Prices for DXY Dollar Index including live quotes, historical charts and news. DXY Dollar Index was last updated by Trading Economics this July 13 of 2025.

Graph and download economic data for Nominal Broad U.S. Dollar Index (DTWEXBGS) from 2006-01-02 to 2025-07-03 about trade-weighted, broad, exchange rate, currency, goods, services, rate, indexes, and USA.

USD index is expected to strengthen in the near term due to persistent safe-haven demand amid global economic uncertainties. The risk associated with this prediction is the potential for a correction if risk appetite improves or the Federal Reserve signals a dovish pivot.

The DXY exchange rate rose to 97.8683 on July 11, 2025, up 0.22% from the previous session. Over the past month, the United States Dollar has weakened 0.05%, and is down by 5.98% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on July 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.

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

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

The average for 2022 based on 7 countries was 6218.31 billion U.S. dollars. The highest value was in the USA: 40297.98 billion U.S. dollars and the lowest value was in Bermuda: 0.21 billion U.S. dollars. The indicator is available from 1975 to 2022. Below is a chart for all countries where data are available.

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.

[Video] US Dollar Index: Charting a New Course?

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

Prices for USDQAR US Dollar Qatari Rial including live quotes, historical charts and news. USDQAR US Dollar Qatari Rial was last updated by Trading Economics this July 12 of 2025.

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's Fate Uncertain as Analysts Weigh Future of U.S. 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

Argentina Index: BCBA: Merval: US Dollars data was reported at 1,795.593 30Jun1986=0.01 in Apr 2025. This records a decrease from the previous number of 2,177.617 30Jun1986=0.01 for Mar 2025. Argentina Index: BCBA: Merval: US Dollars data is updated monthly, averaging 604.550 30Jun1986=0.01 from Dec 1990 (Median) to Apr 2025, with 413 observations. The data reached an all-time high of 2,453.251 30Jun1986=0.01 in Dec 2024 and a record low of 88.208 30Jun1986=0.01 in May 2002. Argentina Index: BCBA: Merval: US Dollars data remains active status in CEIC and is reported by Buenos Aires Stock Exchange. The data is categorized under Global Database’s Argentina – Table AR.Z001: Buenos Aires Stock Exchange: Index.

As of May 2025, there were more than ***** companies listed on stock exchanges worldwide worth more than one billion U.S. dollars. The country with the highest number was the *************, which counted more than ***** public companies with a market capitalization over of *********** U.S. dollars. Second in the list was *****, with *** companies, followed by *****, with ***.

Prices for USDUST US Dollar Tether including live quotes, historical charts and news. USDUST US Dollar Tether was last updated by Trading Economics this July 13 of 2025.

Ukraine: Stock market capitalization, billion USD: The latest value from 2018 is 4.42 billion U.S. dollars, a decline from 5.2 billion U.S. dollars in 2017. In comparison, the world average is 894.42 billion U.S. dollars, based on data from 76 countries. Historically, the average for Ukraine from 2010 to 2018 is 14.83 billion U.S. dollars. The minimum value, 4.42 billion U.S. dollars, was reached in 2018 while the maximum of 38.9 billion U.S. dollars was recorded in 2010.

The statistic shows the development of the MSCI World USD Index from 1986 to 2024. The 2024 year-end value of the MSCI World USD index amounted to ******** points. MSCI World USD index – additional information The MSCI World Index, developed by Morgan Stanley Capital International (MSCI), is one of the most important stock indices. It includes stocks from developed countries all over the world and is regarded as benchmark of global stock market. According to MSCI, this index covers about ** percent of the free float-adjusted market capitalization in each country. As seen on the statistics above, in 2024, MSCI World USD index reported its highest value since 1986 amounting, a threefold increase from the figure recorded in 2013, when the year-end value of the MSCI World index was equal to ********. Along with the S&P Global Broad Market, the MSCI World is one of the most important global stock market performance indexes. Aside of including markets around the globe, these two indexes are global in a sense that they disregard where the companies are domiciled or traded, whereas other important indexes such as the Dow Jones Industrial Average, the Japanese index Nikkei 225, Wilshire 5000, the NASDAQ 100 index, have different approaches.

Panama: Stock market capitalization, billion USD: The latest value from 2022 is 15.63 billion U.S. dollars, an increase from 15.46 billion U.S. dollars in 2021. In comparison, the world average is 1244.55 billion U.S. dollars, based on data from 74 countries. Historically, the average for Panama from 1993 to 2022 is 8.05 billion U.S. dollars. The minimum value, 0.44 billion U.S. dollars, was reached in 1993 while the maximum of 17.27 billion U.S. dollars was recorded in 2019.

Prices for MNTUSD Mongolian Tugrik US Dollar including live quotes, historical charts and news. MNTUSD Mongolian Tugrik US Dollar was last updated by Trading Economics this July 13 of 2025.

Chile Santiago Stock Exchange: Market Capitalization: USD data was reported at 199.986 USD bn in Apr 2025. This records an increase from the previous number of 193.811 USD bn for Mar 2025. Chile Santiago Stock Exchange: Market Capitalization: USD data is updated monthly, averaging 248.918 USD bn from Dec 2005 (Median) to Apr 2025, with 233 observations. The data reached an all-time high of 409.454 USD bn in Apr 2018 and a record low of 129.350 USD bn in Nov 2008. Chile Santiago Stock Exchange: Market Capitalization: USD data remains active status in CEIC and is reported by Santiago Stock Exchange. The data is categorized under Global Database’s Chile – Table CL.Z004: Santiago Stock Exchange: Statistics.

Belarus: Stock market capitalization, billion USD: The latest value from 2022 is 2.5 billion U.S. dollars, a decline from 2.83 billion U.S. dollars in 2021. In comparison, the world average is 1244.55 billion U.S. dollars, based on data from 74 countries. Historically, the average for Belarus from 2014 to 2022 is 1.53 billion U.S. dollars. The minimum value, 0.53 billion U.S. dollars, was reached in 2015 while the maximum of 2.83 billion U.S. dollars was recorded in 2021.