diff --git a/cycle_detector.py b/cycle_detector.py
index 2192a0b..a86f014 100644
--- a/cycle_detector.py
+++ b/cycle_detector.py
@@ -1,248 +1,248 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from scipy.signal import argrelextrema
-
-class CycleDetector:
-    def __init__(self, data, timeframe='daily'):
-        """
-        Initialize the CycleDetector with price data.
-        
-        Parameters:
-        - data: DataFrame with at least 'date' or 'datetime' and 'close' columns
-        - timeframe: 'daily', 'weekly', or 'monthly'
-        """
-        self.data = data.copy()
-        self.timeframe = timeframe
-        
-        # Ensure we have a consistent date column name
-        if 'datetime' in self.data.columns and 'date' not in self.data.columns:
-            self.data.rename(columns={'datetime': 'date'}, inplace=True)
-        
-        # Convert data to specified timeframe if needed
-        if timeframe == 'weekly' and 'date' in self.data.columns:
-            self.data = self._convert_data(self.data, 'W')
-        elif timeframe == 'monthly' and 'date' in self.data.columns:
-            self.data = self._convert_data(self.data, 'M')
-            
-        # Add columns for local minima and maxima detection
-        self._add_swing_points()
-    
-    def _convert_data(self, data, timeframe):
-        """Convert daily data to 'timeframe' timeframe."""
-        data['date'] = pd.to_datetime(data['date'])
-        data.set_index('date', inplace=True)
-        weekly = data.resample(timeframe).agg({
-            'open': 'first',
-            'high': 'max',
-            'low': 'min',
-            'close': 'last',
-            'volume': 'sum'
-        })
-        return weekly.reset_index()
-    
-    
-    def _add_swing_points(self, window=5):
-        """
-        Identify swing points (local minima and maxima).
-        
-        Parameters:
-        - window: The window size for local minima/maxima detection
-        """
-        # Set the index to make calculations easier
-        if 'date' in self.data.columns:
-            self.data.set_index('date', inplace=True)
-        
-        # Detect local minima (swing lows)
-        min_idx = argrelextrema(self.data['low'].values, np.less, order=window)[0]
-        self.data['swing_low'] = False
-        self.data.iloc[min_idx, self.data.columns.get_loc('swing_low')] = True
-        
-        # Detect local maxima (swing highs)
-        max_idx = argrelextrema(self.data['high'].values, np.greater, order=window)[0]
-        self.data['swing_high'] = False
-        self.data.iloc[max_idx, self.data.columns.get_loc('swing_high')] = True
-        
-        # Reset index
-        self.data.reset_index(inplace=True)
-    
-    def find_cycle_lows(self):
-        """Find all swing lows which represent cycle lows."""
-        swing_low_dates = self.data[self.data['swing_low']]['date'].values
-        return swing_low_dates
-    
-    def calculate_cycle_lengths(self):
-        """Calculate the lengths of each cycle between consecutive lows."""
-        swing_low_indices = np.where(self.data['swing_low'])[0]
-        cycle_lengths = np.diff(swing_low_indices)
-        return cycle_lengths
-    
-    def get_average_cycle_length(self):
-        """Calculate the average cycle length."""
-        cycle_lengths = self.calculate_cycle_lengths()
-        if len(cycle_lengths) > 0:
-            return np.mean(cycle_lengths)
-        return None
-    
-    def get_cycle_window(self, tolerance=0.10):
-        """
-        Get the cycle window with the specified tolerance.
-        
-        Parameters:
-        - tolerance: The tolerance as a percentage (default: 10%)
-        
-        Returns:
-        - tuple: (min_cycle_length, avg_cycle_length, max_cycle_length)
-        """
-        avg_length = self.get_average_cycle_length()
-        if avg_length is not None:
-            min_length = avg_length * (1 - tolerance)
-            max_length = avg_length * (1 + tolerance)
-            return (min_length, avg_length, max_length)
-        return None
-    
-    def detect_two_drives_pattern(self, lookback=10):
-        """
-        Detect 2-drives pattern: a swing low, counter trend bounce, and a lower low.
-        
-        Parameters:
-        - lookback: Number of periods to look back
-        
-        Returns:
-        - list: Indices where 2-drives patterns are detected
-        """
-        patterns = []
-        
-        for i in range(lookback, len(self.data) - 1):
-            if not self.data.iloc[i]['swing_low']:
-                continue
-                
-            # Get the segment of data to check for pattern
-            segment = self.data.iloc[i-lookback:i+1]
-            swing_lows = segment[segment['swing_low']]['low'].values
-            
-            if len(swing_lows) >= 2 and swing_lows[-1] < swing_lows[-2]:
-                # Check if there was a bounce between the two lows
-                between_lows = segment.iloc[-len(swing_lows):-1]
-                if len(between_lows) > 0 and max(between_lows['high']) > swing_lows[-2]:
-                    patterns.append(i)
-        
-        return patterns
-    
-    def detect_v_shaped_lows(self, window=5, threshold=0.02):
-        """
-        Detect V-shaped cycle lows (sharp decline followed by sharp rise).
-        
-        Parameters:
-        - window: Window to look for sharp price changes
-        - threshold: Percentage change threshold to consider 'sharp'
-        
-        Returns:
-        - list: Indices where V-shaped patterns are detected
-        """
-        patterns = []
-        
-        # Find all swing lows
-        swing_low_indices = np.where(self.data['swing_low'])[0]
-        
-        for idx in swing_low_indices:
-            # Need enough data points before and after
-            if idx < window or idx + window >= len(self.data):
-                continue
-            
-            # Get the low price at this swing low
-            low_price = self.data.iloc[idx]['low']
-            
-            # Check for sharp decline before low (at least window bars before)
-            before_segment = self.data.iloc[max(0, idx-window):idx]
-            if len(before_segment) > 0:
-                max_before = before_segment['high'].max()
-                decline = (max_before - low_price) / max_before
-                
-                # Check for sharp rise after low (at least window bars after)
-                after_segment = self.data.iloc[idx+1:min(len(self.data), idx+window+1)]
-                if len(after_segment) > 0:
-                    max_after = after_segment['high'].max()
-                    rise = (max_after - low_price) / low_price
-                    
-                    # Both decline and rise must exceed threshold to be considered V-shaped
-                    if decline > threshold and rise > threshold:
-                        patterns.append(idx)
-        
-        return patterns
-    
-    def plot_cycles(self, pattern_detection=None, title_suffix=''):
-        """
-        Plot the price data with cycle lows and detected patterns.
