Wheat rose to 508.51 USd/Bu on September 2, 2025, up 1.25% from the previous day. Over the past month, Wheat's price has fallen 1.59%, and is down 7.88% 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 September of 2025.

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

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.

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

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.

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 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

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.

Corn rose to 399.02 USd/BU on September 1, 2025, up 0.26% from the previous day. Over the past month, Corn's price has risen 3.11%, but it is still 0.49% 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 September of 2025.

Tick (Bids | Asks | Trades | Settle) sample data for Wheat-Spring (Globex)-MGE MWE timestamped in Chicago time

Tick (Bids | Asks | Trades | Settle) sample data for Wheat (Pit) W timestamped in Chicago time

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

By the end of 2020, derivative trading in Italy had fallen to its lowest market volume for the last decade, with just over ** million standard contracts being issued over the year. this compares to a peak of ** million standard contracts in 2016, after which the volume of derivatives traded on the Milan Stock Exchange declined sharply. The trading volume began to increase again in 2022, reaching ** million standard contracts. In 2023, this figure declined again, to around ** million. Equity derivatives related to trades, in the form of an index and single name futures and options; energy and wheat futures accounted for a very small share of the total derivatives trade in Italy.

Soybeans fell to 1,027.42 USd/Bu on September 2, 2025, down 0.73% from the previous day. Over the past month, Soybeans's price has risen 6.03%, and is up 3.10% compared to the same time last year, 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 September of 2025.

Tick (Bids | Asks | Trades | Settle) sample data for Wheat Kansas KW timestamped in Chicago time

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 Forecast: Slight Uptick Predicted

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

Stock Price Time Series for Bunge Limited. Bunge Global SA operates as an agribusiness and food company worldwide. It operates through four segments: Agribusiness, Refined and Specialty Oils, Milling, and Sugar and Bioenergy. The Agribusiness segment purchases, stores, transports, processes, and sells agricultural commodities and commodity products, including oilseeds primarily soybeans, rapeseed, canola, and sunflower seeds, as well as grains comprising wheat and corn; and processes oilseeds into vegetable oils and protein meals. This segment offers its products for animal feed manufacturers, livestock producers, wheat and corn millers, and other oilseed processors, as well as third-party edible oil processing and biofuel companies for biofuel production applications. The Refined and Specialty Oils segment sells packaged and bulk oils and fats that comprise cooking oils, shortenings, margarines, mayonnaise, renewable diesel feedstocks, and other products for baked goods companies, snack food producers, confectioners, restaurant chains, foodservice operators, infant nutrition companies, and other food manufacturers, as well as grocery chains, wholesalers, distributors, and other retailers. This segment also refines and fractionates palm oil, palm kernel oil, coconut oil, and shea butter, and olive oil; and produces specialty ingredients derived from vegetable oils, such as lecithin. The Milling segment provides wheat flours and bakery mixes; corn milling products that comprise dry-milled corn meals and flours, wet-milled masa and flours, and flaking and brewer's grits, as well as soy-fortified corn meal, corn-soy blends, and other products; whole grain and fiber ingredients; die-cut pellets; and non-GMO products. The Sugar and Bioenergy segment produces sugar and ethanol; and generates electricity from burning sugarcane bagasse. Bunge Global SA was founded in 1818 and is headquartered in Chesterfield, Missouri.

Tick (Bids | Asks | Trades | Settle) sample data for Wheat Kansas (Combined) KWA timestamped in Chicago time

Tick (Bids | Asks | Trades | Settle) sample data for Milling Wheat PV timestamped in Chicago time

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