Metadata-Version: 2.1
Name: fortitudo-tech
Version: 0.9.1
Summary: Investment and risk technologies maintained by Fortitudo Technologies.
Home-page: https://fortitudo.tech
License: GPL-3.0-or-later
Keywords: CVaR,Efficient Frontier,Entropy Pooling,Mathematical Finance,Portfolio Optimization
Author: Fortitudo Technologies
Author-email: software@fortitudo.tech
Requires-Python: >=3.8,<3.11
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Office/Business :: Financial :: Investment
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Dist: cvxopt (==1.3.0)
Requires-Dist: matplotlib (>=3.4,<4.0)
Requires-Dist: numpy (>=1.22,<2.0)
Requires-Dist: pandas (>=1.3.4,<2.0.0)
Requires-Dist: scipy (>=1.6,<2.0)
Project-URL: Documentation, https://os.fortitudo.tech
Project-URL: Issues, https://github.com/fortitudo-tech/fortitudo.tech/issues
Project-URL: Repository, https://github.com/fortitudo-tech/fortitudo.tech
Description-Content-Type: text/x-rst

.. image:: https://github.com/fortitudo-tech/fortitudo.tech/actions/workflows/tests.yml/badge.svg
   :target: https://github.com/fortitudo-tech/fortitudo.tech/actions/workflows/tests.yml

.. image:: https://codecov.io/gh/fortitudo-tech/fortitudo.tech/branch/main/graph/badge.svg?token=Z16XK92Gkl 
   :target: https://codecov.io/gh/fortitudo-tech/fortitudo.tech

.. image:: https://mybinder.org/badge_logo.svg
   :target: https://mybinder.org/v2/gh/fortitudo-tech/fortitudo.tech/main?labpath=examples

Fortitudo Technologies Open Source
==================================

This package allows you to freely explore open-source implementations of some
of our fundamental technologies, e.g., Entropy Pooling and CVaR optimization 
in Python.

The package is intended for advanced users who are comfortable specifying
portfolio constraints and Entropy Pooling views using matrices and vectors.
This gives full flexibility in relation to working with these technologies.
Hence, input checking is intentionally kept to a minimum.

Fortitudo Technologies is a fintech offering novel investment technologies
as well as quantitative and digitalization consultancy to the investment
management industry. For more information, please visit our
`website <https://fortitudo.tech>`_.

Installation Instructions
-------------------------

Installation can be done via pip::

   pip install fortitudo.tech

For best performance, we recommend that you install the package in a `conda environment
<https://conda.io/projects/conda/en/latest/user-guide/concepts/environments.html>`_
and let conda handle the installation of dependencies before installing the
package using pip. You can do this by following these steps::

   conda create -n fortitudo.tech python=3.10 scipy pandas matplotlib -y
   conda activate fortitudo.tech
   conda install -c conda-forge cvxopt=1.3 -y
   pip install fortitudo.tech

The examples might require you to install additional packages, e.g., seaborn and
ipykernel / notebook / jupyterlab if you want to run the notebooks. Using pip to
install these packages should not cause any dependency issues.

You can also explore the examples in the cloud without any local installations using
`Binder <https://mybinder.org/v2/gh/fortitudo-tech/fortitudo.tech/main?labpath=examples>`_.
However, note that Binder servers have very limited ressources and might not support
some of the optimized routines this package uses. For best performance, you should
install the package on a machine that supports the `Math Kernel Library <https://en.
wikipedia.org/wiki/Math_Kernel_Library>`_.

Disclaimer
----------

This package is completely separate from our proprietary solutions and therefore
not representative of neither the quality nor the functionality offered by the Simulation
Engine and Investment Analysis modules. If you are an institutional investor and want
to experience how these methods can be used for sophisticated analysis in practice,
please request a demo by sending an email to demo@fortitudo.tech.

