Get the Real Effective Exchange Rate (REER) for a variety of countries over time from the Global Macro Database (GMDB). The REER is a trade-weighted average of a country’s currency relative to a basket of other major currencies, adjusted for relative price levels (inflation) between the country and its trading partners.

Unlike a simple bilateral exchange rate, the REER captures a currency’s overall competitiveness: a rising REER indicates that a country’s exports are becoming more expensive (and imports cheaper) relative to its trading partners after accounting for inflation differentials, while a falling REER indicates the opposite. The index is set to 100 in the base year, which can vary per country.

Data comes from the Global Macro Database (GMDB), further information about the variable can be found within https://www.globalmacrodata.com/documentation.html

Also known as: REER, trade-weighted exchange rate, currency competitiveness index.

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

Calculate the Real Effective Exchange Rate in Python

The Real Effective Exchange Rate is available in the Economics module of the open-source Finance Toolkit. Install it with:

pip install financetoolkit -U

Then call get_real_effective_exchange_rate as shown below.

from financetoolkit import Economics

economics = Economics(start_date='2018-01-01')

economics.get_real_effective_exchange_rate(countries=['United States', 'Japan', 'Netherlands'])

Which returns:

  Japan Netherlands United States
2021 70.6912 102.098 115.627
2022 61.011 102.238 126.626
2023 58.1149 103.352 127.54
2024 55.9376 104.859 134.572
2025 55.5007 104.174 134.22

Parameters

get_real_effective_exchange_rate accepts the following parameters:

  • countries (list[str] | str | None, optional): A list of countries or a single country to include in the results. 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. Defaults to False.
  • lag (int, optional): The number of periods to lag the growth data. Defaults to 1.
  • 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.
Composite Leading Indicator House Prices Rent Prices Share Prices Exchange Rates Producer Price Index Household Savings Rate Household Debt to Income Ratio Misery Index Retail Sales Industrial Production Index Housing Starts

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

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