fmt

.pre-commit-config.yaml

@@ -5,12 +5,15 @@ repos:
       - id: check-toml
       - id: end-of-file-fixer
       - id: trailing-whitespace
+      - id: check-yaml
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     rev: 'v0.8.4'
     hooks:
       - id: ruff
         args: [ --fix, --exit-non-zero-on-fix ]
+      # Run the formatter
+      - id: ruff-format
 
   - repo: https://github.com/igorshubovych/markdownlint-cli
     rev: v0.43.0
@@ -38,3 +41,19 @@ repos:
           files: ^(cvx)
           args:
             ['--license-filepath', 'copyright.txt', '--no-extra-eol']
+
+  - repo: https://github.com/rhysd/actionlint
+    rev: v1.7.5
+    hooks:
+      - id: actionlint
+        args: [-ignore, SC]
+
+  - repo: https://github.com/abravalheri/validate-pyproject
+    rev: v0.23
+    hooks:
+      - id: validate-pyproject
+
+  - repo: https://github.com/crate-ci/typos
+    rev: v1.29.0
+    hooks:
+      - id: typos

book/marimo/demo.py

@@ -34,9 +34,7 @@ def __(__file__):
 @app.cell
 def __(path, pd):
     # Load some historic stock prices
-    prices = pd.read_csv(
-        path / "data" / "stock_prices.csv", index_col=0, parse_dates=True, header=0
-    )
+    prices = pd.read_csv(path / "data" / "stock_prices.csv", index_col=0, parse_dates=True, header=0)
 
     # Estimate a series of historic covariance matrices
     returns = prices.pct_change().dropna(axis=0, how="all")

book/marimo/factormodel.py

@@ -23,9 +23,7 @@ def __():
 @app.cell
 def __(path, pd):
     # Load some historic stock prices
-    prices = pd.read_csv(
-        path / "data" / "stock_prices.csv", index_col=0, parse_dates=True, header=0
-    )
+    prices = pd.read_csv(path / "data" / "stock_prices.csv", index_col=0, parse_dates=True, header=0)
 
     # Estimate a series of historic covariance matrices
     returns = prices.pct_change().dropna(axis=0, how="all")

book/marimo/large.py

@@ -66,11 +66,7 @@ def __(beta, cvx, factors, minrisk_problem, np, pd, ret, triangle):
     triangle.update(
         exposure=beta.values,
         cov=factors.cov().values,
-        idiosyncratic_risk=pd.DataFrame(
-            data=ret - factors @ beta, index=ret.index, columns=ret.columns
-        )
-        .std()
-        .values,
+        idiosyncratic_risk=pd.DataFrame(data=ret - factors @ beta, index=ret.index, columns=ret.columns).std().values,
         lower_assets=np.zeros(1000),
         upper_assets=np.ones(1000),
         lower_factors=-0.1 * np.ones(100),
@@ -86,9 +82,7 @@ def __(beta, factors, minrisk_problem, np, pd, ret, triangle, w, y):
         triangle.update(
             exposure=beta.values,
             cov=factors.cov().values,
-            idiosyncratic_risk=pd.DataFrame(
-                data=ret - factors @ beta, index=ret.index, columns=ret.columns
-            )
+            idiosyncratic_risk=pd.DataFrame(data=ret - factors @ beta, index=ret.index, columns=ret.columns)
             .std()
             .values,
             lower_assets=np.zeros(1000),

cvx/portfolio/min_risk.py

@@ -19,8 +19,7 @@
 def minrisk_problem(riskmodel, weights, **kwargs):
     problem = cp.Problem(
         cp.Minimize(riskmodel.estimate(weights, **kwargs)),
-        [cp.sum(weights) == 1.0, weights >= 0]
-        + riskmodel.constraints(weights, **kwargs),
+        [cp.sum(weights) == 1.0, weights >= 0] + riskmodel.constraints(weights, **kwargs),
     )
 
     return problem

cvx/risk/bounds.py

@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Bounds"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass

cvx/risk/cvar/cvar.py

@@ -37,9 +37,7 @@ class CVar(Model):
 
     def __post_init__(self):
         self.k = int(self.n * (1 - self.alpha))
-        self.parameter["R"] = cvx.Parameter(
-            shape=(self.n, self.m), name="returns", value=np.zeros((self.n, self.m))
-        )
+        self.parameter["R"] = cvx.Parameter(shape=(self.n, self.m), name="returns", value=np.zeros((self.n, self.m)))
         self.bounds = Bounds(m=self.m, name="assets")
 
     def estimate(self, weights, **kwargs):

cvx/risk/factor/factor.py

@@ -11,8 +11,8 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
-"""Factor risk model
-"""
+"""Factor risk model"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -59,15 +59,11 @@ def estimate(self, weights, **kwargs):
         """
         Compute the total variance
         """
-        var_residual = cvx.norm2(
-            cvx.multiply(self.parameter["idiosyncratic_risk"], weights)
-        )
+        var_residual = cvx.norm2(cvx.multiply(self.parameter["idiosyncratic_risk"], weights))
 
         y = kwargs.get("y", self.parameter["exposure"] @ weights)
 
