Black Format

Author: dcajasn

riskfolio/src/AuxFunctions.py

@@ -769,7 +769,7 @@ def std_silhouette_score(dist, clusters, max_k=10):
     scores_list = []
 
     # get within-cluster dissimilarity for each k
-    for k in range(1, min(len(num_k)-1, max_k)):
+    for k in range(1, min(len(num_k) - 1, max_k)):
         level = cluster_lvls.iloc[:, k]  # get k clusters
         b = silhouette_samples(dist, level)
         scores_list.append(b.mean() / b.std())

riskfolio/src/HCPortfolio.py

@@ -226,9 +226,9 @@ def _opt_w(
                 else:
                     method_kurt = None
 
-                port.assets_stats(method_mu="hist",
-                                  method_cov="hist",
-                                  method_kurt=method_kurt)
+                port.assets_stats(
+                    method_mu="hist", method_cov="hist", method_kurt=method_kurt
+                )
                 if self.solvers is not None:
                     port.solvers = self.solvers
                 if mu is not None:
@@ -247,9 +247,9 @@ def _opt_w(
                     method_kurt = "hist"
                 else:
                     method_kurt = None
-                port.assets_stats(method_mu="hist",
-                                  method_cov="hist",
-                                  method_kurt=method_kurt)
+                port.assets_stats(
+                    method_mu="hist", method_cov="hist", method_kurt=method_kurt
+                )
                 if self.solvers is not None:
                     port.solvers = self.solvers
                 if mu is not None:
@@ -697,7 +697,10 @@ def _intra_weights(
                 rf=rf,
                 l=l,
             )
-            weights = pd.Series(weights.flatten(), index=cluster_cov.index,)
+            weights = pd.Series(
+                weights.flatten(),
+                index=cluster_cov.index,
+            )
             intra_weights[i] = weights
 
         intra_weights = intra_weights.fillna(0)
@@ -951,9 +954,7 @@ def optimization(
                 else:
                     self.cov = custom_cov.copy()
         else:
-            self.cov = pe.covar_matrix(
-                self.returns, method=method_cov, **dict_cov
-            )
+            self.cov = pe.covar_matrix(self.returns, method=method_cov, **dict_cov)
 
         # Mean vector
         if method_mu == "custom_mu":
@@ -969,12 +970,10 @@ def optimization(
             else:
                 raise NameError("custom_mu must be a column DataFrame or Series")
         else:
-            self.mu = pe.mean_vector(
-                self.returns, method=method_mu, **dict_mu
-            )
-        if rm == 'KT':
+            self.mu = pe.mean_vector(self.returns, method=method_mu, **dict_mu)
+        if rm == "KT":
             self.kurt, self.skurt = True, False
-        elif rm == 'SKT':
+        elif rm == "SKT":
             self.kurt, self.skurt = False, True
         else:
             self.kurt, self.skurt = False, False
@@ -1081,13 +1080,7 @@ def optimization(
             )
 
             # Step-3.2: Determine inter-cluster weights and multiply with 􏰁→ intra-cluster weights
-            weights = self._inter_weights(
-                intra_weights,
-                obj=obj,
-                rm=rm,
-                rf=rf,
-                l=l
-            )
+            weights = self._inter_weights(intra_weights, obj=obj, rm=rm, rf=rf, l=l)
 
         weights = weights.loc[self.assetslist]
 

riskfolio/src/PlotFunctions.py

@@ -425,8 +425,6 @@ def plot_frontier(
                 a2 = str(w_.shape)
                 raise ValueError("shapes " + a1 + " and " + a2 + " not aligned")
 
-
-
     if beta is None:
         beta = alpha
     if b_sim is None:
@@ -1032,13 +1030,7 @@ def plot_bar(
 
 
 def plot_frontier_area(
-    w_frontier,
-    nrow=25,
-    cmap="tab20",
-    n_colors=20,
-    height=6,
-    width=10,
-    ax=None
+    w_frontier, nrow=25, cmap="tab20", n_colors=20, height=6, width=10, ax=None
 ):
     r"""
     Create a chart with the asset composition of the efficient frontier.
@@ -1897,7 +1889,7 @@ def plot_hist(
         label=r"Normal: $\mu=$"
         + r"{0:.2%},".format(mu)
         + r" $\sigma=$"
-        + r"{0:.2%}".format(sigma)
+        + r"{0:.2%}".format(sigma),
     )
 
     factor = (np.max(a) - np.min(a)) / bins
@@ -2995,7 +2987,7 @@ def plot_clusters(
         ax1.set_title(title)
     elif dendrogram == False:
         ax.set_title(title)
-        ax.set_position([0,0.17,1,1])
+        ax.set_position([0, 0.17, 1, 1])
         axcolor.set_position([1.2, 0.17, 0.035, 1])
 
     try:

riskfolio/src/Portfolio.py

@@ -1172,9 +1172,7 @@ def blfactors_stats(
 
         if B is None:
             if self.B is None:
-                self.B = pe.loadings_matrix(X=F,
-                                            Y=X,
-                                            **dict_load)
+                self.B = pe.loadings_matrix(X=F, Y=X, **dict_load)
                 const = True
             elif self.B is not None:
                 pass