v1.2 (#125)

* Fully Flexible Resampling intermediate commit

* Fully Flexible Resampling intermediate commit 2

* Several docs updates

* Final Fully Flexible resampling commit

* Initial Fully Flexible Resampling test commit

* Full coverage for FullyFlexibleResampling class

* README and docs updates

* Minor docstrings updates

* Docstrings and type hints updates

* Docs and README update

---------

Co-authored-by: Anton Vorobets <admin@fortitudo.tech>

Author: antonvorobets

README.rst

@@ -13,7 +13,8 @@ Fortitudo Technologies Open Source
 ==================================
 
 This package allows you to explore open-source implementations of some of our
-fundamental methods, e.g., Entropy Pooling and CVaR optimization in Python.
+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.
@@ -35,13 +36,6 @@ 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.
 
-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>`_.
-
 Installation Instructions
 -------------------------
 
@@ -59,7 +53,7 @@ package using pip. You can do this by following these steps::
    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
+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
@@ -69,14 +63,21 @@ 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 comparison of which CVaR problems the Investment Analysis module can solve
+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>`_.
 

docs/build/doctrees/documentation.doctree

@@ -12,13 +12,13 @@ given by `the Danish common return expectations for the 2nd half of 2021
 
 The parameters and P&L simulation are used in the examples and allow you to
 immediately start exploring the functionality of this package. You can also
-use it to test your understanding of the theory by replicating results, e.g.,
-the sequential Entropy Pooling results from :cite:t:`Vorobets2021`.
+use it to test your understanding of the theory by replicating results, for
+example, the sequential Entropy Pooling results from :cite:t:`Vorobets2021`.
 
 In addition to the above, simulated time series of an equity index, an
 associated implied volatility surface, a risk-free zero-coupon curve, and
 a credit spread curve is included. You can use this time series data to,
-e.g., validate your risk modeling approach. See `this introduction example
+for example, validate your risk modeling approach. See `this introduction example
 <https://github.com/fortitudo-tech/fortitudo.tech/blob/main/examples/4_TimeSeries
 .ipynb>`_ and `this modeling example <https://github.com/fortitudo-tech/
 fortitudo.tech/blob/main/examples/7_RiskFactorViews.ipynb>`_ for an example
@@ -28,6 +28,26 @@ views on risk factors.
 .. automodule:: fortitudo.tech.data
    :members:
 
+Simulation
+----------
+
+This package includes a Fully Flexible Resampling (FFR) method as well as a
+very simple exponential decay simulation model. The FFR functionality, originally
+introduced in the `Portfolio Construction and Risk Management book
+<https://antonvorobets.substack.com/p/pcrm-book>`_, is only implemented with
+one continous state variable.
+
+For detailed proofs of all the nice properties of the Fully Flexible Resampling
+method, see :cite:t:`KristensenVorobets2025`.
+
+The exponentially decaying probabilities can be used directly with historical
+scenarios, for example, as a prior probability for Sequential Entropy Pooling (SeqEP)
+views/stress tests, the FFR method, or for a normal distribution calibration
+which new samples can be generated from.
+
+.. automodule:: fortitudo.tech.simulation
+   :members:
+
 Entropy Pooling
 ---------------
 
@@ -42,15 +62,15 @@ subject to the constraints
 The approach was first introduced by :cite:t:`EntropyPooling`, while the
 code is implemented using notation from :cite:t:`Vorobets2021`. For an
 introduction to Entropy Pooling, see `this video <https://youtu.be/qk_5l4ICXfY>`_
-or `this Substack post <https://open.substack.com/pub/antonvorobets/p/entropy-pooling-vs-black-litterman-abb608b810cd>`_.
+or `this Substack post <https://antonvorobets.substack.com/p/entropy-pooling-collection>`_.
 
 .. automodule:: fortitudo.tech.entropy_pooling
    :members:
 
 Functions
 ---------
 
-The functions below are useful when working with Entropy Pooling, e.g.,
+The functions below are useful when working with Entropy Pooling, for example,
 they can be used to calculate posterior moments and correlation matrix
 to verify that views have been implemented correctly. The covariance
 matrix function is useful for mean-variance optimization with Entropy
@@ -138,18 +158,3 @@ or :const:`'abstol'` conditions are satisfied. The parameters have been tested
 with "percentage return" P&L and work well. In most cases, the algorithm stops
 due to relative convergence in less than 100 iterations. If you use P&L
 simulations that are scaled differently, you might need to adjust them.
-
-Simulation
-----------
-
-This package includes a very simple exponential decay simulation model. The
-exponentially decaying probabilities can be used directly with historical
-scenarios, e.g., as a prior probability for Entropy Pooling views / stress-tests,
-or for a normal distribution calibration which new samples can be generated from.
-
-You can see how to use this functionality in `the exponential decay modeling
-example <https://github.com/fortitudo-tech/fortitudo.tech/blob/main/examples/
-6_SimulationExpDecay.ipynb>`_.
-
-.. automodule:: fortitudo.tech.simulation
-   :members:

docs/build/doctrees/environment.pickle

@@ -35,6 +35,8 @@ The repository contains the following examples:
 12) The accompanied code for :cite:t:`Vorobets2025` that performs tests
     for normality of US equity index returns and rejects the Aggregational
     Gaussianity hypothesis
+13) High-dimensional CVaR portfolio optimization code, where a portfolio of
+    2000 stocks is optimized over 10000 joint scenarios.
 
 Watch this `YouTube playlist <https://www.youtube.com/playlist?list=PLfI2BKNVj_b2rurUsCtc2F8lqtPWqcs2K>`_
 for a walkthrough of the package's functionality and examples. The examples are

docs/build/doctrees/examples.doctree

@@ -2,7 +2,8 @@ Fortitudo Technologies Open Source
 ==================================
 
 This package allows you to explore open-source implementations of some of our
-fundamental methods, e.g., Entropy Pooling and CVaR optimization in Python.
+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.
@@ -35,10 +36,10 @@ 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.
+not representative of the quality and functionality offered by the `Investment Simulation
+and Investment Analysis modules <https://fortitudo.tech/solutions>`_.
 
-For a short comparison of which CVaR problems the Investment Analysis module can solve
+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>`_.
 

docs/build/doctrees/index.doctree

@@ -15,7 +15,7 @@ package using pip. You can do this by following these steps::
    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
+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