Fix black formatting with line-length 100

Author: johngeorgea157-stack

classical/greedy.py

@@ -32,9 +32,7 @@ def load_portfolio_data(
     sigma = np.load(os.path.join(cached_dir, "sigma.npy"))
 
     # Load stock names from CSV
-    expected_returns_df = pd.read_csv(
-        os.path.join(cached_dir, "expected_returns.csv"), index_col=0
-    )
+    expected_returns_df = pd.read_csv(os.path.join(cached_dir, "expected_returns.csv"), index_col=0)
     stock_names = expected_returns_df.index.tolist()
 
     return mu, sigma, stock_names

classical/sim_annealing.py

@@ -3,9 +3,7 @@
 import math
 
 
-def simulated_annealing_qubo(
-    Q, k, T_start=1000.0, cooling_rate=0.99, max_iter=10000, seed=None
-):
+def simulated_annealing_qubo(Q, k, T_start=1000.0, cooling_rate=0.99, max_iter=10000, seed=None):
     """
     Simulated Annealing heuristic for the QUBO formulation of the portfolio problem.
     Maintains exactly k selected assets at all times to satisfy the constraint naturally.

classical/test_classical.py

@@ -137,6 +137,4 @@ def test_sa_better_than_random(Q_matrix):
         random_objs.append(compute_objective(Q, x))
 
     avg_random = np.mean(random_objs)
-    assert (
-        sa_obj <= avg_random
-    ), f"SA obj {sa_obj:.4f} worse than random avg {avg_random:.4f}"
+    assert sa_obj <= avg_random, f"SA obj {sa_obj:.4f} worse than random avg {avg_random:.4f}"

data/preprocess.py

@@ -76,9 +76,7 @@ def load_prices(input_dir):
     """
     csv_files = glob.glob(os.path.join(input_dir, "*.csv"))
     if not csv_files:
-        raise FileNotFoundError(
-            f"No CSV files found in '{input_dir}'. Run fetch_data.py first."
-        )
+        raise FileNotFoundError(f"No CSV files found in '{input_dir}'. Run fetch_data.py first.")
 
     adj_close_data = {}
     for file in csv_files:
@@ -95,9 +93,7 @@ def load_prices(input_dir):
         elif "Close" in df.columns:
             adj_close_data[ticker] = df["Close"]
         else:
-            print(
-                f"  ⚠  Could not find Close prices for {ticker}. Columns: {list(df.columns)}"
-            )
+            print(f"  ⚠  Could not find Close prices for {ticker}. Columns: {list(df.columns)}")
 
     if not adj_close_data:
         raise ValueError("No valid price data could be loaded from any CSV file.")
@@ -201,13 +197,9 @@ def compute_correlation(returns_df):
     print(f"   Mean correlation:     {corr_flat.mean():.4f}")
 
     if corr_flat.mean() > 0.6:
-        print(
-            "\n💡 Portfolio Insight: HIGH average correlation → Limited diversification"
-        )
+        print("\n💡 Portfolio Insight: HIGH average correlation → Limited diversification")
     elif corr_flat.mean() > 0.4:
-        print(
-            "\n💡 Portfolio Insight: MODERATE average correlation → Some diversification"
-        )
+        print("\n💡 Portfolio Insight: MODERATE average correlation → Some diversification")
     else:
         print("\n💡 Portfolio Insight: LOW average correlation → Good diversification")
 
@@ -276,19 +268,15 @@ def plot_return_distributions(returns_df, results_dir):
     axes = axes.flatten()
 
     for idx, stock in enumerate(returns_df.columns):
-        axes[idx].hist(
-            returns_df[stock], bins=50, alpha=0.7, color="steelblue", edgecolor="black"
-        )
+        axes[idx].hist(returns_df[stock], bins=50, alpha=0.7, color="steelblue", edgecolor="black")
         axes[idx].axvline(
             returns_df[stock].mean(),
             color="red",
             linestyle="--",
             linewidth=2,
             label="Mean",
         )
-        axes[idx].set_title(
-            f"{stock} – Daily Log Return Distribution", fontweight="bold"
-        )
+        axes[idx].set_title(f"{stock} – Daily Log Return Distribution", fontweight="bold")
         axes[idx].set_xlabel("Daily Log Return")
         axes[idx].set_ylabel("Frequency")
         axes[idx].legend()

qaoa/test_circuit.py

@@ -69,9 +69,7 @@ def test_circuit_depth_increases_with_p(simple_Q):
     """Deeper p should produce a deeper circuit."""
     c1, _, _ = create_qaoa_circuit(simple_Q, p=1)
     c2, _, _ = create_qaoa_circuit(simple_Q, p=2)
-    assert (
-        c2.decompose().depth() > c1.decompose().depth()
-    ), "p=2 circuit should be deeper than p=1"
+    assert c2.decompose().depth() > c1.decompose().depth(), "p=2 circuit should be deeper than p=1"
 
 
 def test_circuit_has_measurements(simple_Q):