import pandas as pd import numpy as np import matplotlib.pyplot as plt import re # Define a function to convert the date format def convert_japanese_date(date_str): era, year, month, day = re.match(r'([A-Z])(\d+)\.(\d+)\.(\d+)', date_str).groups() if era == 'S': # Showa era year = int(year) + 1925 elif era == 'H': # Heisei era year = int(year) + 1988 elif era == 'R': # Reiwa era year = int(year) + 2018 return f'{year}-{month}-{day}' # Load the data with Shift-JIS encoding data = pd.read_csv('/mnt/data/jgbcm_all.csv', encoding='shift_jis') # Remove header row and reset column names data.columns = data.iloc[0] data = data[1:] # Convert the date column to datetime data['基準日'] = pd.to_datetime(data['基準日'].apply(convert_japanese_date)) # Replace '-' with NaN and convert the 10 year bond yield to numeric data['10年'] = pd.to_numeric(data['10年'].replace('-', float('NaN'))) # Filter the data starting from Showa 61 year, July 5th start_date = pd.to_datetime('1986-07-05') filtered_data = data[data['基準日'] >= start_date].copy() # Calculate the 30-day moving average of the yield filtered_data['10年_30日移動平均'] = filtered_data['10年'].rolling(window=30).mean() # Plot the 30-day moving average of the 10-year bond yield plt.figure(figsize=(10, 6)) plt.plot(filtered_data['基準日'], filtered_data['10年_30日移動平均']) plt.title('30-Day Moving Average of 10 Year Japanese Government Bond Yield Over Time') plt.xlabel('Date') plt.ylabel('Yield (%)') plt.yticks(np.arange(0, filtered_data['10年_30日移動平均'].max() + 0.5, 0.5)) plt.grid(True) plt.show()