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 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.

Crude commodity prices refer to the rates at which crude oil is being traded in various markets. Today, the price of Brent crude oil is $60 per barrel, while the price of WTI crude oil is $58 per barrel. Factors such as supply and demand dynamics, geopolitical tensions, and economic indicators influence these prices, which are subject to constant fluctuations. Traders and investors closely monitor crude commodity prices to make informed decisions and assess market trends.

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).

Polypropylene rose to 6,407 CNY/T on December 2, 2025, up 0.03% from the previous day. Over the past month, Polypropylene's price has fallen 2.29%, and is down 14.24% compared to the same time last year, 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 Polypropylene.

This paper describes the regulated agricultural commodity futures market of China, focusing on six actively traded futures: corn, strong gluten wheat, No.1 soybean, soymeal, cotton, and white sugar. A novel skew Ornstein-Uhlenbeck model is employed to characterize price dynamics with government controls. The empirical analysis reveals significant skew phenomena in these six futures and indicates that the price dynamics are influenced by state policy. The observed skew phenomena are most notable in grain futures, with relatively weaker, but statistically significant, evidence of skew phenomena in oilseed and soft futures markets. In addition, generalized quasi-likelihood ratio tests show that the skew Ornstein-Uhlenbeck model is superior to the Ornstein-Uhlenbeck model.

This data comprises the data utilised in the analysis of the above titled manuscript.

This dataset contains real trade-weighted exchange rate indices for many commodities and aggregations important to U.S. agriculture. The data covers information from 1970 to 2024.

Replication file for "Dynamic Volatility Connectedness across Commodity Futures: The Role of Macroeconomic Attention"

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!

This dataset provides a comprehensive collection of time-series data related to economic indicators in Indonesia and global commodity markets, designed for insightful analysis into price trends, market dynamics, and consumer interest. The data spans various categories, offering a multi-faceted view of factors influencing commodity prices and economic behavior.

Data Categories and Contents:

The dataset is organized into several key categories: - Currency Exchange Rates * data/Mata Uang/: This directory contains historical daily exchange rate data for several currencies against the US Dollar, including Malaysian Ringgit (MYRUSD=X.csv), Singapore Dollar (SGDUSD=X.csv), Thai Baht (THBUSD=X.csv), and Indonesian Rupiah (USDIDR=X.csv). Each file typically includes columns such as Date, Price, Adj Close, Close, High, Low, and Volume.

• Indonesian Food Prices

  • data/Harga Bahan Pangan (Cleaned)/: This folder provides cleaned daily price data for essential food commodities across various provinces in Indonesia. Commodities include:

    • Shallots (Bawang Merah.csv)
    • Garlic (Bawang Putih Bonggol.csv)
    • Medium Rice (Beras Medium.csv)
    • Premium Rice (Beras Premium.csv)
    • Curly Red Chili (Cabai Merah Keriting.csv)
    • Red Cayenne Pepper (Cabai Rawit Merah.csv)
    • Broiler Chicken Meat (Daging Ayam Ras.csv)
    • Pure Beef (Daging Sapi Murni.csv)
    • Consumption Sugar (Gula Konsumsi.csv)
    • Bulk Cooking Oil (Minyak Goreng Curah.csv)
    • Simple Packaged Cooking Oil (Minyak Goreng Kemasan Sederhana.csv)
    • Chicken Eggs (Telur Ayam Ras.csv)
    • Bulk Wheat Flour (Tepung Terigu (Curah).csv) Each file contains daily prices (in Indonesian Rupiah) for individual provinces across Indonesia, with columns for Date and each province.

  • data/Harga Bahan Pangan/: This directory contains raw daily price data for various food commodities, organized by province in separate CSV files (e.g., Aceh.xlsx - Aceh.csv, Bali.xlsx - Bali.csv, Banten.xlsx - Banten.csv). These files likely contain similar price information as their "Cleaned" counterparts but might require additional processing.

• Google Trends Search Interest

  • data/Google Trend/: This extensive collection includes Google Trends search interest data for various commodities and their related terms, categorized by province and for Indonesia nationally. These files (e.g., tepung terigu/Aceh.csv, telur ayam/Indonesia.csv, minyak goreng/Bali.csv, gula/Jawa Barat.csv, daging sapi/DKI Jakarta.csv, daging ayam/Jawa Timur.csv, daging/Sumatera Utara.csv, cabai rawit/Indonesia.csv, cabai merah/Jawa Barat.csv, cabai/Jawa Tengah.csv, beras/Jawa Timur.csv, bawang putih/Jawa Tengah.csv, bawang merah/Jawa Barat.csv, bawang/DKI Jakarta.csv) show the popularity of search queries over time, with columns for Day and search interest values.

