GARCH Hyperparameter Optimization

This project provides a GARCH hyperparameter grid search optimizer using Optuna.

Project Structure

GARCH-Models-Hyperparameter-Optimizer/
│
├── garch_optimizer/
│   ├── __init__.py
│   └── garch_optimizer.py
│
├── tests/
│   ├── __init__.py
│   └── test_garch_optimizer.py
│
├── Analysis.ipynb  # Jupyter notebook
│
├── setup.py
├── requirements.txt
├── LICENSE
└── README.md

• garch_optimizer/: Contains the main code for GARCH hyperparameter optimization. The core functionality is implemented in garch_optimizer.py.
• tests/: Includes unit tests to verify the correctness of the optimizer. The test_garch_optimizer.py file contains test cases for the GARCH optimizer.
• Analysis.ipynb: A Jupyter notebook demonstrating how the GARCH optimizer works, with examples and visualizations.
• setup.py: Handles the packaging and installation of the project, specifying dependencies and metadata.
• requirements.txt: Lists the required Python packages needed to run the project.
• LICENSE: The license under which the project is released.
• README.md: The main documentation file providing an overview of the project, installation instructions, and usage examples.

Installation

Ensure you have the necessary packages installed. You can install them using pip:

pip install -r requirements.txt

Usage

Import the garch_parameter function from the garch_optimizer module and use it to find the best GARCH model parameters:

from garch_optimizer.garch_optimizer import garch_parameter

# Sample data for testing
import pandas as pd
import numpy as np
np.random.seed(42)
data = pd.Series(np.random.randn(100))

best_params = garch_parameter(
        data=data, 
        max_p=3, 
        max_q=3, 
        n_trials=200
    )

print(best_params)

Testing

To run the tests, use the following command:

python -m unittest discover

Contributing

Feel free to submit issues or pull requests to enhance the project.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

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