More analysis WIP

main.py

@@ -15,8 +15,8 @@ daily_data = data[(pd.to_datetime(data['datetime']) >= start_date) &
 print(f"Number of data points: {len(daily_data)}")
 
 trend_detector = TrendDetectorSimple(daily_data, verbose=True)
-trends = trend_detector.detect_trends()
-trend_detector.plot_trends(trends)
+trends, analysis_results = trend_detector.detect_trends()
+trend_detector.plot_trends(trends, analysis_results)
 
 #trend_detector = TrendDetectorMACD(daily_data, True)
 #trends = trend_detector.detect_trends_MACD_signal()

trend_detector_simple.py

@@ -3,6 +3,7 @@ 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):
@@ -63,11 +64,50 @@ class TrendDetectorSimple:
         for peak in min_peaks:
             df.at[peak, 'is_min'] = True
         
-        result = df[['datetime', 'close', 'is_min', 'is_max']]
+        result = df[['datetime', 'close', 'is_min', 'is_max']].copy()
         
-        return result
+        # 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)
 
-    def plot_trends(self, trend_data):
+        # 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.
         
@@ -116,12 +156,35 @@ class TrendDetectorSimple:
         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='black', s=200, marker='^', label='Local Minima', zorder=100)
+            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='black', s=200, marker='v', label='Local Maxima', zorder=100)
+            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)