-        
-        Parameters:
-        - pattern_detection: 'two_drives', 'v_shape', or None
-        - title_suffix: Optional suffix for the plot title
-        """
-        plt.figure(figsize=(14, 7))
-        
-        # Determine the date column name (could be 'date' or 'datetime')
-        date_col = 'date' if 'date' in self.data.columns else 'datetime'
-        
-        # Plot price data
-        plt.plot(self.data[date_col], self.data['close'], label='Close Price')
-        
-        # Calculate a consistent vertical position for indicators based on price range
-        price_range = self.data['close'].max() - self.data['close'].min()
-        indicator_offset = price_range * 0.01  # 1% of price range
-        
-        # Plot cycle lows (now at a fixed offset below the low price)
-        swing_lows = self.data[self.data['swing_low']]
-        plt.scatter(swing_lows[date_col], swing_lows['low'] - indicator_offset, 
-                   color='green', marker='^', s=100, label='Cycle Lows')
-        
-        # Plot specific patterns if requested
-        if 'two_drives' in pattern_detection:
-            pattern_indices = self.detect_two_drives_pattern()
-            if pattern_indices:
-                patterns = self.data.iloc[pattern_indices]
-                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
-                           color='red', marker='o', s=150, label='Two Drives Pattern')
-        
-        elif 'v_shape' in pattern_detection:
-            pattern_indices = self.detect_v_shaped_lows()
-            if pattern_indices:
-                patterns = self.data.iloc[pattern_indices]
-                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
-                           color='purple', marker='o', s=150, label='V-Shape Pattern')
-        
-        # Add cycle lengths and averages
-        cycle_lengths = self.calculate_cycle_lengths()
-        avg_cycle = self.get_average_cycle_length()
-        cycle_window = self.get_cycle_window()
-        
-        window_text = ""
-        if cycle_window:
-            window_text = f"Tolerance Window: [{cycle_window[0]:.2f} - {cycle_window[2]:.2f}]"
-        
-        plt.title(f"Detected Cycles - {self.timeframe.capitalize()} Timeframe {title_suffix}\n"
-                 f"Average Cycle Length: {avg_cycle:.2f} periods, {window_text}")
-        
-        plt.legend()
-        plt.grid(True)
-        plt.show()
-
-# Usage example:
-# 1. Load your data
-# data = pd.read_csv('your_price_data.csv')
-
-# 2. Create cycle detector instances for different timeframes
-# weekly_detector = CycleDetector(data, timeframe='weekly')
-# daily_detector = CycleDetector(data, timeframe='daily')
-
-# 3. Analyze cycles
-# weekly_cycle_length = weekly_detector.get_average_cycle_length()
-# daily_cycle_length = daily_detector.get_average_cycle_length()
-
-# 4. Detect patterns
-# two_drives = weekly_detector.detect_two_drives_pattern()
-# v_shapes = daily_detector.detect_v_shaped_lows()
-
-# 5. Visualize
-# weekly_detector.plot_cycles(pattern_detection='two_drives')
-# daily_detector.plot_cycles(pattern_detection='v_shape')
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.signal import argrelextrema
+
+class CycleDetector:
+    def __init__(self, data, timeframe='daily'):
+        """
+        Initialize the CycleDetector with price data.
+        
+        Parameters:
+        - data: DataFrame with at least 'date' or 'datetime' and 'close' columns
+        - timeframe: 'daily', 'weekly', or 'monthly'
+        """
+        self.data = data.copy()
+        self.timeframe = timeframe
+        
+        # Ensure we have a consistent date column name
+        if 'datetime' in self.data.columns and 'date' not in self.data.columns:
+            self.data.rename(columns={'datetime': 'date'}, inplace=True)
+        
+        # Convert data to specified timeframe if needed
+        if timeframe == 'weekly' and 'date' in self.data.columns:
+            self.data = self._convert_data(self.data, 'W')
+        elif timeframe == 'monthly' and 'date' in self.data.columns:
+            self.data = self._convert_data(self.data, 'M')
+            
+        # Add columns for local minima and maxima detection
+        self._add_swing_points()
+    
+    def _convert_data(self, data, timeframe):
+        """Convert daily data to 'timeframe' timeframe."""
+        data['date'] = pd.to_datetime(data['date'])
+        data.set_index('date', inplace=True)
+        weekly = data.resample(timeframe).agg({
+            'open': 'first',
+            'high': 'max',
+            'low': 'min',
+            'close': 'last',
+            'volume': 'sum'
+        })
+        return weekly.reset_index()
+    
+    
+    def _add_swing_points(self, window=5):
+        """
+        Identify swing points (local minima and maxima).
+        
+        Parameters:
+        - window: The window size for local minima/maxima detection
+        """
+        # Set the index to make calculations easier
+        if 'date' in self.data.columns:
+            self.data.set_index('date', inplace=True)
+        
+        # Detect local minima (swing lows)
+        min_idx = argrelextrema(self.data['low'].values, np.less, order=window)[0]
+        self.data['swing_low'] = False
+        self.data.iloc[min_idx, self.data.columns.get_loc('swing_low')] = True
+        
+        # Detect local maxima (swing highs)
+        max_idx = argrelextrema(self.data['high'].values, np.greater, order=window)[0]
+        self.data['swing_high'] = False
+        self.data.iloc[max_idx, self.data.columns.get_loc('swing_high')] = True
+        
+        # Reset index
+        self.data.reset_index(inplace=True)
+    
+    def find_cycle_lows(self):
+        """Find all swing lows which represent cycle lows."""
+        swing_low_dates = self.data[self.data['swing_low']]['date'].values
+        return swing_low_dates
+    
+    def calculate_cycle_lengths(self):
+        """Calculate the lengths of each cycle between consecutive lows."""
+        swing_low_indices = np.where(self.data['swing_low'])[0]
+        cycle_lengths = np.diff(swing_low_indices)
+        return cycle_lengths
+    
+    def get_average_cycle_length(self):
+        """Calculate the average cycle length."""
+        cycle_lengths = self.calculate_cycle_lengths()
+        if len(cycle_lengths) > 0:
+            return np.mean(cycle_lengths)
+        return None
+    
+    def get_cycle_window(self, tolerance=0.10):
+        """
+        Get the cycle window with the specified tolerance.
