Crude Oil fell to 59.17 USD/Bbl on December 2, 2025, down 0.25% from the previous day. Over the past month, Crude Oil's price has fallen 3.08%, and is down 15.40% 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 December of 2025.

This dataset contains historical daily price data for WTI (West Texas Intermediate) and Brent Crude Oil futures contracts. The data spans from April 5, 2017, to April 10, 2024, and includes key pricing information such as opening, closing, high, low, average prices, and volume for each trading day. The data was sourced using the Interactive Brokers API and includes futures contract details for both WTI and Brent Crude Oil traded on the NYMEX exchange. This dataset can be used for time series analysis, forecasting, and other financial applications related to the oil market.

On October 27, 2025, the Brent crude oil price stood at 65.14 U.S. dollars per barrel, compared to 61.31 U.S. dollars for WTI oil and 67.54 U.S. dollars for the OPEC basket. Oil prices rose slightly that week.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 global 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 63.05 USD/Bbl on December 2, 2025, down 0.19% from the previous day. Over the past month, Brent's price has fallen 2.84%, and is down 14.36% 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 December of 2025.

WTI Crude Oil Futures data, recent 36 years (traceable to Jan 02,1990), the unit is USD/bbl, latest value is 59.84, updated at Nov 07,2025

Brent crude oil is projected to have an average annual spot price of 67.22 U.S. dollars per barrel in 2025, according to a forecast from May 2025. This would mean a decrease of more than 13 U.S. dollars compared to the previous year and also reflect 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 futures price live refers to the current and real-time price of crude oil futures contracts. Monitoring the live price of crude oil futures helps stakeholders make informed decisions and navigate the dynamic and volatile oil market.

The annual price of West Texas Intermediate (WTI) crude oil is expected to reach an average of 63.58 U.S. dollars per barrel in 2025, according to an August 2025 forecast. This would be a decrease of roughly 13 U.S. dollar compared to the previous year. In the first eight months of 2025, weekly crude oil prices largely stayed below 70 U.S. dollars per barrel amid trade tariffs and an 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.

Context

Crude oil is the world's most critical energy source and one of the most actively traded commodities. Its price is a fundamental driver of the global economy, influencing everything from transportation costs and industrial production to inflation rates and geopolitical policy. This dataset tracks the price of West Texas Intermediate (WTI) crude oil, a high-quality grade that serves as a primary global benchmark.

Access to reliable, long-term historical data is crucial for economists, traders, and data scientists seeking to model market dynamics, analyze the impact of world events, and forecast economic trends. This dataset provides a comprehensive and daily-updated record of crude oil prices, sourced from the Crude Oil Futures (CL=F) market.

Content

This dataset contains daily price information for Crude Oil Futures (CL=F) in a clean, tabular format. Each row represents a single trading day and includes the following columns:

Date: The date of the trading session (YYYY-MM-DD).

Open: The price at which crude oil first traded for the day in USD per barrel.

High: The highest price reached during the trading day in USD per barrel.

Low: The lowest price reached during the trading day in USD per barrel.

Close: The closing price at the end of the trading day in USD per barrel.

Volume: The total number of futures contracts traded during the day.

The WTI Crude Oil Futures Price Chart provides a visual representation of the historical prices of West Texas Intermediate (WTI) crude oil futures. Traders, investors, and analysts can use this chart to track price movements, study historical price patterns, and compare current prices with historical data. Additionally, the chart may include features such as trading volume and open interest to provide further insights into the market. However, it is important to consider other factors such as geopolitical e

The dataset includes monthly WTI crude oil spot and futures prices with the shortest maturity contracts (one-month, two-month, and three-month futures contracts), the US Ending Stocks of Crude Oil and Petroleum Products in thousands of barrels. All the datasets were sourced from US EIA, except for the three-month US treasury bill dataset sourced from the Federal Reserve Economic Data of St. Louis Federal Bank.

Heating Oil rose to 2.35 USD/Gal on December 2, 2025, up 0.21% from the previous day. Over the past month, Heating Oil's price has fallen 2.25%, but it is still 6.31% 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 December of 2025.

US oil price futures are contracts that allow participants to speculate on the future price of crude oil. They serve as a vital tool for risk management and investment in the global oil market. The benchmark contract for US oil price futures is WTI crude oil, and trading occurs on regulated exchanges.

Annual futures contract three price for Cushing Oklahoma crude oil stood at 67.3 U.S. dollars per barrel in 2021, an increase compared to the previous years. During the period in consideration, figures peaked at over 100 U.S. dollars per barrel in 2008.

Brent Crude Oil Futures data, recent 36 years (traceable to Jan 02,1990), the unit is USD/bbl, latest value is 63.7, updated at Nov 07,2025

Murban Crude Oil Futures data, recent 5 years (traceable to Mar 29,2021), the unit is USD/bbl, latest value is 65.95, updated at Nov 07,2025

Introduction:

Crude oil is an essential commodity that is used in almost every aspect of modern life. Its price fluctuations have a significant impact on global economies, especially for countries that heavily depend on oil exports. In this dataset, we will explore the fluctuations of crude oil prices over the past 48 years, from 1974 to 2022, and examine the key factors that have influenced these fluctuations. The dataset provides a valuable resource for researchers and analysts interested in studying the fluctuations in crude oil prices over the past four decades. It can be used to identify patterns and trends in the market, as well as to develop predictive models for future price movements.

This dataset can be used for various purposes, such as:

• Analyzing the trends and fluctuations in crude oil prices over time.
• Identifying the factors that affect crude oil prices, such as supply and demand, geopolitical events, and economic indicators.
• Developing predictive models to forecast crude oil prices.
• Evaluating the impact of crude oil prices on the global economy, energy markets, and individual businesses.

Overall, the Fluctuations of Crude Oil Price dataset is a valuable resource for anyone interested in understanding the dynamics of the global oil market.

Abstract The recent movement of oil prices has brought many forecasts about what is coming in the near future. This is natural since the plunge in prices has been dramatic after 2014 and oil is an essential source of energy worldwide. This paper examines the probabilities of spot price scenarios. We model prices through stochastic processes focusing on the Schwartz-Smith model. The calibration is based on the term structure of future prices. Since the conditional distribution is log-normal we define the probability of a certain value of the spot price in a given time horizon. We found that the recovery of crude oil prices will be slow in the next four years. Moreover, the scenario of prices under US$ 20/barrel has the same probability as being greater than US$ 50/barrel. The methodology has many applications, mainly for government planning and for oil companies in their capital budget decisions.

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.

Oil shocks exert influence on macroeconomic activity through various channels, many of which imply a symmetric effect. However, the effect can also be asymmetric. In particular, sharp oil price changes "either increases or decreases" may reduce aggregate output temporarily because they delay business investment by raising uncertainty or induce costly sectoral resource reallocation. Consistent with these asymmetric-effect hypotheses, the authors find that a volatility measure constructed using daily crude oil futures prices has a negative and significant effect on future gross domestic product (GDP) growth over the period 1984-2004. Moreover, the effect becomes more significant after oil price changes are also included in the regression to control for the symmetric effect. The evidence here provides economic rationales for Hamilton's (2003) nonlinear oil shock measure: It captures overall effects, both symmetric and asymmetric, of oil price shocks on output.