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dewdew

# Function to determine regime characteristics with hysteresis thresholds def determine_regime_characteristics_with_hysteresis ( df, regime_probabilities, tenor, bullish_threshold= 0.6 , bearish_threshold= 0.4 ): """ Determine regime characteristics using hysteresis thresholds to reduce noise. Parameters: - bullish_threshold: Probability threshold to enter bullish regime (default 0.6) - bearish_threshold: Probability threshold to enter bearish regime (default 0.4) """ # Create a dataframe with smoothed probabilities regime_df = pd.DataFrame(index=df.index) # Add regime probabilities regime_df[ 'Prob_Regime_0' ] = regime_probabilities[ 0 ] regime_df[ 'Prob_Regime_1' ] = regime_probabilities[ 1 ] # Add price data regime_df[tenor] = df[tenor] # First, determine which regime is bullish/bearish using the orig...
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Hzksj

# Copy and paste this fixed code into your script # Replace your main function with this version def main(): # Parse command line arguments (if any) args = parse_arguments() lookback_days = args.lookback_days if hasattr(args, 'lookback_days') else 365 # Update data path from arguments if provided data_path = args.data_path if hasattr(args, 'data_path') else DATA_PATH # Check if PDF generation is disabled generate_pdf = not (hasattr(args, 'no_pdf') and args.no_pdf) pdf_filename = args.pdf_filename if hasattr(args, 'pdf_filename') else PDF_FILENAME print("\n*** HMM REGIME DETECTION - MULTI-TENOR PRODUCTION RUN ***") print(f"Run date: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}") # Print active tenors and their window sizes active_tenors = [f"{tenor} (Window: {config['window_size']})" for tenor, config in ASW_TENORS.ite...
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