Natural gas rose to 3.09 USD/MMBtu on August 1, 2025, up 0.10% from the previous day. Over the past month, Natural gas's price has fallen 11.31%, but it is still 57.26% higher than a year ago, 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 August of 2025.

Gold rose to 3,294.43 USD/t.oz on July 31, 2025, up 0.60% from the previous day. Over the past month, Gold's price has fallen 1.31%, but it is still 34.70% 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 July of 2025.

CRB Index fell to 369.61 Index Points on July 31, 2025, down 1.78% from the previous day. Over the past month, CRB Index's price has risen 1.62%, and is up 12.72% 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 August of 2025.

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.

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

Cbonds collects and normalizes indices data, offering daily updated and historical data on over 40,000 indices, including macroeconomic indicators, yield curves and spreads, currency markets, stock and funds markets, and commodities. Using the Indices API, you can access an index's holdings, such as its assets, sectors, and weight, as well as basic data on the asset. You can obtain end-of-day, and historical API indicator prices in CSV, XLS, and JSON formats. Cbonds provides a free Indices API for a limited test period of two weeks or for a longer period with a limited number of instruments.

We present a method to estimate jointly the parameters of a standard commodity storage model and the parameters characterizing the trend in commodity prices. This procedure allows the influence of a possible trend to be removed without restricting the model specification, and allows model and trend selection based on statistical criteria. The trend is modeled deterministically using linear or cubic spline functions of time. The results show that storage models with trend are always preferred to models without trend. They yield more plausible estimates of the structural parameters, with storage costs and demand elasticities that are more consistent with the literature. They imply occasional stockouts, whereas without trend the estimated models predict no stockouts over the sample period for most commodities. Moreover, accounting for a trend in the estimation implies price moments closer to those observed in commodity prices. Our results support the empirical relevance of the speculative storage model, and show that storage model estimations should not neglect the possibility of long-run rice trends.

Corn fell to 389.36 USd/BU on August 1, 2025, down 1.19% from the previous day. Over the past month, Corn's price has fallen 9.29%, and is down 3.45% 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 August of 2025.

The global energy price index stood at around 101.5 in 2024. Energy prices were on a decreasing trend that year, and forecasts suggest the price index would decrease below 80 by 2026. Price indices show the development of prices for goods or services over time relative to a base year. Commodity prices may be dependent on various factors, from supply and demand to overall economic growth. Electricity prices around the world As with overall fuel prices, electricity costs for end users are dependent on power infrastructure, technology type, domestic production, and governmental levies and taxes. Generally, electricity prices are lower in countries with great coal and gas resources, as those have historically been the main sources for electricity generation. This is one of the reasons why electricity prices are lowest in resource-rich countries such as Iran, Qatar, and Russia. Meanwhile, many European governments that have introduced renewable surcharges to support the deployment of solar and wind power and are at the same time dependent on fossil fuel imports, have the highest household electricity prices. Benchmark oil prices One of the commodities found within the energy market is oil. Oil is the main raw material for all common motor fuels, from gasoline to kerosene. In resource-poor and remote regions such as the United States' states of Alaska and Hawaii, or the European country of Cyprus, it is also one of the largest sources for electricity generation. Benchmark oil prices such as Europe’s Brent, the U.S.' WTI, or the OPEC basket are often used as indicators for the overall energy price development.

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.

The objective of the project was to provide econometric analysis and theory for modelling energy and soft commodity prices. This necessitated data analysis and modelling together with theoretical econometrics, dealing with the specific stylised facts of commodity prices. In order to analyse energy and soft commodity prices, the determination of spot energy prices in regulated markets was first considered, from the point of view of the regulator. Direct data analysis of futures commodity prices was then undertaken, resulting in the collection of an extensive dataset of most traded futures commodity prices at a daily frequency, covering 16 different commodities over a 10-year period.

Gain the global energy market information you need with LSEG's energy commodities pricing data. Browse the catalogue.

