Get Advance Retail Sales (Retail and Food Services) for the United States from the Census Bureau (via FRED).

Retail Sales measures nominal spending at retail and food-service establishments. Since Personal Consumption Expenditures make up roughly two-thirds to three-quarters of US GDP, this monthly, high-frequency series is a core input to real-time (nowcast) GDP estimates such as the Federal Reserve Bank of Atlanta’s GDPNow.

The series is monthly, in millions of US dollars, seasonally adjusted, and covers retail trade and food services. It is nominal, so growth=True mixes volume and price changes together; compare against the Consumer Price Index to separate the two.

Requires a free FRED API key, see the fred_api_key parameter of the Economics class.

See definition: https://fred.stlouisfed.org/series/RSAFS

Also known as: retail trade, consumer spending (proxy).

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

Calculate the Retail Sales in Python

The Retail Sales is available in the Economics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_retail_sales as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2020-01-01', fred_api_key='FRED_API_KEY')

economics.get_retail_sales()

Which returns:

Date United States
2026-02-01 741278
2026-03-01 754013
2026-04-01 759097
2026-05-01 766876
2026-06-01 768553

Parameters

get_retail_sales accepts the following parameters:

  • 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. 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.
Exchange Rates Producer Price Index Real Effective Exchange Rate Household Savings Rate Household Debt to Income Ratio Misery Index Industrial Production Index Housing Starts Real Personal Income Commercial Real Estate Prices Commodity Forward Curve Recession Indicator

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

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