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Refactor main.py to remove commented-out cycle detection code and streamline trend plotting. Update TrendDetectorSimple by eliminating unnecessary logging statements for improved clarity during trend detection.

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Simon Moisy 2025-05-09 15:24:10 +08:00
parent f316571a3c
commit 7c4db08b1b
2 changed files with 1 additions and 44 deletions
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38
main.py
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@ -17,40 +17,4 @@ print(f"Number of data points: {len(daily_data)}")
trend_detector = TrendDetectorSimple(daily_data, verbose=True) trend_detector = TrendDetectorSimple(daily_data, verbose=True)
trends, analysis_results = trend_detector.detect_trends() trends, analysis_results = trend_detector.detect_trends()
trend_detector.plot_trends(trends, analysis_results, "supertrend") 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)')

7
trend_detector_simple.py
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@ -90,10 +90,8 @@ class TrendDetectorSimple:
# Convert data to pandas DataFrame if it's not already # Convert data to pandas DataFrame if it's not already
if not isinstance(self.data, pd.DataFrame): if not isinstance(self.data, pd.DataFrame):
if isinstance(self.data, list): if isinstance(self.data, list):
self.logger.info("Converting list to DataFrame")
self.data = pd.DataFrame({'close': self.data}) self.data = pd.DataFrame({'close': self.data})
else: else:
self.logger.error("Invalid data format provided")
raise ValueError("Data must be a pandas DataFrame or a list") raise ValueError("Data must be a pandas DataFrame or a list")
self.logger.info(f"Initialized TrendDetectorSimple with {len(self.data)} data points") 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 - DataFrame with columns for timestamps, prices, and trend indicators
- Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
""" """
self.logger.info(f"Detecting trends")
df = self.data.copy() df = self.data.copy()
close_prices = df['close'].values close_prices = df['close'].values
max_peaks, _ = find_peaks(close_prices) max_peaks, _ = find_peaks(close_prices)
min_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_min'] = False
df['is_max'] = False df['is_max'] = False
@ -192,7 +186,6 @@ class TrendDetectorSimple:
result = df[['datetime', 'close', 'is_min', 'is_max']].copy() result = df[['datetime', 'close', 'is_min', 'is_max']].copy()
# Perform linear regression on min_peaks and max_peaks # 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 min_prices = df['close'].iloc[min_peaks].values
max_prices = df['close'].iloc[max_peaks].values max_prices = df['close'].iloc[max_peaks].values