Wheat fell to 562.25 USd/Bu on July 3, 2025, down 0.31% from the previous day. Over the past month, Wheat's price has risen 3.50%, but it is still 4.78% lower than a year ago, 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 July of 2025.

The dataset contains daily price ranges calculated from the daily high and low prices for Chicago Wheat, Corn, and Oats futures contracts, starting in 1877. The data is manually extracted from the ``Annual Reports of the Trade and Commerce of Chicago'' (today, the Chicago Board of Trade, CBOT, which is part of the CME group).

The price range is calculated as Ranget = ln(Ht) - ln(Lt), where Ht and Lt are the highest and lowest price observed on trading day t.

Description of the dataset:

Date: The trading day, format dd-mm-yyyy

Range_W_F1: Price range Wheat futures, First expiration (nearby contract)

Range_W_F2: Price range Wheat futures, Second expiration

Range_C_F1: Price range Corn futures, First expiration (nearby contract)

Range_C_F2: Price range Corn futures, Second expiration

Range_O_F1: Price range Oats futures, First expiration (nearby contract)

Range_O_F2: Price range Oats futures, Second expiration

Grain Stocks Wheat in the United States decreased to 0.85 Billion Bushels in the second quarter of 2025 from 1.24 Billion Bushels in the first quarter of 2025. This dataset provides - United States Quarterly Grain Stocks - Wheat- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Learn about the various factors that influence the wheat stock market price, including supply and demand dynamics, weather conditions, government policies, and global economic trends. Discover why the wheat market is highly volatile and how farmers, traders, and investors can manage the risks associated with wheat price fluctuations.

Graph and download economic data for Global price of Wheat (PWHEAMTUSDM) from Jan 1990 to Apr 2025 about wheat, World, and price.

Learn about the factors that impact the volatile price of wheat in the market, including weather conditions, global production levels, government policies, and market speculation. Find out how global trade dynamics, currency exchange rates, and demand from end-users also influence wheat prices. Get real-time updates on wheat market prices from reliable financial news sources or commodity trading platforms.

The monthly price of wheat (hard red winter) in the United States reached an all time high in May 2022, at over *** U.S. dollars per metric ton. The unprecedented price increase began in mid-2020, due to the impact of the Covid-19 pandemic, and was later exacerbated by the Russo-Ukrainian War in March 2022. Before the war, Russia and Ukraine were among the world's five largest wheat exporters, and around one third of all international wheat imports came from these two countries. The increase of 96 dollars per ton between February and March 2022 was the single largest price hike in U.S. history, and was only the second time that prices had exceeded 400 dollars - the first time this happened was due to the financial crisis of 2008. In the five years before the Covid-19 pandemic, the price of wheat generally fluctuated between 150 and 230 dollars per ton.

This dataset provides values for WHEAT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Interactive chart of historical daily wheat prices back to 1975. The price shown is in U.S. Dollars per bushel.

Wheat commodity trading refers to the buying and selling of wheat on regulated exchanges. Learn how traders use futures and options contracts to manage risk and speculate on price movements in the global wheat market.

Basis reflects both local and global supply and demand forces. It is calculated as the difference between the local cash price and the futures price. It affects when and where many grain producers and shippers buy and sell grain. Many factors affect basis—such as local supplies, storage and transportation availability, and global demand—and they interact in complex ways. How changes in basis manifest in transportation is likewise complex and not always direct. For instance, an increase in current demand will drive cash prices up relative to future prices, and increase basis. At the same time, grain will enter the transportation system to fulfill that demand. However, grain supplies also affect basis, but will have the opposite effect on transportation. During harvest, the increase in the supply of grain pushes down cash prices relative to futures prices, and basis weakens, but the demand for transportation increases to move the supplies.

For more information on how basis is linked to transportation, see the story, "Grain Prices, Basis, and Transportation" (https://agtransport.usda.gov/stories/s/sjmk-tkh6), and links below for research on the topic.

This data has corn, soybean, and wheat basis for a variety of locations. These include origins—such as Iowa, Minnesota, Nebraska, and many others—and destinations, such as the Pacific Northwest, Louisiana Gulf, Texas Gulf, and Atlantic Coast.

This is one of three companion datasets. The other two are grain prices (https://agtransport.usda.gov/d/g92w-8cn7) and grain price spreads (https://agtransport.usda.gov/d/an4w-mnp7). 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).