+        
+        Parameters:
+        - tolerance: The tolerance as a percentage (default: 10%)
+        
+        Returns:
+        - tuple: (min_cycle_length, avg_cycle_length, max_cycle_length)
+        """
+        avg_length = self.get_average_cycle_length()
+        if avg_length is not None:
+            min_length = avg_length * (1 - tolerance)
+            max_length = avg_length * (1 + tolerance)
+            return (min_length, avg_length, max_length)
+        return None
+    
+    def detect_two_drives_pattern(self, lookback=10):
+        """
+        Detect 2-drives pattern: a swing low, counter trend bounce, and a lower low.
+        
+        Parameters:
+        - lookback: Number of periods to look back
+        
+        Returns:
+        - list: Indices where 2-drives patterns are detected
+        """
+        patterns = []
+        
+        for i in range(lookback, len(self.data) - 1):
+            if not self.data.iloc[i]['swing_low']:
+                continue
+                
+            # Get the segment of data to check for pattern
+            segment = self.data.iloc[i-lookback:i+1]
+            swing_lows = segment[segment['swing_low']]['low'].values
+            
+            if len(swing_lows) >= 2 and swing_lows[-1] < swing_lows[-2]:
+                # Check if there was a bounce between the two lows
+                between_lows = segment.iloc[-len(swing_lows):-1]
+                if len(between_lows) > 0 and max(between_lows['high']) > swing_lows[-2]:
+                    patterns.append(i)
+        
+        return patterns
+    
+    def detect_v_shaped_lows(self, window=5, threshold=0.02):
+        """
+        Detect V-shaped cycle lows (sharp decline followed by sharp rise).
+        
+        Parameters:
+        - window: Window to look for sharp price changes
+        - threshold: Percentage change threshold to consider 'sharp'
+        
+        Returns:
+        - list: Indices where V-shaped patterns are detected
+        """
+        patterns = []
+        
+        # Find all swing lows
+        swing_low_indices = np.where(self.data['swing_low'])[0]
+        
+        for idx in swing_low_indices:
+            # Need enough data points before and after
+            if idx < window or idx + window >= len(self.data):
+                continue
+            
+            # Get the low price at this swing low
+            low_price = self.data.iloc[idx]['low']
+            
+            # Check for sharp decline before low (at least window bars before)
+            before_segment = self.data.iloc[max(0, idx-window):idx]
+            if len(before_segment) > 0:
+                max_before = before_segment['high'].max()
+                decline = (max_before - low_price) / max_before
+                
+                # Check for sharp rise after low (at least window bars after)
+                after_segment = self.data.iloc[idx+1:min(len(self.data), idx+window+1)]
+                if len(after_segment) > 0:
+                    max_after = after_segment['high'].max()
+                    rise = (max_after - low_price) / low_price
+                    
+                    # Both decline and rise must exceed threshold to be considered V-shaped
+                    if decline > threshold and rise > threshold:
+                        patterns.append(idx)
+        
+        return patterns
+    
+    def plot_cycles(self, pattern_detection=None, title_suffix=''):
+        """
+        Plot the price data with cycle lows and detected patterns.
+        
+        Parameters:
+        - pattern_detection: 'two_drives', 'v_shape', or None
+        - title_suffix: Optional suffix for the plot title
+        """
+        plt.figure(figsize=(14, 7))
+        
+        # Determine the date column name (could be 'date' or 'datetime')
+        date_col = 'date' if 'date' in self.data.columns else 'datetime'
+        
+        # Plot price data
+        plt.plot(self.data[date_col], self.data['close'], label='Close Price')
+        
+        # Calculate a consistent vertical position for indicators based on price range
+        price_range = self.data['close'].max() - self.data['close'].min()
+        indicator_offset = price_range * 0.01  # 1% of price range
+        
+        # Plot cycle lows (now at a fixed offset below the low price)
+        swing_lows = self.data[self.data['swing_low']]
+        plt.scatter(swing_lows[date_col], swing_lows['low'] - indicator_offset, 
+                   color='green', marker='^', s=100, label='Cycle Lows')
+        
+        # Plot specific patterns if requested
+        if 'two_drives' in pattern_detection:
+            pattern_indices = self.detect_two_drives_pattern()
+            if pattern_indices:
+                patterns = self.data.iloc[pattern_indices]
+                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
+                           color='red', marker='o', s=150, label='Two Drives Pattern')
+        
+        elif 'v_shape' in pattern_detection:
+            pattern_indices = self.detect_v_shaped_lows()
+            if pattern_indices:
+                patterns = self.data.iloc[pattern_indices]
+                plt.scatter(patterns[date_col], patterns['low'] - indicator_offset * 2, 
+                           color='purple', marker='o', s=150, label='V-Shape Pattern')
+        
+        # Add cycle lengths and averages
+        cycle_lengths = self.calculate_cycle_lengths()
+        avg_cycle = self.get_average_cycle_length()
+        cycle_window = self.get_cycle_window()
+        
+        window_text = ""
+        if cycle_window:
+            window_text = f"Tolerance Window: [{cycle_window[0]:.2f} - {cycle_window[2]:.2f}]"
+        
+        plt.title(f"Detected Cycles - {self.timeframe.capitalize()} Timeframe {title_suffix}\n"
+                 f"Average Cycle Length: {avg_cycle:.2f} periods, {window_text}")
+        
+        plt.legend()
+        plt.grid(True)
+        plt.show()
+
+# Usage example:
+# 1. Load your data
+# data = pd.read_csv('your_price_data.csv')
+
+# 2. Create cycle detector instances for different timeframes
+# weekly_detector = CycleDetector(data, timeframe='weekly')
+# daily_detector = CycleDetector(data, timeframe='daily')
+
+# 3. Analyze cycles
+# weekly_cycle_length = weekly_detector.get_average_cycle_length()
