Merge branch 'main' into ci/homogenize-workflows-with-sktime

Author: tingiskhan

.github/dependabot.yml

@@ -0,0 +1,20 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: "[MNT] [Dependabot]"
+      include: "scope"
+    labels:
+      - "maintenance"
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    commit-message:
+      prefix: "[MNT] [Dependabot]"
+      include: "scope"
+    labels:
+      - "maintenance"

.github/workflows/release.yml

@@ -35,6 +35,7 @@ jobs:
     needs: check_tag
     name: Build wheels
     runs-on: ubuntu-latest
+    needs: [check_tag]
 
     steps:
       - uses: actions/checkout@v6
@@ -52,7 +53,7 @@ jobs:
           UV_SYSTEM_PYTHON: 1
 
       - name: Store wheels
-        uses: actions/upload-artifact@v6
+        uses: actions/upload-artifact@v7
         with:
           name: wheels
           path: wheelhouse/*
@@ -105,7 +106,7 @@ jobs:
       id-token: write
 
     steps:
-      - uses: actions/download-artifact@v7
+      - uses: actions/download-artifact@v8
         with:
           name: wheels
           path: wheelhouse

docs/FAQ.rst

@@ -7,7 +7,7 @@ FAQs
 Constraining a score
 --------------------
 
-Suppose that for each asset you have some "score" – it could be an ESG metric, or some custom risk/return metric. It is simple to specify linear constraints, like "portfolio ESG score must be greater than x": you simply create
+Suppose that for each asset you have some "score" – it could be an ESG (Environmental, Social, and Governance) metric, or some custom risk/return metric. It is simple to specify linear constraints, like "portfolio ESG score must be greater than x": you simply create
 a vector of scores, add a constraint on the dot product of those scores with the portfolio weights, then optimize your objective::
 
     esg_scores = [0.3, 0.1, 0.4, 0.1, 0.5, 0.9, 0.2]

docs/GeneralEfficientFrontier.rst

@@ -63,7 +63,7 @@ annual return of 20%::
     from pypfopt import expected_returns, EfficientSemivariance
 
     df = ... # your dataframe of prices
-    mu = expected_returns.mean_historical_returns(df)
+    mu = expected_returns.mean_historical_return(df)
     historical_returns = expected_returns.returns_from_prices(df)
 
     es = EfficientSemivariance(mu, historical_returns)
@@ -203,7 +203,7 @@ For example, perhaps our objective is to construct a basket of assets that best
 particular index, in other words, to minimise the **tracking error**. This does not fit within
 a mean-variance optimization paradigm, but we can still implement it in PyPortfolioOpt::
 
-    from pypfopt.base_optimizer import BaseConvexOptimizer
+    from pypfopt.base import BaseConvexOptimizer
     from pypfopt.objective_functions import ex_post_tracking_error
 
     historic_rets = ... # dataframe of historic asset returns
@@ -231,7 +231,7 @@ or a ``nonconvex_objective``, which uses ``scipy.optimize`` as the backend and t
 different API. For more examples, check out this `cookbook recipe
 <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/3-Advanced-Mean-Variance-Optimization.ipynb>`_.
 
-    .. class:: pypfopt.base_optimizer.BaseConvexOptimizer
+    .. class:: pypfopt.base.BaseConvexOptimizer
 
         .. automethod:: convex_objective
 

docs/MeanVariance.rst

@@ -32,7 +32,7 @@ language for convex optimization upon which PyPortfolioOpt's efficient frontier
 
 .. tip::
 
-    You can find complete examples in the relevant cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimization.ipynb>`_.
+    You can find complete examples in the relevant cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb>`_.
 
 
 Structure
@@ -114,9 +114,9 @@ Basic Usage
                 If you would like to use the ``portfolio_performance`` function independently of any
                 optimizer (e.g for debugging purposes), you can use::
 
-                    from pypfopt import base_optimizer
+                    from pypfopt.base import portfolio_performance
 
-                    base_optimizer.portfolio_performance(
+                    portfolio_performance(
                         weights, expected_returns, cov_matrix, verbose=True, risk_free_rate=0.02
                     )
 
@@ -135,7 +135,7 @@ EfficientFrontier inherits from the BaseConvexOptimizer class. In particular, th
 add constraints and objectives are documented below:
 
 
-.. class:: pypfopt.base_optimizer.BaseConvexOptimizer
+.. class:: pypfopt.base.BaseConvexOptimizer
     :noindex:
 
     .. automethod:: add_constraint

docs/OtherOptimizers.rst

@@ -95,11 +95,11 @@ to code up compared to custom objective functions.
 
 To implement a custom optimizer that is compatible with the rest of PyPortfolioOpt, just
 extend ``BaseOptimizer`` (or ``BaseConvexOptimizer`` if you want to use ``cvxpy``),
-both of which can be found in ``base_optimizer.py``. This gives you access to utility
+both of which can be found in ``pypfopt.base``. This gives you access to utility
 methods like ``clean_weights()``, as well as making sure that any output is compatible
 with ``portfolio_performance()`` and post-processing methods.
 
-.. automodule:: pypfopt.base_optimizer
+.. automodule:: pypfopt.base
 
     .. autoclass:: BaseOptimizer
         :members:

docs/Roadmap.rst

@@ -8,7 +8,13 @@ Roadmap and Changelog
 Roadmap
 =======
 
-PyPortfolioOpt is now a "mature" package – it is stable and I don't intend to implement major new functionality (though I will endeavour to fix bugs).
+1.6.0
+=====
+
+- Support for python 3.14, end-of-life for python 3.8 and 3.9
+- Support for pandas 3.0
+- Reduction of dependency footprint. Former dependencies are still available as soft dependencies in the new ``all_extras`` depset.
+- Feature additions and bugfixes.
 
 1.5.0
 =====

docs/UserGuide.rst

@@ -301,7 +301,7 @@ in the sidebar to learn more about the parameters and theoretical details of the
 different models offered by PyPortfolioOpt. If you have any questions, please
 raise an issue on GitHub and I will try to respond promptly.
 
-If you'd like even more examples, check out the cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimization.ipynb>`_.
+If you'd like even more examples, check out the cookbook `recipe <https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/cookbook/2-Mean-Variance-Optimisation.ipynb>`_.
 
 
 References

docs/conf.py

@@ -56,9 +56,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = "1.5"
+version = "1.6"
 # The full version, including alpha/beta/rc tags.
-release = "1.5.6"
+release = "1.6.0"
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/index.rst

@@ -15,7 +15,7 @@
                 <img src="https://img.shields.io/badge/python-v3-brightgreen.svg"
                     alt="python"></a> &nbsp;
             <a href="https://pypi.org/project/PyPortfolioOpt/">
-                <img src="https://img.shields.io/badge/pypi-v1.5.6-brightgreen.svg"
+                <img src="https://img.shields.io/badge/pypi-v1.6.0-brightgreen.svg"
                     alt="python"></a> &nbsp;
             <a href="https://opensource.org/licenses/MIT">
                 <img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"