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.

GSCI fell to 556.57 Index Points on December 2, 2025, down 0.34% from the previous day. Over the past month, GSCI's price has fallen 0.80%, but it is still 3.06% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on December of 2025.

Content

File1: Time series of commodity futures from 2000 until present. Includes 23 different commodities. File2: Categories for each commodity.

All prices are quoted in Euros.

The different commodities included in the commodity_prices.csv file are described below:

1. Energy

a/ Natural Gas: quoted in EUR/MMBtu.
b/ Low Sulphur Gas Oil: quoted in EUR/MT.
c/ WTI Crude: quoted in EUR/barrel.
d/ Brent Crude: quoted in EUR/barrel.
e/ ULS Diesel: quoted in EUR/gallon.
f/ Gasoline: quoted in EUR/gallon.

2. Industrial Metals

a/ Copper: quoted in EUR/lb.
b/ Aluminum: quoted in EUR/MT.
c/ Zinc: quoted in EUR/MT.
d/ Nickel: quoted in EUR/MT.

3. Precious Metals

a/ Gold: quoted in EUR/t oz.
b/ Silver: quoted in EUR/t oz.

4. Grains

a/ Corn: quoted in EUR/bushel.
b/ Soybeans: quoted in EUR/bushel.
c/ Wheat: quoted in EUR/bushel.
d/ Soybean Oil: quoted in EUR/lb.
e/ Soybean Meal: quoted in EUR/T.
f/ HRW Wheat: quoted in EUR/bushel.

5. Livestock

a/ Live cattle: quoted in EUR/lb.
b/ Lean Hogs: quoted in EUR/lb.

6. Softs

a/ Sugar: quoted in EUR/lb.
b/ Coffee: quoted in EUR/lb.
c/ Cotton: quoted in EUR/lb.

Inspiration:

Collated for a personal ML project. Plan to update it weekly.

Source:

Web-scraped from multiple sources.

This dataset offers a unified collection of futures data across various commodities, bringing together individual datasets to provide a holistic view of the commodities futures market. If your intent is to use data from multiple categories simultaneously, this collection is ideal. However, for those looking to delve deeper into specific commodities, individual datasets are also available.

Individual Categories Datasets: If you wish to explore data specific to individual categories, please refer to the datasets linked below:

• Animal Products Futures: https://www.kaggle.com/datasets/guillemservera/animal-products-futures
• Agricultural Futures: https://www.kaggle.com/datasets/guillemservera/agricultural-futures
• Fuels and Energy Futures: https://www.kaggle.com/datasets/guillemservera/fuels-futures-data
• Precious Metals Futures: https://www.kaggle.com/datasets/guillemservera/precious-metals-data
• Grains and Cereals Futures: https://www.kaggle.com/datasets/guillemservera/grains-and-cereals-futures

File Structure: - all_commodities_futures_collection.csv: A consolidated file containing futures data across various commodities. Each row includes a Category column that indicates the specific commodity type, making filtering and exploration more manageable. - individual_data/: This directory houses separate files for each commodity, allowing for more detailed explorations of specific commodities without the need to filter from the master dataset.

Learn to Extract the Data:

If you're interested in learning how to obtain this type of data on your own, you can refer to an example notebook that showcases the process for Fuels and Energy Futures. Check it out here: Downloading Fuels & Energy Data with yfinance.

Use Cases: 1. Market Analysis: Investigate price trends across different commodities and identify potential interdependencies. 2. Machine Learning: Utilize the diverse dataset for model training, testing and forecasting of futures prices. 3. Educational Purpose: Understand and teach the dynamics of different commodities in the futures market.

Image Credit:

The dataset's cover image is sourced from The Balance Money and is credited to tradeandexportme.

Data Source: All datasets within this collection have been extracted from Yahoo Finance using the yfinance Python module.

The World Bank’s Commodity Price historical data and forecasts are published quarterly, in January, April, July and October. The price forecasts go up to 2030. Topics: Agriculture & Rural Development

This paper constructs daily measures of the real interest rate and expected inflation using commodity futures prices and the term structure of Treasury yields. We find that commodity futures markets respond to surprise increases in the federal funds rate target by raising the inflation rate expected over the next three to nine months. There is no evidence that the real interest rate responds to surprises in the federal funds target. The data from the commodity futures markets are highly volatile. We show that one can substantially reduce the noise using limited information estimators such as the median change. Nevertheless, the basket of commodities actually traded daily is quite narrow and we do not know whether our observable rates are closely connected to the unobservable inflation and real rates that affect economy-wide consumption and investment decisions.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

✍️ Personal Note: While I'm presenting this dataset for analysis and insights, I want to emphasize the importance of ethical sourcing and consumption, especially in commodities like cocoa and coffee which have known ethical concerns in their supply chains.

About This Dataset:

This dataset delivers an extensive and current assortment of futures related to soft commodities. Futures are financial contracts obligating the buyer to purchase, and the seller to sell, a specified amount of a particular commodity at a predetermined price on a set date in the future.