+# daily_cycle_length = daily_detector.get_average_cycle_length()
+
+# 4. Detect patterns
+# two_drives = weekly_detector.detect_two_drives_pattern()
+# v_shapes = daily_detector.detect_v_shaped_lows()
+
+# 5. Visualize
+# weekly_detector.plot_cycles(pattern_detection='two_drives')
+# daily_detector.plot_cycles(pattern_detection='v_shape')
diff --git a/main.py b/main.py
index 4df366c..06a343b 100644
--- a/main.py
+++ b/main.py
@@ -6,6 +6,7 @@ from cycle_detector import CycleDetector
 # Load data from CSV file instead of database
 data = pd.read_csv('data/btcusd_1-day_data.csv')
 
+
 # Convert datetime column to datetime type
 start_date = pd.to_datetime('2025-04-01')
 stop_date = pd.to_datetime('2025-05-06')
diff --git a/trend_detector_macd.py b/trend_detector_macd.py
index e3bf538..045f604 100644
--- a/trend_detector_macd.py
+++ b/trend_detector_macd.py
@@ -1,259 +1,259 @@
-import pandas as pd
-import numpy as np
-import ta
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-import logging
-import mplfinance as mpf
-from matplotlib.patches import Rectangle
-
-class TrendDetectorMACD:
-    def __init__(self, data, verbose=False):
-        self.data = data
-        self.verbose = verbose
-        # Configure logging
-        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
-                           format='%(asctime)s - %(levelname)s - %(message)s')
-        self.logger = logging.getLogger('TrendDetector')
-        
-        # 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 TrendDetector with {len(self.data)} data points")
-
-    def detect_trends_MACD_signal(self):
-        self.logger.info("Starting trend detection")
-        if len(self.data) < 3:
-            self.logger.warning("Not enough data points for trend detection")
-            return {"error": "Not enough data points for trend detection"}
-        
-        # Create a copy of the DataFrame to avoid modifying the original
-        df = self.data.copy()
-        self.logger.info("Created copy of input data")
-        
-        # If 'close' column doesn't exist, try to use a relevant column
-        if 'close' not in df.columns and len(df.columns) > 0:
-            self.logger.info(f"'close' column not found, using {df.columns[0]} instead")
-            df['close'] = df[df.columns[0]]  # Use the first column as 'close'
-        
-        # Add trend indicators
-        self.logger.info("Calculating MACD indicators")
-        # Moving Average Convergence Divergence (MACD)
-        df['macd'] = ta.trend.macd(df['close'])
-        df['macd_signal'] = ta.trend.macd_signal(df['close'])
-        df['macd_diff'] = ta.trend.macd_diff(df['close'])
-        
-        # Directional Movement Index (DMI)
-        if all(col in df.columns for col in ['high', 'low', 'close']):
-            self.logger.info("Calculating ADX indicators")
-            df['adx'] = ta.trend.adx(df['high'], df['low'], df['close'])
-            df['adx_pos'] = ta.trend.adx_pos(df['high'], df['low'], df['close'])
-            df['adx_neg'] = ta.trend.adx_neg(df['high'], df['low'], df['close'])
-        
-        # Identify trend changes
-        self.logger.info("Identifying trend changes")
-        df['trend'] = np.where(df['macd'] > df['macd_signal'], 'up', 'down')
-        df['trend_change'] = df['trend'] != df['trend'].shift(1)
-        
-        # Generate trend segments
-        self.logger.info("Generating trend segments")
-        trends = []
-        trend_start = 0
-        
-        for i in range(1, len(df)):
-
-            if df['trend_change'].iloc[i]:
-                if i > trend_start:
-                    trends.append({
-                        "type": df['trend'].iloc[i-1],
-                        "start_index": trend_start,
-                        "end_index": i-1,
-                        "start_value": df['close'].iloc[trend_start],
-                        "end_value": df['close'].iloc[i-1]
-                    })
-                trend_start = i
-        
-        # Add the last trend
-        if trend_start < len(df):
-            trends.append({
-                "type": df['trend'].iloc[-1],
-                "start_index": trend_start,
-                "end_index": len(df)-1,
-                "start_value": df['close'].iloc[trend_start],
-                "end_value": df['close'].iloc[-1]
-            })
-        
-        self.logger.info(f"Detected {len(trends)} trend segments")
-        return trends
-        
-    def get_strongest_trend(self):
-        self.logger.info("Finding strongest trend")
-        trends = self.detect_trends_MACD_signal()
-        if isinstance(trends, dict) and "error" in trends:
-            self.logger.warning(f"Error in trend detection: {trends['error']}")
-            return trends
-            
-        if not trends:
-            self.logger.info("No significant trends detected")
-            return {"message": "No significant trends detected"}
-        
-        strongest = max(trends, key=lambda x: abs(x["end_value"] - x["start_value"]))
-        self.logger.info(f"Strongest trend: {strongest['type']} from index {strongest['start_index']} to {strongest['end_index']}")
-        return strongest
-    
-    def plot_trends(self, trends):
-        """
-        Plot price data with identified trends highlighted using candlestick charts.
-        """
-        self.logger.info("Plotting trends with candlesticks")
-        if isinstance(trends, dict) and "error" in trends:
-            self.logger.error(trends["error"])
-            print(trends["error"])
-            return
-        
-        if not trends:
-            self.logger.warning("No significant trends detected for plotting")
-            print("No significant trends detected")
-            return
-            
-        # Create a figure with 2 subplots that share the x-axis
-        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={'height_ratios': [2, 1]}, sharex=True)
-        self.logger.info("Creating plot figure with shared x-axis")
-        
-        # Prepare data for candlestick chart
-        df = self.data.copy()
-        
-        # Ensure required columns exist for candlestick
-        required_cols = ['open', 'high', 'low', 'close']
-        if not all(col in df.columns for col in required_cols):
-            self.logger.warning("Missing required columns for candlestick. Defaulting to line chart.")
