Crude Oil WTI Index is a benchmark index used to track the price of West Texas Intermediate (WTI) crude oil. It provides market participants with a transparent and reliable measure of the price performance of WTI crude oil, which is one of the most widely traded oil futures contracts in the world.

Interactive chart showing the daily closing price for West Texas Intermediate (NYMEX) Crude Oil over the last 10 years. The prices shown are in U.S. dollars.

Crude Oil rose to 64.67 USD/Bbl on June 9, 2025, up 0.13% from the previous day. Over the past month, Crude Oil's price has risen 4.39%, but it is still 16.82% 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 June of 2025.

The WTI (West Texas Intermediate) oil index is a benchmark for crude oil prices in the United States. It serves as a reference point for pricing oil contracts and other financial instruments in the energy market. This article explains how the index is calculated, its importance for market participants, and its broader implications for the global economy.

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

On June 2, 2025, the Brent crude oil price stood at 64.5 U.S. dollars per barrel, compared to 62.52 U.S. dollars for WTI oil and 65.13 U.S. dollars for the OPEC basket. Crude oil prices were some of the lowest they had been since February 2021.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 April 2025, the price for one barrel of West Texas Intermediate (WTI) crude oil averaged 63.54 U.S. dollars. This was a decrease compared to the previous month amid continued weak demand outlooks and expectations for production increases. WTI and other benchmark crudes WTI is also known as "Texas light sweet", and is a grade of crude oil used as a benchmark for oil produced in the United States. It has an API gravity of around 39.6 and specific gravity of about 0.827, which, relative to other crude oils, is considered “light,” hence the name. WTI also contains about 0.24 percent sulfur, making it a “sweet” crude oil. The price of WTI can be compared to the prices other of crude oils, i.e. UK Brent, the OPEC basket, and Dubai Fateh oil. WTI crude oil is the underlying commodity of the Chicago Mercantile Exchange’s oil futures contracts. U.S. oil production and its influence on light oil prices The price development of WTI crude oil relative to Brent crude oil has been influenced by variances in U.S. crude oil transportation and increased U.S. oil production. New transportation infrastructure became operational in early 2013, easing the movement of crude oil in the mid-continent and raising the price of WTI. Since then, U.S. refineries have increased production of crude oil to record levels, also raising the price of WTI. Meanwhile, expedited crude transport in the U.S. put downward pressure on Brent crude oil as domestic crude replaced some imported Brent crude. Between 2014 and 2016, UK Brent prices dropped rapidly, as was the case for all other crude oils.

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.

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

The WTI (West Texas Intermediate) oil futures price chart provides historical price data for WTI crude oil futures contracts. It offers insights into market trends, volatility, and allows users to analyze the data using technical indicators. Traders can identify patterns and opportunities, monitor support and resistance levels, and compare oil prices to other financial instruments or indices.

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: Will it Soar or Sink?

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

Graph and download economic data for CBOE Crude Oil ETF Volatility Index (OVXCLS) from 2007-05-10 to 2025-06-05 about ETF, VIX, volatility, crude, oil, stock market, and USA.

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 Triple-Leveraged USD Index: A Volatile Ride?

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

An oil price market index is a measure of the average price of a set of crude oils in the global market. It represents the value of the oil traded on a specific exchange or market and serves as a benchmark for pricing and trading purposes in the oil industry. This article discusses various oil price market indexes, including the well-known West Texas Intermediate (WTI) and Brent crude oil price indexes, and highlights the importance of these indexes for producers, consumers, and investors in the oil industr

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.

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 Forecast

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

Japan Wholesale Trade Index (WTI): 2000=100: sa data was reported at 104.100 2000=100 in Feb 2007. This records a decrease from the previous number of 105.700 2000=100 for Jan 2007. Japan Wholesale Trade Index (WTI): 2000=100: sa data is updated monthly, averaging 94.800 2000=100 from Jan 1999 (Median) to Feb 2007, with 98 observations. The data reached an all-time high of 105.700 2000=100 in Jan 2007 and a record low of 86.400 2000=100 in Feb 2002. Japan Wholesale Trade Index (WTI): 2000=100: sa data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H003: Wholesale and Retail Trade Index: Seasonally Adjusted. Rebased from 2000=100 to 2005=100. Replacement Series ID: 136512201

As of April 2025, the average annual price of Brent crude oil stood at 73.89 U.S. dollars per barrel. This is some seven U.S. dollars lower than the 2024 average. Brent is the world's leading price benchmark for Atlantic basin crude oils. Crude oil is one of the most closely observed commodity prices as it influences costs across all stages of the production process and consequently alters the price of consumer goods as well. What determines crude oil benchmarks? In the past decade, crude oil prices have been especially volatile. Their inherent inelasticity regarding short-term changes in demand and supply means that oil prices are erratic by nature. However, since the 2009 financial crisis, many commercial developments have greatly contributed to price volatility; such as economic growth by BRIC countries like China and India, and the advent of hydraulic fracturing and horizontal drilling in the U.S. The outbreak of the coronavirus pandemic and the Russia-Ukraine war are examples of geopolitical events dictating prices. Light crude oils - Brent and WTI Brent Crude is considered a classification of sweet light crude oil and acts as a benchmark price for oil around the world. It is considered a sweet light crude oil due to its low sulfur content and a low density and may be easily refined into gasoline. This oil originates in the North Sea and comprises several different oil blends, including Brent Blend and Ekofisk crude. Often, this crude oil is refined in Northwest Europe. Another sweet light oil often referenced alongside UK Brent is West Texas Intermediate (WTI). WTI oil prices amounted to 76.55 U.S. dollars per barrel in 2024.

Japan Wholesale Trade Index (WTI): 2000=100 data was reported at 96.200 2000=100 in Feb 2007. This records an increase from the previous number of 90.700 2000=100 for Jan 2007. Japan Wholesale Trade Index (WTI): 2000=100 data is updated monthly, averaging 92.900 2000=100 from Jan 1999 (Median) to Feb 2007, with 98 observations. The data reached an all-time high of 139.800 2000=100 in Mar 1999 and a record low of 76.500 2000=100 in Jan 2003. Japan Wholesale Trade Index (WTI): 2000=100 data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.H002: Wholesale and Retail Trade Index. Rebased from 2000=100 to 2005=100. Replacement Series ID: 136510001

The p values of PP test of WTI, Brent and 11 Chinese sector volatilities estimated from GARCH models.