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.

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.

Goldman Sachs updates its Brent and WTI crude oil price forecasts, projecting significant declines by 2026 due to economic and geopolitical influences.

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.

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

United States EIA Forecast: Crude Oil Price: WTI Spot Average data was reported at 67.000 USD/Barrel in Dec 2019. This stayed constant from the previous number of 67.000 USD/Barrel for Nov 2019. United States EIA Forecast: Crude Oil Price: WTI Spot Average data is updated monthly, averaging 58.250 USD/Barrel from Mar 2016 (Median) to Dec 2019, with 46 observations. The data reached an all-time high of 74.000 USD/Barrel in Oct 2018 and a record low of 32.000 USD/Barrel in Mar 2016. United States EIA Forecast: Crude Oil Price: WTI Spot Average data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s United States – Table US.P003: Energy Price: Forecast: Energy Information Administration.

Graph and download economic data for Spot Crude Oil Price: West Texas Intermediate (WTI) (WTISPLC) from Jan 1946 to Jul 2025 about WTI, crude, oil, price, and USA.

Why did the Crude Oil Price Change in July 2025? Crude oil prices declined by 10.2 on % quarter-on-quarter basis. Prices have declined in April and May; however, in June, crude oil prices rebounded to settle at WTI at USD 68.04/barrel by the last week of June—the highest since January.

This dataset contains Oil price short term forecast, including STEO and historical from 2016-2022. Data from U.S. Energy Information Administration. Follow datasource.kapsarc.org for timely data to advance energy economics research.

Urals Oil fell to 63.17 USD/Bbl on August 7, 2025, down 2.76% from the previous day. Over the past month, Urals Oil's price has fallen 4.94%, and is down 15.37% 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.

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.

Learn about the various factors that influence the forecast for WTI (West Texas Intermediate) crude oil, including geopolitical tensions, OPEC & non-OPEC production, economic outlook, supply and demand dynamics, crude oil inventories, environmental and renewable energy policies, and speculative trading. Understand the challenges of accurate forecasting and the importance of consulting a professional before making investment decisions.

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.

The subject of predicting global crude oil prices is well recognized in academic circles. The notion of hybrid modeling suggests that the integration of several methodologies has the potential to optimize advantages while reducing limitations. Consequently, hybrid techniques are extensively used in contemporary research. In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). Furthermore, the aforementioned approaches, namely BBO-MLP, BSA-MLP, and TLBO-MLP, include the de-compose-ensemble technique into the individual artificial intelligence model in order to enhance the accuracy of predictions. In order to validate the findings, the forecast is conducted using the authoritative data on oil prices. This study will use three primary indicators, including EMA 20, EMA 60, EMA 100, ROC, and AUC assessments, to assess and evaluate the efficacy of the five methodologies under investigation. The below findings are derived from the conducted research: Based on the achieved AUC values of 0.9567 and 0.9429, it can be concluded that using a multi-verse optimization technique is considered the most suitable strategy for effectively handling the dataset pertaining to crude oil revenue. The next four approaches likewise have a significant AUC value, surpassing 0.8. The AUC values for the BBO-MLP, BSA-MLP, TLBO-MLP, and COA-MLP approaches were obtained as follows: (0.874 and 0.792) for training and testing stages, (0.809 and 0.792) for training and testing stages, (0.9353 and 0.9237) for training and testing stages, and (0.9092 and 0.8927) for training and testing stages, respectively. This model has the potential to contribute to the resolution of default probability and is very valuable to the credit card industry. Broadly speaking, this novel forecasting approach serves as a notable predictor of crude oil prices.

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.

In June 2025, the average price of one barrel of Brent crude oil was 71.44 U.S. dollars. This was around seven U.S. dollars more than prices the previous month, which were the lowest value in the past 24-month period. Brent terminology and most common uses Brent is the world's leading price benchmark for Atlantic basin crude oils. It is used to price two thirds of the internationally traded crude oil supplies and is also the most significant crude oil benchmark for Europe. Brent crude originates in the North Sea and includes oils from Brent and Forties Oil Field in the United Kingdom, and from the Oseborg and Ekofisk oil fields, both oil reserves in Norway. Other names for Brent are Brent Blend, London Brent and Brent petroleum. The name Brent comes from the Brent oil field, located north-east of the Shetland Islands, and thus part of the United Kingdom. Because the Brent oil field already passed its production peak, today the benchmark Brent includes oil from the other three major oil fields. Brent, next to West Texas Intermediate (WTI), is one of the lightest crude oils. With a low content of sulfur, it is ranged among the so-called sweet crude oils. Most of the Brent crude oil is refined into gasoline and middle distillates in Northwest Europe. Benchmark oil prices Other crucial benchmarks for crude oil prices are the already mentioned U.S.- WTI and Dubai Crude (Fateh). They are indispensable for referencing the many types and grades of oil on the global market. In the past 20 years, the annual price for one barrel of Brent crude oil saw a net increase. For example, the average price per barrel stood at 80.53 U.S. dollars in 2024.

United States - Spot Crude Oil Price: West Texas Intermediate (WTI) was 68.17000 $ per Barrel in June of 2025, according to the United States Federal Reserve. Historically, United States - Spot Crude Oil Price: West Texas Intermediate (WTI) reached a record high of 133.93000 in June of 2008 and a record low of 1.17000 in February of 1946. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Spot Crude Oil Price: West Texas Intermediate (WTI) - last updated from the United States Federal Reserve on August of 2025.

Discover the potential for gains in crude oil prices with October micro futures, backed by economic resilience and a bullish market trend.

Natural gas fell to 2.98 USD/MMBtu on August 11, 2025, down 0.47% from the previous day. Over the past month, Natural gas's price has fallen 14.14%, but it is still 35.95% 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 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.

WTI Futures x3 Leveraged USD Index: The Next Big 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