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.

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.

Trading Signals (NASDAQ Composite Index Stock 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

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.

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.

NASDAQ Composite Index NASDAQ Composite Index

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: Lumber and Wood Products: Hardwood Cut Stock and Dimension (WPU081203) from Dec 1980 to May 2025 about floor coverings, stocks, wood, commodities, PPI, 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.

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 statistic shows the stock prices of selected food commodities from January 2, 2020 to February 6, 2025. After the Russian invasion of Ukraine in February 2022, wheat prices increased significantly since both Russia and Ukraine are the key suppliers of the product. With the beginning of 2023, prices of selected food commodities started to decrease, but still stood higher than early-2020 levels.

Commodity Index Funds Market Outlook

The global commodity index funds market size was valued at approximately $200 billion in 2023 and is projected to reach nearly $400 billion by 2032, growing at a robust CAGR of 7.5% during the forecast period. The significant growth in this market can be attributed to the increasing demand for diversification in investment portfolios and the inherent benefits of hedging against inflation that commodity investments provide. Furthermore, the volatility in global stock markets and geopolitical uncertainties have led investors to seek safer, more stable investment avenues, thus driving the growth of commodity index funds.

One of the primary growth factors propelling the commodity index funds market is the rising awareness among investors about the advantages of commodity investments as a hedge against inflation. Commodities, unlike stocks and bonds, often move inversely to the stock market, providing a cushion during market downturns. This characteristic makes commodity index funds an attractive option for risk-averse investors and those looking to balance their portfolios. Additionally, the globalization of trade and the increasing demand for raw materials in emerging markets have further spurred the demand for commodity investments.

Technological advancements in trading platforms have also significantly contributed to the growth of this market. The advent of sophisticated online platforms has made it easier for retail investors to access and invest in commodity index funds. These platforms offer a range of tools and resources that help investors make informed decisions, thereby democratizing access to commodity investments. Moreover, the rise of robo-advisors and algorithm-based trading strategies has further simplified the investment process, attracting a new generation of tech-savvy investors.

The regulatory landscape has also played a crucial role in shaping the commodity index funds market. Governments and financial regulatory bodies across the globe have been working to create a transparent and secure trading environment. Regulatory reforms aimed at reducing market manipulation and increasing transparency have instilled confidence among investors, thereby boosting the market. Additionally, tax incentives and favorable policies for commodity investments in various countries have also contributed to market growth.

In terms of regional outlook, North America holds a significant share of the global commodity index funds market, followed by Europe and Asia Pacific. The presence of well-established financial markets and a high level of investor awareness in North America are key factors driving the market in this region. Europe, with its strong regulatory framework and increasing adoption of alternative investment strategies, is also witnessing substantial growth. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by the rapid economic growth in countries like China and India, and the increasing interest in commodity investments among institutional and retail investors.

Fund Type Analysis

When analyzing the market by fund type, Broad Commodity Index Funds dominate the landscape. These funds invest in a diversified portfolio of commodities, making them a popular choice for investors seeking broad exposure to the commodity markets. The broad commodity index funds are designed to track the performance of a basket of commodities, ranging from energy products to metals and agricultural goods. This diversification helps mitigate risks associated with the volatility of individual commodities, thereby providing a more stable investment option for risk-averse investors.

Single Commodity Index Funds, on the other hand, focus on specific commodities such as gold, oil, or agricultural products. These funds appeal to investors who have a strong conviction about the performance of a particular commodity. For instance, during periods of economic uncertainty, gold-focused funds often see a surge in demand as investors flock to the safe-haven asset. Similarly, energy-focused funds attract investors when there are disruptions in oil supply or significant geopolitical events affecting oil prices. While these funds offer the potential for high returns, they also come with higher risks due to their lack of diversification.

Sector Commodity Index Funds are another important segment within the commodity index funds market. These funds concentrate on commodities within a specific sector, such as energy, agriculture, or metals, allowing investors to target particular segments of the commo

This dataset contains monthly historical prices of 10 different commodities from January 1980 to April 2023. The data was collected from the Alpha Vantage API using Python. The commodities included in the dataset are WTI crude oil, cotton, natural gas, coffee, sugar, aluminum, Brent crude oil, corn, copper, and wheat. Prices are reported in USD per unit of measurement for each commodity. The dataset contains 520 rows and 12 columns, with each row representing a monthly observation of the prices of the 10 commodities. The 'All_Commodities' column is new.

WTI: WTI crude oil price per unit of measurement (USD). COTTON: Cotton price per unit of measurement (USD). NATURAL_GAS: Natural gas price per unit of measurement (USD). ALL_COMMODITIES: A composite index that represents the average price of all 10 commodities in the dataset, weighted by their individual market capitalizations. Prices are reported in USD per unit of measurement. COFFEE: Coffee price per unit of measurement (USD). SUGAR: Sugar price per unit of measurement (USD). ALUMINUM: Aluminum price per unit of measurement (USD). BRENT: Brent crude oil price per unit of measurement (USD). CORN: Corn price per unit of measurement (USD). COPPER: Copper price per unit of measurement (USD). WHEAT: Wheat price per unit of measurement (USD).

Note that some values are missing in the dataset, represented by NaN. These missing values occur for some of the commodities in the earlier years of the dataset.

It may be useful for time series analysis and predictive modeling.

NaN values were included so that you as a Data Scientist can get some practice on dealing with NaN values.

https://www.alphavantage.co/documentation/

Graph and download economic data for Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, All Fiber and Composite (WPU09150731) from Dec 2009 to May 2025 about fiber, composite, paper, commodities, PPI, 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 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

General Index provides robust, reliable price transparency for the world’s commodity markets with quick and convenient access through a single desktop.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, Tubes, and Similar Fiber Products was 513.60000 Index 1982=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, Tubes, and Similar Fiber Products reached a record high of 513.60000 in May of 2025 and a record low of 32.50000 in January of 1964. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Pulp, Paper, and Allied Products: Fiber Cans, Tubes, and Similar Fiber Products - last updated from the United States Federal Reserve on July 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.

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.

Shenzhen Stock Exchange: Index: SZSE Commodity Producer data was reported at 1,535.910 NA in 13 May 2025. This records an increase from the previous number of 1,528.920 NA for 12 May 2025. Shenzhen Stock Exchange: Index: SZSE Commodity Producer data is updated daily, averaging 2,248.665 NA from Dec 2012 (Median) to 13 May 2025, with 3000 observations. The data reached an all-time high of 4,760.880 NA in 15 Jun 2015 and a record low of 1,297.900 NA in 18 Sep 2024. Shenzhen Stock Exchange: Index: SZSE Commodity Producer data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under High Frequency Database’s Financial and Futures Market – Table CN.EDI.SE: Shenzhen Stock Exchange.

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 Index: The Next Big Thing?

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

United States - Producer Price Index by Commodity: Lumber and Wood Products: Hardwood Cut Stock and Dimension was 382.87700 Index 1982=100 in May of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Lumber and Wood Products: Hardwood Cut Stock and Dimension reached a record high of 382.87700 in May of 2025 and a record low of 94.80000 in December of 1980. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Lumber and Wood Products: Hardwood Cut Stock and Dimension - last updated from the United States Federal Reserve 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