CRB Index rose to 457.52 Index Points on March 26, 2026, up 1.55% from the previous day. Over the past month, CRB Index's price has risen 16.06%, and is up 22.67% 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 March of 2026.

📈 Global Commodities Market Dataset (1997–Present)

Overview

This dataset provides a comprehensive historical record of global commodity prices across energy, metals, agriculture, and construction-related commodities. It is designed for time-series analysis, forecasting, econometrics, quantitative finance, and machine learning research.

The dataset aggregates daily market data spanning more than two decades, enriched with engineered temporal features to support predictive modeling and pattern discovery.

📊 What’s Inside

• Time Span: October 1997 → Recent years
• Frequency: Daily
• Total Rows: 7,103
• Total Features: 131 columns

Each commodity includes standard OHLCV-style market data where available.

🛢️ Covered Commodity Categories

Energy

• Crude Oil (WTI)
• Brent Oil
• Natural Gas

Precious & Industrial Metals

• Gold
• Silver
• Copper
• Aluminum
• Zinc
• Nickel
• Lead

Agricultural Commodities

• Corn
• Wheat
• Soybeans
• Rice
• Cotton
• Sugar
• Coffee
• Cocoa

Construction & Industrial Inputs

• Lumber

📌 Feature Breakdown

1. Market Price Data

For each commodity, the dataset may include: - Open - High - Low - Close - Volume

Note: Some commodities have missing values during early years due to data availability.

2. Temporal & Calendar Features

To enable advanced time-series modeling and seasonality analysis: - Year - Month - Quarter - Day_of_Week - Week_of_Year

These features are pre-engineered for immediate use in ML pipelines.

🧠 Ideal Use Cases

• Commodity price forecasting
• Inflation and macroeconomic analysis
• Volatility modeling
• Correlation and co-movement studies
• Feature engineering practice for time-series ML
• LSTM / Transformer-based forecasting models
• Algorithmic trading research (educational)

⚠️ Notes on Data Quality

• Missing values exist, especially in earlier years for some commodities.
• Users are encouraged to apply:
  • Forward-filling
  • Interpolation
  • Asset-specific filtering
• Prices are nominal and not inflation-adjusted.

📂 File Format

• CSV
• UTF-8 encoded
• Ready for use with:
  • Pandas
  • NumPy
  • Scikit-learn
  • PyTorch / TensorFlow
  • R

Wheat rose to 605.75 USd/Bu on March 27, 2026, up 0.12% from the previous day. Over the past month, Wheat's price has risen 5.44%, and is up 14.67% 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 March of 2026.

Corn fell to 462 USd/BU on March 27, 2026, down 1.07% from the previous day. Over the past month, Corn's price has risen 6.64%, and is up 1.93% compared to the same time last year, 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 March of 2026.

Iron Ore rose to 106.14 USD/T on March 26, 2026, up 0.11% from the previous day. Over the past month, Iron Ore's price has risen 7.15%, and is up 3.69% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Iron Ore - values, historical data, forecasts and news - updated on March of 2026.

The commodity prices displayed in Trading Economics are based on over-the-counter (OTC) and contract for difference (CFD) financial instruments. Our market prices are intended to provide you with a reference only, rather than as a basis for making trading decisions. Trading Economics does not verify any data and disclaims any obligation to do so. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes. This dataset provides a table with prices for several commodities including the latest price for the nearby futures contract, yesterday close, plus weekly, monthly and yearly percentage changes.

Up to 30 year Historical Price Data of 8 common Edible Oils

Oils include coconut oil, olive oil, palm kernel oil, palm oil, peanut oil, rapeseed oil, soybean oil, sunflower oil

Coconut oil (Philippines/Indonesia), bulk, c.i.f. Rotterdam Olive Oil, extra virgin less than 1% free fatty acid, ex-tanker price U.K. Palm kernel Oil (Malaysia), c.I.f. Rotterdam Palm oil (Malaysia), 5% bulk, c.i.f. N. W. Europe Groundnut oil (any origin), c.i.f. Rotterdam Rapeseed Oil; Crude, fob Rotterdam Soybean oil (Any origin), crude, f.o.b. ex-mill Netherlands Sunflower Oil, US export price from Gulf of Mexico

A tabular presentation of hitorical commodity prices in United States dollars, 2000 - 2010 (Sources: Ministry of Finance; Ministry of Energy and Mines; Ministry of Forests, Lands and Natural Resource Operations; US Federal Reserve Bank).

