Crude Oil fell to 64.01 USD/Bbl on August 29, 2025, down 0.91% from the previous day. Over the past month, Crude Oil's price has fallen 8.56%, 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. Crude Oil - values, historical data, forecasts and news - updated on August of 2025.

The annual price of West Texas Intermediate (WTI) crude oil is expected to reach an average of 61.81 U.S. dollars per barrel in 2025, according to a May 2025 forecast. This would be a decrease of roughly 15 U.S. dollar compared to the previous year. In the first months weeks of 2025, weekly crude oil prices largely stayed below 70 U.S. dollars per barrel amid trade tariffs and expected economic downturn. What are benchmark crudes? WTI is often used as a price reference point called a benchmark (or ”marker”) crude. This category includes Brent crude from the North Sea, Dubai Crude, as well as blends in the OPEC reference basket. WTI, Brent, and the OPEC basket have tended to trade closely, but since 2011, Brent has been selling at a higher annual spot price than WTI, largely due to increased oil production in the United States. What causes price volatility? Oil prices are historically volatile. While mostly shaped by demand and supply like all consumer goods, they may also be affected by production limits, a change in U.S. dollar value, and to an extent by market speculation. In 2022, the annual average price for WTI was close to the peak of nearly 100 U.S. dollars recorded in 2008. In the latter year, multiple factors, such as strikes in Nigeria, an oil sale stop in Venezuela, and the continuous increase in oil demand from China were partly responsible for the price surge. Higher oil prices allowed the pursuit of extraction methods previously deemed too expensive and risky, such as shale gas and tight oil production in the U.S. The widespread practice of fracturing source rocks for oil and gas extraction led to the oil glut in 2016 and made the U.S. the largest oil producer in the world.

Brent rose to 68.10 USD/Bbl on September 1, 2025, up 0.92% from the previous day. Over the past month, Brent's price has fallen 0.95%, and is down 11.87% 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 September of 2025.

Brent crude oil is projected to have an average annual spot price of 65.85 U.S. dollars per barrel in 2025, according to a forecast from May 2025. This would mean a decrease of nearly 15 U.S. dollars compared to the previous year, and also reflects a reduced forecast WTI crude oil price. Lower economic activity, an increase in OPEC+ production output, and uncertainty over trade tariffs all impacted price forecasting. All about Brent Also known as Brent Blend, London Brent, and Brent petroleum, Brent Crude is a crude oil benchmark named after the exploration site in the North Sea's Brent oilfield. It is a sweet light crude oil but slightly heavier than West Texas Intermediate. In this context, sweet refers to a low sulfur content and light refers to a relatively low density when compared to other crude oil benchmarks. Price development in the 2020s Oil prices are volatile, impacted by consumer demand and discoveries of new oilfields, new extraction methods such as fracking, and production caps routinely placed by OPEC on its member states. The price for Brent crude oil stood at an average of just 42 U.S. dollars in 2020, when the coronavirus pandemic resulted in a sudden demand drop. Two years later, sanctions on Russian energy imports, had pushed up prices to a new decade-high, above 100 U.S. dollars per barrel.

Heating Oil rose to 2.31 USD/Gal on September 1, 2025, up 1.65% from the previous day. Over the past month, Heating Oil's price has fallen 0.45%, but it is still 1.17% 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 September of 2025.

On August 18, 2025, the Brent crude oil price stood at 66.54 U.S. dollars per barrel, compared to 63.42 U.S. dollars for WTI oil and 68.21 U.S. dollars for the OPEC basket. Oil prices remained largely unchanged that week as economic expectations stayed low.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 (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.

Palm Oil rose to 4,474 MYR/T on September 2, 2025, up 2.22% from the previous day. Over the past month, Palm Oil's price has risen 6.88%, and is up 13.76% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Palm Oil - values, historical data, forecasts and news - updated on September of 2025.

Explore the complex factors impacting crude oil prices, including geopolitical events, supply-demand dynamics, technological advancements, and economic trends. Understand how major producers like OPEC+, global economic health, and the shift towards renewable energy influence the future of oil markets.

Explore the dynamic factors influencing Brent crude oil prices, including geopolitical tensions, supply-demand shifts, economic trends, and technological advancements. This comprehensive analysis highlights how these elements shape future price forecasts.

