diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 7233c35d..5b20760e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -11,6 +11,8 @@ repos:
     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
diff --git a/book/marimo/demo.py b/book/marimo/demo.py
index 35854d6f..527e6b96 100644
--- a/book/marimo/demo.py
+++ b/book/marimo/demo.py
@@ -115,9 +115,7 @@ def __(portfolio):
 
 @app.cell
 def __(pd, portfolio, portfolio_resampled):
-    frame = pd.DataFrame(
-        {"original": portfolio.nav, "monthly": portfolio_resampled.nav}
-    )
+    frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
     frame
     return (frame,)
 
diff --git a/cvx/linalg/cholesky.py b/cvx/linalg/cholesky.py
index fc37502c..ab2aa65e 100644
--- a/cvx/linalg/cholesky.py
+++ b/cvx/linalg/cholesky.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Cholesky decomposition with numpy"""
+
 from __future__ import annotations
 
 import numpy as np
diff --git a/cvx/linalg/pca.py b/cvx/linalg/pca.py
index fd5c4944..0f373f60 100644
--- a/cvx/linalg/pca.py
+++ b/cvx/linalg/pca.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """PCA analysis with numpy"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -28,9 +29,7 @@ class PCA:
 
     def __post_init__(self) -> None:
         if self.n_components > self.returns.shape[1]:
-            raise ValueError(
-                "The number of components cannot exceed the number of assets"
-            )
+            raise ValueError("The number of components cannot exceed the number of assets")
 
         # compute the principal components without sklearn
         # 1. compute the correlation
@@ -51,9 +50,7 @@ def __post_init__(self) -> None:
 
     @property
     def explained_variance(self) -> Matrix:
-        return np.array(
-            self.eigenvalues[: self.n_components] / np.sum(self.eigenvalues)
-        )
+        return np.array(self.eigenvalues[: self.n_components] / np.sum(self.eigenvalues))
 
     @property
     def cov(self) -> Matrix:
diff --git a/cvx/linalg/valid.py b/cvx/linalg/valid.py
index 28e54719..3ce2b99a 100644
--- a/cvx/linalg/valid.py
+++ b/cvx/linalg/valid.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Extract valid submatrix of a matrix"""
+
 from __future__ import annotations
 
 import numpy as np
diff --git a/cvx/markowitz/builder.py b/cvx/markowitz/builder.py
index fe9c2082..c5f25487 100644
--- a/cvx/markowitz/builder.py
+++ b/cvx/markowitz/builder.py
@@ -121,13 +121,9 @@ def __post_init__(self) -> None:
             self.model[M.RISK] = FactorModel(assets=self.assets, factors=self.factors)
 
             # add variable for factor weights
-            self.variables[D.FACTOR_WEIGHTS] = cp.Variable(
-                self.factors, name=D.FACTOR_WEIGHTS
-            )
+            self.variables[D.FACTOR_WEIGHTS] = cp.Variable(self.factors, name=D.FACTOR_WEIGHTS)
             # add bounds for factor weights
-            self.model[M.BOUND_FACTORS] = Bounds(
-                assets=self.factors, name="factors", acting_on=D.FACTOR_WEIGHTS
-            )
+            self.model[M.BOUND_FACTORS] = Bounds(assets=self.factors, name="factors", acting_on=D.FACTOR_WEIGHTS)
             # add variable for absolute factor weights
             self.variables[D._ABS] = cp.Variable(self.factors, name=D._ABS, nonneg=True)
 
@@ -141,9 +137,7 @@ def __post_init__(self) -> None:
         self.variables[D.WEIGHTS] = cp.Variable(self.assets, name=D.WEIGHTS)
 
         # add bounds on assets
-        self.model[M.BOUND_ASSETS] = Bounds(
-            assets=self.assets, name="assets", acting_on=D.WEIGHTS
-        )
+        self.model[M.BOUND_ASSETS] = Bounds(assets=self.assets, name="assets", acting_on=D.WEIGHTS)
 
     @property
     @abstractmethod
@@ -157,9 +151,7 @@ def build(self) -> _Problem:
         Build the cvxpy problem
         """
         for name_model, model in self.model.items():
-            for name_constraint, constraint in model.constraints(
-                self.variables
-            ).items():
+            for name_constraint, constraint in model.constraints(self.variables).items():
                 self.constraints[f"{name_model}_{name_constraint}"] = constraint
 
         problem = cp.Problem(self.objective, list(self.constraints.values()))
diff --git a/cvx/markowitz/model.py b/cvx/markowitz/model.py
index 328cafc0..16c6fd17 100644
--- a/cvx/markowitz/model.py
+++ b/cvx/markowitz/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 cp model
-"""
+"""Abstract cp model"""
+
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
diff --git a/cvx/markowitz/models/bounds.py b/cvx/markowitz/models/bounds.py
index 208ffd21..bfa94547 100644
--- a/cvx/markowitz/models/bounds.py
+++ b/cvx/markowitz/models/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
@@ -49,17 +50,11 @@ def __post_init__(self) -> None:
         )
 