The global commodity retail market is experiencing robust growth, driven by several key factors. While the exact market size for 2025 is not provided, considering typical growth rates in established retail sectors and the expansive geographic scope encompassing North America, Europe, Asia-Pacific, and other regions, a reasonable estimate for the 2025 market size would be $2 trillion USD. This valuation reflects the immense scale of the industry, encompassing diverse product categories ranging from daily necessities to luxury goods. The market’s strong performance is fueled by rising consumer disposable incomes, especially in developing economies, expanding e-commerce penetration, and the increasing preference for convenience and readily available goods. The diverse segmentation within the market, catering to various consumer needs and preferences through channels like ending consumers, alliance businesses, and others, further contributes to overall growth. The market's CAGR is provided as XX. Let's assume a conservative CAGR of 5% for illustration, suggesting consistent, steady growth over the forecast period. This steady expansion is anticipated to continue through 2033, driven by ongoing improvements in logistics, supply chain efficiency, and evolving consumer demands. However, the commodity retail market is not without its challenges. Factors such as fluctuating commodity prices, economic downturns, and increasing competition from online retailers can pose significant restraints on growth. Furthermore, evolving consumer preferences and the need for retailers to adapt to sustainable and ethical sourcing practices add complexity to the sector's operating environment. Nevertheless, the sector's resilience, driven by the fundamental demand for essential goods and the ongoing evolution of retail strategies, indicates a continued upward trajectory in the foreseeable future. The robust presence of major players such as Albertsons, Carrefour, Kroger, Tesco, and Walgreens Boots Alliance underscores the market's competitiveness and significant scale. Strategic alliances, technological advancements, and targeted marketing initiatives will likely shape the market landscape in the coming years, allowing businesses to successfully navigate the challenges and seize growth opportunities.

Commodity Management Software Market Outlook

The global commodity management software market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 3.2 billion by 2032, showcasing a robust CAGR of 8.9% during the forecast period. This impressive growth is primarily driven by increasing demand for efficient supply chain management, rising commodity price volatility, and the integration of advanced technologies such as AI and blockchain.

The demand for commodity management software is significantly influenced by the need for enhanced supply chain visibility and risk management. As global trade continues to expand, companies are increasingly seeking advanced solutions to mitigate risks associated with commodity price fluctuations and supply chain disruptions. The ability of commodity management software to provide real-time data analytics and insights is a major growth factor, helping organizations make informed decisions and optimize their operations.

Another critical growth factor driving the commodity management software market is the adoption of advanced technologies such as artificial intelligence (AI) and blockchain. These technologies enhance the capabilities of commodity management software by enabling predictive analytics, improving transaction transparency, and automating complex processes. AI-driven analytics can forecast market trends and commodity prices with higher accuracy, while blockchain ensures secure and transparent transactions, reducing the risk of fraud.

Additionally, the increasing regulatory requirements and compliance standards in various industries are fueling the adoption of commodity management software. Governments and regulatory bodies are imposing stringent regulations to ensure transparency and accountability in commodity trading. This has led organizations to invest in robust software solutions that can help them adhere to these regulations and avoid hefty penalties. The software's ability to streamline compliance processes and provide comprehensive reporting is a significant advantage driving market growth.

Regionally, North America dominates the commodity management software market, accounting for the largest market share. This is attributed to the presence of major commodity trading hubs and advanced technological infrastructure in the region. Europe follows closely, driven by stringent regulatory frameworks and a strong focus on sustainability. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid industrialization, increasing commodity trading activities, and rising adoption of digital solutions in emerging economies such as China and India.

Component Analysis

The commodity management software market is segmented by component into software and services. The software segment holds the largest market share, driven by the increasing need for advanced software solutions that offer real-time data analytics, risk management, and supply chain optimization. The software segment encompasses various applications, including trading and risk management (TRM), procurement, logistics, and inventory management. These applications enable organizations to streamline their operations, reduce costs, and improve decision-making processes.

Trading and risk management (TRM) software is a critical component of the commodity management software market. It helps organizations manage their trading activities, mitigate risks, and ensure compliance with regulatory requirements. The growing volatility in commodity prices and increasing regulatory scrutiny have led to a surge in demand for TRM software. This software provides real-time market data, advanced analytics, and risk assessment tools, enabling organizations to make informed trading decisions and minimize risks.

Procurement software is another vital component, helping organizations manage their procurement processes more efficiently. It offers tools for supplier management, contract management, and procurement analytics, allowing organizations to optimize their procurement strategies, reduce costs, and enhance supplier relationships. The increasing complexity of global supply chains and the need for efficient procurement processes are driving the demand for procurement software.

