Heating Oil Stocks in the United States increased to 603 Thousand Barrels in July 4 from -202 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 rose to 2.47 USD/Gal on July 11, 2025, up 3.46% from the previous day. Over the past month, Heating Oil's price has risen 11.10%, but it is still 1.60% lower 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 July of 2025.

Graph and download economic data for No. 2 Heating Oil Prices: New York Harbor (WHOILNYH) from 1986-06-06 to 2025-06-13 about new york harbor, heating, New York, oil, commodities, 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.

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

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 Index Forecast: Slight Uptick Predicted

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

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

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

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

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 Index Anticipates Seasonal 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

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

In 2023, the tank stock of fuel oil in Hong Kong amounted to 229.83 megaliter. Vast majority of energy in Hong Kong was derived from external sources.

On July 7, 2025, the Brent crude oil price stood at 69.62 U.S. dollars per barrel, compared to 67.93 U.S. dollars for WTI oil and 69.92 U.S. dollars for the OPEC basket. Prices rose slightly that week, following signs of an increase in demand.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.

The global oil inventory chart provides valuable insights into the levels of oil stockpiles worldwide. It tracks the inventory of crude oil and petroleum products, such as gasoline, diesel, and heating oil. The chart is crucial for understanding the supply and demand dynamics of the oil market and is closely monitored by industry experts, governments, and investors.

In 2024, around 56 percent of dwellings in Germany were heated with gas. Gas heating figures were mostly unchanged in recent years. Around 17.3 percent of dwellings used heating oil, which makes it the second-most relevant heating technology in Germany.

The datasets for the Role of Financial Investors on Commodity Futures Risk Premium are weekly datasets for the period from 1995 to 2015 for three commodities in the energy market: crude oil (WTI), heating oil, and natural gas. These datasets contain futures prices for different maturities, open interest positions for each commodity (long and short open interest positions), and S&P 500 composite index. The selected commodities are traded on the New York Mercantile Exchange (NYMEX). The data comes from the Thomson Reuters Datastream and from the Commodity Futures Trading Commission (CFTC).

Slovenia Energy Trade: Low Sulphur Fuel Oil: Opening Stocks data was reported at 0.000 Ton in Mar 2025. This stayed constant from the previous number of 0.000 Ton for Feb 2025. Slovenia Energy Trade: Low Sulphur Fuel Oil: Opening Stocks data is updated monthly, averaging 0.000 Ton from Jan 2004 (Median) to Mar 2025, with 255 observations. The data reached an all-time high of 5,868.000 Ton in Sep 2006 and a record low of 0.000 Ton in Mar 2025. Slovenia Energy Trade: Low Sulphur Fuel Oil: Opening Stocks data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.RB003: Energy Trade and Nuclear Heat.

This feature service represents Oil Refineries and Oil Terminals as overseen by the California Energy Commission.An oil depot (sometimes called a tank farm, tankfarm, installation or oil terminal) is an industrial facility for the storage of oil and/or petrochemical products and from which these products are usually transported to end users or further storage facilities. An oil depot typically has tankage, either above ground or below ground, and gantries (framework) for the discharge of products into road tankers or other vehicles (such as barges) or pipelines.An oil refinery or petroleum refinery is an industrial process plant where crude oil is transformed and refined into more useful products such as petroleum naphtha, gasoline, diesel fuel, asphalt base, heating oil, kerosene, liquefied petroleum gas, jet fuel and fuel oils. Petrochemicals feed stock like ethylene and propylene can also be produced directly by cracking crude oil without the need of using refined products of crude oil such as naphtha.Additional data at the CA Energy Commission Open Data Portal.

Oil Furnaces Market Outlook

The oil furnaces market size is projected to reach USD 8.5 billion by 2032 from USD 5.6 billion in 2023, at a compound annual growth rate (CAGR) of 4.8% during the forecast period. This growth can be attributed to increasing demand for energy-efficient heating solutions, particularly in regions with harsh winters, and the continuous advancements in furnace technology. The market is also being driven by the retrofitting of older heating systems with high-efficiency models, which offer improved energy savings and lower emissions.

