Heating Oil rose to 2.31 USD/Gal on September 1, 2025, up 1.65% from the previous day. Over the past month, Heating Oil's price has fallen 0.45%, but it is still 1.17% 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 September of 2025.

Heating Oil Stocks in the United States increased to 102 Thousand Barrels in August 22 from -503 Thousand Barrels in the previous week. This dataset provides - United States Heating Oil Stocks - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Heating oil price in the United States has peaked in winter 2022/23 at 4.31 U.S. dollars per gallon and has decreased ever since. Heating oil is a liquid petroleum product that is, among other things, used in residential buildings as a fuel oil in furnaces or boilers. Chemically, most heating oils are similar to motor diesel fuels and are often sold interchangeably. Forecast heating price in the U.S. The average price of heating oil in the United States in the winter of 2024/25 is expected to reach 3.44 U.S. dollars per gallon. Energy prices are projected to see a decrease this winter, because of increased production of heating fuels. The number of heating degree days, which are the days in which the average temperature is below 18 degrees Celsius (65 degrees Fahrenheit), also helps quantify the energy demand required to heat a building. What determines heating oil price? Generally, heating oil prices are collected during the heating season between October and March. In the U.S., the greatest determining factor for heating oil prices is the WTI crude oil price. Consumers can lower heating oil bills by considering when they purchase, reducing consumption, and through government assistance programs.

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The fuel oil market size will decrease by USD 84.77 billion during 2020-2024. This report provides a detailed analysis of the market by application (marine, industrial, and others) and geography (APAC, Europe, MEA, North America, and South America). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including BP Plc, Chevron Corp., Exxon Mobil Corp., JXTG Holdings Inc., PJSC LUKOIL, PT Pertamina(Persero), Qatar Petroleum, Reliance Industries Ltd., Royal Dutch Shell Plc, and SK Innovation Co. Ltd.

Market Overview

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Market Competitive Analysis

The fuel oil market is fragmented with numerous vendors that produce and supply fuel oil to customers. Vendors need to make high capital investments to remain competitive in the market. BP Plc, Chevron Corp., and Exxon Mobil Corp. are some of the major market participants. Although the rise in world energy demand will offer immense growth opportunities, the fluctuations in crude oil prices will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this fuel oil market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and offers information on the products offered by various companies. Moreover, this fuel oil market analysis report also provides information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

This report provides information on the production, sustainability, and prospects of several leading companies, including:

• BP Plc
• Chevron Corp.
• Exxon Mobil Corp.
• JXTG Holdings Inc.
• PJSC LUKOIL
• PT Pertamina(Persero)
• Qatar Petroleum
• Reliance Industries Ltd.
• Royal Dutch Shell Plc
• SK Innovation Co. Ltd.

Fuel Oil Market: Segmentation by Application

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The primary requirement of any marine engine is to propel the ship or generate onsite power by using the energy obtained from burning fuel oil. The mega marine engines of ships burn tons of fuel every day to propel the massively loaded ships. The rise in demand for bunker fuel oil due to the growing seaborne trade and growing naval activities will drive the demand for fuel oil for marine.

However, market growth in this segment will be slower than the growth of the market in the industrial and other segments. This report provides an accurate prediction of the contribution of all the segments to the growth of the fuel oil market size.

Fuel Oil Market: Segmentation by Geography

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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.

Is the Heating Oil Index a Reliable Indicator of Market Trends?

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

On August 18, 2025, the Brent crude oil price stood at 66.54 U.S. dollars per barrel, compared to 63.42 U.S. dollars for WTI oil and 68.21 U.S. dollars for the OPEC basket. Oil prices remained largely unchanged that week as economic expectations stayed low.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 (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.

API Heating Oil in the United States increased to -0.43 BBL/1Million in April 12 from -0.47 BBL/1Million in the previous week. This dataset provides - United States API Heating Oil- actual values, historical data, forecast, chart, statistics, economic calendar and news.

23899 Global export shipment records of Heating Oil with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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.

Heating Oil TR/CC CRB Forecast Predicts Volatile Trading for Coming 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

29 Global import shipment records of Heating Oil with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset contains Production, Trade, and Supply of Fuel Oil 1990-2020. Data from United Nations Statistics Division. Follow datasource.kapsarc.org for timely data to advance energy economics research.Notes: - Refer back to the original source for numbers estimated by the United Nations Statistics Division (numbers with * symbol).- Production includes output from refineries and plants.- Please refer to the Definitions Section on pages ix to xv for the appropriate product description/ classification, and xvi to xvii for the descriptions of relevant flows.

