Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this March 27 of 2025.

The US dollar index of February 2025 was higher than it was in 2024, although below the peak in late 2022. This reveals itself in a historical graphic on the past 50 years, measuring the relative strength of the U.S. dollar. This metric is different from other FX graphics that compare the U.S. dollar against other currencies. The history of the DXY Index The index shown here – often referred to with the code DXY, or USDX – measures the value of the U.S. dollar compared to a basket of six other foreign currencies. This basket includes the euro, the Swiss franc, the Japanese yen, the Canadian dollar, the British pound, and the Swedish króna. The index was created in 1973, after the arrival of the petrodollar and the dissolution of the Bretton Woods Agreement. Today, most of these currencies remain connected to the United States' largest trade partners. The relevance of the DXY Index The index focuses on trade and the strength of the U.S. dollar against specific currencies. It less on inflation or devaluation, which is measured in alternative metrics like the Big Mac Index. Indeed, as the methodology behind the DXY Index has only been updated once – when the euro arrived in 1999 – some argue this composition is not accurate to the current state of the world. The price development of the U.S. dollar affects many things, including commodity prices in general.

Long term historical dataset of the broad price-adjusted U.S. dollar index published by the Federal Reserve. The index is adjusted for the aggregated home inflation rates of all included currencies. The price adjustment is especially important with our Asian and South American trading partners due to their significant inflation episodes of the 80s and 90s.

Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2025-03-21 about trade-weighted, broad, exchange rate, currency, goods, services, rate, indexes, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dollar Index (Live)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Nominal Emerging Market Economies U.S. Dollar Index (DTWEXEMEGS) from 2006-01-02 to 2025-03-07 about trade-weighted, emerging markets, exchange rate, currency, goods, services, rate, indexes, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

US Dollar Index: Bullish Break or Bearish Trap?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The DXY decreased 0.1629 or 0.16% to 104.3841 on Thursday March 27 from 104.5470 in the previous trading session. United States Dollar - values, historical data, forecasts and news - updated on March of 2025.

United States - Nominal Broad U.S. Dollar Index (Goods Only) (DISCONTINUED) was 128.00970 Index Jan 1997=100 in December of 2019, according to the United States Federal Reserve. Historically, United States - Nominal Broad U.S. Dollar Index (Goods Only) (DISCONTINUED) reached a record high of 131.88080 in September of 2019 and a record low of 89.02590 in May of 1995. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Nominal Broad U.S. Dollar Index (Goods Only) (DISCONTINUED) - last updated from the United States Federal Reserve on March of 2025.

United States USD Trade Weighted Index: Nominal: Emerging Market Economies data was reported at 121.368 2006=100 in Jan 2019. This records a decrease from the previous number of 123.885 2006=100 for Dec 2018. United States USD Trade Weighted Index: Nominal: Emerging Market Economies data is updated monthly, averaging 98.829 2006=100 from Jan 2006 (Median) to Jan 2019, with 157 observations. The data reached an all-time high of 124.362 2006=100 in Nov 2018 and a record low of 89.858 2006=100 in Jul 2008. United States USD Trade Weighted Index: Nominal: Emerging Market Economies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dollar index faces uncertain future amid economic headwinds

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

United States USD Trade Weighted Index: Real: Emerging Market Economies data was reported at 97.567 Jan2006=100 in Jan 2019. This records a decrease from the previous number of 99.691 Jan2006=100 for Dec 2018. United States USD Trade Weighted Index: Real: Emerging Market Economies data is updated monthly, averaging 92.630 Jan2006=100 from Jan 2006 (Median) to Jan 2019, with 157 observations. The data reached an all-time high of 102.109 Jan2006=100 in Jan 2017 and a record low of 82.473 Jan2006=100 in Apr 2013. United States USD Trade Weighted Index: Real: Emerging Market Economies data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.M016: US Dollar Trade Weighted Index.

Graph and download economic data for Real Broad Dollar Index (RTWEXBGS) from Jan 2006 to Feb 2025 about trade-weighted, broad, goods, services, real, indexes, and USA.

Graph and download economic data for Trade Weighted U.S. Dollar Index: Other Important Trading Partners, Goods (DISCONTINUED) from 1995-01-04 to 2020-01-01 about trade-weighted, trade, exchange rate, currency, goods, rate, indexes, and USA.

The official currency of Puerto Rico is the US Dollar. This dataset displays a chart with historical values for the US Dollar Index. United States Dollar - values, historical data, forecasts and news - updated on March of 2025.

Japan Exports Nowcast: YoY: Contribution: Foreign Exchange Rates: U.S. Dollar Index: Futures: Open Interest data was reported at 5.147 % in 10 Mar 2025. This stayed constant from the previous number of 5.147 % for 03 Mar 2025. Japan Exports Nowcast: YoY: Contribution: Foreign Exchange Rates: U.S. Dollar Index: Futures: Open Interest data is updated weekly, averaging 0.095 % from Mar 2020 (Median) to 10 Mar 2025, with 262 observations. The data reached an all-time high of 22.114 % in 15 May 2023 and a record low of 0.000 % in 18 Dec 2023. Japan Exports Nowcast: YoY: Contribution: Foreign Exchange Rates: U.S. Dollar Index: Futures: Open Interest data remains active status in CEIC and is reported by CEIC Data. The data is categorized under Global Database’s Japan – Table JP.CEIC.NC: CEIC Nowcast: Exports.

Graph and download economic data for from 1973-01-03 to 2020-01-01 about major, trade-weighted, exchange rate, currency, goods, indexes, rate, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dollars and Sense: The Correlation Between US Total Reserves and the Dollar Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Nominal Major Currencies U.S. Dollar Index (Goods Only) (DISCONTINUED) from Jan 1973 to Dec 2019 about major, trade-weighted, exchange rate, currency, goods, rate, indexes, and USA.

At 8.07 U.S. dollars, Switzerland has the most expensive Big Macs in the world, according to the July 2024 Big Mac index. Concurrently, the cost of a Big Mac was 5.69 dollars in the U.S., and 6.06 U.S. dollars in the Euro area. What is the Big Mac index? The Big Mac index, published by The Economist, is a novel way of measuring whether the market exchange rates for different countries’ currencies are overvalued or undervalued. It does this by measuring each currency against a common standard – the Big Mac hamburger sold by McDonald’s restaurants all over the world. Twice a year the Economist converts the average national price of a Big Mac into U.S. dollars using the exchange rate at that point in time. As a Big Mac is a completely standardized product across the world, the argument goes that it should have the same relative cost in every country. Differences in the cost of a Big Mac expressed as U.S. dollars therefore reflect differences in the purchasing power of each currency. Is the Big Mac index a good measure of purchasing power parity? Purchasing power parity (PPP) is the idea that items should cost the same in different countries, based on the exchange rate at that time. This relationship does not hold in practice. Factors like tax rates, wage regulations, whether components need to be imported, and the level of market competition all contribute to price variations between countries. The Big Mac index does measure this basic point – that one U.S. dollar can buy more in some countries than others. There are more accurate ways to measure differences in PPP though, which convert a larger range of products into their dollar price. Adjusting for PPP can have a massive effect on how we understand a country’s economy. The country with the largest GDP adjusted for PPP is China, but when looking at the unadjusted GDP of different countries, the U.S. has the largest economy.