One of the key growth factors for the oil furnaces market is the rising awareness and demand for energy-efficient heating solutions. Governments and environmental agencies worldwide are pushing for the adoption of high-efficiency furnaces due to their lower carbon emissions and reduced energy consumption. This is further complemented by financial incentives and rebates offered to consumers and businesses for upgrading to more efficient heating systems. Consequently, high-efficiency oil furnaces are gaining traction as they provide significant cost savings over their lifespan despite their higher initial costs.

Additionally, technological advancements in oil furnace designs are also driving market growth. Modern oil furnaces are equipped with advanced features such as variable-speed blowers, electronic ignition systems, and smart thermostats, which enhance their performance and energy efficiency. These innovations not only improve the operational efficiency of the furnaces but also offer enhanced comfort and convenience to the users. The integration of IoT technology is another significant trend, enabling remote monitoring and control of oil furnaces, thus contributing to their increasing adoption.

Furthermore, the replacement of aging heating infrastructure in residential, commercial, and industrial buildings is fueling market demand. Many existing oil furnaces are reaching the end of their operational life and are being replaced with newer, more efficient models. This trend is particularly noticeable in regions with older building stock, where upgrading heating systems is essential for meeting modern energy efficiency standards. The emphasis on reducing greenhouse gas emissions and enhancing energy conservation is prompting both public and private sectors to invest in upgraded heating solutions.

Regionally, the oil furnaces market is seeing significant growth in North America and Europe due to the extreme winter conditions and the high prevalence of oil-based heating systems. In North America, the market is driven by the widespread adoption of oil furnaces in rural and suburban areas where natural gas infrastructure is limited. Europe, on the other hand, is experiencing growth due to stringent energy efficiency regulations and increasing consumer awareness about the benefits of high-efficiency heating systems. Meanwhile, emerging economies in the Asia Pacific, Latin America, and the Middle East & Africa are gradually increasing their adoption of oil furnaces, driven by rising urbanization and industrialization.

Product Type Analysis

Oil furnaces can be broadly categorized into standard oil furnaces and high-efficiency oil furnaces. Standard oil furnaces are typically less expensive and are widely used in regions with moderate heating requirements. They have a simpler design and fewer advanced features compared to their high-efficiency counterparts. However, their lower initial cost makes them an attractive option for cost-conscious consumers. Despite their lower efficiency, standard oil furnaces continue to hold a significant market share, particularly in regions where heating oil prices are relatively low.

High-efficiency oil furnaces, on the other hand, are designed to maximize energy savings and minimize emissions. These furnaces often feature advanced components such as variable-speed blowers, modulating burners, and electronic ignition systems that enhance their performance. High-efficiency oil furnaces are typically rated with Annual Fuel Utilization Efficiency (AFUE) ratings of 90% and above, meaning they convert more fuel into usable heat compared to standard models. The higher initial investment in high-efficiency furnaces is offset by the long-term savings in fuel costs and the availability of government incentives and rebates for energy-efficient upgrades.

The growing emphasis on sustainability and energy efficiency is driving the demand for high-efficiency oil furnaces. Consumers and businesses are increasingly prioritizing long-term savings and environme

Net heat generation, fuel input, supply, stock of heating plants: Germany, years, energy sources

Slovenia Energy Trade: Transport Diesel Oil: Opening Stocks data was reported at 446,886.242 Ton in Mar 2025. This records an increase from the previous number of 433,968.124 Ton for Feb 2025. Slovenia Energy Trade: Transport Diesel Oil: Opening Stocks data is updated monthly, averaging 91,113.000 Ton from Jan 2004 (Median) to Mar 2025, with 255 observations. The data reached an all-time high of 446,886.242 Ton in Mar 2025 and a record low of 2,333.000 Ton in May 2013. Slovenia Energy Trade: Transport Diesel Oil: Opening Stocks data remains active status in CEIC and is reported by Statistical Office of the Republic of Slovenia. The data is categorized under Global Database’s Slovenia – Table SI.RB003: Energy Trade and Nuclear Heat.