Metadata-Version: 2.1
Name: fortitudo-tech
Version: 1.2.4
Summary: Entropy Pooling views and stress testing combined with Conditional Value-at-Risk (CVaR) portfolio optimization in Python.
Home-page: https://fortitudo.tech
License: GPL-3.0-or-later
Keywords: CVaR,Efficient Frontier,Entropy Pooling,Quantitative Finance,Portfolio Optimization
Author: Fortitudo Technologies
Author-email: software@fortitudo.tech
Requires-Python: >=3.9,<3.15
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 :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
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,<2.0.0)
Requires-Dist: matplotlib (>=3.4,<4.0)
Requires-Dist: numpy (>=2.0)
Requires-Dist: pandas (>=1.3.4)
Requires-Dist: scipy (>=1.10,<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

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

.. |Binder| image:: https://mybinder.org/badge_logo.svg
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Fortitudo Technologies Open Source
==================================

This package allows you to explore open-source implementations of some of our
fundamental methods, for example, Sequential Entropy Pooling (SeqEP), CVaR optimization,
and Fully Flexible Resampling (FFR) in Python.

You can watch this `YouTube playlist <https://www.youtube.com/playlist?list=PLfI2BKNVj_b2rurUsCtc2F8lqtPWqcs2K>`_
for a walkthrough of the package's functionality and examples.

For a high-level introduction to the investment framework, watch this `YouTube video <https://youtu.be/4ESigySdGf8>`_
and `Substack post <https://open.substack.com/pub/antonvorobets/p/entropy-pooling-and-cvar-portfolio-optimization-in-python-ffed736a8347>`_.

For a pedagogical and deep presentation of the investment framework and its methods,
see the `Portfolio Construction and Risk Management Book <https://antonvorobets.substack.com/p/pcrm-book>`_.

To build the deepest understanding of all the theories and methods, you can
complete the `Applied Quantitative Investment Management Course <https://antonvorobets.substack.com/t/course>`_.

Audience
--------

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.

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

Installation can be done via pip::

   pip install -U 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 -c conda-forge python scipy pandas matplotlib cvxopt
   conda activate fortitudo.tech
   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 resources and might not support
some of the optimized routines this package uses. If you want access to a stable
and optimized environment with persistent storage, please subscribe to our Data
Science Server.

Company
-------

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

Disclaimer
----------

This package is completely separate from our proprietary solutions and therefore
not representative of the quality and functionality offered by the Investment Simulation
and Investment Analysis modules.

For a short presentation of which CVaR problems the Investment Analysis module can solve
and at what speed, see the
`cvar-optimization-benchmarks repository <https://github.com/fortitudo-tech/cvar-optimization-benchmarks>`_.

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.