The cash price comes from the grain prices dataset and the futures price comes from the appropriate futures market, which is Chicago Board of Trade (CME Group) for corn, soybeans, and soft red winter wheat; Kansas City Board of Trade (CME Group) for hard red winter wheat; and the Minneapolis Grain Exchange for hard red spring wheat.

United States Commodity Trade: Grains: Wheat data was reported at 24.524 Metric Ton mn in 2017. This records a decrease from the previous number of 28.602 Metric Ton mn for 2016. United States Commodity Trade: Grains: Wheat data is updated yearly, averaging 28.531 Metric Ton mn from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 48.191 Metric Ton mn in 1981 and a record low of 14.805 Metric Ton mn in 1968. United States Commodity Trade: Grains: Wheat data remains active status in CEIC and is reported by US Department of Agriculture. The data is categorized under Global Database’s USA – Table US.B069: Agricultural Demand and Supply Estimates. Exports Only

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.

TR/CC CRB Wheat Index: A Reliable Indicator of Global Wheat 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

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.

Wheat Futures: A Threat to Employment in Agriculture?

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

Series Is Presented Here As Two Variables--(1)-- Original Data, 1842-1944 (2)--Original Data, 1938-1952. "Statistics Of Wheat Prices For The Period 1841-1870 Were Taken From Newspapers Of That Period, Namely The American, The Democrat, And The Tribune; From The Annual Review Of The Trade And Commerce Of Chicago, Published By The Tribune, And All Found In The Archives Of The Chicago Historical Society. The Statistics For The Period 1871-1922 Are All From The Annual Reports Published By The Chicago Board Of Trade." (J.E. Boyle In Source). Average Monthly Price Is Computed (NBER) By Averaging Monthly High And Low. In All Cases The Effort Was Made To Use That Grade Of Wheat In Which The Most Transactions Were Had. On This Basis, Therefore, These Grades Were Used. (All Cash Prices.): 1841-57, Spring Wheat; 1858-59, Standard Spring; 1860-63, No. 2 Spring; 1864-1870, No. 1 Spring; 1871-1897, No. 2 Spring; 1898-1904,"Regular" Wheat (Deliverable On Contracts); 1905-1918, No. 2 Red Winter; 1919-1920, No. 2 Northern, 1921-1922, No. 2 Red Northern. Future Trading In Wheat At The Chicago Board Of Trade Ceased August 25, 1917, And Was Resumed July 15, 1920. The Price Of Cash Wheat Was Fixed By The Government Beginning In September, 1917. Government Control Of Cash Wheat Ceased On June 30, 1920. Beginning In 1883, The Basic Cash Price Of Wheat Is That Spot Price Of Such Wheat As Is Being Delivered On Chicago Future Contracts, Or Is Expected To Be Delivered On Them, Adjusted For Any Premium Or Discount Applicable On Delivery. Listing Of Prices Was Discontinued In The Wheat Studies For May, 1944, Vol Xx, No. 5. June And July Prices Are From "World Grain Review And Outlook, 1945," P.281. Source: James E. Boyle, Chicago Wheat Prices For Eighty-One Years, Pp. 69-71, For 1841-1882; Food Research Institute, Stanford University, "Wheat Studies, " November, 1934, P.118 And The Following December Issues For 1883-1944.

This NBER data series m04001a appears on the NBER website in Chapter 4 at http://www.nber.org/databases/macrohistory/contents/chapter04.html.

NBER Indicator: m04001a

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

United States Turnover: CBOT: Agricultural Futures: Wheat data was reported at 3,415,945.000 Contract in Oct 2018. This records an increase from the previous number of 2,831,916.000 Contract for Sep 2018. United States Turnover: CBOT: Agricultural Futures: Wheat data is updated monthly, averaging 594,842.500 Contract from Jan 1985 (Median) to Oct 2018, with 406 observations. The data reached an all-time high of 5,870,011.000 Contract in Aug 2018 and a record low of 103,067.000 Contract in Dec 1986. United States Turnover: CBOT: Agricultural Futures: Wheat 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 dataset shows the long-run projections (Wheat Trade) for the US agricultural sector to 2031 includes assumptions for the US and international macroeconomic conditions and projections for major commodities, farm income, and the US agricultural trade value. Values are from the publication United States Department of Agriculture (USDA) Agricultural Projections to 2032, October 2021.

The global Wheat market stood at approximately 810 million tonnes in 2024 and is anticipated to grow at a CAGR of 2.36% during the forecast period until 2035.

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.

KC wheat 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