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Calculate Composite Rate for Series I Savings Bonds (I Bonds) using CPI

Install Packages

import numpy as np
import pandas as pd
from pandas_datareader.fred import FredReader

Download CPI Data from FRED

Download Consumer Price Index for All Urban Consumers - Not Seasonaly Adjusted CPIAUCNS.

end_date = pd.to_datetime('today').date()
start_date = (end_date + pd.DateOffset(years=-5)).date()

print(f'start_date: {start_date} to end_date: {end_date}')

start_date: 2017-08-11 to end_date: 2022-08-11


df = FredReader('CPIAUCNS', start=start_date).read()

df.tail(12)
CPIAUCNS
DATE
2021-08-01 273.567
2021-09-01 274.310
2021-10-01 276.589
2021-11-01 277.948
2021-12-01 278.802
2022-01-01 281.148
2022-02-01 283.716
2022-03-01 287.504
2022-04-01 289.109
2022-05-01 292.296
2022-06-01 296.311
2022-07-01 296.276

Plot CPI Data

df.plot(title='CPI - CPIAUCNS', figsize=(15,10));
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Calculate Percent Change

df['CPIAUCNS-1m'] = df['CPIAUCNS'].shift(periods=1)
df['CPIAUCNS-6m'] = df['CPIAUCNS'].shift(periods=6)
df['CPIAUCNS-12m'] = df['CPIAUCNS'].shift(periods=12)
df.tail(12)
CPIAUCNS CPIAUCNS-1m CPIAUCNS-6m CPIAUCNS-12m
DATE
2021-08-01 273.567 273.003 263.014 259.918
2021-09-01 274.310 273.567 264.877 260.280
2021-10-01 276.589 274.310 267.054 260.388
2021-11-01 277.948 276.589 269.195 260.229
2021-12-01 278.802 277.948 271.696 260.474
2022-01-01 281.148 278.802 273.003 261.582
2022-02-01 283.716 281.148 273.567 263.014
2022-03-01 287.504 283.716 274.310 264.877
2022-04-01 289.109 287.504 276.589 267.054
2022-05-01 292.296 289.109 277.948 269.195
2022-06-01 296.311 292.296 278.802 271.696
2022-07-01 296.276 296.311 281.148 273.003

Composite Rate is 6 months percentage change multiplied by 2 (annualized).

df['percent_change_1m'] = (df['CPIAUCNS'] - df['CPIAUCNS-1m'])*100 / df['CPIAUCNS-1m']
df['percent_change_6m'] = (df['CPIAUCNS'] - df['CPIAUCNS-6m'])*100 / df['CPIAUCNS-6m']
df['percent_change_12m'] = (df['CPIAUCNS'] - df['CPIAUCNS-12m'])*100 / df['CPIAUCNS-12m']

# Composite Rate
df['percent_change_6m_annualized'] = df['percent_change_6m'] *2
cols = ['percent_change_1m', 'percent_change_6m', 'percent_change_12m', 'percent_change_6m_annualized']
df[cols].tail(12)
percent_change_1m percent_change_6m percent_change_12m percent_change_6m_annualized
DATE
2021-08-01 0.206591 4.012334 5.251272 8.024668
2021-09-01 0.271597 3.561276 5.390349 7.122551
2021-10-01 0.830812 3.570439 6.221869 7.140878
2021-11-01 0.491343 3.251546 6.809003 6.503093
2021-12-01 0.307252 2.615423 7.036403 5.230846
2022-01-01 0.841457 2.983484 7.479872 5.966967
2022-02-01 0.913398 3.709877 7.871064 7.419755
2022-03-01 1.335138 4.809887 8.542456 9.619773
2022-04-01 0.558253 4.526572 8.258629 9.053144
2022-05-01 1.102352 5.162117 8.581512 10.324233
2022-06-01 1.373608 6.280084 9.059758 12.560168
2022-07-01 -0.011812 5.380796 8.524815 10.761592

Composite Rate

Following we can see the composite rate based on the latest CPI data.

composite_rate = df.iloc[-1]['percent_change_6m_annualized'].round(2)
inflation_rate = df.iloc[-1]['percent_change_12m'].round(2)
latest_CPI_date = df.iloc[-1].name.date()

print(f'Latest CPI Date: {latest_CPI_date}, Inflation Rate: {inflation_rate}%, Composite Rate: {composite_rate}%')

Latest CPI Date: 2022-07-01, Inflation Rate: 8.52%, Composite Rate: 10.76%

Plot Percent Changes over Time

# pct_columns = ['percent_change_1m', 'percent_change_6m', 'percent_change_12m', 'percent_change_6m_annualized']
pct_columns = ['percent_change_6m_annualized']

df[pct_columns].tail(12)
percent_change_6m_annualized
DATE
2021-08-01 8.024668
2021-09-01 7.122551
2021-10-01 7.140878
2021-11-01 6.503093
2021-12-01 5.230846
2022-01-01 5.966967
2022-02-01 7.419755
2022-03-01 9.619773
2022-04-01 9.053144
2022-05-01 10.324233
2022-06-01 12.560168
2022-07-01 10.761592 
df[pct_columns].plot(title='Calculated Inflation Rate', figsize=(15, 10));
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print(f"Last Updated {pd.to_datetime('today')}")

Last Updated 2022-08-11 12:03:35.659083