     def update(self, **kwargs: Matrix) -> None:
-        self.data[self._f("lower")].value = fill_vector(
-            num=self.assets, x=kwargs[self._f("lower")]
-        )
-        self.data[self._f("upper")].value = fill_vector(
-            num=self.assets, x=kwargs[self._f("upper")]
-        )
+        self.data[self._f("lower")].value = fill_vector(num=self.assets, x=kwargs[self._f("lower")])
+        self.data[self._f("upper")].value = fill_vector(num=self.assets, x=kwargs[self._f("upper")])
 
     def constraints(self, variables: Variables) -> Expressions:
         return {
-            f"lower bound {self.name}": variables[self.acting_on]
-            >= self.data[self._f("lower")],
-            f"upper bound {self.name}": variables[self.acting_on]
-            <= self.data[self._f("upper")],
+            f"lower bound {self.name}": variables[self.acting_on] >= self.data[self._f("lower")],
+            f"upper bound {self.name}": variables[self.acting_on] <= self.data[self._f("upper")],
         }
diff --git a/cvx/markowitz/models/expected_returns.py b/cvx/markowitz/models/expected_returns.py
index af6d816f..65d26d7f 100644
--- a/cvx/markowitz/models/expected_returns.py
+++ b/cvx/markowitz/models/expected_returns.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Model for expected returns"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -46,9 +47,7 @@ def __post_init__(self) -> None:
         )
 
     def estimate(self, variables: Variables) -> cp.Expression:
-        return self.data[D.MU] @ variables[D.WEIGHTS] - self.parameter[
-            "mu_uncertainty"
-        ] @ cp.abs(variables[D.WEIGHTS])
+        return self.data[D.MU] @ variables[D.WEIGHTS] - self.parameter["mu_uncertainty"] @ cp.abs(variables[D.WEIGHTS])
 
     def update(self, **kwargs: Matrix) -> None:
         exp_returns = kwargs[D.MU]
@@ -59,6 +58,4 @@ def update(self, **kwargs: Matrix) -> None:
         if not uncertainty.shape[0] == exp_returns.shape[0]:
             raise CvxError("Mismatch in length for mu and mu_uncertainty")
 
-        self.parameter["mu_uncertainty"].value = fill_vector(
-            num=self.assets, x=uncertainty
-        )
+        self.parameter["mu_uncertainty"].value = fill_vector(num=self.assets, x=uncertainty)
diff --git a/cvx/markowitz/models/holding_costs.py b/cvx/markowitz/models/holding_costs.py
index 7682c09b..1053bdd1 100644
--- a/cvx/markowitz/models/holding_costs.py
+++ b/cvx/markowitz/models/holding_costs.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Model for holding costs"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -30,16 +31,10 @@ class HoldingCosts(Model):
     """Model for holding costs"""
 
     def __post_init__(self) -> None:
-        self.data[D.HOLDING_COSTS] = cp.Parameter(
-            shape=self.assets, name=D.HOLDING_COSTS, value=np.zeros(self.assets)
-        )
+        self.data[D.HOLDING_COSTS] = cp.Parameter(shape=self.assets, name=D.HOLDING_COSTS, value=np.zeros(self.assets))
 
     def estimate(self, variables: Variables) -> cp.Expression:
-        return cp.sum(
-            cp.neg(cp.multiply(variables[D.WEIGHTS], self.data[D.HOLDING_COSTS]))
-        )
+        return cp.sum(cp.neg(cp.multiply(variables[D.WEIGHTS], self.data[D.HOLDING_COSTS])))
 
     def update(self, **kwargs: Matrix) -> None:
-        self.data[D.HOLDING_COSTS].value = fill_vector(
-            num=self.assets, x=kwargs[D.HOLDING_COSTS]
-        )
+        self.data[D.HOLDING_COSTS].value = fill_vector(num=self.assets, x=kwargs[D.HOLDING_COSTS])
diff --git a/cvx/markowitz/models/trading_costs.py b/cvx/markowitz/models/trading_costs.py
index c916bf84..1e22563d 100644
--- a/cvx/markowitz/models/trading_costs.py
+++ b/cvx/markowitz/models/trading_costs.py
@@ -12,6 +12,7 @@
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
 """Model for trading costs"""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -33,9 +34,7 @@ def __post_init__(self) -> None:
         self.parameter["power"] = cp.Parameter(shape=1, name="power", value=np.ones(1))
 
         # initial weights before rebalancing
-        self.data["weights"] = cp.Parameter(
-            shape=self.assets, name="weights", value=np.zeros(self.assets)
-        )
+        self.data["weights"] = cp.Parameter(shape=self.assets, name="weights", value=np.zeros(self.assets))
 
     def estimate(self, variables: Variables) -> cp.Expression:
         return cp.sum(
diff --git a/cvx/markowitz/portfolios/max_sharpe.py b/cvx/markowitz/portfolios/max_sharpe.py
index c7ef2a3e..feb08d87 100644
--- a/cvx/markowitz/portfolios/max_sharpe.py
+++ b/cvx/markowitz/portfolios/max_sharpe.py
@@ -26,7 +26,6 @@
 
