Cycles/trend_detector_simple.py

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()