bolster.data_sources.boe_base_rate

Bank of England official Bank Rate (base rate).

Wraps the Bank of England’s published base-rate spreadsheet to provide standardised access to the UK policy interest rate:

A unified daily rate series back to 1973, formed by coalescing the successive rate regimes the BoE has used (Minimum Lending Rate, Minimum Band 1 Dealing Rate, Repo Rate and the current Official Bank Rate) into a single continuous level.
An event-based history of rate changes back to 1694, exposed via get_rate_changes().

The base rate is a level (a standing interest rate), not a flow, so when it is resampled to coarser resolutions the last observation in each period is used rather than a sum or mean.

This is the second of three macroeconomic context modules and emits the same fixed output schema as bolster.data_sources.ons_cpi, so that the macro series can be joined and resampled against one another:

Column	Description
date	`datetime64[ns]` period-start (Jan 2024 -> 2024-01-01, Q1 2024 -> 2024-01-01, year 2024 -> 2024-01-01, a daily observation keeps its own date)
year	`int` calendar year
quarter	`str` “Q1”..”Q4”, or `pd.NA` for annual/monthly/daily
month	`int` 1-12, or `pd.NA` for quarterly/annual/daily
resolution	`str` “daily” \| “monthly” \| “quarterly” \| “annual”
series	`str` always “base_rate”
value	`float` rate in per-cent
unit	`str` always “%”
geography	`str` always “UK”
source	`str` always “BoE”

Note

The Bank of England’s dynamic statistics API (/boe-apps/statistics-api/, /boe-apps/sdw/) returns HTTP 403 for automated requests. The static spreadsheet linked below is the only reliable programmatic route and is what this module uses.

Source:: https://www.bankofengland.co.uk/boeapps/database/Bank-Rate.asp

Example

>>> from bolster.data_sources import boe_base_rate
>>> df = boe_base_rate.get_latest_data(resolution="annual")  
>>> sorted(df.columns)  
['date', 'geography', 'month', 'quarter', 'resolution', 'series', 'source', 'unit', 'value', 'year']

Attributes

`logger`
`DATA_URL`
`RAW_DATA_SHEET`
`HISTORICAL_SHEET`
`GEOGRAPHY`
`SOURCE`
`SERIES`
`UNIT`
`SCHEMA_COLUMNS`
`RATE_CHANGES_COLUMNS`
`RESOLUTIONS`

Exceptions

`BoEDataError`	Base exception for Bank of England data errors.
`BoEValidationError`	Raised when a DataFrame fails `validate_data()`.

Functions

`get_latest_data`([resolution, force_refresh])	Fetch the unified UK base rate at a given resolution.
`get_rate_changes`([force_refresh])	Fetch the event-based history of base-rate changes back to 1694.
`validate_data`(df)	Validate that a DataFrame conforms to the macroeconomic schema.

Module Contents

bolster.data_sources.boe_base_rate.logger[source]

bolster.data_sources.boe_base_rate.DATA_URL = 'https://www.bankofengland.co.uk/-/media/boe/files/monetary-policy/baserate.xls'[source]

bolster.data_sources.boe_base_rate.RAW_DATA_SHEET = 'Raw Data'[source]

bolster.data_sources.boe_base_rate.HISTORICAL_SHEET = 'HISTORICAL SINCE 1694'[source]

bolster.data_sources.boe_base_rate.GEOGRAPHY = 'UK'[source]

bolster.data_sources.boe_base_rate.SOURCE = 'BoE'[source]

bolster.data_sources.boe_base_rate.SERIES = 'base_rate'[source]

bolster.data_sources.boe_base_rate.UNIT = '%'[source]

bolster.data_sources.boe_base_rate.SCHEMA_COLUMNS = ['date', 'year', 'quarter', 'month', 'resolution', 'series', 'value', 'unit', 'geography', 'source'][source]

bolster.data_sources.boe_base_rate.RATE_CHANGES_COLUMNS = ['date', 'year', 'series', 'value', 'unit', 'geography', 'source'][source]

bolster.data_sources.boe_base_rate.RESOLUTIONS = ('daily', 'monthly', 'quarterly', 'annual')[source]

exception bolster.data_sources.boe_base_rate.BoEDataError[source]

Bases: Exception

Base exception for Bank of England data errors.

Initialize self. See help(type(self)) for accurate signature.

exception bolster.data_sources.boe_base_rate.BoEValidationError[source]

Bases: BoEDataError

Raised when a DataFrame fails validate_data().

Initialize self. See help(type(self)) for accurate signature.

bolster.data_sources.boe_base_rate.get_latest_data(resolution='monthly', force_refresh=False)[source]

Fetch the unified UK base rate at a given resolution.

The base rate is a standing level, so coarser resolutions take the last observation in each period (i.e. the rate in force at period end).

Parameters:

resolution (str) – “daily”, “monthly” (default), “quarterly” or “annual”.
force_refresh (bool) – Accepted for API parity; binary downloads are not cached.

Returns:

DataFrame conforming to SCHEMA_COLUMNS, sorted by date.

Raises:

ValueError – If resolution is not recognised.
BoEDataError – If the workbook download or parse fails.

Return type:

pandas.DataFrame

Example

>>> df = get_latest_data(resolution="annual")  
>>> df.iloc[-1][["series", "unit", "geography", "source"]].tolist()  
['base_rate', '%', 'UK', 'BoE']

bolster.data_sources.boe_base_rate.get_rate_changes(force_refresh=False)[source]

Fetch the event-based history of base-rate changes back to 1694.

Each row is a rate change effective on a given date (the value is the new rate). The series is irregular by nature. Where the source records only a year and month (the earliest entries pre-date daily records), the change is dated to the first of that month.

Parameters:: force_refresh (bool) – Accepted for API parity; binary downloads are not cached.
Returns:: DataFrame with columns RATE_CHANGES_COLUMNS, sorted by date.
Raises:: BoEDataError – If the workbook download or parse fails, or no changes could be parsed.
Return type:: pandas.DataFrame

Example

>>> df = get_rate_changes()  
>>> df.iloc[0][["date", "value"]].tolist()  
[Timestamp('1694-10-01 00:00:00'), 6.0]

bolster.data_sources.boe_base_rate.validate_data(df)[source]

Validate that a DataFrame conforms to the macroeconomic schema.

Checks performed:

All SCHEMA_COLUMNS are present.
At least one row is present.
geography is exclusively “UK” and source exclusively “BoE”.
series is exclusively “base_rate” and unit exclusively “%”.
resolution only contains known values.
value is numeric, non-null and within a sane 0-25% range.

Parameters:: df (pandas.DataFrame) – DataFrame to validate.
Returns:: True if all checks pass.
Raises:: BoEValidationError – If any check fails.
Return type:: bool

Example

>>> df = get_latest_data(resolution="annual")  
>>> validate_data(df)  
True