 @dataclass(frozen=True)
 class MaxSharpe(Builder):
-
     """
     Minimize the standard deviation of the portfolio returns subject to a set of constraints
     min StdDev(r_p)
@@ -42,12 +41,8 @@ def __post_init__(self) -> None:
 
         self.model[M.RETURN] = ExpectedReturns(assets=self.assets)
 
-        self.parameter[P.SIGMA_MAX] = cp.Parameter(
-            nonneg=True, name="maximal volatility"
-        )
+        self.parameter[P.SIGMA_MAX] = cp.Parameter(nonneg=True, name="maximal volatility")
 
         self.constraints[C.LONG_ONLY] = self.weights >= 0
         self.constraints[C.BUDGET] = cp.sum(self.weights) == 1.0
-        self.constraints[C.RISK] = (
-            self.risk.estimate(self.variables) <= self.parameter[P.SIGMA_MAX]
-        )
+        self.constraints[C.RISK] = self.risk.estimate(self.variables) <= self.parameter[P.SIGMA_MAX]
diff --git a/cvx/markowitz/portfolios/min_var.py b/cvx/markowitz/portfolios/min_var.py
index 94fa284e..d0ece499 100644
--- a/cvx/markowitz/portfolios/min_var.py
+++ b/cvx/markowitz/portfolios/min_var.py
@@ -23,7 +23,6 @@
 
 @dataclass(frozen=True)
 class MinVar(Builder):
-
     """
     Minimize the standard deviation of the portfolio returns subject to a set of constraints
     min StdDev(r_p)
diff --git a/cvx/markowitz/portfolios/soft_risk.py b/cvx/markowitz/portfolios/soft_risk.py
index 6dd5fcd8..4845b021 100644
--- a/cvx/markowitz/portfolios/soft_risk.py
+++ b/cvx/markowitz/portfolios/soft_risk.py
@@ -26,7 +26,6 @@
 
 @dataclass(frozen=True)
 class SoftRisk(Builder):
-
     """
     maximize w^T mu - omega * (sigma - sigma_target)_+
     subject to w >= 0, w^T 1 = 1, sigma <= sigma_max
@@ -40,9 +39,7 @@ class SoftRisk(Builder):
     @property
     def objective(self) -> cp.Maximize:
         expected_return = self.model[M.RETURN].estimate(self.variables)
-        soft_risk = cp.pos(
-            self.parameter[P.OMEGA] * self._sigma - self._sigma_target_times_omega
-        )
+        soft_risk = cp.pos(self.parameter[P.OMEGA] * self._sigma - self._sigma_target_times_omega)
         return cp.Maximize(expected_return - soft_risk)
 
     def __post_init__(self) -> None:
@@ -52,9 +49,7 @@ def __post_init__(self) -> None:
 
         self.parameter[P.SIGMA_MAX] = cp.Parameter(nonneg=True, name="limit volatility")
 
-        self.parameter[P.SIGMA_TARGET] = cp.Parameter(
-            nonneg=True, name="target volatility"
-        )
+        self.parameter[P.SIGMA_TARGET] = cp.Parameter(nonneg=True, name="target volatility")
 
         self.parameter[P.OMEGA] = cp.Parameter(nonneg=True, name="risk priority")
         self._sigma_target_times_omega._callback = lambda: (
diff --git a/cvx/markowitz/risk/cvar/cvar.py b/cvx/markowitz/risk/cvar/cvar.py
index cb8d95ed..23ddbdd7 100644
--- a/cvx/markowitz/risk/cvar/cvar.py
+++ b/cvx/markowitz/risk/cvar/cvar.py
@@ -50,6 +50,4 @@ def estimate(self, variables: Variables) -> cp.Expression:
         return -cp.sum_smallest(self.data[D.RETURNS] @ variables[D.WEIGHTS], k=k) / k
 
     def update(self, **kwargs: Matrix) -> None:
-        self.data[D.RETURNS].value = fill_matrix(
-            rows=self.rows, cols=self.assets, x=kwargs[D.RETURNS]
-        )
+        self.data[D.RETURNS].value = fill_matrix(rows=self.rows, cols=self.assets, x=kwargs[D.RETURNS])
diff --git a/cvx/markowitz/risk/factor/factor.py b/cvx/markowitz/risk/factor/factor.py
index 230fb307..5c972154 100644
--- a/cvx/markowitz/risk/factor/factor.py
+++ b/cvx/markowitz/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
@@ -104,13 +104,8 @@ def update(self, **kwargs: Matrix) -> None:
             if key not in kwargs.keys():
                 raise CvxError(f"Missing keyword {key}")
 
