Corn fell to 413.26 USd/BU on October 10, 2025, down 1.19% from the previous day. Over the past month, Corn's price has fallen 1.55%, and is down 0.60% compared to the same time last year, 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.

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).

View weekly updates and historical trends for CBOT Corn Futures Open Interest. Source: US Commodity Futures Trading Commission. Track economic data with Y…

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 Range_t = ln(H_t) - ln(L_t), where H_t and L_t 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

View weekly updates and historical trends for CBOT Corn Futures Managed Money Spread Positions. Source: US Commodity Futures Trading Commission. Track eco…

Discover the latest shifts in corn futures as volume and open interest see significant changes, affecting global commodity trends.

Corn futures opened the week with slight declines due to market adjustments and shifts in export and production data.

China Settlement Price: Dalian Commodity Exchange: Corn: 2nd Month data was reported at 2,365.000 RMB/Ton in Apr 2025. This records an increase from the previous number of 2,250.000 RMB/Ton for Mar 2025. China Settlement Price: Dalian Commodity Exchange: Corn: 2nd Month data is updated monthly, averaging 1,983.500 RMB/Ton from Sep 2004 (Median) to Apr 2025, with 248 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.

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.

Corn Futures: TR/CC CRB Corn Index Projects Moderate Price Fluctuations

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

Analysis of the October 2, 2025, corn futures session, showing a flat close for the front-month contract, decreased volume, and a rise in open interest.

The Corn Trading Signal dataset provides structured insights into market sentiment, narrative drivers, and price action affecting corn futures. The most recent update issues a NEUTRAL signal with 70% confidence, reflecting uncertainty in the market as traders weigh competing forces. Key factors include: Volatility in pricing: Corn surged to a 16-month high before retracing, with fluctuations centring around the $515 level. Export demand and trade flows: Strong demand initially supported prices, but subsequent export data and profit-taking triggered downward pressure. Narrative sensitivity: Market sentiment shifted rapidly in response to export reports, highlighting the importance of demand-driven narratives. Balancing signals: While bullish themes persist around consumer trends, policy support, and technological advancements in farming, they are offset by bearish pressures from profit-taking and supply concerns. The dataset tracks top themes such as price commentary, export dynamics, consumer demand, and supply-side developments, as well as influential events like general market declines. For systematic and quantitative traders, this dataset helps evaluate whether short-term volatility around key price levels may evolve into a trend. By identifying how narratives interact with demand and supply data, it highlights potential leading indicators for corn market positioning.

A sharp decline in CBOT corn futures trading volume and open interest on August 25th suggests a market consolidation phase, as analyzed with data from Yahoo Finance and IndexBox.

This statistic shows the development of corn prices within the American market from 1936 to 2023, per metric ton. In 1956, the price for one bushel of corn in the United States was around **** U.S. dollars. In 2016, one bushel of corn cost about **** U.S. dollars and was projected to decrease to *** U.S. dollars in 2023. The United States was the largest producer of corn worldwide in 2022.

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.

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.

An overview of the October 7, 2025, corn futures session on the CBOT, detailing price gains for near-term contracts, a dip in trading volume, and a rise in open interest.

Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":

Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)

Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)

Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report

China Turnover: Volume: Dalian Commodity Exchange: Corn data was reported at 941.054 Lot th in 13 May 2025. This records an increase from the previous number of 892.132 Lot th for 12 May 2025. China Turnover: Volume: Dalian Commodity Exchange: Corn data is updated daily, averaging 511.975 Lot th from Sep 2004 (Median) to 13 May 2025, with 5012 observations. The data reached an all-time high of 4,702.794 Lot th in 06 Feb 2017 and a record low of 13.272 Lot th in 24 Jun 2014. China Turnover: Volume: Dalian Commodity Exchange: Corn data remains active status in CEIC and is reported by Dalian Commodity Exchange. The data is categorized under High Frequency Database’s Commodity Prices and Futures – Table CN.ZB: Dalian Commodity Exchange: Commodity Futures: Turnover: Daily.

View weekly updates and historical trends for CBOT Corn Combined Swap Dealers Spread Positions WoW. Source: US Commodity Futures Trading Commission. Track…

Corn futures prices have seen a modest rise due to bullish market activities and short covering, reflecting a positive trend for corn producers and exporters.