Search LSEG's Commodities Data, and find global pricing, exchanges, and fundamentals for energy, agriculture, and metals.

CRB Index rose to 373.34 Index Points on July 11, 2025, up 1.06% from the previous day. Over the past month, CRB Index's price has risen 0.59%, and is up 9.33% compared to the same time last year, 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 July of 2025.

Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil. Data comes from the International Monetary Fund (IMF).All rights are reservedDataDataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).LicenseThe IMF grants permission to visit its Sites and to download and copy information, documents, and materials from the Sites for personal, noncommercial usage only, without any right to resell or redistribute or to compile or create derivative works, subject to these Terms and Conditions of Usage and also subject to more specific restrictions that may apply to particular information within the Sites. Any rights not expressly granted herein are reserved.For more information please visit: Copyright and Usage.

Analysis of ‘Sentiment Analysis of Commodity News (Gold)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ankurzing/sentiment-analysis-in-commodity-market-gold on 14 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This is a news dataset for the commodity market where we have manually annotated 11,412 news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021).

Content

The dataset has been collected from various news sources and annotated by three human annotators who were subject experts. Each news headline was evaluated on various dimensions, for instance - if a headline is a price related news then what is the direction of price movements it is talking about; whether the news headline is talking about the past or future; whether the news item is talking about asset comparison; etc.

Acknowledgements

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." In Future of Information and Communication Conference, pp. 589-601. Springer, Cham, 2021.

https://arxiv.org/abs/2009.04202 Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." arXiv preprint arXiv:2009.04202 (2020)

We would like to acknowledge the financial support provided by the India Gold Policy Centre (IGPC).

Inspiration

Commodity prices are known to be quite volatile. Machine learning models that understand the commodity news well, will be able to provide an additional input to the short-term and long-term price forecasting models. The dataset will also be useful in creating news-based indicators for commodities.

Apart from researchers and practitioners working in the area of news analytics for commodities, the dataset will also be useful for researchers looking to evaluate their models on classification problems in the context of text-analytics. Some of the classes in the dataset are highly imbalanced and may pose challenges to the machine learning algorithms.

--- Original source retains full ownership of the source dataset ---

In this paper, we extract latent factors from a large cross-section of commodity prices, including fuel and non-fuel commodities. We decompose each commodity price series into a global (or common) component, block-specific components, and a purely idiosyncratic component. We find that the bulk of the fluctuations in commodity prices are well summarized by a single global factor. This global factor is closely related to fluctuations in global economic activity and, since the early 2000s, has become more important in explaining variations in commodity prices.

Country-specific commodity price indices, including export, import, and terms-of-trade indices. For each country, the change in the international price of up to 45 individual commodities is weighted using commodity-level trade data. See “Commodity Terms of Trade: A New Database,” by Bertrand Gruss and Suhaib Kebhaj, for further details.

In 2023, the rate of return on gold was 13.1 percent, making gold the leading commodity based on return rate in that year. Natural resources like any other investments exhibit a wide range of fluctuations over time.

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.

This dataset is designed for the commodity market, featuring over 10,000 manually annotated news headlines. It aims to provide deep insights into news sentiment and its implications for commodity prices. The headlines were collected from various news sources and evaluated by three subject experts over a period of more than 20 years, from 2000 to 2021. Each news item has been assessed across multiple dimensions, including implied price direction (up, down, or constant), whether the news discusses past or future events, and if it involves asset comparisons. This dataset is particularly valuable for developing machine learning models that can understand commodity news, which can then serve as an additional input for both short-term and long-term price forecasting models. It is also useful for creating news-based indicators for commodities. Researchers focused on text analytics and classification problems will find this dataset beneficial, although some classes are highly imbalanced, which may present challenges for machine learning algorithms.

Columns

The dataset includes the following columns:

• Dates: The date of the news headline.
• URL: The URL where the news headline was published.
• News: The actual news headline text.
• Price Direction Up: A binary indicator (1 for Yes, 0 for No) if the news headline suggests an increase in price.
• Price Direction Constant: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a stable price (no change).
• Price Direction Down: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a decrease in price.
• Asset Comparison: A binary indicator (1 for Yes, 0 for No) if the news headline compares different assets.
• Past Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to past events.
• Future Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to future events.
• Price Sentiment: The overall sentiment of the gold commodity price based on the headline, categorised as positive, negative, or other.

Distribution

The dataset contains over 10,000 unique news headlines and corresponding metadata. Data files are typically provided in CSV format. Key distribution statistics for some dimensions are as follows:

• Dates: 3,761 unique values.
• URL: 10,570 unique values.
• News: 10,570 unique values.
• Price Direction Up: 6,158 headlines do not imply up, 4,412 imply up.
• Price Direction Constant: 10,126 headlines do not imply constant, 444 imply constant.
• Price Direction Down: 6,658 headlines do not imply down, 3,912 imply down.
• Asset Comparison: 8,569 headlines do not compare assets, 2,001 compare assets.
• Past Information: 318 headlines do not discuss past information, 10,252 discuss past information.
• Future Information: 10,251 headlines do not discuss future information, 319 discuss future information.
• Price Sentiment: Approximately 42% positive, 36% negative, and 22% other sentiment.

Usage

This dataset is ideally suited for:

• Developing machine learning models that understand commodity news for price forecasting.
• Creating news-based indicators for commodity markets.
• Evaluating text classification models in the context of news analytics.
• Research into the impact of news on commodity market volatility.

Coverage

The dataset has a global regional coverage. It spans a significant time range of over 20 years, from 2000 to 2021, with headlines collected across this period. There are no specific demographic notes beyond the focus on gold commodity news.