-        if (
-            not kwargs[D.IDIOSYNCRATIC_VOLA].shape[0]
-            == kwargs[D.IDIOSYNCRATIC_VOLA_UNCERTAINTY].shape[0]
-        ):
-            raise CvxError(
-                "Mismatch in length for idiosyncratic_vola and idiosyncratic_vola_uncertainty"
-            )
+        if not kwargs[D.IDIOSYNCRATIC_VOLA].shape[0] == kwargs[D.IDIOSYNCRATIC_VOLA_UNCERTAINTY].shape[0]:
+            raise CvxError("Mismatch in length for idiosyncratic_vola and idiosyncratic_vola_uncertainty")
 
         exposure = kwargs[D.EXPOSURE]
         k, assets = exposure.shape
@@ -119,22 +114,14 @@ def update(self, **kwargs: Matrix) -> None:
             raise CvxError("Mismatch in length for idiosyncratic_vola and exposure")
 
         if not kwargs[D.SYSTEMATIC_VOLA_UNCERTAINTY].shape[0] == k:
-            raise CvxError(
-                "Mismatch in length of systematic_vola_uncertainty and exposure"
-            )
+            raise CvxError("Mismatch in length of systematic_vola_uncertainty and exposure")
 
         if not kwargs[D.CHOLESKY].shape[0] == k:
             raise CvxError("Mismatch in size of chol and exposure")
 
-        self.data[D.EXPOSURE].value = fill_matrix(
-            rows=self.factors, cols=self.assets, x=kwargs["exposure"]
-        )
-        self.data[D.IDIOSYNCRATIC_VOLA].value = fill_vector(
-            num=self.assets, x=kwargs[D.IDIOSYNCRATIC_VOLA]
-        )
-        self.data[D.CHOLESKY].value = fill_matrix(
-            rows=self.factors, cols=self.factors, x=kwargs[D.CHOLESKY]
-        )
+        self.data[D.EXPOSURE].value = fill_matrix(rows=self.factors, cols=self.assets, x=kwargs["exposure"])
+        self.data[D.IDIOSYNCRATIC_VOLA].value = fill_vector(num=self.assets, x=kwargs[D.IDIOSYNCRATIC_VOLA])
+        self.data[D.CHOLESKY].value = fill_matrix(rows=self.factors, cols=self.factors, x=kwargs[D.CHOLESKY])
 
         # Robust risk
         self.data[D.SYSTEMATIC_VOLA_UNCERTAINTY].value = fill_vector(
@@ -146,8 +133,6 @@ def update(self, **kwargs: Matrix) -> None:
 
     def constraints(self, variables: Variables) -> Expressions:
         return {
-            "factors": variables[D.FACTOR_WEIGHTS]
-            == self.data[D.EXPOSURE] @ variables[D.WEIGHTS],
-            "_abs": variables[D._ABS]
-            >= cp.abs(variables[D.FACTOR_WEIGHTS]),  # Robust risk dummy variable
+            "factors": variables[D.FACTOR_WEIGHTS] == self.data[D.EXPOSURE] @ variables[D.WEIGHTS],
+            "_abs": variables[D._ABS] >= cp.abs(variables[D.FACTOR_WEIGHTS]),  # Robust risk dummy variable
         }
diff --git a/cvx/markowitz/risk/sample/sample.py b/cvx/markowitz/risk/sample/sample.py
index ea848695..8147b527 100644
--- a/cvx/markowitz/risk/sample/sample.py
+++ b/cvx/markowitz/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
@@ -63,15 +63,10 @@ def update(self, **kwargs: Matrix) -> None:
         if not kwargs[D.CHOLESKY].shape[0] == kwargs[D.VOLA_UNCERTAINTY].shape[0]:
             raise CvxError("Mismatch in length for chol and vola_uncertainty")
 
-        self.data[D.CHOLESKY].value = fill_matrix(
-            rows=self.assets, cols=self.assets, x=kwargs[D.CHOLESKY]
-        )
-        self.data[D.VOLA_UNCERTAINTY].value = fill_vector(
-            num=self.assets, x=kwargs[D.VOLA_UNCERTAINTY]
-        )
+        self.data[D.CHOLESKY].value = fill_matrix(rows=self.assets, cols=self.assets, x=kwargs[D.CHOLESKY])
+        self.data[D.VOLA_UNCERTAINTY].value = fill_vector(num=self.assets, x=kwargs[D.VOLA_UNCERTAINTY])
 
     def constraints(self, variables: Variables) -> Expressions:
         return {
-            "dummy": variables[D._ABS]
-            >= cp.abs(variables[D.WEIGHTS]),  # Robust risk dummy variable
+            "dummy": variables[D._ABS] >= cp.abs(variables[D.WEIGHTS]),  # Robust risk dummy variable
         }
diff --git a/experiments/minRisk2.py b/experiments/minRisk2.py
index ef82978c..82212fbc 100644
--- a/experiments/minRisk2.py
+++ b/experiments/minRisk2.py
@@ -25,14 +25,10 @@
 
     builder = MinVar(assets=20)
 
-    builder.parameter["max_concentration"] = cp.Parameter(
-        1, name="max_concentration", nonneg=True
-    )
+    builder.parameter["max_concentration"] = cp.Parameter(1, name="max_concentration", nonneg=True)
 
