Wheat fell to 545.50 USd/Bu on July 11, 2025, down 1.62% from the previous day. Over the past month, Wheat's price has risen 3.61%, but it is still 0.95% 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.

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.

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

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

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.

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

In 2024, the global wheat market decreased by -3.8% to $258.7B, falling for the second consecutive year after two years of growth. The market value increased at an average annual rate of +1.1% from 2012 to 2024; the trend pattern remained relatively stable, with somewhat noticeable fluctuations throughout the analyzed period. As a result, consumption attained the peak level of $289.4B. From 2023 to 2024, the growth of the global market remained at a lower figure.

Explore the various factors influencing wheat stock prices, including supply and demand dynamics, geopolitical events, weather conditions, policy regulations, and financial market sentiments. This article provides insight into the complexities impacting wheat markets and highlights the importance of accurate market data sourcing.

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

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 Prices to Face Volatility: TR/CC CRB Wheat Index Outlook Uncertain

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

The Wheat Market Report is Segmented by Geography (North America, Europe, Asia-Pacific, South America, and Middle East and Africa). The Report Includes Production Analysis (Volume), Consumption Analysis (Volume and Value), Import Analysis (Volume and Value), Export Analysis (Volume and Value), and Price Trend Analysis of the Wheat Market. The Report Offers Market Estimation and Forecasts in Value (USD) and Volume (metric Tons).

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 Price Outlook: TR/CC CRB Index Signals Potential Volatility

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

The North America Wheat Market Report Includes Production Analysis (Volume), Consumption Analysis (Value and Volume), Import Analysis (Value and Volume), Export Analysis (Value and Volume), and Price Trend Analysis. The Market is Segmented by Country (United States, Canada, and Mexico). The Report Offers the Market Size and Forecasts Regarding Volume (Metric Tons) and Value (USD) for all the Above Segments.

In 2024, the Indian wheat market decreased by -0.5% to $32B for the first time since 2020, thus ending a three-year rising trend. The market value increased at an average annual rate of +1.8% from 2012 to 2024; the trend pattern remained consistent, with somewhat noticeable fluctuations being observed in certain years. Over the period under review, the market hit record highs at $32.2B in 2023, and then declined slightly in the following year.

This statistic shows the stock prices of selected food commodities from January 2, 2020 to February 6, 2025. After the Russian invasion of Ukraine in February 2022, wheat prices increased significantly since both Russia and Ukraine are the key suppliers of the product. With the beginning of 2023, prices of selected food commodities started to decrease, but still stood higher than early-2020 levels.

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

The Wheat In Europe Market report segments the industry into Geography (Spain, France, United Kingdom, Germany, Russia). The report includes Production Analysis in Volume, Consumption Analysis by Volume and Value, Import Analysis by Value and Volume, Export Analysis by Value and Volume, and Price Trend Analysis. Get five years of historical data alongside five-year market forecasts.

The global roasted wheat market is estimated at US$ 541.3 million in 2024 and is forecasted to expand at a steady CAGR of 5.4% to reach a valuation of US$ 912.3 million by 2034-end.

Report Attributes	Details
Roasted Wheat Market Size (2024E)	US$ 541.3 Million
Forecasted Market Value (2034F)	US$ 912.3 Million
Global Market Growth Rate (2024 to 2034)	5.4% CAGR
Canada Market Value (2034F)	US$ 40.7 Million
South Korea Market Value (2024E)	US$ 13.4 Million
Organic Roasted Wheat Demand Growth Rate (2024 to 2034)	4.6% CAGR
Key Companies Profiled	Müller Albert Malzfabrik GmbH & Co. Thomas Fawcett & Sons Ltd. Muntons Plc Barley Agro Foods Pvt. Ltd. Simpsons Malt The Swaen Mouterij Dingemans Interquell GmbH Castle Malting IREKS GmbH

Country-wise Analysis

Attribute	United States
Market Value (2024E)	US$ 99.1 Million
Growth Rate (2024 to 2034)	4.6% CAGR
Projected Value (2034F)	US$ 154.7 Million

Attribute	Japan
Market Value (2024E)	US$ 24.6 Million
Growth Rate (2024 to 2034)	6.6% CAGR
Projected Value (2034F)	US$ 46.8 Million

Category-wise Analysis

Attribute	Conventional Roasted Wheat
Segment Value (2024E)	US$ 347 Million
Growth Rate (2024 to 2034)	5.8% CAGR
Projected Value (2034F)	US$ 607 Million

The average annual wheat price in the U.S. is forecast to drop by 2% y-o-y to $250 per ton in 2022, falling on reduced domestic consumption coupled with stable supply worldwide. The market balance will be buoyed by production gains in Argentina and the EU that will offset decreasing output in Brazil and Paraguay.

This year, harvests in the EU, the U.S., the UK, Argentina, Morocco and Ukraine are expected to increase, leading to a growth in wheat production. Even though global stockpiles of grains will remain high, there are boosted expectations for inflation due to forecasts of record demand and increased prices for other cereal grains. The rising global population and bioethanol production are key factors leading to this growth in demand for wheat. Another driving factor is the emerging trend in the EU to use more wheat in animal feed rather than barley.