Refactor cycle detection and trend analysis; enhance trend detection with linear regression and moving averages. Update main script for improved data handling and visualization.

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')

trend_detector_simple.py

@@ -4,6 +4,7 @@ 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):
@@ -18,6 +19,44 @@ class TrendDetectorSimple:
         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')
@@ -34,6 +73,7 @@ class TrendDetectorSimple:
         
         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
@@ -47,10 +87,13 @@ class TrendDetectorSimple:
         - 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)
         
@@ -79,11 +122,9 @@ class TrendDetectorSimple:
         # 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 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
-        sma_7 = df['close'].rolling(window=7).mean().dropna().values
+        sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
-        sma_15 = df['close'].rolling(window=15).mean().dropna().values
         
-        # Add SMA values to regression_results
         analysis_results = {}
         analysis_results['linear_regression'] = {
             'min': {
@@ -107,6 +148,7 @@ class TrendDetectorSimple:
 
         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.
@@ -120,18 +162,18 @@ class TrendDetectorSimple:
         import matplotlib.pyplot as plt
         from matplotlib.patches import Rectangle
                 
-        # Create the figure and axis
+        # Create the figure and axis with specified background
-        fig, ax = plt.subplots(figsize=(12, 8))
+        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()
         
-        # 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)):
@@ -142,26 +184,29 @@ class TrendDetectorSimple:
             low_val = df['low'].iloc[i]
             
             # Determine candle color
-            color = up_color if close_val >= open_val else down_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 - width/2, bottom), width, body_height, color=color, alpha=0.8)
+            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='black', linewidth=1)
+            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='darkred', s=200, marker='^', label='Local Minima', zorder=100)
+            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='darkgreen', s=200, marker='v', label='Local Maxima', zorder=100)
+            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))
@@ -169,33 +214,38 @@ class TrendDetectorSimple:
             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')
+            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, 'r--', linewidth=2, label='Maxima Regression')
+            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, 'y-', linewidth=2, label='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']
+            sma_15 = analysis_results['sma']['15']
-            # valid_idx_15 = ~np.isnan(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')
+            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=14)
+        ax.set_title('Price Candlestick Chart with Local Minima and Maxima', 
-        ax.set_xlabel('Date', fontsize=12)
+                    fontsize=self.title_size, color=self.title_color)
-        ax.set_ylabel('Price', fontsize=12)
+        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='best')
+        ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
         
         # Adjust layout
         plt.tight_layout()