Use Cases: 1. Price Forecasting: Harness machine learning to predict the price dynamics of commodities like coffee and cocoa, aiding stakeholders in their decision-making. 2. Supply Chain Analysis: Evaluate the correlation between futures prices and global events, offering insights into potential supply chain disruptions. 3. Demand Projections: Utilize deep learning techniques to correlate historical consumption patterns with price movements, projecting future demand.

Dataset Image Source: Photo by Tom Fisk from Pexels: https://www.pexels.com/photo/aerial-shot-of-green-milling-tractor-1595108/

Column Descriptions: 1. Date: The date when the data was recorded. Format: YYYY-MM-DD. 2. Open: The opening market price for the day. 3. High: Maximum price achieved during the trading session. 4. Low: Lowest traded price during the session. 5. Close: Market's concluding price. 6. Volume: Count of contracts traded throughout the session. 7. Ticker: Distinct market quotation symbol for the commodity future. 8. Commodity: Indicates the type of soft commodity the futures contract pertains to (e.g., Cocoa, Coffee).

Remember to link to the correct image source for your dataset's image!

CRB Index rose to 378.33 Index Points on December 1, 2025, up 0.45% from the previous day. Over the past month, CRB Index's price has fallen 0.80%, but it is still 10.95% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. CRB Commodity Index - values, historical data, forecasts and news - updated on December of 2025.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

This statistic depicts global commodity price indexes for energy, metal, and agriculture from January 2018 to November 2019. In November 2019, the commodity index for energy stood at 87.7, compared to 86.1 for metals, and 98.4 for agriculture.

Tradefeeds Commodity Prices API enables you to get commodity prices data of the major commodity asset types like energy commodities, metals, industrial commodities, agricultural commodities and livestock commodities. Each commodity asset type has a different historical coverage. A commodity within its commodity asset type has not the same historical coverage as another commodity from the group. For example, within the group of energy commodities, crude oil has a historical coverage of 34 years while coal of only 13 years. Tradefeeds offers commodity prices data either via JSON REST API, or via downloadable databases in CSV or Excel format.

If you are interested to learn more, check out the company website: https://tradefeeds.com/commodities-prices-api/

This table contains 23 series, with data for years 1956 - 2013 (not all combinations necessarily have data for all years), and was last released on 2014-01-06. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Commodities (23 items: Total; all commodities; Fruit; vegetable; feeds and other food products; Meat; fish and dairy products; Total; excluding petroleum and coal products ...).

Russia Commodity Price: Channels data was reported at 43,802.000 RUB/Ton in 15 May 2020. This stayed constant from the previous number of 43,802.000 RUB/Ton for 14 May 2020. Russia Commodity Price: Channels data is updated daily, averaging 26,714.000 RUB/Ton from May 2005 (Median) to 15 May 2020, with 4595 observations. The data reached an all-time high of 52,297.000 RUB/Ton in 17 May 2018 and a record low of 14,356.000 RUB/Ton in 31 Mar 2006. Russia Commodity Price: Channels data remains active status in CEIC and is reported by Metal.Com.Ru Trade System. The data is categorized under Daily Database’s Commodity Prices and Futures – Table PG003: Metals Trading Price.

Silver futures contracts to be settled in December 2028 were trading on U.S. markets at around ** U.S. dollars per troy ounce on June 20, 2023. This is above the price of ***** U.S. dollars per troy ounce for contracts to be settled in May 2024, indicating silver traders expect the price of silver to decrease over the next five years. Silver futures are contracts that effectively lock in a price for an amount of silver to be purchased at a time in the future, which can then be traded on markets. Futures markets therefore provide an indicator of how investors think a commodities market will develop in the future.

Purchase Order commodity line level detail for City of Austin Commodities/Goods purchases dating back to October 1st, 2009. Each line includes the NIGP Commodity Code/COA Inventory Code, commodity description, quantity, unit of measure, unit price, total amount, referenced Master Agreement if applicable, the contract name, purchase order, award date, and vendor information. The data contained in this data set is for informational purposes only. Certain Austin Energy transactions have been excluded as competitive matters under Texas Government Code Section 552.133 and City Council Resolution 20051201-002.

Commodity prices are updated in the second business day of the month. Commodity price forecasts are updated twice a year (April and October). The Manufacture Unit Value Index (MUV), also updated twice a year, can be found in the in the worksheet “Annual Price” excel file, “Annual Indices (Real)” worksheet.

This dataset includes data previously published as the "Global Economic Monitor (GEM) Commodities" and "Manufactures Unit Value Index (MUV Index)".

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

Long term price performance of all major primary commodities. This includes the 3 months, 6 months, 1 year, 3 year, as well as the 5 year change in %.

Nominal prices in USD for selected key international commodity prices relevant to Pacific Island Countries and Territories, extracted from World bank Commodity Prices (« pink sheets ») and from FAO GLOBEFISH European Fish Price Report.

Find more Pacific data on PDH.stat.