-            if 'close' in df.columns:
-                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
-                        df['close'], color='black', alpha=0.7, linewidth=1, label='Price')
-            else:
-                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
-                        df[df.columns[0]], color='black', alpha=0.7, linewidth=1, label='Price')
-        else:
-            # Get x values (dates if available, otherwise indices)
-            if 'datetime' in df.columns:
-                x_label = 'Date'
-                # Format date axis
-                ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
-                ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
-                fig.autofmt_xdate()
-                self.logger.info("Using datetime for x-axis")
-                
-                # For candlestick, ensure datetime is the index
-                if df.index.name != 'datetime':
-                    df = df.set_index('datetime')
-            else:
-                x_label = 'Index'
-                self.logger.info("Using index for x-axis")
-            
-            # Plot candlestick chart
-            up_color = 'green'
-            down_color = 'red'
-            
-            # Draw candlesticks manually 
-            width = 0.6
-            for i in range(len(df)):
-                # Get OHLC values for this candle
-                open_val = df['open'].iloc[i]
-                close_val = df['close'].iloc[i]
-                high_val = df['high'].iloc[i]
-                low_val = df['low'].iloc[i]
-                idx = df.index[i]
-                
-                # Determine candle color
-                color = up_color if close_val >= open_val else down_color
-                
-                # Plot candle body
-                body_height = abs(close_val - open_val)
-                bottom = min(open_val, close_val)
-                rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
-                ax1.add_patch(rect)
-                
-                # Plot candle wicks
-                ax1.plot([i, i], [low_val, high_val], color='black', linewidth=1)
-            
-            # Set appropriate x-axis limits
-            ax1.set_xlim(-0.5, len(df) - 0.5)
-        
-        # Highlight each trend with a different color
-        self.logger.info("Highlighting trends on plot")
-        for trend in trends:
-            start_idx = trend['start_index']
-            end_idx = trend['end_index']
-            trend_type = trend['type']
-            
-            # Get x-coordinates for trend plotting
-            x_start = start_idx
-            x_end = end_idx
-            
-            # Get y-coordinates for trend line
-            if 'close' in df.columns:
-                y_start = df['close'].iloc[start_idx]
-                y_end = df['close'].iloc[end_idx]
-            else:
-                y_start = df[df.columns[0]].iloc[start_idx]
-                y_end = df[df.columns[0]].iloc[end_idx]
-            
-            # Choose color based on trend type
-            color = 'green' if trend_type == 'up' else 'red'
-            
-            # Plot trend line
-            ax1.plot([x_start, x_end], [y_start, y_end], color=color, linewidth=2, 
-                    label=f"{trend_type.capitalize()} Trend" if f"{trend_type.capitalize()} Trend" not in ax1.get_legend_handles_labels()[1] else "")
-            
-            # Add markers at start and end points
-            ax1.scatter(x_start, y_start, color=color, marker='o', s=50)
-            ax1.scatter(x_end, y_end, color=color, marker='s', s=50)
-        
-        # Configure first subplot
-        ax1.set_title('Price with Trends (Candlestick)', fontsize=16)
-        ax1.set_ylabel('Price', fontsize=14)
-        ax1.grid(alpha=0.3)
-        ax1.legend()
-        
-        # Create MACD in second subplot
-        self.logger.info("Creating MACD subplot")
-        
-        # Calculate MACD indicators if not already present
-        if 'macd' not in df.columns:
-            if 'close' not in df.columns and len(df.columns) > 0:
-                df['close'] = df[df.columns[0]]
-                
-            df['macd'] = ta.trend.macd(df['close'])
-            df['macd_signal'] = ta.trend.macd_signal(df['close'])
-            df['macd_diff'] = ta.trend.macd_diff(df['close'])
-        
-        # Plot MACD components on second subplot
-        x_indices = np.arange(len(df))
-        ax2.plot(x_indices, df['macd'], label='MACD', color='blue')
-        ax2.plot(x_indices, df['macd_signal'], label='Signal', color='orange')
-        
-        # Plot MACD histogram
-        for i in range(len(df)):
-            if df['macd_diff'].iloc[i] >= 0:
-                ax2.bar(i, df['macd_diff'].iloc[i], color='green', alpha=0.5, width=0.8)
-            else:
-                ax2.bar(i, df['macd_diff'].iloc[i], color='red', alpha=0.5, width=0.8)
-        
-        ax2.set_title('MACD Indicator', fontsize=16)
-        ax2.set_xlabel(x_label, fontsize=14)
-        ax2.set_ylabel('MACD', fontsize=14)
-        ax2.grid(alpha=0.3)
-        ax2.legend()
-        
-        # Enable synchronized zooming
-        plt.tight_layout()
-        plt.subplots_adjust(hspace=0.1)
-        plt.show()
-        
-        return plt
+import pandas as pd
+import numpy as np
+import ta
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+import logging
+import mplfinance as mpf
+from matplotlib.patches import Rectangle
+
+class TrendDetectorMACD:
+    def __init__(self, data, verbose=False):
+        self.data = data
+        self.verbose = verbose
+        # Configure logging
+        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
+                           format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger('TrendDetector')
+        
+        # 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 TrendDetector with {len(self.data)} data points")
+
+    def detect_trends_MACD_signal(self):
+        self.logger.info("Starting trend detection")
+        if len(self.data) < 3:
+            self.logger.warning("Not enough data points for trend detection")
+            return {"error": "Not enough data points for trend detection"}
+        
+        # Create a copy of the DataFrame to avoid modifying the original
+        df = self.data.copy()
+        self.logger.info("Created copy of input data")
+        
+        # If 'close' column doesn't exist, try to use a relevant column
+        if 'close' not in df.columns and len(df.columns) > 0:
+            self.logger.info(f"'close' column not found, using {df.columns[0]} instead")
+            df['close'] = df[df.columns[0]]  # Use the first column as 'close'
+        
+        # Add trend indicators
+        self.logger.info("Calculating MACD indicators")
+        # Moving Average Convergence Divergence (MACD)
+        df['macd'] = ta.trend.macd(df['close'])
+        df['macd_signal'] = ta.trend.macd_signal(df['close'])
+        df['macd_diff'] = ta.trend.macd_diff(df['close'])
+        
+        # Directional Movement Index (DMI)
+        if all(col in df.columns for col in ['high', 'low', 'close']):
+            self.logger.info("Calculating ADX indicators")
+            df['adx'] = ta.trend.adx(df['high'], df['low'], df['close'])
+            df['adx_pos'] = ta.trend.adx_pos(df['high'], df['low'], df['close'])
+            df['adx_neg'] = ta.trend.adx_neg(df['high'], df['low'], df['close'])
+        
+        # Identify trend changes
+        self.logger.info("Identifying trend changes")
+        df['trend'] = np.where(df['macd'] > df['macd_signal'], 'up', 'down')
+        df['trend_change'] = df['trend'] != df['trend'].shift(1)
+        
+        # Generate trend segments
+        self.logger.info("Generating trend segments")
+        trends = []
+        trend_start = 0
+        
+        for i in range(1, len(df)):
+
+            if df['trend_change'].iloc[i]:
+                if i > trend_start:
+                    trends.append({
+                        "type": df['trend'].iloc[i-1],
+                        "start_index": trend_start,
+                        "end_index": i-1,
+                        "start_value": df['close'].iloc[trend_start],
+                        "end_value": df['close'].iloc[i-1]
+                    })
+                trend_start = i
+        
+        # Add the last trend
+        if trend_start < len(df):
+            trends.append({
+                "type": df['trend'].iloc[-1],
+                "start_index": trend_start,
+                "end_index": len(df)-1,
+                "start_value": df['close'].iloc[trend_start],
+                "end_value": df['close'].iloc[-1]
+            })
+        
+        self.logger.info(f"Detected {len(trends)} trend segments")
+        return trends
+        
+    def get_strongest_trend(self):
+        self.logger.info("Finding strongest trend")
+        trends = self.detect_trends_MACD_signal()
+        if isinstance(trends, dict) and "error" in trends:
+            self.logger.warning(f"Error in trend detection: {trends['error']}")
+            return trends
+            
+        if not trends:
+            self.logger.info("No significant trends detected")
+            return {"message": "No significant trends detected"}
+        
+        strongest = max(trends, key=lambda x: abs(x["end_value"] - x["start_value"]))
+        self.logger.info(f"Strongest trend: {strongest['type']} from index {strongest['start_index']} to {strongest['end_index']}")
+        return strongest
+    
+    def plot_trends(self, trends):
+        """
+        Plot price data with identified trends highlighted using candlestick charts.
