This dataset provides a comprehensive and up-to-date collection of futures related to corn, oat, and other grains. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular grain at a predetermined price on a future date.

Use Cases: 1. Crop Yield Predictions: Use machine learning models to correlate grain futures prices with historical data, predicting potential harvest yields. 2. Impact Analysis of Weather Events: Implement deep learning techniques to understand the relationship between grain price movements and significant weather patterns. 3. Grain Price Forecasting: Develop time-series forecasting models to predict future grain prices, assisting traders and stakeholders in decision-making.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/agriculture-arable-barley-bread-265242/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Maximum price reached during the trading session. 4. Low: Minimum traded price during the day. 5. Close: Market's closing price. 6. Volume: Number of contracts traded during the session. 7. Ticker: Unique market quotation symbol for the grain future. 8. Commodity: Specifies the type of grain the future contract represents (e.g., corn, oat).

Corn rose to 433.53 USd/BU on December 2, 2025, up 0.01% from the previous day. Over the past month, Corn's price has fallen 0.17%, but it is still 2.43% higher 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 December 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

Wheat fell to 529.25 USd/Bu on December 1, 2025, down 0.33% from the previous day. Over the past month, Wheat's price has fallen 2.62%, and is down 1.53% 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 December of 2025.

Analysis of the October 1, 2025, wheat futures market, detailing price declines across all major contracts, changes in trading volume, and an increase in open interest.

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.

Prices are a fundamental component of exchange and have long been important to the functioning of agricultural markets. Grain prices are closely related to grain transportation, where the supply and demand for grain simultaneously determines both the price of grain, as well as the demand for grain transportation.

This data has corn, soybean, and wheat prices 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.

The data come from three sources: USDA-AMS Market News price reports, GeoGrain, and U.S. Wheat Associates. Links are included below. GeoGrain offers granular data for purchase. The GeoGrain data here is an average of those granular prices for a given state (and the "Southeast" region, which combines Arkansas, Mississippi, and Alabama).

This is one of three companion datasets. The other two are grain basis (https://agtransport.usda.gov/d/v85y-3hep) 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).

Learn about the various factors that influence grain commodity prices, including supply and demand, weather patterns, transportation costs, and government policies. Gain insight into how traders and analysts make predictions about price movements and why understanding these factors is crucial for farmers, traders, and consumers.

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.

Commodity Price Range 1877-Present

Historical Futures Contract Pricing for Wheat, Corn, and Oats

By [source]

About this dataset

This dataset contains over 140 years of historical commodity price ranges for wheat, corn, and oats futures contracts. It provides a glimpse into the evolution of our modern economic markets and the constant fluctuations in demand and supply that occur at all times. This data is invaluable to academic researchers, corporate strategists, economists, investors and traders alike as it reveals timely insight into various commodities' pricing trends over time. Each record includes the corresponding highest and lowest prices for each particular well-traded commodity on each particular day since 1877— providing an essential view into market dynamics across multiple decades. Use this data to identify recent pricing patterns or make predictions about future prices – no matter how you decide to use it–this may give you further insight into the ever-changing marketplace

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How to use the dataset

This dataset contains historical commodity price ranges for wheat, corn, and oats futures contracts from 1877 to present. It is a great resource for anyone interested in analyzing the trends of these commodities over time. Each row contains one day's data on the low and high prices of each contract.

To use this dataset, start by looking at the columns. There are several columns to choose from depending on which type of commodity you would like to analyze: Range_W_F1 (Lowest Price of Wheat Futures Contract), Range_W_F2 (Highest Price of Wheat Futures Contract), Range_C_F1 (Lowest Price of Corn Futures Contract), Range_C_F2 (Highest Price of Corn Futures Contract) and Range_O_F1 & 2 (Lowest and Highest Prices respectively for Oats Futures Contracts). Once you have selected the relevant columns for your analysis, pick a date range to focus on and filter out the rows outside that range. This will leave only those days within your chosen timeframe in the dataset so you can begin analyzing them more closely.

For an in-depth analysis it can be helpful to add other pieces data such as weather information or other economic indicators alongside these price ranges so you can investigate possible correlations between different factors that affect pricing in these markets over time. No matter how complex an analysis you might want to do with this data, this dataset provides a good starting point with reliable historical records dating all the way back over 140 years ago!

