Gasoline fell to 2.08 USD/Gal on June 27, 2025, down 1.15% from the previous day. Over the past month, Gasoline's price has fallen 0.73%, and is down 17.13% 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 June 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.

Natural gas rose to 3.75 USD/MMBtu on June 27, 2025, up 6.24% from the previous day. Over the past month, Natural gas's price has risen 5.31%, and is up 44.02% compared to the same time last year, 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 June of 2025.

In 2024, the price of natural gas in Europe reached 11 constant U.S. dollars per million British thermal units, compared with 2.2 U.S. dollars in the U.S. This was a notable decrease compared to the previous year, which had seen a steep increase in prices due to an energy supply shortage exacerbated by the Russia-Ukraine war. Since 1980, natural gas prices have typically been higher in Europe than in the United States and are expected to remain so for the coming two years. This is due to the U.S. being a significantly larger natural gas producer than Europe. What is natural gas and why is it gaining ground in the energy market? Natural gas is commonly burned in power plants with combustion turbines that generate electricity or used as a heating fuel. Given the fact that the world’s energy demand continues to grow, natural gas was seen by some industry leaders as an acceptable "bridge-fuel" to overcome the use of more emission-intensive energy sources such as coal. Subsequently, natural gas has become the main fuel for electricity generation in the U.S., while the global gas power generation share has reached over 22 percent. How domestic production shapes U.S. natural gas prices The combination of hydraulic fracturing (“fracking”) and horizontal drilling can be regarded as one of the oil and gas industry’s biggest breakthroughs in decades, with the U.S. being the largest beneficiary. This technology has helped the industry release unprecedented quantities of gas from deposits, mainly shale and tar sands that were previously thought either inaccessible or uneconomic. It is forecast that U.S. shale gas production could reach 36 trillion cubic feet in 2050, up from 1.77 trillion cubic feet in 2000.

TTF Gas fell to 33.43 EUR/MWh on June 27, 2025, down 0.20% from the previous day. Over the past month, TTF Gas's price has fallen 8.48%, and is down 3.11% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. EU Natural Gas TTF - values, historical data, forecasts and news - updated on June 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.

Interactive chart illustrating the history of Henry Hub natural gas prices. The prices shown are in U.S. dollars.

In February 2025, crude oil accounted for a 58 percent share of the retail cost of gasoline in the United States. Crude oil costs are the greatest determining factor for petroleum product prices such as gasoline. This is also reflected in the U.S. diesel price breakdown, with crude oil making up 49 percent of the retail price that same month. U.S. gasoline cost breakdown The end price that consumers pay for gasoline in the U.S. is made up of several different components. The weighting of these different costs in the end price of gasoline is also very dynamic depending on the market. In April 2020, just 25 percent of the cost of one U.S. gallon of gasoline was from the cost of crude oil, as a result of the 2020 oil crisis. Other cost components are those associated with refining, distribution and marketing, and taxes. Residents in California pay the highest gasoline taxes out of all U.S. states, at 68.1 cents per gallon as of January 2024. U.S. gasoline prices Since 1980, U.S. gasoline prices have fluctuated greatly, usually reflecting the volatile nature of crude oil prices. The U.S. has some of the lowest unleaded premium prices in the world. When looking at the real U.S. gasoline price to end users, figures amounted to 1.24 real U.S. dollars per gallon in 2023. This ‘real’ U.S. dollars figure refers to the purchasing power of one U.S. dollar relative to the period of time between 1982 and 1984.

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The global gasoline and fuel market, valued at $1.85 billion in 2025, is projected to experience steady growth, driven primarily by the continued reliance on internal combustion engine vehicles, particularly in developing economies experiencing rapid motorization. While the Compound Annual Growth Rate (CAGR) of 1.06% suggests a moderate expansion, this figure likely underrepresents the market's dynamism, as it doesn't account for fluctuating oil prices and evolving geopolitical landscapes. Significant growth drivers include the increasing demand from transportation sectors (both passenger and commercial vehicles), power generation in regions with limited access to renewable energy, and the continued use of gasoline in other applications such as machinery and equipment. However, restraining factors include the rising adoption of electric vehicles (EVs), government regulations promoting cleaner energy sources, and the intermittent fluctuations in global crude oil prices that impact fuel costs and availability. The market segmentation reveals that transportation holds the largest share, closely followed by power generation, with 'others' representing a smaller but still significant portion. Major players, including Chevron, ExxonMobil, PetroChina, and others, are strategically navigating these dynamics through investments in renewable energy and efficiency improvements in fuel production and distribution, while also adapting to the growing EV market. The geographical distribution of the market reflects established economic patterns. North America and Asia Pacific (particularly China and India) represent significant market segments, fueled by high vehicle ownership rates and energy demands in rapidly industrializing regions. Europe, while showing relatively mature markets, continues to contribute substantially. South America and the Middle East & Africa represent markets with varying growth potentials, influenced by their specific economic conditions and energy policies. Future growth will depend critically on the balance between increasing vehicle ownership and the global shift toward electric and alternative fuel vehicles, coupled with the ongoing influence of energy policies and geopolitical factors on global oil prices. While a relatively low CAGR is projected, specific regional markets and segments within the industry are likely to show more pronounced variations in growth rates, creating opportunities for strategic market players. Recent developments include: In October 2023, Aramco and ENOWA, NEOM’s energy and water company, have signed a joint development agreement to construct and establish a first-of-its-kind synthetic electro-fuel (e-fuel) demonstration plant aimed to display technological feasibility and commercial viability by developing thirty five barrels per day of low-carbon, synthetic gasoline from renewable-based hydrogen and captured carbon dioxide (CO2)., In July 2022, the U.S. Department of Energy (DOE) announced that contracts have been awarded for the purchase of crude oil from the Strategic Petroleum Reserve (SPR). This contract awards are part announcement of president to release one million barrels of crude oil a day for six months to address the significant global supply disruption caused by Russia-Ukraine war.. Key drivers for this market are: 4., Rising Adoption of Automobiles such as Passenger Cars, Motorcycles Across the World4.; Easy Availability of Gasoline. Potential restraints include: 4., Rising Adoption of Automobiles such as Passenger Cars, Motorcycles Across the World4.; Easy Availability of Gasoline. Notable trends are: Transportation Segment is Expected to Dominate in the Market.