-        return cvx.norm2(
-            cvx.vstack([cvx.norm2(self.parameter["chol"] @ y), var_residual])
-        )
+        return cvx.norm2(cvx.vstack([cvx.norm2(self.parameter["chol"] @ y), var_residual]))
 
     def update(self, **kwargs):
         self.parameter["exposure"].value = np.zeros((self.k, self.assets))
@@ -83,9 +79,7 @@ def update(self, **kwargs):
         assert assets <= self.assets
 
         self.parameter["exposure"].value[:k, :assets] = kwargs["exposure"]
-        self.parameter["idiosyncratic_risk"].value[:assets] = kwargs[
-            "idiosyncratic_risk"
-        ]
+        self.parameter["idiosyncratic_risk"].value[:assets] = kwargs["idiosyncratic_risk"]
         self.parameter["chol"].value[:k, :k] = cholesky(kwargs["cov"])
         self.bounds_assets.update(**kwargs)
         self.bounds_factors.update(**kwargs)

cvx/risk/linalg/pca.py

@@ -11,8 +11,8 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
-"""PCA analysis
-"""
+"""PCA analysis"""
+
 from __future__ import annotations
 
 from collections import namedtuple
@@ -48,9 +48,7 @@ def pca(returns, n_components=10):
         factors=factors,
         exposure=pd.DataFrame(data=exposure, columns=returns.columns),
         cov=factors.cov(),
-        systematic=pd.DataFrame(
-            data=factors.values @ exposure, index=returns.index, columns=returns.columns
-        ),
+        systematic=pd.DataFrame(data=factors.values @ exposure, index=returns.index, columns=returns.columns),
         idiosyncratic=pd.DataFrame(
             data=returns.values - factors.values @ exposure,
             index=returns.index,

cvx/risk/model.py

@@ -11,8 +11,8 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
-"""Abstract risk model
-"""
+"""Abstract risk model"""
+
 from __future__ import annotations
 
 from abc import ABC, abstractmethod

cvx/risk/sample/sample.py

@@ -11,8 +11,8 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
-"""Risk models based on the sample covariance matrix
-"""
+"""Risk models based on the sample covariance matrix"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass

pyproject.toml

@@ -26,13 +26,16 @@ dev = [
 ]
 
 [tool.ruff]
-select = ["E", "F", "I"]
 line-length = 120
 target-version = "py310"
 exclude = [
     "*__init__.py"
 ]
 
+[tool.ruff.lint]
+select = ["E", "F", "I"]
+
+
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"

tests/test_risk/test_bounds.py

@@ -18,15 +18,9 @@ def test_raise_not_implemented():
 def test_constraints():
     weights = cp.Variable(3)
     bounds = Bounds(m=3, name="assets")
-    bounds.update(
-        lower_assets=np.array([0.1, 0.2]), upper_assets=np.array([0.3, 0.4, 0.5])
-    )
-
-    assert bounds.parameter["lower_assets"].value == pytest.approx(
-        np.array([0.1, 0.2, 0])
-    )
-    assert bounds.parameter["upper_assets"].value == pytest.approx(
-        np.array([0.3, 0.4, 0.5])
-    )
+    bounds.update(lower_assets=np.array([0.1, 0.2]), upper_assets=np.array([0.3, 0.4, 0.5]))
+
+    assert bounds.parameter["lower_assets"].value == pytest.approx(np.array([0.1, 0.2, 0]))
+    assert bounds.parameter["upper_assets"].value == pytest.approx(np.array([0.3, 0.4, 0.5]))
 
     assert len(bounds.constraints(weights)) == 2

tests/test_risk/test_factor/test_factor.py

@@ -13,9 +13,7 @@
 
 @pytest.fixture()
 def returns(resource_dir):
-    prices = pd.read_csv(
-        resource_dir / "stock_prices.csv", index_col=0, header=0, parse_dates=True
-    )
+    prices = pd.read_csv(resource_dir / "stock_prices.csv", index_col=0, header=0, parse_dates=True)
     return prices.pct_change().fillna(0.0)
 
 
@@ -93,9 +91,7 @@ def test_estimate_risk():
     assert np.array(weights.value[20:]) == pytest.approx(np.zeros(5), abs=1e-6)
 
     # test that the exposure is correct, e.g. the factor weights match the exposure * asset weights
-    assert model.parameter["exposure"].value @ weights.value == pytest.approx(
-        y.value, abs=1e-6
-    )
+    assert model.parameter["exposure"].value @ weights.value == pytest.approx(y.value, abs=1e-6)
 
     # test all entries of y are smaller than 0.1
     assert np.all([y.value <= 0.1 + 1e-6])
@@ -125,8 +121,6 @@ def test_dynamic_exposure():
         upper_factors=np.array([1.0]),
     )
 
-    np.testing.assert_array_equal(
-        model.parameter["exposure"].value, np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
-    )
+    np.testing.assert_array_equal(model.parameter["exposure"].value, np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]))
 
     # assert False

tests/test_risk/test_linalg/test_pca.py

@@ -9,9 +9,7 @@
 
 @pytest.fixture()
 def returns(resource_dir):
-    prices = pd.read_csv(
-        resource_dir / "stock_prices.csv", index_col=0, header=0, parse_dates=True
-    )
+    prices = pd.read_csv(resource_dir / "stock_prices.csv", index_col=0, header=0, parse_dates=True)
     return prices.pct_change().fillna(0.0)
 
 

tests/test_risk/test_sample/test_sample.py

@@ -41,9 +41,7 @@ def test_min_variance():
         upper_assets=np.ones(2),
     )
     problem.solve()
-    np.testing.assert_almost_equal(
-        weights.value, np.array([0.75, 0.25, 0.0, 0.0]), decimal=5
-    )
+    np.testing.assert_almost_equal(weights.value, np.array([0.75, 0.25, 0.0, 0.0]), decimal=5)
 
     # It's enough to only update the value for the cholesky decomposition
     riskmodel.update(
@@ -52,6 +50,4 @@ def test_min_variance():
         upper_assets=np.ones(2),
     )
     problem.solve()
-    np.testing.assert_almost_equal(
-        weights.value, np.array([0.875, 0.125, 0.0, 0.0]), decimal=5
-    )
+    np.testing.assert_almost_equal(weights.value, np.array([0.875, 0.125, 0.0, 0.0]), decimal=5)