diff --git a/main.py b/main.py
index 0e54834..7b2d4fb 100644
--- a/main.py
+++ b/main.py
@@ -17,40 +17,4 @@ print(f"Number of data points: {len(daily_data)}")
 
 trend_detector = TrendDetectorSimple(daily_data, verbose=True)
 trends, analysis_results = trend_detector.detect_trends()
-trend_detector.plot_trends(trends, analysis_results, "supertrend")
-
-#trend_detector = TrendDetectorMACD(daily_data, True)
-#trends = trend_detector.detect_trends_MACD_signal()
-#trend_detector.plot_trends(trends)
-
-# # Cycle detection (new code)
-# print("\n===== CYCLE DETECTION =====")
-
-# # Daily cycles
-# daily_detector = CycleDetector(daily_data, timeframe='daily')
-# daily_avg_cycle = daily_detector.get_average_cycle_length()
-# daily_window = daily_detector.get_cycle_window()
-
-# print(f"Daily Timeframe: Average Cycle Length = {daily_avg_cycle:.2f} days")
-# if daily_window:
-#     print(f"Daily Cycle Window: {daily_window[0]:.2f} to {daily_window[2]:.2f} days")
-
-# weekly_detector = CycleDetector(daily_data, timeframe='weekly')
-# weekly_avg_cycle = weekly_detector.get_average_cycle_length()
-# weekly_window = weekly_detector.get_cycle_window()
-
-# print(f"\nWeekly Timeframe: Average Cycle Length = {weekly_avg_cycle:.2f} weeks")
-# if weekly_window:
-#     print(f"Weekly Cycle Window: {weekly_window[0]:.2f} to {weekly_window[2]:.2f} weeks")
-
-# # Detect patterns
-# two_drives = daily_detector.detect_two_drives_pattern()
-# v_shapes = daily_detector.detect_v_shaped_lows()
-
-# print(f"\nDetected {len(two_drives)} 'Two Drives' patterns in daily data")
-# print(f"Detected {len(v_shapes)} 'V-Shaped' lows in daily data")
-
-# # Plot cycles with detected patterns
-# print("\nPlotting cycles and patterns...")
-# daily_detector.plot_cycles(pattern_detection=['two_drives', 'v_shape'], title_suffix='(with Two Drives Pattern)')
-# weekly_detector.plot_cycles(title_suffix='(Weekly View)')
\ No newline at end of file
+trend_detector.plot_trends(trends, analysis_results, "supertrend")
\ No newline at end of file
diff --git a/trend_detector_simple.py b/trend_detector_simple.py
index bf1ff60..9563bcb 100644
--- a/trend_detector_simple.py
+++ b/trend_detector_simple.py
@@ -90,10 +90,8 @@ class TrendDetectorSimple:
         # Convert data to pandas DataFrame if it's not already
         if not isinstance(self.data, pd.DataFrame):
             if isinstance(self.data, list):
-                self.logger.info("Converting list to DataFrame")
                 self.data = pd.DataFrame({'close': self.data})
             else:
-                self.logger.error("Invalid data format provided")
                 raise ValueError("Data must be a pandas DataFrame or a list")
         
         self.logger.info(f"Initialized TrendDetectorSimple with {len(self.data)} data points")
@@ -171,16 +169,12 @@ class TrendDetectorSimple:
         - DataFrame with columns for timestamps, prices, and trend indicators
         - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
         """
-        self.logger.info(f"Detecting trends")
-        
         df = self.data.copy()
         close_prices = df['close'].values
         
         max_peaks, _ = find_peaks(close_prices)
         min_peaks, _ = find_peaks(-close_prices)
         
-        self.logger.info(f"Found {len(min_peaks)} local minima and {len(max_peaks)} local maxima")
-        
         df['is_min'] = False
         df['is_max'] = False
         
@@ -192,7 +186,6 @@ class TrendDetectorSimple:
         result = df[['datetime', 'close', 'is_min', 'is_max']].copy()
         
         # Perform linear regression on min_peaks and max_peaks
-        self.logger.info("Performing linear regression on min and max peaks")
         min_prices = df['close'].iloc[min_peaks].values
         max_prices = df['close'].iloc[max_peaks].values