Silver Market Intelligence Dataset (2000–Present)

Silver is one of the most dynamic commodities in global financial markets. It serves both as an industrial metal and a monetary asset, making its price behavior unique compared to other commodities.

This dataset provides historical daily silver prices from 2000 onwards in OHLC format (Open, High, Low, Close). The dataset is suitable for finance research, volatility analysis, and time-series modeling.

The goal of releasing this dataset is to enable researchers, data scientists, and analysts to explore the structural dynamics of silver markets.

Dataset Fields:

Date – Trading date
Open – Opening silver price
High – Highest price during the trading day
Low – Lowest price during the trading day
Close – Closing price of the day

Potential Applications:

• Commodity market research
• Volatility modeling
• Trading strategy development
• Financial time-series forecasting
• Market regime analysis
• Data visualization and exploratory analysis

This dataset is designed specifically for financial data science.

View monthly updates and historical trends for Non-Fuel Commodities Price Index. Source: International Monetary Fund. Track economic data with YCharts ana…

Dataset Overview

A comprehensive monthly commodity price dataset spanning January 1960 to February 2026, covering 71 commodities across 10 categories — Energy, Metals & Minerals, Precious Metals, Grains, Beverages, Oils & Meals, Fertilizers, Timber, Other Food, and Other Raw Materials.

Primary data sourced from the World Bank Pink Sheet (CMO Historical Data Monthly), the world's most authoritative commodity price series. Six key commodities (WTI Oil, Brent Oil, Copper, Wheat, Maize, European Natural Gas) are extended through February 2026 via the FRED API (Federal Reserve Bank of St. Louis), bridging the Pink Sheet's December 2024 cutoff.

Each record includes the nominal USD price, unit of measurement, commodity category, and 20+ derived analytical columns including month-on-month and year-on-year price changes, 3/12/60-month rolling averages, 12-month rolling volatility, a base-2000 price index, all-time high/low flags, price regime classification, decade and era labels. 49,093 records in total.

Data Science Applications

• Time series forecasting: predict commodity prices using rolling averages, volatility and MoM/YoY features
• Correlation analysis: identify cross-commodity price relationships (e.g. crude oil vs fertilizers vs food)
• Regime detection: classify market states using price_regime_mom and rolling volatility
• Inflation research: track commodity price cycles across 6 decades and major economic shocks
• Portfolio analysis: use price index (base 2000) to compare relative returns across commodity classes
• EDA: explore price behaviour across Oil Crisis, Commodity Supercycle, GFC, COVID and post-pandemic eras

Column Descriptors

date — First day of month (ISO format YYYY-MM-DD) year — Calendar year month — Month number (1–12) month_name — Month name (January–December) quarter — Quarter label (Q1–Q4) decade — Decade label (e.g. 1970s) era — Economic era classification commodity_name — Full World Bank commodity name commodity_code — Short ticker-style code (e.g. OIL_WTI, GOLD) category — Commodity category (Energy, Grains etc.) unit — Unit of measurement ($/bbl, $/mt, $/mmbtu etc.) price_nominal_usd — Monthly price in nominal USD price_mom_pct — Month-on-month % price change price_mom_abs — Month-on-month absolute price change (USD) price_yoy_pct — Year-on-year % price change price_3m_avg — 3-month rolling average price price_12m_avg — 12-month rolling average price price_60m_avg — 60-month (5-year) rolling average price price_12m_volatility — 12-month rolling standard deviation price_index_2000_base — Price index rebased to Jan 2000 = 100 price_regime_mom — MoM regime: Sharp Rise / Rising / Stable / Falling / Sharp Fall is_all_time_high — 1 if price is a new all-time high at that date is_all_time_low — 1 if price is a new all-time low at that date source_desc — Full commodity source description from World Bank Pink Sheet documentation data_source — World Bank Pink Sheet or FRED dataset_version — 1.0 retrieved_date — Date data was retrieved build_timestamp — Full build timestamp row_completeness_pct — % of critical fields populated

Acknowledgements

World Bank Group — Commodity Markets Outlook, Pink Sheet (CMO Historical Data Monthly) https://www.worldbank.org/en/research/commodity-markets

Federal Reserve Bank of St. Louis — FRED Economic Data https://fred.stlouisfed.org

All World Bank data is open access under CC BY 4.0. FRED data is public domain.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: International Commodity Prices

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.

