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Deep-MacroFin

Deep-MacroFin is a comprehensive deep-learning framework designed to solve equilibrium economic models in continuous time. The library leverages deep learning to alleviate curse of dimensionality.

Installation

Install from PyPI

The stable version of the package can be installed from PyPI.

pip install deep-macrofin

Build from Source (with uv)

The project is now configured with uv for dependency management and packaging. To install the dependencies and run the code:

  1. Clone the repository 

    git clone https://github.com/rotmanfinhub/deep-macrofin.git

  2. Install uv by following the official documentation here

  3. Create a uv virtual environment and install the dependencies and the package 

    uv sync # this will automatically download suitable python versions, create virtual environment and install packages in pyproject.toml file

Build from Source (without uv)

For developers, you should clone the folder to your local machine and install from the local folder.

  1. Clone the repository 

    git clone https://github.com/rotmanfinhub/deep-macrofin.git

  2. Create a virtual environment (Optional, but recommended) 

    python -m venv venv
source venv/bin/activate # venv/Scripts/activate using Windows powershell

  3. Install dependencies 

    pip install -r requirements.txt
pip install -r requirements-dev.txt
pip install -r requirements-doc.txt

  4. Install the package 

    pip install -e .

Docs

The documentation site is based on mkdocs and mkdocs-mateiral.

Layouts 

mkdocs.yml    # The configuration file.
docs/
    index.md  # The documentation homepage.
    ...       # Other markdown pages, images and other files.

To see the site locally, run the following command: 

mkdocs serve

Cite Deep-MacroFin

If you use Deep-MacroFin for academic research, you are encouraged to cite the following paper:

@misc{wu2024deepmacrofin,
      title={Deep-MacroFin: Informed Equilibrium Neural Network for Continuous Time Economic Models}, 
      author={Yuntao Wu and Jiayuan Guo and Goutham Gopalakrishna and Zisis Poulos},
      year={2024},
      eprint={2408.10368},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2408.10368}, 
}