chore(docs): fix typo [skip ci]

* fix(typos): fix typos

* docs: add @rriski as a contributor

.all-contributorsrc

@@ -12,4 +12,15 @@
   "contributorsPerLine": 7,
   "skipCi": true,
   "commitType": "docs",
+  "contributors": [
+    {
+      "login": "rriski",
+      "name": "rriski",
+      "avatar_url": "https://avatars.githubusercontent.com/u/25483483?v=4",
+      "profile": "https://github.com/rriski",
+      "contributions": [
+        "doc"
+      ]
+    }
+  ]
 }

pyproject.toml

@@ -33,7 +33,7 @@ keywords = [
     "quantitative",
     "quant",
     "investment",
-    "startegy",
+    "strategy",
     "machine-learning",
     "scikit-learn",
     "data-mining",

src/skfolio/model_selection/_walk_forward.py

@@ -39,7 +39,7 @@ class WalkForward(skm.BaseCrossValidator):
 
     reduce_test : bool, default=False
         If this is set to True, the last train/test split will be returned even if the
-        test set is partial (if it constains less observations than `test_size`),
+        test set is partial (if it contains less observations than `test_size`),
         otherwise it will be ignored.
         The default is `False`
 

src/skfolio/optimization/convex/_base.py

@@ -43,7 +43,7 @@ class ObjectiveFunction(AutoEnum):
     MAXIMIZE_UTILITY : str
         Maximize the utility  :math:`w^T\mu - \lambda \times risk(w)`.
 
-    MAXIMIZE_UTILITY : str
+    MAXIMIZE_RATIO : str
         Maximize the ratio  :math:`\frac{w^T\mu - R_{f}}{risk(w)}`.
     """
 

src/skfolio/optimization/convex/_maximum_diversification.py

@@ -21,7 +21,7 @@ class MaximumDiversification(MeanRisk):
 
     It is a special case of the :class:`~skfolio.optimization.MeanRisk` estimator where
     the expected return from the objective function is replaced by the weighted
-    volatilies.
+    volatilities.
 
     Parameters
     ----------

src/skfolio/optimization/convex/_mean_risk.py

@@ -931,7 +931,7 @@ def fit(self, X: npt.ArrayLike, y: npt.ArrayLike | None = None) -> "MeanRisk":
             case ObjectiveFunction.MAXIMIZE_RATIO:
                 if expected_return.is_affine():
                     # Charnes-Cooper's variable transformation for Fractional
-                    # Programing problem :Max(f1/f2) with f2 linear
+                    # Programming problem :Max(f1/f2) with f2 linear
                     constraints += [
                         expected_return * self._scale_constraints
                         - cp.Constant(self.risk_free_rate)
@@ -941,7 +941,7 @@ def fit(self, X: npt.ArrayLike, y: npt.ArrayLike | None = None) -> "MeanRisk":
                     ]
                 else:
                     # Schaible's generalization of Charnes-Cooper's variable
-                    # transformation for Fractional Programing problem :Max(f1/f2)
+                    # transformation for Fractional Programming problem :Max(f1/f2)
                     # with f1 concave instead of linear: Schaible,"Parameter-free
                     # Convex Equivalent and Dual Programs of Fractional Programming
                     # Problems".

src/skfolio/portfolio/_base.py

@@ -705,7 +705,7 @@ def copy(self):
         return self.__copy__()
 
     def clear(self) -> None:
-        """CLear all measures, fitness, cumulative returns and drawdowns in slots"""
+        """Clear all measures, fitness, cumulative returns and drawdowns in slots"""
         attrs = ["_fitness", "_cumulative_returns", "_drawdowns"]
         for attr in attrs + list(_MEASURES_VALUES):
             delattr(self, attr)