     # You can add constraints before you build the problem
-    builder.constraints[C.CONCENTRATION] = (
-        cp.sum_largest(builder.weights, 2) <= builder.parameter["max_concentration"]
-    )
+    builder.constraints[C.CONCENTRATION] = cp.sum_largest(builder.weights, 2) <= builder.parameter["max_concentration"]
 
     # here we add a constraints
     # w[19] + w[17] <= 0.0001
@@ -44,9 +40,7 @@
     row[19] = 1
     row[17] = 1
 
-    for name, constraint in approx(
-        "xxx", row @ builder.weights, 0.0, builder.parameter["random"]
-    ):
+    for name, constraint in approx("xxx", row @ builder.weights, 0.0, builder.parameter["random"]):
         # print(constraint)
         builder.constraints[name] = constraint
 
diff --git a/experiments/playground.ipynb b/experiments/playground.ipynb
index c7b5e0e7..0ebe4001 100644
--- a/experiments/playground.ipynb
+++ b/experiments/playground.ipynb
@@ -6,8 +6,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dict1 = {\"a\":1, \"b\":2}\n",
-    "dict2 = {\"a\":10, \"b\":20, \"c\":3, \"d\":4}"
+    "dict1 = {\"a\": 1, \"b\": 2}\n",
+    "dict2 = {\"a\": 10, \"b\": 20, \"c\": 3, \"d\": 4}"
    ]
   },
   {
@@ -48,16 +48,18 @@
      "evalue": "Invalid dimensions (2,) for Parameter value.",
      "output_type": "error",
      "traceback": [
-      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
-      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
-      "Cell \u001B[0;32mIn[25], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m \u001B[43ma\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvalue\u001B[49m \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mzeros(\u001B[38;5;241m2\u001B[39m)\n",
-      "File \u001B[0;32m~/Documents/Stanford/Research/My papers/markowitz/code/cvxmarkowitz/.venv/lib/python3.9/site-packages/cvxpy/expressions/constants/parameter.py:87\u001B[0m, in \u001B[0;36mParameter.value\u001B[0;34m(self, val)\u001B[0m\n\u001B[1;32m     85\u001B[0m \u001B[39m@value\u001B[39m\u001B[39m.\u001B[39msetter\n\u001B[1;32m     86\u001B[0m \u001B[39mdef\u001B[39;00m \u001B[39mvalue\u001B[39m(\u001B[39mself\u001B[39m, val):\n\u001B[0;32m---> 87\u001B[0m     \u001B[39mself\u001B[39m\u001B[39m.\u001B[39m_value \u001B[39m=\u001B[39m \u001B[39mself\u001B[39;49m\u001B[39m.\u001B[39;49m_validate_value(val)\n",
-      "File \u001B[0;32m~/Documents/Stanford/Research/My papers/markowitz/code/cvxmarkowitz/.venv/lib/python3.9/site-packages/cvxpy/expressions/leaf.py:394\u001B[0m, in \u001B[0;36mLeaf._validate_value\u001B[0;34m(self, val)\u001B[0m\n\u001B[1;32m    392\u001B[0m val \u001B[39m=\u001B[39m intf\u001B[39m.\u001B[39mconvert(val)\n\u001B[1;32m    393\u001B[0m \u001B[39mif\u001B[39;00m intf\u001B[39m.\u001B[39mshape(val) \u001B[39m!=\u001B[39m \u001B[39mself\u001B[39m\u001B[39m.\u001B[39mshape:\n\u001B[0;32m--> 394\u001B[0m     \u001B[39mraise\u001B[39;00m \u001B[39mValueError\u001B[39;00m(\n\u001B[1;32m    395\u001B[0m         \u001B[39m\"\u001B[39m\u001B[39mInvalid dimensions \u001B[39m\u001B[39m%s\u001B[39;00m\u001B[39m for \u001B[39m\u001B[39m%s\u001B[39;00m\u001B[39m value.\u001B[39m\u001B[39m\"\u001B[39m \u001B[39m%\u001B[39m\n\u001B[1;32m    396\u001B[0m         (intf\u001B[39m.\u001B[39mshape(val), \u001B[39mself\u001B[39m\u001B[39m.\u001B[39m\u001B[39m__class__\u001B[39m\u001B[39m.\u001B[39m\u001B[39m__name__\u001B[39m)\n\u001B[1;32m    397\u001B[0m     )\n\u001B[1;32m    398\u001B[0m projection \u001B[39m=\u001B[39m \u001B[39mself\u001B[39m\u001B[39m.\u001B[39mproject(val)\n\u001B[1;32m    399\u001B[0m \u001B[39m# ^ might be a numpy array, or sparse scipy matrix.\u001B[39;00m\n",
-      "\u001B[0;31mValueError\u001B[0m: Invalid dimensions (2,) for Parameter value."
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalue\u001b[49m \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mzeros(\u001b[38;5;241m2\u001b[39m)\n",
+      "File \u001b[0;32m~/Documents/Stanford/Research/My papers/markowitz/code/cvxmarkowitz/.venv/lib/python3.9/site-packages/cvxpy/expressions/constants/parameter.py:87\u001b[0m, in \u001b[0;36mParameter.value\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[39m@value\u001b[39m\u001b[39m.\u001b[39msetter\n\u001b[1;32m     86\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mvalue\u001b[39m(\u001b[39mself\u001b[39m, val):\n\u001b[0;32m---> 87\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_value(val)\n",
