Use ruff linter.

model.py

@@ -1,11 +1,9 @@
 import pandas as pd
 import numpy as np
-from datetime import datetime, timedelta
+from datetime import timedelta
 import matplotlib.pyplot as plt
 import seaborn as sns
-from scipy.stats import norm
-from scipy.signal import savgol_filter
-from multiprocessing import Process, Pool
+from multiprocessing import Pool
 
 
 # Utility functions
@@ -455,7 +453,7 @@ def create_plots(df, start=None, end=None, project_days=365):
     plt.style.use("seaborn-v0_8")
 
     # Create figure with adjusted size for additional subplot
-    fig = plt.figure(figsize=(15, 15))  # Increased height to accommodate new subplot
+    _ = plt.figure(figsize=(15, 15))  # Increased height to accommodate new subplot
 
     # Date range for titles
     hist_date_range = f" ({plot_df['Date'].min().strftime('%Y-%m-%d')} to {plot_df['Date'].max().strftime('%Y-%m-%d')})"
@@ -850,7 +848,7 @@ def create_backtest_plot(
 
     # Set up the plot
     plt.style.use("seaborn-v0_8")
-    fig, ax = plt.figure(figsize=(15, 10)), plt.gca()
+    _, ax = plt.figure(figsize=(15, 10)), plt.gca()
 
     # Plot training data
     heading_label = f'Historical Price (Training: {start_date.strftime("%Y-%m-%d")} to {backtest_date.strftime("%Y-%m-%d")})'
@@ -1015,7 +1013,7 @@ def create_backtest_plot(
 
 def run_projection(args):
     df, start = args
-    projections = create_plots(df, start=start, project_days=365 * 4)
+    _ = create_plots(df, start=start, project_days=365 * 4)
 
 
 def run_projections(df):
@@ -1205,7 +1203,7 @@ def run_systematic_backtests(df, validation_years=1, min_training_years=8):
     # Sort periods by backtest date for clearer analysis
     unique_periods.sort(key=lambda x: pd.Timestamp(x["backtest_date"]))
 
-    print(f"\nRunning backtests with:")
+    print("\nRunning backtests with:")
     print(
         f"- Start dates range: {unique_periods[0]['start_date']} to {unique_periods[-1]['start_date']}"
     )