and early stopping to prevent overfitting. The neural network is trained on a subset of the data.
Hyperparameter Tuning: Grid search and cross-validation are used to fine-tune the models and optimize their hyperparameters.
Model Evaluation: The pipeline evaluates the models using mean squared error (MSE) and R^2 score, which measure the accuracy of the predictions.
Implementation Details
The pipeline starts by importing necessary libraries and modules, followed by loading the Bitcoin price data from a CSV file. The data is preprocessed, resampled, and saved into a new CSV file. The input features and target variables are defined, and the data is split into training and testing sets.
An ensemble of machine learning models is trained on the data, and predictions are made using these models. The ensemble approach aims to combine the strengths of different models to produce more accurate predictions. The predictions from each model are averaged to produce the final forecast.
A neural network with two hidden layers is created and trained on a subset of the data. Early stopping is used to prevent overfitting by monitoring the validation loss and stopping the training when it stops improving.
Hyperparameter tuning is performed using grid search and cross-validation to optimize the models' performance. This process helps identify the best combination of hyperparameters for each model.
Finally, the models are evaluated using mean squared error (MSE) and R^2 score. These metrics help measure the accuracy of the predictions and the performance of the models.
Conclusion
The Bitcoin price prediction pipeline presented in this article combines various machine learning techniques and deep learning algorithms to forecast the closing price of Bitcoin. This ensemble approach aims to improve prediction accuracy by leveraging the strengths of different models. While predicting the price of cryptocurrencies remains a challenging task, this pipeline provides a solid foundation for further experimentation and improvements. To explore the code further, visit the GitHub repository at https://github.com/amar-muratovic/bitcoin-price-prediction-pipeline.