# -*- coding: utf-8 -*-
from setuptools import setup

packages = \
['tech']

package_data = \
{'': ['*'], 'tech': ['data/*']}

install_requires = \
['cvxopt>=1.3.0,<2.0.0',
 'matplotlib>=3.4,<4.0',
 'numpy>=1.22,<2.0',
 'pandas>=1.3.4,<2.0.0',
 'scipy>=1.6,<2.0']

setup_kwargs = {
    'name': 'fortitudo-tech',
    'version': '0.10.1',
    'description': 'Investment and risk technologies maintained by Fortitudo Technologies.',
    'long_description': '.. image:: https://github.com/fortitudo-tech/fortitudo.tech/actions/workflows/tests.yml/badge.svg\n   :target: https://github.com/fortitudo-tech/fortitudo.tech/actions/workflows/tests.yml\n\n.. image:: https://codecov.io/gh/fortitudo-tech/fortitudo.tech/branch/main/graph/badge.svg?token=Z16XK92Gkl \n   :target: https://codecov.io/gh/fortitudo-tech/fortitudo.tech\n\n.. image:: https://static.pepy.tech/personalized-badge/fortitudo-tech?period=total&units=international_system&left_color=grey&right_color=blue&left_text=Downloads\n :target: https://pepy.tech/project/fortitudo-tech\n\n.. image:: https://mybinder.org/badge_logo.svg\n   :target: https://mybinder.org/v2/gh/fortitudo-tech/fortitudo.tech/main?labpath=examples\n\nFortitudo Technologies Open Source\n==================================\n\nThis package allows you to explore open-source implementations of some of our\nfundamental technologies, e.g., Entropy Pooling and CVaR optimization in Python.\n\nThe package is intended for advanced users who are comfortable specifying\nportfolio constraints and Entropy Pooling views using matrices and vectors.\nThis gives full flexibility in relation to working with these technologies.\nHence, input checking is intentionally kept to a minimum.\n\nFortitudo Technologies offers novel investment software as well as quantitative and\ndigitalization consultancy to the investment management industry. For more information,\nplease visit our `website <https://fortitudo.tech>`_.\n\nInstallation Instructions\n-------------------------\n\nInstallation can be done via pip::\n\n   pip install fortitudo.tech\n\nFor best performance, we recommend that you install the package in a `conda environment\n<https://conda.io/projects/conda/en/latest/user-guide/concepts/environments.html>`_\nand let conda handle the installation of dependencies before installing the\npackage using pip. You can do this by following these steps::\n\n   conda create -n fortitudo.tech -c conda-forge python=3.11 scipy pandas=1.5 matplotlib cvxopt\n   conda activate fortitudo.tech\n   pip install fortitudo.tech\n\nThe examples might require you to install additional packages, e.g., seaborn and\nipykernel / notebook / jupyterlab if you want to run the notebooks. Using pip to\ninstall these packages should not cause any dependency issues.\n\nYou can also explore the examples in the cloud without any local installations using\n`Binder <https://mybinder.org/v2/gh/fortitudo-tech/fortitudo.tech/main?labpath=examples>`_.\nHowever, note that Binder servers have very limited ressources and might not support\nsome of the optimized routines this package uses. For best performance, you should\ninstall the package on a machine that supports the `Math Kernel Library <https://en.\nwikipedia.org/wiki/Math_Kernel_Library>`_.\n\nDisclaimer\n----------\n\nThis package is completely separate from our proprietary solutions and therefore\nnot representative of neither the quality nor the functionality offered by the Simulation\nEngine and Investment Analysis modules. If you are an institutional investor who wants\nto experience how these methods can be used for sophisticated analysis in practice,\nplease request a demo by sending an email to demo@fortitudo.tech.\n',
    'author': 'Fortitudo Technologies',
    'author_email': 'software@fortitudo.tech',
    'maintainer': 'None',
    'maintainer_email': 'None',
    'url': 'https://fortitudo.tech',
    'packages': packages,
    'package_data': package_data,
    'install_requires': install_requires,
    'python_requires': '>=3.8,<3.12',
}


setup(**setup_kwargs)
