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.

The 'EIA Heating Oil Stocks Change' is a weekly report published by the U.S. Energy Information Administration that measures the change in the volume of heating oil held in inventory by commercial firms.

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.

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 dataset contains Weekly U.S. Ending Stocks of Distillate Fuel Oil 2015-2022. Data from US Energy information administration.This series is available through the EIA open data API and can be downloaded to Excel or embedded as an interactive chart or map on your website.

European Fuel Oil Closing Stock by Country, 2023 Discover more data with ReportLinker!

United States Ending Stocks: Residual Fuel Oil (RF) data was reported at 32,349.000 Barrel th in Apr 2018. This records a decrease from the previous number of 35,042.000 Barrel th for Mar 2018. United States Ending Stocks: Residual Fuel Oil (RF) data is updated monthly, averaging 45,668.000 Barrel th from Jan 1936 (Median) to Apr 2018, with 988 observations. The data reached an all-time high of 103,423.000 Barrel th in Oct 1938 and a record low of 29,359.000 Barrel th in Dec 2017. United States Ending Stocks: Residual Fuel Oil (RF) data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB030: Petroleum Stocks.

Graph and download economic data for No. 2 Heating Oil Prices: New York Harbor (WHOILNYH) from 1986-06-06 to 2025-07-25 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.

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

Forecast: Fuel Oil Stock Change in the UK 2024 - 2028 Discover more data with ReportLinker!

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

United States Petroleum Stocks: Distillate Fuel Oil (DF) data was reported at 121,210.000 Barrel th in 20 Jul 2018. This records a decrease from the previous number of 121,311.000 Barrel th for 13 Jul 2018. United States Petroleum Stocks: Distillate Fuel Oil (DF) data is updated weekly, averaging 126,452.000 Barrel th from Aug 1982 (Median) to 20 Jul 2018, with 1869 observations. The data reached an all-time high of 186,012.000 Barrel th in 10 Dec 1982 and a record low of 87,153.000 Barrel th in 19 Apr 1996. United States Petroleum Stocks: Distillate Fuel Oil (DF) data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB029: Petroleum Stocks: Weekly Report.

Forecast: Fuel Oil Stock Change in Italy 2024 - 2028 Discover more data with ReportLinker!

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

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

United States Residual Fuel Oil: Ending Stocks data was reported at 29.561 Barrel mn in Oct 2018. This records an increase from the previous number of 28.446 Barrel mn for Sep 2018. United States Residual Fuel Oil: Ending Stocks data is updated monthly, averaging 41.373 Barrel mn from Jan 1973 (Median) to Oct 2018, with 550 observations. The data reached an all-time high of 97.187 Barrel mn in Jan 1980 and a record low of 27.812 Barrel mn in Aug 2018. United States Residual Fuel Oil: Ending Stocks 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.RB017: Petroleum Overview: by Product.

Common-Stock Time Series for Star Gas Partners LP. Star Group, L.P., together with its subsidiaries, provides home heating oil and propane products and services to residential and commercial customers in the United States. It offers gasoline and diesel fuel; and installs, maintain, and repairs heating and air conditioning equipment. As of September 30, 2024, the company served approximately 404,600 full service residential and commercial home heating oil and propane customers and 61,700 customers on a delivery only basis. It sells gasoline and diesel fuel to approximately 26,800 customers. The company was formerly known as Star Gas Partners, L.P. and changed its name to Star Group, L.P. in October 2017. The company was incorporated in 1995 and is based in Stamford, Connecticut.

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.

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

Forecast: Fuel Oil Opening Stock in Italy 2023 - 2027 Discover more data with ReportLinker!