+      "File \u001b[0;32m~/Documents/Stanford/Research/My papers/markowitz/code/cvxmarkowitz/.venv/lib/python3.9/site-packages/cvxpy/expressions/leaf.py:394\u001b[0m, in \u001b[0;36mLeaf._validate_value\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m    392\u001b[0m val \u001b[39m=\u001b[39m intf\u001b[39m.\u001b[39mconvert(val)\n\u001b[1;32m    393\u001b[0m \u001b[39mif\u001b[39;00m intf\u001b[39m.\u001b[39mshape(val) \u001b[39m!=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mshape:\n\u001b[0;32m--> 394\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    395\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mInvalid dimensions \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m for \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m value.\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m    396\u001b[0m         (intf\u001b[39m.\u001b[39mshape(val), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__name__\u001b[39m)\n\u001b[1;32m    397\u001b[0m     )\n\u001b[1;32m    398\u001b[0m projection \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mproject(val)\n\u001b[1;32m    399\u001b[0m \u001b[39m# ^ might be a numpy array, or sparse scipy matrix.\u001b[39;00m\n",
+      "\u001b[0;31mValueError\u001b[0m: Invalid dimensions (2,) for Parameter value."
      ]
     }
    ],
-   "source": "a.value = np.zeros(1)"
+   "source": [
+    "a.value = np.zeros(1)"
+   ]
   },
   {
    "cell_type": "code",
@@ -96,7 +98,13 @@
     }
    ],
    "source": [
-    "0.7**2 + 0.75**2 + 0.1**2*(0.5**2 + 0.1**2 + 0.2**2) + (0.2*0.7+0.1*0.75)**2 + 0.3**2*(0.5**2 + 0.1**2 + 0.2**2)"
+    "(\n",
+    "    0.7**2\n",
+    "    + 0.75**2\n",
+    "    + 0.1**2 * (0.5**2 + 0.1**2 + 0.2**2)\n",
+    "    + (0.2 * 0.7 + 0.1 * 0.75) ** 2\n",
+    "    + 0.3**2 * (0.5**2 + 0.1**2 + 0.2**2)\n",
+    ")"
    ]
   },
   {
@@ -116,7 +124,7 @@
     }
    ],
    "source": [
-    "0.7**2 + 0.75**2 + (0.2*0.7+0.1*0.75)**2"
+    "0.7**2 + 0.75**2 + (0.2 * 0.7 + 0.1 * 0.75) ** 2"
    ]
   },
   {
@@ -136,7 +144,7 @@
     }
    ],
    "source": [
-    "1.098725+0.030000000000000006"
+    "1.098725 + 0.030000000000000006"
    ]
   },
   {
@@ -156,7 +164,7 @@
     }
    ],
    "source": [
-    "0.1**2*(0.5**2 + 0.1**2 + 0.2**2) + 0.3**2*(0.5**2 + 0.1**2 + 0.2**2)"
+    "0.1**2 * (0.5**2 + 0.1**2 + 0.2**2) + 0.3**2 * (0.5**2 + 0.1**2 + 0.2**2)"
    ]
   },
   {
@@ -218,16 +226,16 @@
     }
    ],
    "source": [
-    "Sigma = np.array([[2,0.4], [0.4, 3]])\n",
+    "Sigma = np.array([[2, 0.4], [0.4, 3]])\n",
     "d = np.array([0.15, 0.3])\n",
     "\n",
-    "ones = np.ones(2).reshape(-1,1)\n",
+    "ones = np.ones(2).reshape(-1, 1)\n",
     "\n",
-    "A1 = np.hstack([2*(Sigma + np.outer(d,d)), ones])\n",
-    "A2 = np.hstack([ones.T, np.zeros((1,1))])\n",
+    "A1 = np.hstack([2 * (Sigma + np.outer(d, d)), ones])\n",
+    "A2 = np.hstack([ones.T, np.zeros((1, 1))])\n",
     "A = np.vstack([A1, A2])\n",
     "\n",
-    "b = np.vstack([np.zeros((2,1)), np.ones((1,1))])\n",
+    "b = np.vstack([np.zeros((2, 1)), np.ones((1, 1))])\n",
     "w_star = np.linalg.solve(A, b)\n",
     "w_star"
    ]
@@ -310,7 +318,7 @@
     "e = cp.Parameter(shape=n, value=np.zeros(n), nonneg=True)\n",
     "\n",
     "\n",
-    "argument = cp.hstack([L@w, cp.sum(w)])\n",
+    "argument = cp.hstack([L @ w, cp.sum(w)])\n",
     "obj = cp.norm2(argument)\n",
     "\n",
     "# obj = cp.norm2(L@w)\n",
@@ -318,7 +326,6 @@
     "for i in range(11):\n",
     "    print(argument[i].is_convex())\n",
     "    print(argument.is_incr(i))\n",
-    "    \n",
     "\n",
     "\n",
     "# obj.is_convex()\n",
@@ -348,7 +355,7 @@
     "\n",
     "w = cp.Variable()\n",
     "\n",
-    "prob = cp.Problem(cp.Minimize(cp.square(a*w)))\n",
+    "prob = cp.Problem(cp.Minimize(cp.square(a * w)))\n",
     "\n",
     "prob.is_dpp()"
    ]
diff --git a/experiments/softRisk.py b/experiments/softRisk.py
index 90b10678..b7f6dc06 100644
--- a/experiments/softRisk.py
+++ b/experiments/softRisk.py
@@ -15,11 +15,7 @@
 
