Wheat rose to 507.44 USd/Bu on October 8, 2025, up 0.14% from the previous day. Over the past month, Wheat's price has fallen 2.46%, and is down 15.28% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Wheat - values, historical data, forecasts and news - updated on October of 2025.

A "spread" can have multiple meanings, but it generally implies a difference between two comparable measures. These can be differences across space, across time, or across anything with a similar attribute. For example, in the stock market, there is a spread between the highest price a buyer is willing to pay and the lowest price a seller is willing to accept.

In this dataset, spread refers to differences in prices between two locations, an origin (e.g., Illinois, Iowa, etc.) and a destination (e.g., Louisiana Gulf, Pacific Northwest, etc.). Mathematically, it is the destination price minus the origin price.

Price spreads are closely linked to transportation. They tend to reflect the costs of moving goods from one point to another, all else constant. Fluctuations in spreads can change the flow of goods (where it may be more profitable to ship to a different location), as well as indicate changes in transportation availability (e.g., disruptions). For more information on how price spreads are linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6).

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain basis (https://agtransport.usda.gov/d/v85y-3hep). These datasets are separate, because the coverage lengths differ and missing values are removed (e.g., there needs to be a cash price and a futures price to have a basis price, and there needs to be both an origin and a destination to have a price spread).

The origin and destination prices come from the grain prices dataset.

Corn rose to 420.55 USd/BU on October 8, 2025, up 0.19% from the previous day. Over the past month, Corn's price has risen 0.19%, but it is still 0.11% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on October of 2025.

Grain Stocks Wheat in the United States increased to 2.12 Billion Bushels in the third quarter of 2025 from 0.85 Billion Bushels in the second quarter of 2025. This dataset provides - United States Quarterly Grain Stocks - Wheat- actual values, historical data, forecast, chart, statistics, economic calendar and news.

For the fourth year in a row, the Indian cereal grain market recorded growth in sales value, which increased by 12% to $150.9B in 2024. The market value increased at an average annual rate of +3.4% over the period from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. As a result, consumption attained the peak level and is likely to continue growth in the immediate term.

In 2024, the global cereal grain market decreased by -0.9% to $3,876.1B, falling for the second year in a row after three years of growth. The market value increased at an average annual rate of +1.4% from 2012 to 2024; the trend pattern remained consistent, with only minor fluctuations in certain years. Over the period under review, the global market hit record highs at $3,926.8B in 2022; however, from 2023 to 2024, consumption stood at a somewhat lower figure.

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.

Will the Commodity Grains Index Drive Future Prices?

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

In 2024, after three years of growth, there was significant decline in the U.S. grain market, when its value decreased by -7.3% to $96.6B. The market value increased at an average annual rate of +1.1% from 2012 to 2024; the trend pattern indicated some noticeable fluctuations being recorded throughout the analyzed period. Grain consumption peaked at $107B in 2014; however, from 2015 to 2024, consumption stood at a somewhat lower figure.

Grain Stocks Corn in the United States decreased to 1.53 Billion Bushels in the third quarter of 2025 from 4.64 Billion Bushels in the second quarter of 2025. This dataset provides - United States Quarterly Grain Stocks - Corn- 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.

Will the DJ Commodity Grains Index Rise?

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.

DJ Commodity Grains Index forecast stable.

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.

DJ Commodity Grains Index Faces Uncertain Future Amidst Shifting Market Dynamics

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

Corn futures dropped significantly after USDA's quarterly Grain Stocks report showed higher-than-expected corn inventories, with December 2025 contracts closing at $4.15 1/2 amid ongoing harvest progress and international crop updates.

After four years of growth, the Vietnamese cereal grain market decreased by -0.5% to $32.2B in 2024. The market value increased at an average annual rate of +1.7% from 2012 to 2024; the trend pattern remained consistent, with only minor fluctuations throughout the analyzed period. Over the period under review, the market reached the maximum level at $32.4B in 2023, and then shrank modestly in the following year.

Index Time Series for Bloomberg Grains. The frequency of the observation is daily. Moving average series are also typically included.

GrainCorp stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Soybeans fell to 1,021.44 USd/Bu on October 8, 2025, down 0.05% from the previous day. Over the past month, Soybeans's price has fallen 0.95%, but it is still 0.12% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Soybeans - values, historical data, forecasts and news - updated on October of 2025.

The North America Grain Seed Market is segmented by Breeding Technology (Hybrids, Open Pollinated Varieties & Hybrid Derivatives), by Crop (Corn, Rice, Sorghum, Wheat) and by Country (Canada, Mexico, United States). The market volume and value are presented in metric ton and USD respectively. The key data points include the market size of seeds by breeding technology, cultivation mechanism and crop.

A Detailed Analysis of the Market Report is Provided by Giving the Production Analysis, Consumption Analysis (value and Volume), Import Analysis (value and Volume), Export Analysis (value and Volume), and Price Trend Analysis Within the Country. The Report Offers Market Estimation and Forecasts in Value (USD) and Volume (metric Tons) for the Above-Mentioned Segments.

Preferred-Stock-and-Other-Adjustments Time Series for Daodaoquan Grain and Oil Co Ltd. Daodaoquan Grain and Oil Co.,Ltd. operates as an oil processing company in China. The company is involved in the scientific research, production, trading, warehousing, and logistics of edible vegetable oils and related by-products. It offers packaged edible vegetable oils, including rapeseed, soybean, corn, coriander, sunflower, and other blended oils under Daodaoquan, Caiziwang, Jincaiwang, and other edible oil brands. The company was founded in 1999 and is headquartered in Yueyang, China.