Consumer Confidence Index

This consumer confidence indicator provides an indication of future developments of households consumption and saving, based upon answers regarding their expected financial situation, their sentiment about the general economic situation, unemployment and capability of savings.

An indicator above 100 signals a boost in the consumers’ confidence towards the future economic situation, as a consequence of which they are less prone to save, and more inclined to spend money on major purchases in the next 12 months. Values below 100 indicate a pessimistic attitude towards future developments in the economy, possibly resulting in a tendency to save more and consume less.

See definition: https://data.oecd.org/leadind/consumer-confidence-index-cci.htm

Also known as: consumer sentiment, spending outlook.

No programming experience? With the Finance Toolkit MCP server, AI assistants such as Claude and ChatGPT can calculate the Consumer Confidence Index for you. Just ask in plain English.

Calculate the Consumer Confidence Index in Python

The Consumer Confidence Index is available in the Economics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_consumer_confidence_index as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2008-09-01', end_date='2009-03-01')

economics.get_consumer_confidence_index(countries=['Germany', 'France', 'Portugal'])

Which returns:

Parameters

get_consumer_confidence_index accepts the following parameters:

• countries (list[str] | str | None, optional): The countries to include in the data. Defaults to None.
• rolling (int, optional): The rolling window size to use for smoothing the data (simple moving average). Defaults to None.
• trailing (int, optional): The trailing window size to use for summing the data over trailing periods (e.g. a trailing-4-quarter sum). Defaults to None.
• growth (bool, optional): Whether to return the growth data or the actual data.
• lag (int, optional): The number of periods to lag the data by.
• standardize (bool, optional): Whether to standardize (Z-Score) the result. When combined with growth=True, standardizes the growth values instead of the raw values. Defaults to False.
• rounding (int | None, optional): The number of decimals to round the results to. Defaults to None.

Related Economy

The Economics module page introduces the module, and the sidebar lists all of its functions.