okama.Inflation

class Inflation(symbol='RUB.INFL', first_date=None, last_date=None)

Bases: MacroABC

Inflation related data and methods.

Inflation symbols are in ‘.INFL’ namespace.

Parameters:

symbol: str: Inflation symbol is unique series of letters with namespace after dot (EUR.INFL).
first_datestr, default None: First date of the values time series (2020-01).
last_datestr, default None: Last date of the values time series (2022-03).

Methods & Attributes

`annual_inflation_ts`	Calculate annual inflation time series.
`cumulative_inflation`	Calculate cumulative inflation rate time series for the whole period.
`describe`([years])	Generate descriptive inflation statistics for YTD and a given list of periods.
`purchasing_power_1000`	Calculate purchasing power of 1000 (in the currency of inflation) after period from first_date to last_date.
`rolling_inflation`	Calculate 12 months rolling inflation time series.
`set_values_monthly`(date, value)	Set monthly value for the past or future date.
`values_monthly`	Return values time series historical monthly data.

property cumulative_inflation

Calculate cumulative inflation rate time series for the whole period.

Returns:

Series: Cumulative inflation rate.

Examples

>>> x = ok.Inflation('RUB.INFL', first_date='2020-01', last_date='2020-12')
>>> x.cumulative_inflation
date
2020-01    0.004000
2020-02    0.007313
2020-03    0.012853
2020-04    0.021260
2020-05    0.024018
2020-06    0.026270
2020-07    0.029862
2020-08    0.029450
2020-09    0.028730
2020-10    0.033153
2020-11    0.040489
2020-12    0.049125
Freq: M, Name: RUB.INFL, dtype: float64

property annual_inflation_ts

Calculate annual inflation time series.

Inflation is calculated for each calendar year.

Returns:

Series: Calendar annual Inflation time series.

Examples

>>> import matplotlib.pyplot as plt
>>> infl = ok.Inflation('EUR.INFL', first_date='2016-01', last_date='2021-12')
>>> infl.annual_inflation_ts.plot(kind='bar')
>>> plt.show()

property purchasing_power_1000

Calculate purchasing power of 1000 (in the currency of inflation) after period from first_date to last_date.

Returns:

float: The Purchasing power of 1000 currency units.

Examples

>>> x = ok.Inflation('RUB.INFL', first_date='2000-01', last_date='2020-12')
>>> x.purchasing_power_1000
145.8118461948026

property rolling_inflation

Calculate 12 months rolling inflation time series.

Returns:

Series: 12 months rolling inflation time series.

Examples

>>> import matplotlib.pyplot as plt
>>> infl = ok.Inflation('ILS.INFL', first_date='1980-01', last_date='1989-12')
>>> infl.rolling_inflation.plot()
>>> plt.show()

describe(years=(1, 5, 10))

Generate descriptive inflation statistics for YTD and a given list of periods. Statistics includes: - YTD compound inflation - Annual inflation (geometric mean) for a given list of periods - max 12 months inflation for the periods - Annual inflation (geometric mean) for the whole history

Parameters:

yearstuple of (int,), default (1, 5, 10): List of periods in years for the Inflation.

Returns:

DataFrame: Table of descriptive statistics for Inflation.

Examples

>>> infl = ok.Inflation('USD.INFL', last_date='2022-04')
>>> infl.describe(years=(1, 15, 50))
         property               period    USD.INFL
    compound inflation                  YTD    0.036987
 1000 purchasing power                  YTD  964.332475
      annual inflation              1 years    0.082611
    compound inflation              1 years    0.082611
     max 12m inflation              2022-03    0.085410
 1000 purchasing power              1 years  923.692547
      annual inflation             15 years    0.022632
    compound inflation             15 years    0.398916
     max 12m inflation              2022-03    0.085410
 1000 purchasing power             15 years  714.839226
     annual inflation             50 years    0.039595
   compound inflation             50 years    5.969612
    max 12m inflation              1980-03    0.147383
1000 purchasing power             50 years  143.480004
     annual inflation  109 years, 3 months    0.031470
   compound inflation  109 years, 3 months   28.519646
    max 12m inflation              1920-06    0.236888
1000 purchasing power  109 years, 3 months   33.875745

set_values_monthly(date, value)

Set monthly value for the past or future date.

The date should be in month period format (“2023-12”). T The result stored only in the class instance. It can be used to analyze inflation with forecast or corrected data.

Examples

>>> infl = ok.Inflation('USD.INFL', last_date='2022-04')
>>> infl.values_monthly.tail()  # last 5 months statistics
date
2021-12    0.0031
2022-01    0.0084
2022-02    0.0091
2022-03    0.0134
2022-04    0.0056
Freq: M, Name: USD.INFL, dtype: float64
>>> infl.set_values_monthly(date='2024-05', value=0.09)  # set forecasted inflation value
>>> infl.values_monthly.tail()  # check the statistics again (May data is available now)
date
2022-01    0.0084
2022-02    0.0091
2022-03    0.0134
2022-04    0.0056
2024-05    0.0900
Freq: M, Name: USD.INFL, dtype: float64

property values_monthly

Return values time series historical monthly data.

Returns:

Series: Time series of values historical data (monthly).