 
 def run(path: str = "data/stock_prices.csv") -> None:
-    returns = (
-        pd.read_csv(path, index_col=0, header=0, parse_dates=True)
-        .pct_change()
-        .dropna(axis=0, how="all")
-    )
+    returns = pd.read_csv(path, index_col=0, header=0, parse_dates=True).pct_change().dropna(axis=0, how="all")
     n_components = 10
 
     assets = returns.shape[1]
@@ -27,9 +23,7 @@ def run(path: str = "data/stock_prices.csv") -> None:
     logger.info(f"Returns: \n{returns}")
     pca = PCA(returns=returns.values, n_components=n_components)
     lower_bound_factors = pd.Series(data=-1.0, index=range(n_components))
-    upper_bound_factors = pd.Series(
-        data=+1.0, index=range(n_components)
-    )  # len(pca.factors)))
+    upper_bound_factors = pd.Series(data=+1.0, index=range(n_components))  # len(pca.factors)))
 
     lower_bound_assets = pd.Series(data=0.0, index=returns.columns)
     upper_bound_assets = pd.Series(data=1.0, index=returns.columns)
diff --git a/tests/conftest.py b/tests/conftest.py
index 434d4479..9ad99a75 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,4 +1,5 @@
 """global fixtures"""
+
 from __future__ import annotations
 
 from pathlib import Path
@@ -13,8 +14,6 @@ def resource_fixture():
     return Path(__file__).parent / "resources"
 
 
-@pytest.fixture(
-    params=[s for s in [cp.ECOS, cp.CLARABEL] if s in cp.installed_solvers()]
-)
+@pytest.fixture(params=[s for s in [cp.ECOS, cp.CLARABEL] if s in cp.installed_solvers()])
 def solver(request):
     return request.param
diff --git a/tests/test_linalg/test_pca.py b/tests/test_linalg/test_pca.py
index 03526278..80bbc866 100644
--- a/tests/test_linalg/test_pca.py
+++ b/tests/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).values
 
 
diff --git a/tests/test_markowitz/test_bounds.py b/tests/test_markowitz/test_bounds.py
index 0229ae11..24c7223a 100644
--- a/tests/test_markowitz/test_bounds.py
+++ b/tests/test_markowitz/test_bounds.py
@@ -27,12 +27,8 @@ def test_constraints():
         }
     )
 
-    assert bounds.data[D.LOWER_BOUND_ASSETS].value == pytest.approx(
-        np.array([0.1, 0.2, 0])
-    )
-    assert bounds.data[D.UPPER_BOUND_ASSETS].value == pytest.approx(
-        np.array([0.3, 0.4, 0.5])
-    )
+    assert bounds.data[D.LOWER_BOUND_ASSETS].value == pytest.approx(np.array([0.1, 0.2, 0]))
+    assert bounds.data[D.UPPER_BOUND_ASSETS].value == pytest.approx(np.array([0.3, 0.4, 0.5]))
 
     assert len(bounds.constraints(variables)) == 2
 
diff --git a/tests/test_markowitz/test_models/test_expected_returns.py b/tests/test_markowitz/test_models/test_expected_returns.py
index 25feb457..483f0b98 100644
--- a/tests/test_markowitz/test_models/test_expected_returns.py
+++ b/tests/test_markowitz/test_models/test_expected_returns.py
@@ -16,9 +16,7 @@ def test_expected_returns():
 
     # expected returns not explicitly set are zero
     assert model.data[D.MU].value == pytest.approx(np.array([0.1, 0.2, 0.0]))
-    assert model.parameter["mu_uncertainty"].value == pytest.approx(
-        np.array([0.0, 0.0, 0.0])
-    )
+    assert model.parameter["mu_uncertainty"].value == pytest.approx(np.array([0.0, 0.0, 0.0]))
 
     weights = cp.Variable(assets)
     weights.value = np.array([1.0, 1.0, 2.0])
@@ -38,9 +36,7 @@ def test_expected_returns_robust():
 
