United States U.S. Dollar Index: Futures: Volume data was reported at 36,443.810 Unit in Apr 2025. This records an increase from the previous number of 26,125.524 Unit for Mar 2025. United States U.S. Dollar Index: Futures: Volume data is updated monthly, averaging 3,678.275 Unit from Nov 1985 (Median) to Apr 2025, with 474 observations. The data reached an all-time high of 77,809.773 Unit in Mar 2015 and a record low of 210.783 Unit in Oct 1986. United States U.S. Dollar Index: Futures: Volume data remains active status in CEIC and is reported by Barchart.com, Inc.. The data is categorized under Global Database’s United States – Table US.M036: US Dollar Index.

United States U.S. Dollar Index: Futures: High data was reported at 101.014 Mar1973=100 in Apr 2025. This records a decrease from the previous number of 104.350 Mar1973=100 for Mar 2025. United States U.S. Dollar Index: Futures: High data is updated monthly, averaging 93.879 Mar1973=100 from Nov 1985 (Median) to Apr 2025, with 474 observations. The data reached an all-time high of 126.365 Mar1973=100 in Dec 1985 and a record low of 72.793 Mar1973=100 in Apr 2008. United States U.S. Dollar Index: Futures: High data remains active status in CEIC and is reported by Barchart.com, Inc.. The data is categorized under Global Database’s United States – Table US.M036: US Dollar Index.

United States U.S. Dollar Index: Futures: Open Interest data was reported at 28,716.190 Unit in Apr 2025. This records a decrease from the previous number of 34,969.952 Unit for Mar 2025. United States U.S. Dollar Index: Futures: Open Interest data is updated monthly, averaging 22,840.175 Unit from Nov 1985 (Median) to Apr 2025, with 474 observations. The data reached an all-time high of 129,685.045 Unit in Mar 2015 and a record low of 1,125.000 Unit in Nov 1985. United States U.S. Dollar Index: Futures: Open Interest data remains active status in CEIC and is reported by Barchart.com, Inc.. The data is categorized under Global Database’s United States – Table US.M036: US Dollar Index.

Download Historical U.S. Dollar Index (Settlement) Futures Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

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.

WTI Futures x3 Leveraged USD Index: The Next Big Play?

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

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 97.4335 on July 23, 2025, up 0.07% from the previous session. Over the past month, the United States Dollar has weakened 0.43%, and is down by 6.65% over the last 12 months. United States Dollar - values, historical data, forecasts and news - updated on July of 2025.

United States U.S. Dollar Index: Futures: Open data was reported at 100.690 Mar1973=100 in Apr 2025. This records a decrease from the previous number of 104.086 Mar1973=100 for Mar 2025. United States U.S. Dollar Index: Futures: Open data is updated monthly, averaging 93.525 Mar1973=100 from Nov 1985 (Median) to Apr 2025, with 474 observations. The data reached an all-time high of 126.074 Mar1973=100 in Dec 1985 and a record low of 72.360 Mar1973=100 in Apr 2008. United States U.S. Dollar Index: Futures: Open data remains active status in CEIC and is reported by Barchart.com, Inc.. The data is categorized under Global Database’s United States – Table US.M036: US Dollar 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 (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 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 July of 2025.

Daily sample data for U.S. Dollar Index (ICE) DXE timestamped in Chicago time

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

United States U.S. Dollar Index: Futures: Low data was reported at 99.955 Mar1973=100 in Apr 2025. This records a decrease from the previous number of 103.664 Mar1973=100 for Mar 2025. United States U.S. Dollar Index: Futures: Low data is updated monthly, averaging 93.156 Mar1973=100 from Nov 1985 (Median) to Apr 2025, with 474 observations. The data reached an all-time high of 125.701 Mar1973=100 in Dec 1985 and a record low of 72.031 Mar1973=100 in Apr 2008. United States U.S. Dollar Index: Futures: Low data remains active status in CEIC and is reported by Barchart.com, Inc.. The data is categorized under Global Database’s United States – Table US.M036: US Dollar Index.

The EUR/USD exchange rate rose to 1.1750 on July 22, 2025, up 0.47% from the previous session. Over the past month, the Euro US Dollar Exchange Rate - EUR/USD has strengthened 1.49%, and is up by 8.30% over the last 12 months. Euro US Dollar Exchange Rate - EUR/USD - values, historical data, forecasts and news - updated on July 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.

[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

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.

WTI Futures x3 Leveraged USD Index Forecast

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.

WTI Futures x3 Leveraged USD Index: Will it Soar or Sink?

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

Baltic Dry fell to 2,016 Index Points on July 21, 2025, down 1.75% from the previous day. Over the past month, Baltic Dry's price has risen 20.43%, and is up 6.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on July of 2025.

CRB Index fell to 375.35 Index Points on July 21, 2025, down 0.40% from the previous day. Over the past month, CRB Index's price has risen 0.25%, and is up 12.61% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

A graphic that displays the dollar performance against other currencies reveals that economic developments had mixed results on currency exchanges. The third quarter of 2023 marked a period of disinflation in the euro area, while China's projected growth was projected to go up. The United States economy was said to have a relatively strong performance in Q3 2023, although growing capital market interest rate and the resumption of student loan repayments might dampen this growth at the end of 2023. A relatively weak Japanese yen Q3 2023 saw pressure from investors towards Japanese authorities on how they would respond to the situation surrounding the Japanese yen. The USD/JPY rate was close to ***, whereas analysts suspected it should be around ** given the country's purchase power parity. The main reason for this disparity is said to be the differences in central bank interest rates between the United States, the euro area, and Japan. Any future aggressive changes from, especially the U.S. Fed might lower those differences. Financial markets responded somewhat disappoint when Japan did not announce major plans to tackle the situation. Potential rent decreases in 2024 Central bank rates peak in 2023, although it is expected that some of these will decline in early 2024. That said, analysts expect overall policies will remain restrictive. For example, the Bank of England's interest rate remained unchanged at **** percent in Q3 2023. It is believed the United Kingdom's central bank will ease its interest rate in 2024 but less than either the U.S. Fed or the European Central Bank. This should be a positive development for the pound compared to either the euro or the dollar.