Access CME futures and options data for interest rate markets, including U.S. Treasuries, SOFR, Federal Funds, ESTR, and more with Databento's APIs or web portal.

Our continuous contract symbology is a notation that maps to an actual, tradable instrument on any given date. The prices returned are real, unadjusted prices. We do not create a synthetic time series by adjusting the prices to remove jumps during rollovers.

In 2023, 3-Month SOFR (Secured Overnight Financing Rate) futures had the highest trading volume of all exchange-traded interest rate derivatives in 2023, with 809 million contracts traded on the CME. 10-year Treasury Notes futures followed, with 498 million contracts traded on the same exchange.

United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data is updated monthly, averaging 13,704.000 Contract from Oct 2001 (Median) to May 2018, with 200 observations. The data reached an all-time high of 66,730.000 Contract in Aug 2007 and a record low of 0.000 Contract in May 2018. United States Open Interest: CBOT: Financial Futures: Interest Rate Swap: 10 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z022: CBOT: Futures: Open Interest.

CBOT operates as part of the CME Group, offering a wide range of futures and options contracts across various asset classes. CBOT specializes in trading futures and options contracts for agricultural commodities, such as corn, soybeans, wheat, and oats, as well as financial instruments, including interest rates and stock indexes.

CME reported $41.7M in Interest Expense on Debt for its fiscal quarter ending in March of 2025. Data for CME - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last July in 2025.

United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data is updated monthly, averaging 26,040.500 Contract from Oct 2001 (Median) to May 2018, with 200 observations. The data reached an all-time high of 209,087.000 Contract in Jun 2009 and a record low of 0.000 Contract in May 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 10 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s United States – Table US.Z021: CBOT: Futures: Turnover.

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.

CME CME Group Inc. Class A Common Stock

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 Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data was reported at 0.000 Contract in May 2018. This stayed constant from the previous number of 0.000 Contract for Apr 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data is updated monthly, averaging 11,527.000 Contract from Jun 2002 (Median) to May 2018, with 192 observations. The data reached an all-time high of 231,912.000 Contract in Jun 2009 and a record low of 0.000 Contract in May 2018. United States Turnover: CBOT: Financial Futures: Interest Rate Swap: 5 Years data remains active status in CEIC and is reported by CME Group. The data is categorized under Global Database’s USA – Table US.Z021: CBOT: Futures: Turnover.

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

CME lumber futures

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.

CME feeder cattle futures

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.

What are buy sell or hold recommendations? (CME Stock 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

Graph and download economic data for Secured Overnight Financing Rate (SOFR) from 2018-04-03 to 2025-07-10 about financing, overnight, securities, rate, and USA.

Context

Soy beans are a major agricultural crop.

Content

Compilation of Soybean prices and factors that effect soybean prices. Daily data. Temp columns are daily temperatures of the major U.S. grow areas. Production and Area are the annual counts from each country (2018 being the estimates). Prices of commodities are from CME futures and are NOT adjusted for inflation. Updates of these CME futures can be found on Quandl. Additional data could be added, such as, interest rates, country currency prices, country import data, country temperatures.

More raw data I used to assemble this.
https://github.com/MotorCityCobra/Soy_Data_Collection Browse my other projects and offer me a job.

Acknowledgements

https://www.quandl.com/

Banner Photo by rawpixel on Unsplash

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

The STLFSI4 measures the degree of financial stress in the markets and is constructed from 18 weekly data series: seven interest rate series, six yield spreads and five other indicators. Each of these variables captures some aspect of financial stress. Accordingly, as the level of financial stress in the economy changes, the data series are likely to move together.

How to Interpret the Index: The average value of the index, which begins in late 1993, is designed to be zero. Thus, zero is viewed as representing normal financial market conditions. Values below zero suggest below-average financial market stress, while values above zero suggest above-average financial market stress.

More information: The STLFSI4 is the third revision (i.e., STLFSI3 (https://fred.stlouisfed.org/series/STLFSI3) and STLFSI2 (https://fred.stlouisfed.org/series/STLFSI2) of the original STLFSI (https://fred.stlouisfed.org/series/STLFSI). Whereas the STLFSI3 used the past 90-day average backward-looking secured overnight financing rate (SOFR) (https://fred.stlouisfed.org/series/SOFR90DAYAVG) in two of their yield spreads, the STLFSI4 uses the 90-day forward-looking SOFR (https://www.cmegroup.com/market-data/cme-group-benchmark-administration/term-sofr.html) in its place. For more information, see "The St. Louis Fed’s Financial Stress Index, Version 4.0" (https://fredblog.stlouisfed.org/2022/11/the-st-louis-feds-financial-stress-index-version-4/). For information on earlier STLFSIs, see "Measuring Financial Market Stress" (https://files.stlouisfed.org/files/htdocs/publications/es/10/ES1002.pdf), "The St. Louis Fed’s Financial Stress Index, Version 2.0." (https://fredblog.stlouisfed.org/2020/03/the-st-louis-feds-financial-stress-index-version-2-0/), and "The St. Louis Fed’s Financial Stress Index, Version 3.0" (https://fredblog.stlouisfed.org/2022/01/the-st-louis-feds-financial-stress-index-version-3-0/).