formatting based on the .pre-commit hooks

.pre-commit-config.yaml

@@ -0,0 +1,30 @@
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v5.0.0
+    hooks:
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/psf/black
+    rev: 24.10.0
+    hooks:
+      - id: black
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: 'v0.8.1'
+    hooks:
+      - id: ruff
+        args: [ --fix, --exit-non-zero-on-fix ]
+
+  - repo: https://github.com/asottile/pyupgrade
+    rev: v3.19.0
+    hooks:
+      - id: pyupgrade
+
+  - repo: https://github.com/python-jsonschema/check-jsonschema
+    rev: 0.30.0
+    hooks:
+      - id: check-dependabot
+        args: ["--verbose"]
+      - id: check-github-workflows
+        args: ["--verbose"]

pypfopt/black_litterman.py

@@ -135,7 +135,7 @@ def __init__(
         view_confidences=None,
         tau=0.05,
         risk_aversion=1,
-        **kwargs
+        **kwargs,
     ):
         """
         :param cov_matrix: NxN covariance matrix of returns

pypfopt/cla.py

@@ -314,11 +314,11 @@ def _solve(self):
                 covarF_inv = np.linalg.inv(covarF)
                 j = 0
                 for i in f:
-                    l, bi = self._compute_lambda(
+                    lamb, bi = self._compute_lambda(
                         covarF_inv, covarFB, meanF, wB, j, [self.lB[i], self.uB[i]]
                     )
-                    if CLA._infnone(l) > CLA._infnone(l_in):
-                        l_in, i_in, bi_in = l, i, bi
+                    if CLA._infnone(lamb) > CLA._infnone(l_in):
+                        l_in, i_in, bi_in = lamb, i, bi
                     j += 1
             # 2) case b): Free one bounded weight
             l_out = None
@@ -327,18 +327,18 @@ def _solve(self):
                 for i in b:
                     covarF, covarFB, meanF, wB = self._get_matrices(f + [i])
                     covarF_inv = np.linalg.inv(covarF)
-                    l, bi = self._compute_lambda(
+                    lamb, bi = self._compute_lambda(
                         covarF_inv,
                         covarFB,
                         meanF,
                         wB,
                         meanF.shape[0] - 1,
                         self.w[-1][i],
                     )
-                    if (self.ls[-1] is None or l < self.ls[-1]) and l > CLA._infnone(
-                        l_out
-                    ):
-                        l_out, i_out = l, i
+                    if (
+                        self.ls[-1] is None or lamb < self.ls[-1]
+                    ) and lamb > CLA._infnone(l_out):
+                        l_out, i_out = lamb, i
             if (l_in is None or l_in < 0) and (l_out is None or l_out < 0):
                 # 3) compute minimum variance solution
                 self.ls.append(0)

pypfopt/discrete_allocation.py

@@ -82,7 +82,7 @@ def __init__(
         self.latest_prices = latest_prices
         self.total_portfolio_value = total_portfolio_value
         if short_ratio is None:
-            self.short_ratio = sum((-x[1] for x in self.weights if x[1] < 0))
+            self.short_ratio = sum(-x[1] for x in self.weights if x[1] < 0)
         else:
             self.short_ratio = short_ratio
 

pypfopt/risk_models.py

@@ -181,7 +181,7 @@ def semicovariance(
     benchmark=0.000079,
     frequency=252,
     log_returns=False,
-    **kwargs
+    **kwargs,
 ):
     """
     Estimate the semicovariance matrix, i.e the covariance given that
@@ -290,7 +290,7 @@ def min_cov_determinant(
     frequency=252,
     random_state=None,
     log_returns=False,
-    **kwargs
+    **kwargs,
 ):  # pragma: no cover
     warnings.warn("min_cov_determinant is deprecated and will be removed in v1.5")
 

tests/test_base_optimizer.py

@@ -189,17 +189,17 @@ def test_save_weights_to_file():
         json.dump(weights, f)
 
     # Test reading
-    with open(test_file_path_txt, "r") as f:
+    with open(test_file_path_txt) as f:
         parsed = json.load(f)
     assert ef.clean_weights() == parsed
 
     ef.save_weights_to_file(test_file_path_json)
-    with open(test_file_path_json, "r") as f:
+    with open(test_file_path_json) as f:
         parsed = json.load(f)
     assert ef.clean_weights() == parsed
 
     ef.save_weights_to_file(test_file_path_csv)
-    with open(test_file_path_csv, "r") as f:
+    with open(test_file_path_csv) as f:
         df = pd.read_csv(
             f,
             header=None,

tests/test_plotting.py

@@ -58,12 +58,12 @@ def test_dendrogram_plot():
 
     ax = plotting.plot_dendrogram(hrp, showfig=False)
     assert len(ax.findobj()) > 180
-    assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection
+    assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection)
     plt.clf()
 
     ax = plotting.plot_dendrogram(hrp, show_tickers=False, showfig=False)
     assert len(ax.findobj()) > 60
-    assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection
+    assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection)
     plt.clf()
     plt.close()
 
@@ -78,7 +78,7 @@ def test_dendrogram_plot():
             == "hrp param has not been optimized.  Attempting optimization."
         )
         assert len(ax.findobj()) > 60
-        assert type(ax.findobj()[0]) == matplotlib.collections.LineCollection
+        assert isinstance(ax.findobj()[0], matplotlib.collections.LineCollection)
     plt.clf()
     plt.close()