Crude Oil rose to 63.59 USD/Bbl on August 10, 2025, up 0.39% from the previous day. Over the past month, Crude Oil's price has fallen 7.09%, and is down 20.57% 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.

On August 4, 2025, the Brent crude oil price stood at 68.59 U.S. dollars per barrel, compared to 66.29 U.S. dollars for WTI oil and 71.58 U.S. dollars for the OPEC basket. OPEC prices rose slightly that week, while Brent and WTI prices fell.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.

Brent fell to 66.11 USD/Bbl on August 8, 2025, down 0.48% from the previous day. Over the past month, Brent's price has fallen 5.81%, and is down 17.01% 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 August of 2025.

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

Forecast: Average Daily Crude Oil Consumption in China 2022 - 2026 Discover more data with ReportLinker!

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

The global demand for crude oil (including biofuels) in 2024 amounted to 103.75 million barrels per day. The source expects economic activity and related oil demand to pick up by the end of the year, with forecast suggesting it could increase to more than 105 million barrels per day. Motor fuels make up majority of oil demand Oil is an important and versatile substance, used in different ways and in different forms for many applications. The road sector is the largest oil consuming sector worldwide. It accounts for nearly one half of the global demand for oil, largely due to reliance on motor spirits made from petroleum. The OPEC projects global oil product demand to reach 120 million barrels per day by 2050, with transportation fuels such as gasoline and diesel expected to remain the most consumed products. Diesel and gasoil demand is forecast to amount to 32.5 million barrels per day in 2050, up from 29 million barrels in 2023. Gasoline demand is forecast at 27 million barrels by 2050. Differences in forecast oil demand widen between major energy institutions Despite oil producing bodies such as the OPEC seeing continued importance for crude oil in the future, other forecast centers have been more moderate in their demand outlooks. For example, between the EIA, IEA, and OPEC, the latter was the only one to expect significant growth for oil demand until 2030.

Heating Oil rose to 2.27 USD/Gal on August 8, 2025, up 0.09% from the previous day. Over the past month, Heating Oil's price has fallen 5.82%, and is down 3.06% compared to the same time last year, 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 August 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.

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

Canada NEB Forecast: Crude Oil Production: Heavy data was reported at 393,683.090 Cub m/Day in Dec 2020. This records an increase from the previous number of 336,029.391 Cub m/Day for Nov 2020. Canada NEB Forecast: Crude Oil Production: Heavy data is updated monthly, averaging 372,750.377 Cub m/Day from Oct 2017 (Median) to Dec 2020, with 39 observations. The data reached an all-time high of 400,197.677 Cub m/Day in Feb 2020 and a record low of 301,653.187 Cub m/Day in Feb 2019. Canada NEB Forecast: Crude Oil Production: Heavy data remains active status in CEIC and is reported by Canada Energy Regulator. The data is categorized under Global Database’s Canada – Table CA.RB022: Crude Oil Production: Forecast.

Complete replication and data package. Abstract: This paper proposes methods to include information from the underlying nominal daily series in model-based forecasts of average real series. We apply these methods to forecasts of the real price of crude oil. Models utilizing information from daily prices yield large forecast improvements and, in some cases, almost halve the forecast error compared to current specifications. We demonstrate for the first time that model-based forecasts of the real price of crude oil can outperform the traditional random walk forecast, that is the end-of-month no-change forecast, at short forecast horizons.

Canada NEB Forecast: Crude Oil Production: Light: Conventional data was reported at 106,617.916 Cub m/Day in Dec 2020. This records an increase from the previous number of 106,115.993 Cub m/Day for Nov 2020. Canada NEB Forecast: Crude Oil Production: Light: Conventional data is updated monthly, averaging 125,312.612 Cub m/Day from Aug 2017 (Median) to Dec 2020, with 41 observations. The data reached an all-time high of 135,966.312 Cub m/Day in Nov 2017 and a record low of 100,249.779 Cub m/Day in May 2020. Canada NEB Forecast: Crude Oil Production: Light: Conventional data remains active status in CEIC and is reported by Canada Energy Regulator. The data is categorized under Global Database’s Canada – Table CA.RB022: Crude Oil Production: Forecast.

While major energy institutions IEA, OPEC, and EIA used to have little differences in their long-term growth projections for the oil market, their demand outlooks have become more divergent in recent years. In its 2024 outlook, OPEC expected global oil demand to increase to more than 113 million barrels per day by 2030. In comparison, the IEA's stated policies scenario (STEPS) from 2024 sees oil demand coming to merely 101.7 million barrels per day by 2030. A figure that was similar to the EIA's latest outlook.

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 Price Forecasting

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 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

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

Palm Oil rose to 4,254 MYR/T on August 8, 2025, up 0.31% from the previous day. Over the past month, Palm Oil's price has risen 2.28%, and is up 13.56% 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 August of 2025.

Canada NEB Forecast: Crude Oil Production: Light: Condensate data was reported at 69,074.925 Cub m/Day in Dec 2020. This records a decrease from the previous number of 70,893.375 Cub m/Day for Nov 2020. Canada NEB Forecast: Crude Oil Production: Light: Condensate data is updated monthly, averaging 66,574.463 Cub m/Day from Aug 2017 (Median) to Dec 2020, with 41 observations. The data reached an all-time high of 71,591.922 Cub m/Day in Oct 2020 and a record low of 48,459.534 Cub m/Day in Aug 2017. Canada NEB Forecast: Crude Oil Production: Light: Condensate data remains active status in CEIC and is reported by Canada Energy Regulator. The data is categorized under Global Database’s Canada – Table CA.RB022: Crude Oil Production: Forecast.

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 113 million barrels per day by 2030, while the IEA's stated policies scenario anticipates demand reaching only 101.7 million barrels per day in the same year. Diesel and gasoil currently account for the largest share of oil product demand at 28.38 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.

Find out how various factors such as supply and demand, geopolitical events, weather conditions, and economic factors influence the daily fluctuations in crude oil prices and their impact on the global economy.