Crude Oil rose to 63.21 USD/Bbl on August 18, 2025, up 0.66% from the previous day. Over the past month, Crude Oil's price has fallen 4.15%, and is down 14.18% 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 August of 2025.

MetaData

Crude Oil Stock Price Data (2000-2024)

Dataset Description

This dataset contains historical stock price data for Crude Oil from 2000 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Crude Oil's stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Crude Oil stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Crude Oil stock at the end of each trading day.

High: The highest price reached by Crude Oil stock during the trading day.

Low: The lowest price reached by Crude Oil stock during the trading day.

Open: The price of Crude Oil stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

API Crude Oil Stock Change in the United States increased to 1.50 BBL/1Million in August 8 from -4.20 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Trading oil futures online offers convenience, leverage, and real-time data for making informed trading decisions. However, it comes with risks due to the volatility of oil prices. Learn about the benefits and precautions of trading oil futures online.

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.

S&P GSCI Crude Oil Index: A Reliable Gauge of Global Oil Prices?

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

Brent fell to 66.16 USD/Bbl on August 15, 2025, down 1.02% from the previous day. Over the past month, Brent's price has fallen 3.44%, and is down 16.97% 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 August of 2025.

USO is an exchange-traded fund (ETF) that tracks the performance of WTI crude oil futures, allowing individuals to speculate on the direction of oil prices. Investors should carefully consider their investment objectives and be aware of the fund's short-term trading focus.

On August 4, 2025, the Brent crude oil price stood at 66.65 U.S. dollars per barrel, compared to 63.96 U.S. dollars for WTI oil and 68.64 U.S. dollars for the OPEC basket. Oil prices fell that week as economic performance from the U.S. and China, the largest oil consumers, remained 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.

Oil trading online is a popular method for individuals, corporations, and financial institutions to participate in the global oil market. Discover the advantages, instruments, and risks associated with this convenient and accessible form of trading.

This statistic displays the total market value of oil and gas companies on the London Stock Exchange (LSE) from January 2018 to June 2019, in billion British pounds. During this time period the total market value of oil and gas companies trading on the LSE fluctuated. The lowest value recorded was of ***** billion British pounds as of December 2018 and the highest value was ****** billion British pounds as of July 2018.

At the beginning of July 2019, FSTE Russel reclassified several industries across global stock exchanges, one of which being oil and gas companies. The Industry Classification Benchmark (ICB) is the categorization and comparison of companies by industry and sector across global exchanges. Oil and gas companies are now classified under the broader industry of energy.

Palm Oil rose to 4,517 MYR/T on August 15, 2025, up 2.54% from the previous day. Over the past month, Palm Oil's price has risen 6.91%, and is up 22.74% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Palm Oil - values, historical data, forecasts and news - updated on August 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

Content

It's a time series dataset of crude oil

Date: Date of the Stock Data Close/Last: Last close price of the company's shares on the relevant stock exchange Volume: Number of shares sold, traded over a certain period of time (Usually Daily) Open: Opening price of company's shares High: Highest price at which a stock traded during the trading day Low: Lowest price at which a stock traded during the trading day

Inspiration

Perform time series analysis concept on real world scenario and forecast future stock price on real world data and gain some knowledge.
Have a fun !!!

Stock market oil futures are contracts that allow investors to trade oil at a predetermined price on a future date. Learn how these contracts work and how investors can speculate on the future price of oil. Discover the different purposes of hedgers and speculators in the oil futures market. Understand the factors that can impact oil prices and how to analyze them for successful trading.

Crude oil online charts are graphical representations of price movements, allowing traders to analyze historical and current trends, identify patterns, and make informed decisions. Learn how these charts incorporate technical indicators and assist in formulating trading strategies.

Urals Oil rose to 63.49 USD/Bbl on August 14, 2025, up 3.03% from the previous day. Over the past month, Urals Oil's price has fallen 3.01%, and is down 17.51% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urals Crude.

Learn about crude oil futures, a type of financial contract traded on the stock market that allow investors to speculate on the future price of crude oil. Discover how these contracts can be used for hedging and how they are traded on various stock exchanges worldwide. Understand the factors that influence oil prices and how investors can profit from trading crude oil futures, but also be aware of the significant risks involved.

The ticker symbol for crude oil futures is CL. Learn about the importance of this symbol in tracking the price, volume, and other key information related to crude oil futures. Discover how crude oil futures contracts work and the factors that influence their prices. Find out who trades crude oil futures and how to access them through futures brokers or online trading platforms.

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.

Crude Oil Futures Show Bullish Outlook for S&P GSCI Crude Oil Index.

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.

Crude Oil Futures: Experts Predict Volatility for S&P GSCI Crude Oil Index.

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