This dataset provides values for HEATING OIL reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Global Heating Oil Additives market size is expected to reach $8.53 billion by 2029 at 6.7%, soaring automobile manufacturing fuels expansion in the heating oil additives market

26 Active Global Heating Oil buyers list and Global Heating Oil importers directory compiled from actual Global import shipments of Heating Oil.

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 Faces Uncertain Outlook

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

Snapshot img { margin: 10px !important; } The heavy fuel oil market size will decrease by USD 52.68 billion during 2020-2024, and the market’s growth momentum will accelerate during the forecast period.

This report provides a detailed analysis of the market by end-user (shipping and others) and geography (APAC, Europe, MEA, North America, and South America). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including BP Plc, Chevron Corp., Exxon Mobil Corp., Indian Oil Corp. Ltd., Neste Oyj, PetroChina Co. Ltd., Qatar Petroleum, Rosneft Oil Co., Royal Dutch Shell Plc, and TOTAL SA.

Market Overview

Market Competitive Analysis

The market is fragmented, and the degree of fragmentation will remain the same during the forecast period. PetroChina Co. Ltd., Qatar Petroleum, Rosneft Oil Co., Royal Dutch Shell Plc, and TOTAL SA are some of the major market participants. Although the rising seaborne trade will offer growth opportunities, the implementation of MARPOL regulations will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this heavy fuel oil market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and offers information on the products offered by various companies. Moreover, this heavy fuel oil market analysis report also provides information on the upcoming trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

This report provides information on the production, sustainability, and prospects of several leading companies, including:

BP Plc
Chevron Corp.
Exxon Mobil Corp.
Indian Oil Corp. Ltd.
Neste Oyj
PetroChina Co. Ltd.
Qatar Petroleum
Rosneft Oil Co.
Royal Dutch Shell Plc
TOTAL SA

Heavy Fuel Oil Market: Segmentation by Region

APAC had the largest heavy fuel oil market share in 2019. The growing requirement for energy and the growth in seaborne trade will influence the demand for heavy fuel oil in this region.

37% of the market’s decremental growth will originate from APAC during the forecast period. Singapore and China are the key markets for heavy fuel oil in APAC.

Heavy Fuel Oil Market: Segmentation by End-user

Heavy oil is highly preferred in the marine segment as the energy obtained from burning heavy fuel oil inside a combustion chamber rotates the propeller of the ship, thus propelling the vessel.

Market growth in this segment will be slower than the growth of the market in the others’ segment. This report provides an accurate prediction of the contribution of all the segments to the growth of the heavy fuel oil market size.

Heavy Fuel Oil Market: Key Drivers and Trends

The increasing industrialization and liberalization of national economies have fueled the demand for consumer products, thus enhancing trade activities. Heavy fuel oil is mainly used in the shipping industry as marine fuel. It is used to generate motion as well as heat and has high density and viscosity. Furthermore, seaborne transport is a key component of globalization that enables international trade and support supply chains, and also plays a crucial role in cross-border transportation. It further nurtures industrial development by supporting manufacturing growth, bringing together consumers and industries, and promoting regional economic and trade integration. Additionally, the growth in the availability of shipping data and application of Big data analytics in the shipping industry also provides greater visibility into the market as well as the pricing trends. The rise in seaborne trade activities will significantly influence the growth of the heavy fuel oil market during the forecast period.

Scrubbers are used to remove particulate matters and harmful gases, which are generated as a result of combustion processes in marine engines in order to implement pollution control into the environment.
Companies that manufacture marine scrubbers are forming collaborations within the value chain to improve the technology and simplify the installation process.
The scrubbing systems are manufactured and installed to treat exhaust from engines, onshore and onboard marine vessels, auxiliary engines and boilers, to ensure that no damage is done to human life and environment by toxic chemicals.
The development of scrubber technology fuels the use of high-sulfur heavy fuel oil.
During 2020-2024, the market will register a CAGR of over (15)%.

Heavy Fuel Oil Market: Key Highlights of the Report for the Forecast Period 2020-2024

CAGR of the market durin

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

339554 Global exporters importers export import shipment records of Fuel oil with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

United States - No. 2 Heating Oil Prices: New York Harbor was 2.32400 $ per Gallon in July of 2025, according to the United States Federal Reserve. Historically, United States - No. 2 Heating Oil Prices: New York Harbor reached a record high of 4.49700 in May of 2022 and a record low of 0.30400 in February of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - No. 2 Heating Oil Prices: New York Harbor - last updated from the United States Federal Reserve on September 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.

Heating Oil TR/CC CRB Spot Market Signals Potential Price Volatility.

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