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.

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.

View monthly updates and historical trends for All Commodities Price Index. Source: International Monetary Fund. Track economic data with YCharts analytic…

This dataset provides the current daily prices of various commodities sourced from multiple markets (mandis) across different regions. It includes detailed information on the market names, commodity types, and their respective prices, offering a snapshot of real-time agricultural and other commodity market trends. The data is valuable for farmers, traders, and analysts to monitor price fluctuations, compare regional price variations, and make informed decisions. It offers insights into supply and demand dynamics, and market conditions, and helps in understanding the economic factors affecting commodity pricing. This dataset supports decision-making, price forecasting, and market research.

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

Commodity Prices YoY in New Zealand decreased to 4.40 percent in October from 6.20 percent in September of 2025. This dataset includes a chart with historical data for New Zealand Commodity Prices YoY.

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

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.

This dataset contains daily and monthly prices of key commodities in North Sulawesi, Indonesia, from 2020 to 2025. It was compiled to support research in food security, inflation analysis, and predictive modeling.

Besides commodity prices, the dataset also includes: - Rolling averages (7-day, 30-day) - Lag features (1, 7, 30 days) - Macroeconomic indicators such as CPI (Consumer Price Index) and USD/IDR exchange rate - Weather data: temperature, rainfall, humidity - Calendar information: day count, day in month

This dataset can be useful for: - Forecasting models (ARIMA, LSTM, Prophet, etc.) - Economic analysis of price fluctuations - Policy-making support in agriculture and trade - Machine learning projects combining climate and economic features

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

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 reserved

Data

Dataset 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).

License

The 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.

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 %.

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)".

This dataset contains monthly historical prices of 10 different commodities from January 1980 to April 2023. The data was collected from the Alpha Vantage API using Python. The commodities included in the dataset are WTI crude oil, cotton, natural gas, coffee, sugar, aluminum, Brent crude oil, corn, copper, and wheat. Prices are reported in USD per unit of measurement for each commodity. The dataset contains 520 rows and 12 columns, with each row representing a monthly observation of the prices of the 10 commodities. The 'All_Commodities' column is new.

WTI: WTI crude oil price per unit of measurement (USD). COTTON: Cotton price per unit of measurement (USD). NATURAL_GAS: Natural gas price per unit of measurement (USD). ALL_COMMODITIES: A composite index that represents the average price of all 10 commodities in the dataset, weighted by their individual market capitalizations. Prices are reported in USD per unit of measurement. COFFEE: Coffee price per unit of measurement (USD). SUGAR: Sugar price per unit of measurement (USD). ALUMINUM: Aluminum price per unit of measurement (USD). BRENT: Brent crude oil price per unit of measurement (USD). CORN: Corn price per unit of measurement (USD). COPPER: Copper price per unit of measurement (USD). WHEAT: Wheat price per unit of measurement (USD).

Note that some values are missing in the dataset, represented by NaN. These missing values occur for some of the commodities in the earlier years of the dataset.

It may be useful for time series analysis and predictive modeling.

NaN values were included so that you as a Data Scientist can get some practice on dealing with NaN values.

https://www.alphavantage.co/documentation/

Commodity Price Risk Dashboards Market Outlook

According to our latest research, the global commodity price risk dashboards market size reached USD 1.45 billion in 2024, reflecting the growing importance of real-time risk management tools in volatile commodity markets. With a robust compound annual growth rate (CAGR) of 10.6%, the market is projected to expand to USD 3.62 billion by 2033. This impressive growth is primarily driven by the increasing complexity of global supply chains, heightened geopolitical risks, and the escalating demand for data-driven decision-making across industries.

One of the most significant growth factors fueling the commodity price risk dashboards market is the increasing volatility and unpredictability in global commodity prices. Over the past decade, geopolitical tensions, trade disputes, and climate change events have contributed to sharp fluctuations in the prices of essential commodities such as oil, agricultural products, and metals. Enterprises and financial institutions are under mounting pressure to manage exposure to price risks more efficiently. As a result, organizations are rapidly adopting advanced dashboards that offer real-time price monitoring, predictive analytics, and scenario modeling capabilities. These tools empower stakeholders to make informed decisions, optimize procurement strategies, and safeguard profit margins against unpredictable market swings.

Another key driver is the digital transformation sweeping across industries, particularly in sectors with significant exposure to commodity risks such as energy, agriculture, and manufacturing. The integration of artificial intelligence, machine learning, and big data analytics into commodity price risk dashboards has elevated their value proposition. Modern dashboards can now process vast datasets from multiple sources, offering actionable insights and automated alerts. This technological evolution has not only improved the accuracy of risk assessments but also enhanced the speed at which organizations can respond to market movements. The growing emphasis on automation and data-driven strategies is expected to sustain robust demand for commodity price risk dashboards throughout the forecast period.

Furthermore, stringent regulatory requirements and the growing need for transparency in financial reporting have compelled organizations to adopt sophisticated risk management solutions. Regulatory bodies across the globe are mandating more comprehensive reporting and risk disclosure standards, particularly for companies engaged in commodity trading and procurement. Commodity price risk dashboards facilitate compliance by providing auditable records, detailed analytics, and customizable reporting features. This regulatory push, coupled with the increasing adoption of enterprise risk management frameworks, is anticipated to further stimulate market growth, as organizations seek to align their risk management practices with global standards.

From a regional perspective, North America currently leads the commodity price risk dashboards market, accounting for the largest share in 2024. This dominance is attributed to the presence of major commodity trading hubs, advanced technological infrastructure, and a high concentration of multinational corporations. However, Asia Pacific is poised to exhibit the highest growth rate during the forecast period, driven by rapid industrialization, expanding commodity markets, and increasing investments in digital transformation initiatives. Europe also remains a significant market, supported by robust regulatory frameworks and a strong emphasis on sustainability and risk management in commodity-intensive industries.

Component Analysis

The commodity price risk dashboards market is segmented by component into software and services, each playing a pivotal role in addressing the diverse needs of end-users. Software solutions constitute the core of risk management, offering advanced functionalities such as real-time price tracking, analytics,

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to Sep 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

This table contains 5 series, with data for years 1972 - 2010 (not all combinations necessarily have data for all years), and was last released on 2010-05-12. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Commodity (5 items: Total; all commodities; Food; Total excluding energy; Energy ...).

Historical commodity (daily) price from 2000-2022 (March). 1. Gold 2. Palladium 3. Nickel 4. Brent Oil 5. Natural Gas 6. Wheat

• cover image credit: https://www.pexels.com/photo/industrial-machine-during-golden-hour-162568/

Weekly Commodity Prices are made up of four excel spreadsheets and graphs split into commodity groups. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Commodity Price Movements If you require datasets in a more accessible format, please contact prices@defra.gsi.gov.uk.

Argentina Commodities Prices Index: USD data was reported at 264.017 Dec2001=100 in Nov 2025. This records an increase from the previous number of 257.836 Dec2001=100 for Oct 2025. Argentina Commodities Prices Index: USD data is updated monthly, averaging 302.046 Dec2001=100 from Jan 1997 (Median) to Nov 2025, with 347 observations. The data reached an all-time high of 372.811 Dec2001=100 in May 2022 and a record low of 94.700 Dec2001=100 in Feb 1999. Argentina Commodities Prices Index: USD data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.I: Commodities Prices Index.