Research Ideas

• Market analysis to identify trends in prices of different commodities over time.
• Predictive modeling to forecast future prices based on past price ranges and market conditions.
• Proactive risk management strategies by tracking changes in commodity prices and anticipating potential changes in raw material costs for manufacturers or businesses that rely on commodities as part of their production processes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: rangedata_commodities_since1877.csv | Column name | Description | |:---------------|:---------------------------------------------------------------| | Date | Date of the commodity price range. (Date) | | Range_W_F1 | Lowest price of wheat futures contract for the day. (Numeric) | | Range_W_F2 | Highest price of wheat futures contract for the day. (Numeric) | | Range_C_F1 | Lowest price of corn futures contract for the day. (Numeric) | | Range_C_F2 | Highest price of corn futures contract for the day. (Numeric) | | Range_O_F1 | Lowest price of oats futures contract for the day. (Numeric) | | Range_O_F2 | Highest price of oats futures contract for the day. (Numeric) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

The dataset are the individual futures prices for Chicago corn in 2017

This data product provides three Excel file spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat).

Using Futures Prices to Forecast the Season-Average Price and Counter-Cyclical Payment Rate for Corn, Soybeans, and Wheat

Farmers and policymakers are interested in the level of counter-cyclical payments (CCPs) provided by the 2008 Farm Act to producers of selected commodities. CCPs are based on the season-average price received by farmers. (For more information on CCPs, see the ERS 2008 Farm Bill Side-By-Side, Title I: Commodity Programs.)

This data product provides three Excel spreadsheet models that use futures prices to forecast the U.S. season-average price received and the implied CCP for three major field crops (corn, soybeans, and wheat). Users can view the model forecasts or create their own forecast by inserting different values for futures prices, basis values, or marketing weights. Example computations and data are provided on the Documentation page.

Spreadsheet Models

For each of the three major U.S. field crops, the Excel spreadsheet model computes a forecast for:

1. the national-level season-average price received by farmers and
2. the implied counter-cyclical payment rate.

Note: the model forecasts are not official USDA forecasts. See USDA's World Agricultural Supply and Demand Estimates for official USDA season-average price forecasts. See USDA's Farm Service Agency information for official USDA CCP rates.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: Webpage with links to Excel files For complete information, please visit https://data.gov.

This data set contains Ontario wheat grain prices collected by University of Guelph, Ridgetown Campus. The dataset includes daily prices of agricultural commodities at individual elevators in Ontario. Daily highs and lows are given for each commodity, as well as, daily Bank of Canada exchange rates.

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

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.

Learn about live grain markets, a platform for buyers and sellers to exchange information and transact on commodities like wheat, corn, and soybeans. Discover how prices are affected by supply and demand, and the different types of contracts available to manage risk. Understand the importance of live grain markets in the global economy for farmers, grain traders, and other market participants.

In 2022, the average price of one bushel of corn was around **** U.S. dollars. That year, the United States was the largest producer of corn in the world.

China Settlement Price: Dalian Commodity Exchange: Corn: 2nd Month data was reported at 2,241.000 RMB/Ton in Nov 2025. This records an increase from the previous number of 2,118.000 RMB/Ton for Oct 2025. China Settlement Price: Dalian Commodity Exchange: Corn: 2nd Month data is updated monthly, averaging 2,465.000 RMB/Ton from Sep 2004 (Median) to Nov 2025, with 255 observations. The data reached an all-time high of 2,971.000 RMB/Ton in Apr 2022 and a record low of 1,126.000 RMB/Ton in Oct 2004. China Settlement Price: Dalian Commodity Exchange: Corn: 2nd Month data remains active status in CEIC and is reported by Dalian Commodity Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Dalian Commodity Exchange: Commodity Futures: Settlement Price.

CBOT Corn Price data, recent 36 years (traceable to Jan 02,1990), the unit is US¢/bu, latest value is 426.75, updated at Nov 07,2025

Description: This dataset provides daily price records for three key agricultural commodities: coffee, wheat, and corn, spanning five decades from 1973 to 2023. The dataset is a valuable resource for researchers, analysts, and enthusiasts interested in understanding the historical price trends of these essential commodities in the global market.

Columns: - Date: The date of the price record in yyyy-mm-dd format. - Coffee (USD): Daily prices of coffee in US dollars. - Wheat (USD): Daily prices of wheat in US dollars. - Corn (USD): Daily prices of corn in US dollars.

Data Source: The dataset is compiled from reliable sources and represents a comprehensive record of daily commodity prices, making it an ideal tool for studying the dynamics of these agricultural markets over the past fifty years.

Use Cases: - Analyze long-term price trends and patterns for coffee, wheat, and corn. - Create predictive models for commodity price forecasting. - Investigate the impact of various economic and environmental factors on commodity prices. - Explore correlations between commodity prices and global events.

Acknowledgments: We would like to express our gratitude to the data sources that have contributed to the compilation of this dataset, making it freely available for research and analysis.

Note: Please cite this dataset appropriately if you use it in your research or analysis.

Start exploring the world of agricultural commodity prices by downloading this dataset today!