License

CC-BY-NC

Who Can Use It

This dataset is primarily intended for:

• Researchers and practitioners specialising in news analytics for commodities, who can leverage it for building predictive models.
• Data scientists and machine learning engineers working on text classification and natural language processing tasks, especially those dealing with imbalanced datasets.
• Financial analysts and market strategists interested in incorporating news sentiment into their commodity market analysis.

Dataset Name Suggestions

• Gold News Sentiment Analysis Dataset
• Commodity Market News Classifier
• Financial News Headline Sentiment
• Gold Price Direction News Data
• Annotated Commodity News for ML

Attributes

Original Data Source: Sentiment Analysis of Commodity News (Gold)

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 ...).

The global commodity trading services market is experiencing robust growth, driven by increasing globalization, fluctuating commodity prices, and the need for efficient supply chain management. The market size in 2025 is estimated at $2 trillion, exhibiting a Compound Annual Growth Rate (CAGR) of 6% between 2025 and 2033. This growth is fueled by several key factors. Firstly, the rising demand for raw materials across various sectors, including metals, energy, and agriculture, is creating lucrative opportunities for commodity trading firms. Secondly, technological advancements in areas like data analytics and blockchain technology are improving transparency, efficiency, and risk management within commodity trading, further stimulating market expansion. Finally, the increasing complexity of global supply chains necessitates the expertise of specialized commodity traders to navigate market volatility and ensure secure and timely delivery of goods. The market is segmented by commodity type (metals, energy, agricultural, and others) and by the size of the businesses served (large enterprises and SMEs). While large enterprises dominate the market currently, the SME segment shows strong potential for future growth as businesses increasingly rely on external expertise for commodity sourcing. The geographical distribution of the commodity trading services market is diverse, with North America, Europe, and Asia Pacific representing the major regions. However, emerging markets in Asia and Africa are showing significant growth potential due to rapid industrialization and rising consumer demand. Competitive pressures within the industry are high, with numerous large multinational corporations vying for market share. These companies, including Vitol, Glencore, Trafigura, Mercuria, and Cargill, possess extensive global networks, strong financial capabilities, and deep expertise in risk management, allowing them to dominate the market. Nevertheless, smaller, specialized trading firms are also finding success by focusing on niche markets or employing innovative trading strategies. The overall outlook for the commodity trading services market remains optimistic, with continued growth expected over the coming years, albeit with some potential challenges related to geopolitical instability and regulatory changes.

This table contains 7 series, with data starting from 1972 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Commodity (7 items: Total; all commodities; Metals and Minerals; Energy; Total excluding energy ...).

Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data was reported at 2,854.700 2001=1000 in 10 Dec 2018. This records an increase from the previous number of 2,831.800 2001=1000 for 07 Dec 2018. Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data is updated daily, averaging 2,200.475 2001=1000 from Jun 2005 (Median) to 10 Dec 2018, with 3904 observations. The data reached an all-time high of 3,716.580 2001=1000 in 16 Apr 2012 and a record low of 1,277.850 2001=1000 in 28 Jun 2005. Commodity Index: Multi Commodity Exchange of India: Future Price: Agriculture data remains active status in CEIC and is reported by Multi Commodity Exchange of India. The data is categorized under India Premium Database’s Financial Market – Table IN.ZF004: Commodity Index.

Eggs CH rose to 3,450 CNY/T on July 11, 2025, up 0.09% from the previous day. Over the past month, Eggs CH's price has risen 23.26%, but it is still 23.82% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Eggs CH.

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.

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.

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).

TraditionData’s Energy & Commodities Market Data service offers comprehensive coverage across various commodity markets including oil, gas, power, and more.

• Extensive market coverage with data sourced directly from Tradition’s brokerage desks.
• Provides flexible, region and product-specific data packages for energy and commodities.
• Suitable for risk management, trading, and independent risk evaluation.

Visit Energy & Commodities Market Data for a detailed view.

Discover what drives the fluctuating prices of aluminium in the global market and the critical factors that impact its supply and demand, production costs, and geopolitical events in this insightful article. Stay informed and make informed investment decisions!

High price volatility among various commodities and the recent lowering of interest rates has fueled strong growth among commodity contracts intermediation brokers. While the national economy has continued to recover following a period of high inflationary pressures, recent rate cuts by the Federal Reserve and continued price volatility of oil and agricultural products strengthened commodity contracts’ popularity. Short-term contracts and future continue to facilitate interest among brokers, with revenue growing at a CAGR of 4.6% to an estimated $21.8 billion through the end of 2024, including an estimated 2.3% boost in 2024 alone. Profit continues to remain steady, as higher price volatility and lower interest rates continue to facilitate favorable market conditions for commodity traders. Banks, once outsized players in the industry, have significantly downsized or completely ended their commodity trading activities. This has put significant downward pressure on revenue as these institutions have been forced to limit proprietary trading due to the Volcker rule, enacted prior to the current period. The decreased presence of banks in the industry has allowed smaller players to enter the industry, exacerbating fragmentation among various service groups. The inflationary spike played a key role in buoying growth, with recent geopolitical conflicts in the Middle East and Europe strengthening commodity price volatility. Moving forward, commodity contract intermediaries face a less certain landscape, as anticipated declines in global oil prices and the agricultural price index will dampen the popularity of long-term commodity trades. Increased demand for metal and energy products and the low inventories of metal commodities are expected to sustain a significant revenue stream for brokers. However, further uncertainty surrounding rising tensions in the Middle East will impact the types of trades made by commodity traders. Greater automation and adoption of new technologies such as blockchain will offer a workflow enhancement in the longer term. Nonetheless, an expected decline in global oil prices is poised to cause revenue to fall at a CAGR of 1.0% to an estimated $20.8 billion through the end of 2029.