Commodity Price Index: Weights: Rural Commodities: Sugar data was reported at 1.500 % in Feb 2013. This stayed constant from the previous number of 1.500 % for Jan 2013. Commodity Price Index: Weights: Rural Commodities: Sugar data is updated monthly, averaging 1.500 % from Feb 2008 (Median) to Feb 2013, with 61 observations. The data reached an all-time high of 1.500 % in Feb 2013 and a record low of 0.700 % in Aug 2009. Commodity Price Index: Weights: Rural Commodities: Sugar data remains active status in CEIC and is reported by Reserve Bank of Australia. The data is categorized under Global Database’s Australia – Table AU.I051: Commodity Price Index: Weights (Old).

Commodity Price: Ave: Basmati Super: Old data was reported at 36,500.000 PKR/100 kg in 25 Mar 2026. This stayed constant from the previous number of 36,500.000 PKR/100 kg for 24 Mar 2026. Commodity Price: Ave: Basmati Super: Old data is updated daily, averaging 34,000.000 PKR/100 kg from Jul 2022 (Median) to 25 Mar 2026, with 674 observations. The data reached an all-time high of 36,500.000 PKR/100 kg in 25 Mar 2026 and a record low of 21,500.000 PKR/100 kg in 17 Aug 2022. Commodity Price: Ave: Basmati Super: Old data remains active status in CEIC and is reported by Business Recorder. The data is categorized under Global Database’s Pakistan – Table PK.P: Commodity Price.

Commodity Price: High: Basmati Super: New data was reported at 37,000.000 PKR/100 kg in 25 Mar 2026. This stayed constant from the previous number of 37,000.000 PKR/100 kg for 24 Mar 2026. Commodity Price: High: Basmati Super: New data is updated daily, averaging 31,000.000 PKR/100 kg from Jul 2022 (Median) to 25 Mar 2026, with 674 observations. The data reached an all-time high of 37,000.000 PKR/100 kg in 25 Mar 2026 and a record low of 20,000.000 PKR/100 kg in 17 Aug 2022. Commodity Price: High: Basmati Super: New data remains active status in CEIC and is reported by Business Recorder. The data is categorized under Global Database’s Pakistan – Table PK.P: Commodity Price.

Graph and download economic data for Producer Price Index by Commodity: Farm Products: Pistachios (WPU01190106) from Dec 1991 to Feb 2026 about nuts, agriculture, commodities, PPI, inflation, price index, indexes, price, and USA.

Gold rose to 4,533.64 USD/t.oz on March 27, 2026, up 3.51% from the previous day. Over the past month, Gold's price has fallen 14.82%, but it is still 46.99% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on March of 2026.

Commodity Price: High: Ghee data was reported at 8,850.000 PKR/16 kg in 25 Mar 2026. This stayed constant from the previous number of 8,850.000 PKR/16 kg for 24 Mar 2026. Commodity Price: High: Ghee data is updated daily, averaging 7,400.000 PKR/16 kg from Jul 2022 (Median) to 25 Mar 2026, with 674 observations. The data reached an all-time high of 9,200.000 PKR/16 kg in 24 Feb 2023 and a record low of 7,200.000 PKR/16 kg in 22 Jan 2024. Commodity Price: High: Ghee data remains active status in CEIC and is reported by Business Recorder. The data is categorized under Global Database’s Pakistan – Table PK.P: Commodity Price.

Commodity Price: Low: Gur data was reported at 20,000.000 PKR/100 kg in 25 Mar 2026. This stayed constant from the previous number of 20,000.000 PKR/100 kg for 24 Mar 2026. Commodity Price: Low: Gur data is updated daily, averaging 18,000.000 PKR/100 kg from Jul 2022 (Median) to 25 Mar 2026, with 674 observations. The data reached an all-time high of 21,000.000 PKR/100 kg in 01 Dec 2025 and a record low of 9,000.000 PKR/100 kg in 06 Dec 2022. Commodity Price: Low: Gur data remains active status in CEIC and is reported by Business Recorder. The data is categorized under Global Database’s Pakistan – Table PK.P: Commodity Price.