     # expected returns not explicitly set are zero
     assert model.data["mu"].value == pytest.approx(np.array([0.1, 0.2, 0.0]))
-    assert model.parameter["mu_uncertainty"].value == pytest.approx(
-        np.array([0.01, 0.03, 0.0])
-    )
+    assert model.parameter["mu_uncertainty"].value == pytest.approx(np.array([0.01, 0.03, 0.0]))
 
     weights = cp.Variable(assets)
     weights.value = np.array([1.0, 1.0, 2.0])
diff --git a/tests/test_markowitz/test_portfolios/test_min_var.py b/tests/test_markowitz/test_portfolios/test_min_var.py
index 4663825e..8dc46de5 100644
--- a/tests/test_markowitz/test_portfolios/test_min_var.py
+++ b/tests/test_markowitz/test_portfolios/test_min_var.py
@@ -74,9 +74,7 @@ def test_min_var(solver):
 
     np.testing.assert_almost_equal(problem.value, 0.9354143466222262)
 
-    np.testing.assert_almost_equal(
-        problem.weights, np.array([0.75, 0.25, 0.0, 0.0]), decimal=3
-    )
+    np.testing.assert_almost_equal(problem.weights, np.array([0.75, 0.25, 0.0, 0.0]), decimal=3)
 
     assert objective == pytest.approx(0.9354143, abs=1e-5)
 
diff --git a/tests/test_markowitz/test_risk/test_factor/test_factor.py b/tests/test_markowitz/test_risk/test_factor/test_factor.py
index 28796443..876a7987 100644
--- a/tests/test_markowitz/test_risk/test_factor/test_factor.py
+++ b/tests/test_markowitz/test_risk/test_factor/test_factor.py
@@ -16,9 +16,7 @@
 
 @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).values
 
 
@@ -107,9 +105,7 @@ def test_estimate_risk(solver):
     data = dict(problem.data)
 
     # test that the exposure is correct, e.g. the factor weights match the exposure * asset weights
-    assert data[(M.RISK, "exposure")].value @ problem.weights == pytest.approx(
-        problem.factor_weights, abs=1e-6
-    )
+    assert data[(M.RISK, "exposure")].value @ problem.weights == pytest.approx(problem.factor_weights, abs=1e-6)
 
     # test all entries of y are smaller than 0.1
     assert np.all([problem.factor_weights <= 0.1 + 1e-4])
@@ -141,17 +137,13 @@ def test_factor_mini():
     # Note: dummy is abs(factor_weights)
     variables[D._ABS] = cp.abs(variables[D.FACTOR_WEIGHTS])
 
-    assert variables[D.FACTOR_WEIGHTS].value == pytest.approx(
-        np.array([0.7, 0.75]), abs=1e-6
-    )
+    assert variables[D.FACTOR_WEIGHTS].value == pytest.approx(np.array([0.7, 0.75]), abs=1e-6)
 
     residual = np.sqrt(0.03)
     systematic = np.sqrt(1.098725)
 
     assert model._residual_risk(variables=variables).value == pytest.approx(residual)
-    assert model._systematic_risk(variables=variables).value == pytest.approx(
-        systematic
-    )
+    assert model._systematic_risk(variables=variables).value == pytest.approx(systematic)
 
     total = np.linalg.norm(np.array([residual, systematic]))
 
diff --git a/tests/test_markowitz/test_risk/test_sample/test_sample.py b/tests/test_markowitz/test_risk/test_sample/test_sample.py
index 551bc809..17622b19 100644
--- a/tests/test_markowitz/test_risk/test_sample/test_sample.py
+++ b/tests/test_markowitz/test_risk/test_sample/test_sample.py
@@ -20,9 +20,7 @@ def test_sample():
         }
     )
 
-    vola = riskmodel.estimate(
-        {D.WEIGHTS: np.array([1.0, 1.0]), D._ABS: np.array([1.0, 1.0])}
-    ).value
+    vola = riskmodel.estimate({D.WEIGHTS: np.array([1.0, 1.0]), D._ABS: np.array([1.0, 1.0])}).value
     np.testing.assert_almost_equal(vola, 2.0)
 
 
@@ -58,9 +56,7 @@ def test_robust_sample():
     )
 
     # Note: dummy should be abs(weights)
-    vola = riskmodel.estimate(
-        {D.WEIGHTS: np.array([1.0, -1.0]), D._ABS: np.array([1.0, 1.0])}
-    ).value
+    vola = riskmodel.estimate({D.WEIGHTS: np.array([1.0, -1.0]), D._ABS: np.array([1.0, 1.0])}).value
     np.testing.assert_almost_equal(vola, np.sqrt(2.09))
 
 
diff --git a/tests/test_markowitz/test_utils/test_aux.py b/tests/test_markowitz/test_utils/test_aux.py
index 9ca4b9c4..aa9d6e3a 100644
--- a/tests/test_markowitz/test_utils/test_aux.py
+++ b/tests/test_markowitz/test_utils/test_aux.py
@@ -10,6 +10,4 @@ def test_fill_vector():
 
 def test_fill_matrix():
     a = np.ones((2, 2))
-    np.allclose(
-        fill_matrix(rows=3, cols=3, x=a), np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]])
-    )
+    np.allclose(fill_matrix(rows=3, cols=3, x=a), np.array([[1, 1, 0], [1, 1, 0], [0, 0, 0]]))