The services segment includes consulting, implementation, and support services, which are essential for the successful deployment and operation of commodity management software. Consulting services help organizations assess their requireme

The automotive aluminum market offers a diverse range of products, each with specific applications:Rolled Products: Rolled aluminum accounts for the largest market share. It is used for various components, including body panels and structural parts due to its formability and strength.Extruded Products: Extruded aluminum finds applications in frames, bumpers, and interior trim. Its high strength-to-weight ratio makes it suitable for complex shapes and load-bearing components.Castings: Aluminum castings are preferred for high-stress applications such as engine blocks and suspension components. They offer complex geometries and excellent durability. Recent developments include: February 2024: Vedanta Aluminium has come up with Vedanta Metal Bazaar, an e-superstore for primary aluminum that will revolutionize the buying and selling of aluminum in India., The emporium launched with over 750 product variations from Vedanta Aluminium. AI-based pricing discovery gives clients unrivaled value even when commodity prices fluctuate. Ingots, billets, PFA, wire rods, rolled products, flip coils, hot metal, and Restora (India's first low-carbon aluminum) are available. The superstore additionally customizes solutions for its large consumer base., Aluminium is used in aerospace, automotive, building and construction, energy distribution, defense, and other industries. It is called the ‘metal of the future’ because it is essential to the global energy transition, renewable energy, electric vehicles, green infrastructure, and high-tech manufacturing. However, procuring aluminum was formerly complicated and resource-intensive., Vedanta Aluminium created Vedanta Metal Bazaar, a revolutionary e-commerce platform that would change aluminium purchase, to simplify business for customers., It promises to streamline the procurement process, allowing buyers to focus on business growth rather than transactional follow-ups and commodity price and order fulfillment fluctuations. After steel, aluminium is the second most consumed metal worldwide., Buyers may get just-in-time delivery, real-time AI-based pricing discovery, and full visibility from order placement to delivery with Vedanta Metal Bazaar. This enables robust production planning and frees up resources for important expenditures. In a few clicks, clients may use order history, dynamic market circumstances, and competitive rates to make smart purchases., A groundbreaking new platform, Vedanta Metal Bazaar was developed from the ground up to meet consumer expectations and revolutionize the experience., The platform offers global-first features like product availability, online price discovery, long-term contracts, on-the-spot orders, live shipment tracking, financial reconciliation, all critical documentation (such as test certificates, bank guarantees, letters of credit), and a selection of channel finance and logistics providers to help customers procure., September 2022: Alcoa Corporation announced new innovations in alloy development and deployment, further strengthening its position as a supplier of advanced aluminum alloys., May 2022: Novelis Inc. announced it will invest $2.5 billion to build a new low-carbon recycling and rolling plant.. Key drivers for this market are: . Restraints, . Opportunities; . Challenges; . Trends. Potential restraints include: . Opportunities, . Challenges; . Trends. Notable trends are: Growing in the sales and production of several automobiles to boost market growth.

China Imports Price Index: Commodity data was reported at 1.949 Index, 2015 in 2025. This records an increase from the previous number of 1.941 Index, 2015 for 2024. China Imports Price Index: Commodity data is updated yearly, averaging 1.041 Index, 2015 from Dec 1988 (Median) to 2025, with 38 observations. The data reached an all-time high of 2.007 Index, 2015 in 2022 and a record low of 0.258 Index, 2015 in 1988. China Imports Price Index: Commodity data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.EO: Exports and Imports Price Index: Forecast: Non OECD Member: Annual. PMNW - Price of commodity importsIndex, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

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 Energy index: Analysts Predict Volatility Ahead

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

Coffee fell to 286.23 USd/Lbs on August 1, 2025, down 2.90% from the previous day. Over the past month, Coffee's price has fallen 3.17%, but it is still 24.49% higher than a year ago, 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 August of 2025.

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.

Commodity Petroleum Index: The Ultimate Guide?

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

The market for Commodity Hedging Solutions is projected to reach a value of USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period of 2025-2033. This growth can be attributed to the increasing volatility in commodity prices, which is driving businesses to seek ways to mitigate their exposure to price fluctuations. Other factors contributing to the market's expansion include the growing demand for sophisticated risk management solutions, advancements in technology, and the increasing adoption of hedging strategies by various industries. Key market trends include the adoption of cloud-based solutions, the integration of artificial intelligence (AI) and machine learning (ML) into hedging solutions, and the growing popularity of dynamic hedging strategies. The market is segmented into software, service, and other types, as well as oil and gas, metal, agricultural products, and other application segments. North America, Europe, and Asia Pacific are the major regional markets for Commodity Hedging Solutions. The market is dominated by a number of established players, including DBS Corporate Banking, Marex, CIC Market Solutions, and CIMB.