+        """
+        self.logger.info("Plotting trends with candlesticks")
+        if isinstance(trends, dict) and "error" in trends:
+            self.logger.error(trends["error"])
+            print(trends["error"])
+            return
+        
+        if not trends:
+            self.logger.warning("No significant trends detected for plotting")
+            print("No significant trends detected")
+            return
+            
+        # Create a figure with 2 subplots that share the x-axis
+        fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8), gridspec_kw={'height_ratios': [2, 1]}, sharex=True)
+        self.logger.info("Creating plot figure with shared x-axis")
+        
+        # Prepare data for candlestick chart
+        df = self.data.copy()
+        
+        # Ensure required columns exist for candlestick
+        required_cols = ['open', 'high', 'low', 'close']
+        if not all(col in df.columns for col in required_cols):
+            self.logger.warning("Missing required columns for candlestick. Defaulting to line chart.")
+            if 'close' in df.columns:
+                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
+                        df['close'], color='black', alpha=0.7, linewidth=1, label='Price')
+            else:
+                ax1.plot(df.index if 'datetime' not in df.columns else df['datetime'], 
+                        df[df.columns[0]], color='black', alpha=0.7, linewidth=1, label='Price')
+        else:
+            # Get x values (dates if available, otherwise indices)
+            if 'datetime' in df.columns:
+                x_label = 'Date'
+                # Format date axis
+                ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+                ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
+                fig.autofmt_xdate()
+                self.logger.info("Using datetime for x-axis")
+                
+                # For candlestick, ensure datetime is the index
+                if df.index.name != 'datetime':
+                    df = df.set_index('datetime')
+            else:
+                x_label = 'Index'
+                self.logger.info("Using index for x-axis")
+            
+            # Plot candlestick chart
+            up_color = 'green'
+            down_color = 'red'
+            
+            # Draw candlesticks manually 
+            width = 0.6
+            for i in range(len(df)):
+                # Get OHLC values for this candle
+                open_val = df['open'].iloc[i]
+                close_val = df['close'].iloc[i]
+                high_val = df['high'].iloc[i]
+                low_val = df['low'].iloc[i]
+                idx = df.index[i]
+                
+                # Determine candle color
+                color = up_color if close_val >= open_val else down_color
+                
+                # Plot candle body
+                body_height = abs(close_val - open_val)
+                bottom = min(open_val, close_val)
+                rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
+                ax1.add_patch(rect)
+                
+                # Plot candle wicks
+                ax1.plot([i, i], [low_val, high_val], color='black', linewidth=1)
+            
+            # Set appropriate x-axis limits
+            ax1.set_xlim(-0.5, len(df) - 0.5)
+        
+        # Highlight each trend with a different color
+        self.logger.info("Highlighting trends on plot")
+        for trend in trends:
+            start_idx = trend['start_index']
+            end_idx = trend['end_index']
+            trend_type = trend['type']
+            
+            # Get x-coordinates for trend plotting
+            x_start = start_idx
+            x_end = end_idx
+            
+            # Get y-coordinates for trend line
+            if 'close' in df.columns:
+                y_start = df['close'].iloc[start_idx]
+                y_end = df['close'].iloc[end_idx]
+            else:
+                y_start = df[df.columns[0]].iloc[start_idx]
+                y_end = df[df.columns[0]].iloc[end_idx]
+            
+            # Choose color based on trend type
+            color = 'green' if trend_type == 'up' else 'red'
+            
+            # Plot trend line
+            ax1.plot([x_start, x_end], [y_start, y_end], color=color, linewidth=2, 
+                    label=f"{trend_type.capitalize()} Trend" if f"{trend_type.capitalize()} Trend" not in ax1.get_legend_handles_labels()[1] else "")
+            
+            # Add markers at start and end points
+            ax1.scatter(x_start, y_start, color=color, marker='o', s=50)
+            ax1.scatter(x_end, y_end, color=color, marker='s', s=50)
+        
+        # Configure first subplot
+        ax1.set_title('Price with Trends (Candlestick)', fontsize=16)
+        ax1.set_ylabel('Price', fontsize=14)
+        ax1.grid(alpha=0.3)
+        ax1.legend()
+        
+        # Create MACD in second subplot
+        self.logger.info("Creating MACD subplot")
+        
+        # Calculate MACD indicators if not already present
+        if 'macd' not in df.columns:
+            if 'close' not in df.columns and len(df.columns) > 0:
+                df['close'] = df[df.columns[0]]
+                
+            df['macd'] = ta.trend.macd(df['close'])
+            df['macd_signal'] = ta.trend.macd_signal(df['close'])
+            df['macd_diff'] = ta.trend.macd_diff(df['close'])
+        
+        # Plot MACD components on second subplot
+        x_indices = np.arange(len(df))
+        ax2.plot(x_indices, df['macd'], label='MACD', color='blue')
+        ax2.plot(x_indices, df['macd_signal'], label='Signal', color='orange')
+        
+        # Plot MACD histogram
+        for i in range(len(df)):
+            if df['macd_diff'].iloc[i] >= 0:
+                ax2.bar(i, df['macd_diff'].iloc[i], color='green', alpha=0.5, width=0.8)
+            else:
+                ax2.bar(i, df['macd_diff'].iloc[i], color='red', alpha=0.5, width=0.8)
+        
+        ax2.set_title('MACD Indicator', fontsize=16)
+        ax2.set_xlabel(x_label, fontsize=14)
+        ax2.set_ylabel('MACD', fontsize=14)
+        ax2.grid(alpha=0.3)
+        ax2.legend()
+        
+        # Enable synchronized zooming
+        plt.tight_layout()
+        plt.subplots_adjust(hspace=0.1)
+        plt.show()
+        
+        return plt
diff --git a/trend_detector_simple.py b/trend_detector_simple.py
index 141998c..5e92e05 100644
--- a/trend_detector_simple.py
+++ b/trend_detector_simple.py
@@ -1,205 +1,255 @@
-import pandas as pd
-import numpy as np
-import logging
-from scipy.signal import find_peaks
-import matplotlib.dates as mdates
-from scipy import stats
-
-class TrendDetectorSimple:
-    def __init__(self, data, verbose=False):
-        """
-        Initialize the TrendDetectorSimple class.