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.

Will the Natural Gas Futures x3 Short Leveraged Index Ignite a Price Drop?

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

UK Gas rose to 77.96 GBp/thm on June 27, 2025, up 0.21% from the previous day. Over the past month, UK Gas's price has fallen 10.70%, and is down 4.15% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on June 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.

Summer Road Trips Feeling the Burn as Gas Prices Soar

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 June 23, 2025, the Brent crude oil price stood at 70.98 U.S. dollars per barrel, compared to 68.51 U.S. dollars for WTI oil and 76.19 U.S. dollars for the OPEC basket. OPEC prices rose that week following expected supply constraints related to the Israel-Iran conflict.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 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 (whereby a contract is agreed upon, while the 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.

The 2025 annual OPEC oil price stood at ***** U.S. dollars per barrel, as of May. This would be lower than the 2024 average, which amounted to ***** U.S. dollars. The abbreviation OPEC stands for Organization of the Petroleum Exporting Countries and includes Algeria, Angola, Congo, Equatorial Guinea, Gabon, Iraq, Iran, Kuwait, Libya, Nigeria, Saudi Arabia, Venezuela, and the United Arab Emirates. The aim of the OPEC is to coordinate the oil policies of its member states. It was founded in 1960 in Baghdad, Iraq. The OPEC Reference Basket The OPEC crude oil price is defined by the price of the so-called OPEC (Reference) basket. This basket is an average of prices of the various petroleum blends that are produced by the OPEC members. Some of these oil blends are, for example: Saharan Blend from Algeria, Basra Light from Iraq, Arab Light from Saudi Arabia, BCF 17 from Venezuela, et cetera. By increasing and decreasing its oil production, OPEC tries to keep the price between a given maxima and minima. Benchmark crude oil The OPEC basket is one of the most important benchmarks for crude oil prices worldwide. Other significant benchmarks are UK Brent, West Texas Intermediate (WTI), and Dubai Crude (Fateh). Because there are many types and grades of oil, such benchmarks are indispensable for referencing them on the global oil market. The 2025 fall in prices was the result of weakened demand outlooks exacerbated by extensive U.S. trade tariffs.

Dutch TTF gas futures amounted to ***** euros per megawatt hour on June 23, 2025 for contracts with delivery in July 2025. Figures increased compared to the previous week and were higher than they had been a year prior. Dutch TTF is seen as a Europe-wide natural gas price benchmark. Europe more reliant on imports The Groningen gas field is the largest gas field in Europe and the major natural gas source in the Netherlands. In 2014, the first earthquake related to drilling the field occurred, and other seismic activities were also observed. Therefore, the Groningen field has drastically reduced its production output. Since then, natural gas production in the Netherlands has been in a trend of continuous decline. To balance the diminished domestic production, the European market relies on liquefied natural gas imports and pipeline inflow. LNG pricing across European regions The European gas market exhibits regional variations, as evidenced by LNG prices in different parts of the continent. The Southwest Europe LNG price is generally slightly higher than LNG prices in Northwest Europe. The latter reached around **** U.S. dollars per million British thermal units in mid-June 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.

Will Unleaded Gasoline Index Fuel a Surge?

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.

Will Unleaded Gasoline Index Fuel Market Growth?

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 US oil stock market is a key component of the global energy sector and offers individuals and institutions the opportunity to participate in the growth and profitability of the oil and gas industry. This article explores the composition of the market, the influence of factors such as oil prices and government policies, and the emergence of renewable energy companies. It also highlights the risks associated with investing in oil stocks and advises investors to carefully analyze market conditions before m

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