• Global Commodity Prices

  • data/Global Commodity Price/: This section includes historical futures data for key global commodities:
    • Crude Oil WTI (Crude Oil WTI Futures Historical Data.csv)
    • Natural Gas (Natural Gas Futures Historical Data.csv)
    • Newcastle Coal (Newcastle Coal Futures Historical Data.csv)
    • Palm Oil (Palm Oil Futures Historical Data.csv)
    • US Sugar #11 (US Sugar #11 Futures Historical Data.csv)
    • US Wheat (US Wheat Futures Historical Data.csv) These files provide daily data including Date, Price, Open, High, Low, Vol., and Change %.

Potential Use Cases:

This dataset can be utilized for a variety of analytical tasks, including:

• Time Series Analysis: Forecast future prices of food commodities, currencies, and global energy resources.
• Economic Impact Studies: Analyze the correlation between global commodity prices, currency exchange rates, and local food prices in Indonesia.
• Market Trend Analysis: Identify and visualize trends in consumer interest for specific food items using Google Trends data.
• Regional Economic Disparities: Compare food prices and search interest across different Indonesian provinces to identify regional variations.
• Predictive Modeling: Develop models to predict inflation, economic stability, or consumer behavior based on the interplay of these diverse data points.

The combination of local Indonesian market data with global commodity and search interest data makes this dataset particularly valuable for researchers and analysts interested in economic forecasting and market analysis.

Rapeseed fell to 479.03 EUR/T on December 1, 2025, down 0.87% from the previous day. Over the past month, Rapeseed's price has fallen 0.10%, and is down 6.68% compared to the same time last year, 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 Rapeseed Oil.

this data base contains USD/MAD and EUR/MAD returns in addition to S&P GSCI returns from 15 October 2005 until the 31 December 2014 as the main sample,then we divided this sample into four subsamples, (after & before) the subprime crisis, and (before & after) the debt crisis

China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data was reported at 30.000 Unit in 2023. This records a decrease from the previous number of 35.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data is updated yearly, averaging 44.500 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 60.000 Unit in 2012 and a record low of 30.000 Unit in 2023. China Commodity Trading Market over 100 M Yuan: Number of Market: Other Special Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

Urals Oil fell to 54.22 USD/Bbl on December 1, 2025, down 0.37% from the previous day. Over the past month, Urals Oil's price has fallen 7.52%, and is down 17.95% compared to the same time last year, 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 Urals Crude.

China Commodity Trading Market over 100 M Yuan: Number of Market: Stamps and Coins Market data was reported at 1.000 Unit in 2012. China Commodity Trading Market over 100 M Yuan: Number of Market: Stamps and Coins Market data is updated yearly, averaging 1.000 Unit from Dec 2012 (Median) to 2012, with 1 observations. The data reached an all-time high of 1.000 Unit in 2012 and a record low of 1.000 Unit in 2012. China Commodity Trading Market over 100 M Yuan: Number of Market: Stamps and Coins Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

HRC Steel fell to 891.06 USD/T on December 2, 2025, down 0.88% from the previous day. Over the past month, HRC Steel's price has risen 5.08%, and is up 29.14% compared to the same time last year, 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 HRC Steel.

China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data was reported at 163.000 Unit in 2023. This stayed constant from the previous number of 163.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data is updated yearly, averaging 205.000 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 252.000 Unit in 2013 and a record low of 153.000 Unit in 2008. China Commodity Trading Market over 100 M Yuan: Number of Market: Decoration Materials Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Market.

This dataset provides comprehensive and up-to-date information on futures related to oil, gas, and other fuels. Futures are financial contracts obligating the buyer to purchase and the seller to sell a specified amount of a particular fuel at a predetermined price and future date.

Use Cases: 1. Trend Analysis: Scrutinize patterns and price fluctuations to anticipate future market directions in the energy sector. 2. Academic Research: Delve into the historical behavior of oil and gas prices and understand the influence of global events on these commodities. 3. Trading Strategies: Develop and test trading tactics based on the dynamics of oil, gas, and other fuel futures. 4. Risk Management: Utilize the dataset for hedging and risk management for corporations involved in the extraction, refining, or trading of fuels.

Dataset Image Source: Photo by Pixabay: https://www.pexels.com/photo/industrial-machine-during-golden-hour-162568/

Column Descriptions: 1. Date: The date when the data was documented. Format: YYYY-MM-DD. 2. Open: Market's opening price for the day. 3. High: Peak price during the trading window. 4. Low: Lowest traded price during the day. 5. Close: Price at which the market closed. 6. Volume: Number of contracts exchanged during the trading period. 7. Ticker: The unique market quotation symbol for the future. 8. Commodity: Specifies the type of fuel the future contract pertains to (e.g., crude oil, natural gas).

The table presents the correlation among illiquidity series and volatility series for all financial markets. The sample runs from January 1, 2010 to March 22, 2021.