日本の長期金利の推移（1986〜）

https://gyazo.com/f076a5dfba1e9738bae924c6f54881ed

マイナス金利の導入発表を受けて、市中金利は大きく低下しました。長期金利（新発10年物国債）は初めてマイナスになり、2月末以降マイナス圏で推移しています（図表1）。

code:python

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.iloc0

data = data1:

# 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()