-        
-        Parameters:
-        - data: pandas DataFrame containing price data
-        - verbose: boolean, whether to display detailed logging information
-        """
-        
-        self.data = data
-        self.verbose = verbose
-        
-        # Configure logging
-        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
-                           format='%(asctime)s - %(levelname)s - %(message)s')
-        self.logger = logging.getLogger('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")
-
-    def detect_trends(self):
-        """
-        Detect trends by identifying local minima and maxima in the price data
-        using scipy.signal.find_peaks.
-        
-        Parameters:
-        - prominence: float, required prominence of peaks (relative to the price range)
-        - width: int, required width of peaks in data points
-        
-        Returns:
-        - DataFrame with columns for timestamps, prices, and trend 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
-        
-        for peak in max_peaks:
-            df.at[peak, 'is_max'] = True
-        for peak in min_peaks:
-            df.at[peak, 'is_min'] = True
-        
-        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
-        
-        # Linear regression for min peaks if we have at least 2 points
-        min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
-        # Linear regression for max peaks if we have at least 2 points
-        max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
-
-        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods
-        self.logger.info("Calculating SMA-7 and SMA-15")
-        
-        # Calculate SMA values and exclude NaN values
-        sma_7 = df['close'].rolling(window=7).mean().dropna().values
-        sma_15 = df['close'].rolling(window=15).mean().dropna().values
-        
-        # Add SMA values to regression_results
-        analysis_results = {}
-        analysis_results['linear_regression'] = {
-            'min': {
-                'slope': min_slope,
-                'intercept': min_intercept,
-                'r_squared': min_r_value ** 2
-            },
-            'max': {
-                'slope': max_slope,
-                'intercept': max_intercept,
-                'r_squared': max_r_value ** 2
-            }
-        }
-        analysis_results['sma'] = {
-            '7': sma_7,
-            '15': sma_15
-        }
-        
-        self.logger.info(f"Min peaks regression: slope={min_slope:.4f}, intercept={min_intercept:.4f}, r²={min_r_value**2:.4f}")
-        self.logger.info(f"Max peaks regression: slope={max_slope:.4f}, intercept={max_intercept:.4f}, r²={max_r_value**2:.4f}")
-
-        return result, analysis_results
-
-    def plot_trends(self, trend_data, analysis_results):
-        """
-        Plot the price data with detected trends using a candlestick chart.
-        
-        Parameters:
-        - trend_data: DataFrame, the output from detect_trends(). If None, detect_trends() will be called.
-        
-        Returns:
-        - None (displays the plot)
-        """
-        import matplotlib.pyplot as plt
-        from matplotlib.patches import Rectangle
-                
-        # Create the figure and axis
-        fig, ax = plt.subplots(figsize=(12, 8))
-        
-        # Create a copy of the data
-        df = self.data.copy()
-        
-        # Plot candlestick chart
-        up_color = 'green'
-        down_color = 'red'
-        
-        # Draw candlesticks manually 
-        width = 0.6
-        x_values = range(len(df))
-        
-        for i in range(len(df)):
-            # Get OHLC values for this candle
-            open_val = df['open'].iloc[i]
-            close_val = df['close'].iloc[i]
-            high_val = df['high'].iloc[i]
-            low_val = df['low'].iloc[i]
-            
-            # Determine candle color
-            color = up_color if close_val >= open_val else down_color
-            
-            # Plot candle body
-            body_height = abs(close_val - open_val)
-            bottom = min(open_val, close_val)
-            rect = Rectangle((i - width/2, bottom), width, body_height, color=color, alpha=0.8)
-            ax.add_patch(rect)
-            
-            # Plot candle wicks
-            ax.plot([i, i], [low_val, high_val], color='black', linewidth=1)
-        
-        min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
-        if min_indices:
-            min_y = [df['close'].iloc[i] for i in min_indices]
-            ax.scatter(min_indices, min_y, color='darkred', s=200, marker='^', label='Local Minima', zorder=100)
-
-        max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
-        if max_indices:
-            max_y = [df['close'].iloc[i] for i in max_indices]
-            ax.scatter(max_indices, max_y, color='darkgreen', s=200, marker='v', label='Local Maxima', zorder=100)
-        
-        if analysis_results:
-            x_vals = np.arange(len(df))
-            # Minima regression line (support)
-            min_slope = analysis_results['linear_regression']['min']['slope']
-            min_intercept = analysis_results['linear_regression']['min']['intercept']
-            min_line = min_slope * x_vals + min_intercept
-            ax.plot(x_vals, min_line, 'g--', linewidth=2, label='Minima Regression')
-
-            # Maxima regression line (resistance)
-            max_slope = analysis_results['linear_regression']['max']['slope']
-            max_intercept = analysis_results['linear_regression']['max']['intercept']
-            max_line = max_slope * x_vals + max_intercept
-            ax.plot(x_vals, max_line, 'r--', linewidth=2, label='Maxima Regression')
-
-            # SMA-7 line
-            sma_7 = analysis_results['sma']['7']
-            ax.plot(x_vals, sma_7, 'y-', linewidth=2, label='SMA-7')
-
-            # SMA-15 line
-            # sma_15 = analysis_results['sma']['15']
-            # valid_idx_15 = ~np.isnan(sma_15)
-            # ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], 'm-', linewidth=2, label='SMA-15')
-        
-        # Set title and labels
-        ax.set_title('Price Candlestick Chart with Local Minima and Maxima', fontsize=14)
-        ax.set_xlabel('Date', fontsize=12)
-        ax.set_ylabel('Price', fontsize=12)
-        
-        # Set appropriate x-axis limits
-        ax.set_xlim(-0.5, len(df) - 0.5)
-        
-        # Add a legend
-        ax.legend(loc='best')
-        
-        # Adjust layout
-        plt.tight_layout()
-        
-        # Show the plot
-        plt.show()        
+import pandas as pd
+import numpy as np
+import logging
+from scipy.signal import find_peaks
+import matplotlib.dates as mdates
+from scipy import stats
+from scipy import stats
+
+class TrendDetectorSimple:
+    def __init__(self, data, verbose=False):
+        """
+        Initialize the TrendDetectorSimple class.
