GSCI rose to 551.39 Index Points on July 11, 2025, up 0.98% from the previous day. Over the past month, GSCI's price has risen 0.10%, but it is still 3.67% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. GSCI Commodity Index - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q1 2025 about World, commodities, price index, indexes, and price.

CRB Index rose to 373.34 Index Points on July 11, 2025, up 1.06% from the previous day. Over the past month, CRB Index's price has risen 0.59%, and is up 9.33% 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 July of 2025.

LME Index fell to 4,166.90 Index Points on July 11, 2025, down 0.47% from the previous day. Over the past month, LME Index's price has risen 0.85%, but it is still 1.39% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. LME Index - values, historical data, forecasts and news - updated on July of 2025.

Orange Juice rose to 288.86 USd/Lbs on July 12, 2025, up 9.48% from the previous day. Over the past month, Orange Juice's price has risen 5.38%, but it is still 36.04% lower than a year ago, 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 July of 2025.

Graph and download economic data for Producer Price Index by Commodity: All Commodities (PPIACO) from Jan 1913 to May 2025 about commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Wood Pulp (WPU0911) from Jan 1926 to May 2025 about wood, paper, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Processed Foods and Feeds: Fat Free or Skim Milk (WPU02310303) from Dec 1982 to May 2025 about milk, dairy, fat, processed, food, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Producer Price Index by Commodity: Metals and Metal Products: Cold Rolled Steel Sheet and Strip (WPU101707) from Jun 1982 to May 2025 about steel, metals, commodities, PPI, inflation, price index, indexes, price, and USA.

Corn fell to 393.37 USd/BU on July 14, 2025, down 0.66% from the previous day. Over the past month, Corn's price has fallen 9.52%, and is down 2.69% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Bleached Bristol, Clay-Coated, Uncoated, and Industrial Converted Paper (WPU09130119) from Dec 2011 to May 2025 about paper, commodities, PPI, industry, inflation, 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.

Is the TR/CC CRB Corn Index an Accurate Reflection of Corn Market Performance?

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.

DJ Commodity Zinc Index Forecast

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.

S&P GSCI Crude Oil Index: A Reliable Gauge of Global Oil Prices?

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

Graph and download economic data for Producer Price Index by Commodity: Metals and Metal Products: Iron and Steel (WPU101) from Jan 1926 to May 2025 about iron, steel, metals, commodities, PPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Import Price Index (End Use): All Commodities (IR) from Sep 1982 to May 2025 about end use, imports, headline figure, commodities, price index, indexes, price, and USA.

Lead fell to 2,028.48 USD/T on July 11, 2025, down 0.69% from the previous day. Over the past month, Lead's price has risen 1.60%, but it is still 8.21% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lead - values, historical data, forecasts and news - updated on July 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.

CBOE Volatility Index Options & Futures Prediction

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.

TR/CC CRB Ex Energy Index: A Reliable Indicator of Commodity Market Health?

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

Real-Time Material Price Index API Market Outlook

According to our latest research, the global Real-Time Material Price Index API market size reached USD 1.48 billion in 2024, reflecting strong momentum driven by surging demand for dynamic pricing intelligence across industries. The market is projected to grow at a robust CAGR of 16.2% from 2025 to 2033, reaching a forecasted size of USD 5.15 billion by 2033. This accelerated expansion is primarily attributed to the increasing adoption of digital procurement, supply chain automation, and the need for real-time materials cost transparency in volatile global markets.

The growth of the Real-Time Material Price Index API market is propelled by several critical factors. The rise in globalization and the complexity of supply chains have made it imperative for organizations to access accurate, up-to-the-minute pricing data for a wide array of raw materials. As commodity prices continue to fluctuate due to geopolitical tensions, trade policies, and environmental disruptions, the reliance on real-time APIs for price tracking and forecasting has become a strategic necessity. Enterprises are leveraging these APIs to optimize procurement decisions, manage risk, and maintain competitiveness in fast-evolving markets. The integration of artificial intelligence and machine learning into these solutions further enhances their predictive capabilities, enabling organizations to anticipate price shifts and plan accordingly.

Another significant driver is the digital transformation sweeping through traditional sectors such as construction, manufacturing, and energy. These industries are increasingly deploying Real-Time Material Price Index APIs to automate their procurement processes, minimize human error, and ensure compliance with contractual obligations tied to material costs. The ability to seamlessly integrate these APIs with enterprise resource planning (ERP) and supply chain management (SCM) systems has unlocked new efficiencies and cost savings. Furthermore, the proliferation of cloud-based deployment models has democratized access to real-time pricing intelligence, making it feasible for small and medium-sized enterprises (SMEs) to harness the same tools as large corporations.

The market is also benefiting from heightened regulatory scrutiny and sustainability initiatives. Governments and regulatory bodies are mandating greater transparency in sourcing and pricing, particularly for critical and rare materials. Real-Time Material Price Index APIs are playing a pivotal role in helping organizations meet these requirements by providing auditable, real-time data feeds. Additionally, as companies strive to achieve sustainability targets, these APIs aid in evaluating the cost implications of alternative sourcing strategies and greener materials. This confluence of regulatory, operational, and strategic factors is expected to sustain the market’s growth trajectory through the forecast period.

Regionally, North America leads the Real-Time Material Price Index API market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has witnessed widespread adoption across its construction and manufacturing sectors, driven by the rapid digitization of supply chains and robust investment in procurement technologies. Europe is experiencing a surge in demand, fueled by stringent regulatory frameworks and the push for sustainable sourcing. Meanwhile, Asia Pacific is emerging as the fastest-growing region, with countries like China and India investing heavily in digital infrastructure and industrial automation. Latin America and the Middle East & Africa are gradually catching up, propelled by modernization initiatives and the growing need for supply chain resilience.

Component Analysis

The Real-Time Material Price Index API market is segmented by component into software and services. The software segment dominates the market, driven by the proliferation of advanced API platforms that offer real-time da