Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% compared to the same time last year, 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 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.

Orange Juice fell to 147.99 USd/Lbs on December 2, 2025, down 0.38% from the previous day. Over the past month, Orange Juice's price has fallen 15.22%, and is down 71.10% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Orange Juice - values, historical data, forecasts and news - updated on December of 2025.

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.

Coffee fell to 408.66 USd/Lbs on December 2, 2025, down 0.95% from the previous day. Over the past month, Coffee's price has risen 0.50%, and is up 38.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coffee - values, historical data, forecasts and news - updated on December of 2025.

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

The World Bank’s Commodity Markets Outlook is published quarterly, in January, April, July and October. The report provides detailed market analysis for major commodity groups, including energy, metals, agriculture, precious metals and fertilizers. Price forecasts to 2025 for 46 commodities are presented along with historical price data. For more information, please visit: http://www.worldbank.org/commodities For current and past data on Commodity Price Forecasts, please see the Archives data tab on the website.

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.

Graph and download economic data for Producer Price Index by Commodity: Lumber and Wood Products: Softwood Lumber, Made from Purchased Lumber, Cut Stock, and Dimension (WPU081108) from May 2025 to Aug 2025 about stocks, wood, purchase, commodities, PPI, price index, indexes, price, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

DJ Commodity Leadindex: The Future of Commodity Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Overview

The data refers to Daily prices of various commodities in India like Tomato, Potato, Brinjal, Wheat etc. It has the wholesale maximum price, minimum price and modal price on daily basis. the prices in the dataset refer to the wholesale prices of various commodities per quintal (100 kg) in Indian rupees. The wholesale price is the price at which goods are sold in large quantities to retailers or distributors.

.

Features of the dataset include:

• State: The state in India where the market is located.
• District: The district in India where the market is located.
• Market: The name of the market.
• Commodity: The name of the commodity.
• Variety: The variety of the commodity.
• Grade: The grade or quality of the commodity.
• Min Price: (INR) The minimum wholesale price of the commodity on a given day, per quintal (100 kg).
• Max Price: (INR) The maximum wholesale price of the commodity on a given day, per quintal (100 kg).
• Modal Price: (INR) The most common or representative wholesale price of the commodity on a given day, per quintal (100 kg).

1 INR = 0.012 USD (as on 17 August, 2023)

Use Cases

Market analysis: You can use this dataset to analyze trends and patterns in the wholesale prices of various commodities across different markets in India. This can help you understand factors that affect prices, such as supply and demand, seasonality, and market conditions. Commodity recommendation: Develop recommender systems that suggest the best markets or commodities for farmers or traders to sell or buy based on their location, preferences, and market conditions.

Licensed under the Government Open Data License - India (GODL) https://data.gov.in/government-open-data-license-india

Feel free to download the data and use it in your work. I will wait for interesting notebooks from your side. Thank you

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,

Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on December of 2025.

This statistic shows the stock prices of selected oil and gas commodities from January 2, 2020 to February 4, 2025. After the Russian invasion of Ukraine in February 2022, energy prices climbed significantly. The highest increase can be observed for natural gas, whose price peaked in August and September 2022. By the beginning of 2023, natural gas price started to decline.

The San Francisco Controller's Office maintains a database of purchasing activity from fiscal year 2007 forward. This data is presented on the Purchasing Commodity Data report in CSV format, and represents detailed commodity-level data by purchase order. Additional lines have been added to this dataset to reconcile some document totals from the City's purchasing system to the totals from the City's accounting system in cases when the two amounts are different, which sometimes occurs due to adjustments entered into the accounting system but not the purchasing system. We have removed sensitive information from this data – this is intended to show payments made to entities providing goods and services to the City and County and to protect individuals. For example, we have removed payments to employees (reimbursements, garnishments) and jury members, revenue refunds, payments for judgments and claims, witnesses, relocation and rehousing, and a variety of human services payments. New data is added on a weekly basis. Supplier payments represent payments to City contractors and vendors that provide goods and/or services to the City. Certain other non-supplier payee payments, which are made to parties other than traditional City contractors and vendors, are also included in this dataset, These include payments made for tax and fee refunds, rebates, settlements, etc.

Dataset

This dataset was created by supermf

Contents

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

DJ Commodity Grains Index forecast stable.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Monthly export country-by-commodity data on the UK's trade in goods, including trade by all countries and selected commodities, non-seasonally adjusted.

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.

CN: Open Interest: Dalian Commodity Exchange: Corn Starch data was reported at 329.589 Lot th in 02 Dec 2025. This records an increase from the previous number of 326.685 Lot th for 01 Dec 2025. CN: Open Interest: Dalian Commodity Exchange: Corn Starch data is updated daily, averaging 88.357 Lot th from Dec 2014 (Median) to 02 Dec 2025, with 2662 observations. The data reached an all-time high of 1,563.912 Lot th in 14 Feb 2017 and a record low of 7.240 Lot th in 19 Dec 2014. CN: Open Interest: Dalian Commodity Exchange: Corn Starch data remains active status in CEIC and is reported by Dalian Commodity Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Dalian Commodity Exchange: Commodity Futures: Open Position: Daily.