+        
+        Parameters:
+        - data: pandas DataFrame containing price data
+        - verbose: boolean, whether to display detailed logging information
+        """
+        
+        self.data = data
+        self.verbose = verbose
+        
+        # Plot style configuration
+        self.plot_style = 'dark_background' 
+        self.bg_color = '#181C27'
+        self.plot_size = (12, 8)
+        
+        # Candlestick configuration
+        self.candle_width = 0.6
+        self.candle_up_color = '#089981'
+        self.candle_down_color = '#F23645'
+        self.candle_alpha = 0.8
+        self.wick_width = 1
+        
+        # Marker configuration
+        self.min_marker = '^'
+        self.min_color = 'red'
+        self.min_size = 100
+        self.max_marker = 'v'
+        self.max_color = 'green'
+        self.max_size = 100
+        self.marker_zorder = 100
+        
+        # Line configuration
+        self.line_width = 2
+        self.min_line_style = 'g--'  # green dashed
+        self.max_line_style = 'r--'  # red dashed
+        self.sma7_line_style = 'y-'  # yellow solid
+        self.sma15_line_style = 'm-'  # magenta solid
+        
+        # Text configuration
+        self.title_size = 14
+        self.title_color = 'white'
+        self.axis_label_size = 12
+        self.axis_label_color = 'white'
+        
+        # Legend configuration
+        self.legend_loc = 'best'
+        self.legend_bg_color = '#333333'
+        
+        # Configure logging
+        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
+                           format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger('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")
+
+    def detect_trends(self):
+    def detect_trends(self):
+        """
+        Detect trends by identifying local minima and maxima in the price data
+        using scipy.signal.find_peaks.
+        
+        Parameters:
+        - prominence: float, required prominence of peaks (relative to the price range)
+        - width: int, required width of peaks in data points
+        
+        Returns:
+        - DataFrame with columns for timestamps, prices, and trend indicators
+        """
+        self.logger.info(f"Detecting trends")
+        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)
+        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
+        
+        for peak in max_peaks:
+            df.at[peak, 'is_max'] = True
+        for peak in min_peaks:
+            df.at[peak, 'is_min'] = True
+        
+        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
+        
+        # Linear regression for min peaks if we have at least 2 points
+        min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
+        # Linear regression for max peaks if we have at least 2 points
+        max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
+
+        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods
+        self.logger.info("Calculating SMA-7 and SMA-15")
+        
+        sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
+        sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
+        
+        analysis_results = {}
+        analysis_results['linear_regression'] = {
+            'min': {
+                'slope': min_slope,
+                'intercept': min_intercept,
+                'r_squared': min_r_value ** 2
+            },
+            'max': {
+                'slope': max_slope,
+                'intercept': max_intercept,
+                'r_squared': max_r_value ** 2
+            }
+        }
+        analysis_results['sma'] = {
+            '7': sma_7,
+            '15': sma_15
+        }
+        
+        self.logger.info(f"Min peaks regression: slope={min_slope:.4f}, intercept={min_intercept:.4f}, r²={min_r_value**2:.4f}")
+        self.logger.info(f"Max peaks regression: slope={max_slope:.4f}, intercept={max_intercept:.4f}, r²={max_r_value**2:.4f}")
+
+        return result, analysis_results
+
+    def plot_trends(self, trend_data, analysis_results):
+    def plot_trends(self, trend_data, analysis_results):
+        """
+        Plot the price data with detected trends using a candlestick chart.
+        
+        Parameters:
+        - trend_data: DataFrame, the output from detect_trends(). If None, detect_trends() will be called.
+        
+        Returns:
+        - None (displays the plot)
+        """
+        import matplotlib.pyplot as plt
+        from matplotlib.patches import Rectangle
+                
+        # Create the figure and axis with specified background
+        plt.style.use(self.plot_style)
+        fig, ax = plt.subplots(figsize=self.plot_size)
+        
+        # Set the custom background color
+        fig.patch.set_facecolor(self.bg_color)
+        ax.set_facecolor(self.bg_color)
+        
+        # Create a copy of the data
+        df = self.data.copy()
+        
+        # Draw candlesticks manually 
+        x_values = range(len(df))
+        
+        for i in range(len(df)):
+            # Get OHLC values for this candle
+            open_val = df['open'].iloc[i]
+            close_val = df['close'].iloc[i]
+            high_val = df['high'].iloc[i]
+            low_val = df['low'].iloc[i]
+            
+            # Determine candle color
+            color = self.candle_up_color if close_val >= open_val else self.candle_down_color
+            
+            # Plot candle body
+            body_height = abs(close_val - open_val)
+            bottom = min(open_val, close_val)
+            rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height, 
+                            color=color, alpha=self.candle_alpha)
+            ax.add_patch(rect)
+            
+            # Plot candle wicks
+            ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
+        
+        min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
+        if min_indices:
+            min_y = [df['close'].iloc[i] for i in min_indices]
+            ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size, 
+                      marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
+
+        max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
+        if max_indices:
+            max_y = [df['close'].iloc[i] for i in max_indices]
+            ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size, 
+                      marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
+        
+        if analysis_results:
+            x_vals = np.arange(len(df))
+            # Minima regression line (support)
+            min_slope = analysis_results['linear_regression']['min']['slope']
+            min_intercept = analysis_results['linear_regression']['min']['intercept']
+            min_line = min_slope * x_vals + min_intercept
+            ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width, 
+                   label='Minima Regression')
+
+            # Maxima regression line (resistance)
+            max_slope = analysis_results['linear_regression']['max']['slope']
+            max_intercept = analysis_results['linear_regression']['max']['intercept']
+            max_line = max_slope * x_vals + max_intercept
+            ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width, 
+                   label='Maxima Regression')
+
+            # SMA-7 line
+            sma_7 = analysis_results['sma']['7']
+            ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width, 
+                   label='SMA-7')
+
+            # SMA-15 line
+            sma_15 = analysis_results['sma']['15']
+            valid_idx_15 = ~np.isnan(sma_15)
+            ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style, 
+                   linewidth=self.line_width, label='SMA-15')
+        
+        # Set title and labels
+        ax.set_title('Price Candlestick Chart with Local Minima and Maxima', 
+                    fontsize=self.title_size, color=self.title_color)
+        ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
+        ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
+        
+        # Set appropriate x-axis limits
+        ax.set_xlim(-0.5, len(df) - 0.5)
+        
+        # Add a legend
+        ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
+        
+        # Adjust layout
+        plt.tight_layout()
+        
+        # Show the plot
+        plt.show()        
         
\ No newline at end of file