Crude Oil fell to 66.61 USD/Bbl on July 3, 2025, down 1.24% from the previous day. Over the past month, Crude Oil's price has risen 5.99%, but it is still 20.72% lower than a year ago, 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 July 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.

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.

Heating Oil rose to 2.37 USD/Gal on July 4, 2025, up 0.19% from the previous day. Over the past month, Heating Oil's price has risen 13.32%, but it is still 9.47% lower 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 July of 2025.

Brent fell to 68.86 USD/Bbl on July 3, 2025, down 0.36% from the previous day. Over the past month, Brent's price has risen 6.17%, but it is still 21.23% lower than a year ago, 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 July of 2025.

On June 30, 2025, the Brent crude oil price stood at 66.64 U.S. dollars per barrel, compared to 65.11 U.S. dollars for WTI oil and 68.35 U.S. dollars for the OPEC basket. OPEC prices fell that week as concerns over supply constraints related to the Israel-Iran conflict eased.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.

In addition to their theoretical analysis of the joint determination of oil futures prices and oil spot prices, Alquist and Kilian (Journal of Applied Econometrics, 2010, 25(4), 539-573) compare the out-of-sample accuracy of the random walk forecast with that of forecasts based on oil futures prices and other predictors. The results of my replication exercise are very similar to the original forecast accuracy results, but the relative accuracy of the random walk forecast and the futures-based forecast changes when the sample is extended to August 2016, consistent with the results of several other recent studies by Kilian and co-authors.

Palm Oil rose to 4,108 MYR/T on July 4, 2025, up 0.37% from the previous day. Over the past month, Palm Oil's price has risen 5.23%, and is up 1.68% 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 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.

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.

Urals Oil rose to 64.31 USD/Bbl on July 3, 2025, up 1.18% from the previous day. Over the past month, Urals Oil's price has risen 8.43%, but it is still 21.00% lower than a year ago, 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.

Replication package for "A Reappraisal of Real-Time Forecasts of the Real Price of Oil"Abstract: We replicate Baumeister and Kilian (2012) to reappraise real-time forecasts of the real price of crude oil against the end-of-month no-change forecast, the equivalent naive benchmark used for asset prices. We find no consistently significant improvements in the predictive accuracy of model-based forecasts over this naive benchmark at short horizons. Only futures-based forecasts consistently outperform the end-of-month no-change forecast, and only at longer horizons. These results challenge the consensus on the predictability of the real price of crude oil and the merits of alternative forecast approaches. Our findings motivate broader reassessment and replication of forecasting models of temporally aggregated series.

The price of crude coconut oil in the USA hit 1,147 USD/MT in December 2023. In Germany, crude coconut oil prices reached 1,416 USD/MT during December 2023. The role of production costs had been crucial in maintaining price stability, ending the year at around 9,68 USD/MT in Indonesia during December 2023.

Get the latest insights on price movement and trend analysis of Fuel Oil in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

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.

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.

WTI Futures x3 Leveraged USD Index: The Ultimate Oil Play?

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

In Q1 2025, U.S. sunflower oil prices reflected significant volatility, shaped by evolving supply conditions, shifting demand patterns, and broader macroeconomic pressures. January began with a strong upward trajectory as constrained global sunflower seed production—driven by geopolitical tensions and adverse weather in Ukraine and Russia—led to tighter supply. The USDA projected a 10% drop in global output for the 2024/25 season, while rising soybean oil futures further supported sunflower oil’s price surge.

Sunflower Oil rose to 1,240 INR/10 kg on July 2, 2025, up 0.51% from the previous day. Over the past month, Sunflower Oil's price has fallen 2.05%, but it is still 33.42% higher than a year ago, 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 Sunflower Oil.

Get the latest insights on price movement and trend analysis of Rapeseed Oil in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).