Natural gas fell to 3.04 USD/MMBtu on October 14, 2025, down 2.11% from the previous day. Over the past month, Natural gas's price has fallen 0.24%, but it is still 21.52% 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 October of 2025.

Gasoline fell to 1.83 USD/Gal on October 14, 2025, down 0.98% from the previous day. Over the past month, Gasoline's price has fallen 9.02%, and is down 10.28% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gasoline - values, historical data, forecasts and news - updated on October of 2025.

CRB Index rose to 367.43 Index Points on October 13, 2025, up 0.98% from the previous day. Over the past month, CRB Index's price has fallen 2.96%, but it is still 7.44% 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 October 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.

Imports Price Index: Commodity data was reported at 1.140 Index, 2021 in 2026. This stayed constant from the previous number of 1.140 Index, 2021 for 2025. Imports Price Index: Commodity data is updated yearly, averaging 0.630 Index, 2021 from Dec 1988 (Median) to 2026, with 39 observations. The data reached an all-time high of 1.258 Index, 2021 in 2022 and a record low of 0.189 Index, 2021 in 1988. 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 United States – Table US.OECD.EO: Exports and Imports Price Index: Forecast: OECD Member: Annual.

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.

Philadelphia Gold and Silver Index: A Beacon of Precious Metal Value?

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

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

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

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.

Wheat Price Outlook: TR/CC CRB Index Signals Potential Volatility

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

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

Coffee rose to 399.96 USd/Lbs on October 14, 2025, up 4.36% from the previous day. Over the past month, Coffee's price has fallen 4.24%, but it is still 56.10% 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 October of 2025.

United States EIA Projection: PPI: Metals & Metal Products data was reported at 2.532 1982=1 in 2050. This records an increase from the previous number of 2.528 1982=1 for 2049. United States EIA Projection: PPI: Metals & Metal Products data is updated yearly, averaging 2.405 1982=1 from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 2.532 1982=1 in 2050 and a record low of 1.943 1982=1 in 2016. United States EIA Projection: PPI: Metals & Metal Products data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.I018: Producer Price Index: By Commodities: Projection: Energy Information Administration.

Why did the Carbon Black Price Change in July 2025? The Carbon Black Price Index FOB Texas exhibited a mixed trend as of Q2 2025, exhibiting a slight decline during April, followed by moderate recoveries in May and June.

Pakistan Commodity Price: Low: Masoor: Salam-Local data was reported at 33,000.000 PKR/100 kg in 12 May 2025. This stayed constant from the previous number of 33,000.000 PKR/100 kg for 08 May 2025. Pakistan Commodity Price: Low: Masoor: Salam-Local data is updated daily, averaging 30,000.000 PKR/100 kg from Jul 2022 (Median) to 12 May 2025, with 528 observations. The data reached an all-time high of 35,000.000 PKR/100 kg in 24 Oct 2023 and a record low of 0.000 PKR/100 kg in 12 Oct 2022. Pakistan Commodity Price: Low: Masoor: Salam-Local data remains active status in CEIC and is reported by Business Recorder. The data is categorized under Global Database’s Pakistan – Table PK.P001: Commodity Price.

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.

View monthly updates and historical trends for US One-Year Ahead Commodity Price Change Expectations - Gold. from United States. Source: Federal Reserve B…

China Exports Price Index: Commodity data was reported at 1.825 Index, 2015 in 2025. This records an increase from the previous number of 1.815 Index, 2015 for 2024. China Exports Price Index: Commodity data is updated yearly, averaging 1.006 Index, 2015 from Dec 1988 (Median) to 2025, with 38 observations. The data reached an all-time high of 1.846 Index, 2015 in 2022 and a record low of 0.224 Index, 2015 in 1988. China Exports 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. PXNW - Price of commodity exportsIndex, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

Polypropylene fell to 6,639 CNY/T on October 13, 2025, down 0.38% from the previous day. Over the past month, Polypropylene's price has fallen 3.53%, and is down 12.97% 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 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.

Is Natural Gas the Future?

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