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bienvie

Subject: Update on Regime Identification Model for US Asset Swaps Hello everyone, As you may know, we are currently working on the development of a regime identification model for US Asset Swaps. While the project is still in development, with the final results to be presented this Friday, I wanted to draw your attention to a key update. Our model has just identified a shift in the regime for the 5-year asset swap spread from bullish to bearish. This change indicates a transition from an increasing trend to a decreasing trend. Based on historical results, once the model identifies a change to a bearish regime, the US Asset Swap typically remains in a decreasing trend for about 2 months. This means you can expect to see more decreasing values for the 5-year ASW during this period. Disclaimer: Please note that this information is provided for informational purposes only and is based on historical data. As the model is still in development, past performance does not guarantee future ...
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sss

# HMM Regime Detection - Production Script # This script runs a Markov Switching Model to identify market regimes # and visualizes the results for daily trading decisions import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.dates as mdates from statsmodels.tsa.regime_switching.markov_regression import MarkovRegression import warnings from datetime import datetime, timedelta import os import matplotlib.patches as patches # Suppress warnings for cleaner output warnings.filterwarnings( 'ignore' ) # Configuration parameters WINDOW_SIZE = 50 # Optimal window size from backtest DATA_PATH = 'ASW_SY.xlsx' # Update to your data path SAVE_RESULTS = True RESULTS_DIR = 'regime_results' # Create results directory if it doesn't exist if SAVE_RESULTS and not os.path.exists(RESULTS_DIR): os.makedirs(RESULTS_DIR) # Function to load and preprocess data def load_data ( file_path ): print ( f...
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