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

Crude Oil rose to 62.42 USD/Bbl on October 8, 2025, up 1.12% from the previous day. Over the past month, Crude Oil's price has fallen 0.33%, and is down 14.77% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on October of 2025.

This statistic shows the stock price development of selected petroleum companies from January 2, 2020 to April 15, 2024. After the Russian invasion of Ukraine in February 2022, oil prices increased sharply in the first quarter of 2022 since many countries depend on Russian oil. Petroleum companies highly benefited from inclined oil prices, and saw significant increases in their share prices.

Learn about crude oil stock indices, how they track and measure the performance of crude oil related stocks, and their importance in the financial markets.

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.

Oil stock price graphs are crucial tools for analyzing the historical performance of oil stocks. They help investors and financial analysts identify trends, patterns, support and resistance levels, and key chart patterns.

Graph and download economic data for CBOE Crude Oil ETF Volatility Index from 2007-05-10 to 2025-10-06 about ETF, VIX, volatility, crude, stock market, oil, and USA.

The US oil stock chart represents the historical movement of stock prices for companies in the oil industry in the United States. It provides valuable insights into the performance of the oil industry, allowing investors to analyze trends and make informed decisions. Analyzing the chart can help understand historical performance, predict future price movements, and compare different oil companies or indexes.

Stocks of crude oil in the United States increased by 1.79million barrels in the week ending September 26 of 2025. This dataset provides the latest reported value for - United States Crude Oil Stocks Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Short-term predictions indicate a cautiously optimistic outlook for S&P GSCI Crude Oil, with potential for moderate upside driven by a combination of supply constraints and rising global energy demand. However, risks to this prediction include geopolitical uncertainties, increased interest rates, and the potential for new COVID-19 variants to disrupt economic recovery and demand.

Brent rose to 66.10 USD/Bbl on October 8, 2025, up 0.99% from the previous day. Over the past month, Brent's price has fallen 0.44%, and is down 13.69% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Brent crude oil - values, historical data, forecasts and news - updated on October of 2025.

Heating Oil rose to 2.28 USD/Gal on October 8, 2025, up 0.86% from the previous day. Over the past month, Heating Oil's price has fallen 1.51%, but it is still 0.30% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Heating oil - values, historical data, forecasts and news - updated on October of 2025.

Iran Index: TSE: Oil and Gas Extraction and Related Services excl Surveying data was reported at 339.200 21Mar1998=100 in May 2018. This records a decrease from the previous number of 360.100 21Mar1998=100 for Apr 2018. Iran Index: TSE: Oil and Gas Extraction and Related Services excl Surveying data is updated monthly, averaging 437.100 21Mar1998=100 from Jul 2009 (Median) to May 2018, with 107 observations. The data reached an all-time high of 1,051.400 21Mar1998=100 in Jan 2014 and a record low of 115.800 21Mar1998=100 in Jul 2009. Iran Index: TSE: Oil and Gas Extraction and Related Services excl Surveying data remains active status in CEIC and is reported by Tehran Stock Exchange. The data is categorized under Global Database’s Iran – Table IR.Z001: Tehran Stock Exchange: Index.

Graph and download economic data for Producer Price Index by Commodity: Fuels and Related Products and Power: Lubricating Oil Base Stocks (WPU05780121) from Jun 2009 to Aug 2025 about lubricants, stocks, fuels, oil, commodities, PPI, inflation, price index, indexes, price, and USA.

A crude oil stock price graph shows the historical movement and fluctuations in the price of crude oil over a specific period. The graph represents the changes in the value of crude oil stocks, helping investors and analysts analyze trends and make predictions.

Oil stocks graph provides a visual representation of the performance of stocks related to the oil industry over a specified period of time. It is a valuable tool for investors, analysts, and traders who are interested in tracking the movement and trends in oil stock prices. Learn about the importance of oil stocks graph, different types of graphs, and how they support informed decision-making in the oil industry.

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.

Crude Oil Futures: Experts Predict Volatility for S&P GSCI Crude Oil 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

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 Indicator of Global Energy Markets?

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

On September 29, 2025, the Brent crude oil price stood at 66.8 U.S. dollars per barrel, compared to 63.45 U.S. dollars for WTI oil and 70.48 U.S. dollars for the OPEC basket. Oil prices rose slightly that week.Europe's Brent crude oil, the U.S. WTI crude oil, and OPEC's basket are three of the most important benchmarks used by traders as reference for global oil and gasoline prices. Lowest ever oil prices during coronavirus pandemic In 2020, the coronavirus pandemic resulted in crude oil prices hitting a major slump as oil demand drastically declined following lockdowns and travel restrictions. Initial outlooks and uncertainty surrounding the course of the pandemic brought about a disagreement between two of the largest oil producers, Russia and Saudi Arabia, in early March. Bilateral talks between global oil producers ended in agreement on April 13th, with promises to cut petroleum output and hopes rising that these might help stabilize the oil price in the coming weeks. However, with storage facilities and oil tankers quickly filling up, fears grew over where to store excess oil, leading to benchmark prices seeing record negative prices between April 20 and April 22, 2020. How crude oil prices are determined As with most commodities, crude oil prices are impacted by supply and demand, as well as inventories and market sentiment. However, as oil is most often traded in future contracts (where a contract is agreed upon while product delivery will follow in the next two to three months), market speculation is one of the principal determinants for oil prices. Traders make conclusions on how production output and consumer demand will likely develop over the coming months, leaving room for uncertainty. Spot prices differ from futures in so far as they reflect the current market price of a commodity.

An oil price market index is a measure of the average price of a set of crude oils in the global market. It represents the value of the oil traded on a specific exchange or market and serves as a benchmark for pricing and trading purposes in the oil industry. This article discusses various oil price market indexes, including the well-known West Texas Intermediate (WTI) and Brent crude oil price indexes, and highlights the importance of these indexes for producers, consumers, and investors in the oil industr