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 Show Bullish Outlook 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

Urals Oil rose to 62.89 USD/Bbl on August 29, 2025, up 1.08% from the previous day. Over the past month, Urals Oil's price has fallen 8.38%, and is down 15.11% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

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: A Hold for the Next 3 Months

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

As of the fourth quarter of 2024, oil prices in the United Kingdom stood at 74 dollars per barrel, with prices expected to rise to 76.6 dollars a barrel in early 2025, before gradually falling in subsequent quarters.

The WTI oil forecast is a projection of the future price movement of West Texas Intermediate (WTI) crude oil. Traders, investors, and analysts closely monitor these forecasts to make informed decisions regarding oil-related investments and trading strategies. Factors such as supply and demand dynamics, geopolitical events, economic indicators, and weather conditions influence the WTI oil forecast. Different analysis methods including technical analysis, fundamental analysis, and sentiment analysis are used

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

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

The global crude oil trading market, a multi-trillion dollar industry, is characterized by intense competition among major players and significant influence from geopolitical events and macroeconomic factors. While precise market sizing data is not provided, leveraging publicly available information suggests a 2025 market value in the range of $3-4 trillion USD, reflecting the enormous volume of crude oil traded globally. The Compound Annual Growth Rate (CAGR) – while unspecified – is likely to be in the low single digits over the forecast period (2025-2033), influenced by factors such as fluctuating demand driven by global economic growth, the ongoing energy transition toward renewable sources, and OPEC+ production policies. Key drivers include increasing global energy demand from developing economies, particularly in Asia, and the continued reliance on crude oil as a primary energy source. Trends indicate a shift towards greater transparency and digitalization within trading operations, as well as a growing focus on sustainability and environmental concerns impacting trading strategies and investments in carbon capture technologies. Restraints include price volatility caused by geopolitical instability, regulatory changes, and the increasing adoption of alternative energy sources. The market is segmented by various factors including crude type (Brent, WTI, etc.), trading location (spot, futures, etc.), and geographical regions. The major players in this market, including Vitol, Trafigura, Glencore, Gunvor, and the integrated oil majors (BP, Shell, TotalEnergies, Chevron), continue to dominate the landscape due to their established networks, financial strength, and access to vast resources. However, emerging players from Asia and the Middle East are increasingly challenging this dominance. Regional dynamics significantly impact trading patterns, with North America, Europe, and Asia remaining crucial regions. The forecast period will likely witness continued consolidation within the industry, strategic partnerships, and innovation in trading technology. The overall market is expected to demonstrate resilience despite the long-term shift towards decarbonization, largely driven by the continued demand for oil, particularly in transportation and industrial sectors. This necessitates continuous adaptation and strategic planning by market participants to navigate the evolving dynamics of the crude oil trading landscape effectively.

Crude oil daily forecasts provide valuable insights into the future movement of oil prices. Factors such as supply and demand, geopolitical events, economic indicators, weather conditions, and technical analysis are considered in these forecasts. Various methodologies, including statistical models, fundamental analysis, sentiment analysis, and expert opinions, are used to make predictions. However, it is important to recognize the inherent limitations of forecasting accurate oil prices due to unpredictable

The IEA is the energy institute expecting the highest oil surplus for 2025. As demand outlooks remain modest, robust production output throughout 2024 is expected to result in some form of oil surplus, which would also impact oil prices. Woodmac was the only energy institute surveyed that did not see a surplus for the year. Production growth amid lower demand expectations The expected surplus in 2025 is largely attributed to non-OPEC production growth from major producers such as the United States and newcomers like Guyana. Overall, worldwide liquid fuels production could see a steep increase in the first half of 2025, if producers like OPEC stick to their output plans. This would come in spite of modest consumption expectations. Again, the IEA is the institute predicting the lowest growth in global oil demand when compared to other industry bodies such as the EIA and OPEC. Forecasting centers diverge in opinion on oil future Not only near-term, also long-term oil demand projections have become increasingly divergent among major energy institutions. OPEC's 2024 outlook expects global oil demand to surpass *** million barrels per day by 2030, while the IEA's stated policies scenario anticipates demand reaching only ***** million barrels per day in the same year. Diesel and gasoil currently account for the largest share of oil product demand at ***** percent, though this is expected to decrease slightly by 2050. Jet fuel and kerosene are projected to see the greatest increase in demand shares over the coming decades.

The linseed oil market is anticipated to generate USD 9,363.4 million in sales revenue globally in 2025. It is projected to expand at a CAGR of 6%, reaching an estimated worth of USD 16,824.6 million by 2035.

Country-wise Analysis