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.

Crude Oil rose to 68.75 USD/Bbl on July 11, 2025, up 3.27% from the previous day. Over the past month, Crude Oil's price has risen 1.04%, but it is still 16.37% 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.

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 70.45 USD/Bbl on July 14, 2025, up 0.12% from the previous day. Over the past month, Brent's price has fallen 3.80%, and is down 16.98% 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 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: 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

Deterministic and stochastic are two methods for modeling of crude oil and bottled water market. Forecasting the price of the market directly affected energy producer and water user.There are two software, Tableau and Python, which are utilized to model and visualize both markets for the aim of estimating possible price in the future.The role of those software is to provide an optimal alternative with different methods (deterministic versus stochastic). The base of predicted price in Tableau is deterministic—global optimization and time series. In contrast, Monte Carlo simulation as a stochastic method is modeled by Python software. The purpose of the project is, first, to predict the price of crude oil and bottled water with stochastic (Monte Carlo simulation) and deterministic (Tableau software),second, to compare the prices in a case study of Crude Oil Prices: West Texas Intermediate (WTI) and the U.S. bottled water. 1. Introduction Predicting stock and stock price index is challenging due to uncertainties involved. We can analyze with a different aspect; the investors perform before investing in a stock or the evaluation of stocks by means of studying statistics generated by market activity such as past prices and volumes. The data analysis attempt to identify stock patterns and trends that may predict the estimation price in the future. Initially, the classical regression (deterministic) methods were used to predict stock trends; furthermore, the uncertainty (stochastic) methods were used to forecast as same as deterministic. According to Deterministic versus stochastic volatility: implications for option pricing models (1997), Paul Brockman & Mustafa Chowdhury researched that the stock return volatility is deterministic or stochastic. They reported that “Results reported herein add support to the growing literature on preference-based stochastic volatility models and generally reject the notion of deterministic volatility” (Pag.499). For this argument, we need to research for modeling forecasting historical data with two software (Tableau and Python). In order to forecast analyze Tableau feature, the software automatically chooses the best of up to eight models which generates the highest quality forecast. According to the manual of Tableau , Tableau assesses forecast quality optimize the smoothing of each model. The optimization model is global. The main part of the model is a taxonomy of exponential smoothing that analyzes the best eight models with enough data. The real- world data generating process is a part of the forecast feature and to support deterministic method. Therefore, Tableau forecast feature is illustrated the best possible price in the future by deterministic (time – series and prices). Monte Carlo simulation (MCs) is modeled by Python, which is predicted the floating stock market index . Forecasting the stock market by Monte Carlo demonstrates in mathematics to solve various problems by generating suitable random numbers and observing that fraction of the numbers that obeys some property or properties. The method utilizes to obtain numerical solutions to problems too complicated to solve analytically. It randomly generates thousands of series representing potential outcomes for possible returns. Therefore, the variable price is the base of a random number between possible spot price between 2002-2016 that present a stochastic method.

Heating Oil fell to 2.37 USD/Gal on July 15, 2025, down 0.96% from the previous day. Over the past month, Heating Oil's price has fallen 3.34%, and is down 4.06% 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 July of 2025.

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 first quarter of 2025 for Crude Oil prices in the North American region experienced a decline followed by an uptrend. In January 2025 oil prices maintained an upward trajectory.

Quantification and analysis of global oil trade networks reveals deep insights into a nation's development and influence at a global scale. Further

Stocks of crude oil in the United States increased by 7.07million barrels in the week ending July 4 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.

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.

DJ Commodity Heating Oil index: Analysts Predict Moderate Price Fluctuations

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

Sunflower Oil rose to 1,245.50 INR/10 kg on July 14, 2025, up 0.61% from the previous day. Over the past month, Sunflower Oil's price has fallen 3.04%, but it is still 37.43% 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.

The United States is seeing a growing demand for refined olive oil, with market consumption expected to continue to rise over the next decade. Forecasts predict a steady increase in market volume and value, with a projected increase of 807K tons and $2.4B by 2035.

The WTI Crude Oil Futures Chart provides a visual representation of the historical price movements of West Texas Intermediate (WTI) crude oil. Traders, investors, analysts, and industry professionals utilize the chart to analyze price trends and make informed decisions about buying or selling oil futures contracts. By utilizing technical analysis techniques, users can gain insights into past price behavior and potentially predict future price movements. The chart also offers additional tools and features to

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.

Junior Oil Index: Analysts Predict Continued Growth Amidst Shifting Market Dynamics

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

Natural gas account for 1/4 of the global demand and roughly 1/3 of the US energy demand. After oil, Natural gas is the most dominate sort of energy. So, being about to improve natural gas demand prediction is extremely valuable.

Therefore, this project aims to predict the demand of Natural Gas in the US by combining a wide range of datasets including the time series of major Natural Gas Prices including US Henry Hub. Data comes from U.S. Energy Information Administration. Need to forecast the price of natural gas based on the historical data.

Data

Dataset contains Daily prices of Natural gas, starting from January 1997 to current year. Prices are in nominal dollars.

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.

Junior Oil Index Poised for Moderate Growth, Analysts Predict

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

Natural gas rose to 3.36 USD/MMBtu on July 11, 2025, up 0.58% from the previous day. Over the past month, Natural gas's price has fallen 3.89%, but it is still 44.10% higher than a year ago, 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 July of 2025.

The price of heavy crude oil is influenced by supply and demand dynamics, sulfur content, geopolitical factors, and market sentiment. This article discusses the various factors that affect the price of heavy crude oil and provides insights for market participants to assess and predict its future price movements.