Brent rose to 68.10 USD/Bbl on September 1, 2025, up 0.92% from the previous day. Over the past month, Brent's price has fallen 0.95%, and is down 11.87% 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 September of 2025.

Spain Crude Oil Price: Brent North Sea data was reported at 81.870 USD/Barrel in Oct 2018. This records an increase from the previous number of 79.080 USD/Barrel for Sep 2018. Spain Crude Oil Price: Brent North Sea data is updated monthly, averaging 28.330 USD/Barrel from Jan 1986 (Median) to Oct 2018, with 394 observations. The data reached an all-time high of 133.030 USD/Barrel in Jul 2008 and a record low of 9.740 USD/Barrel in Jul 1986. Spain Crude Oil Price: Brent North Sea data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.P006: Crude Oil and Gold Price.

Gold rose to 3,476.40 USD/t.oz on September 1, 2025, up 0.79% from the previous day. Over the past month, Gold's price has risen 3.03%, and is up 39.21% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on September of 2025.

Spain Gold Price: USD data was reported at 1,214.871 USD/Troy oz in Oct 2018. This records an increase from the previous number of 1,197.827 USD/Troy oz for Sep 2018. Spain Gold Price: USD data is updated monthly, averaging 341.747 USD/Troy oz from Jan 1957 (Median) to Oct 2018, with 742 observations. The data reached an all-time high of 1,771.852 USD/Troy oz in Sep 2011 and a record low of 34.870 USD/Troy oz in Jan 1957. Spain Gold Price: USD data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.P006: Crude Oil and Gold Price.

General commodity price information is data that provides market price information for major general commodities provided by the Korea Exchange. This information includes real-time prices, daily rate of increase and decrease, and trading volume for products listed on the Petroleum Electronic Commerce Market, KRX Gold Market, and Carbon Emissions Market. This data consists of three operations. Each operation is as follows. ① Petroleum Price: Searches for price information for petroleum products listed on the Petroleum Electronic Commerce Market. ② Gold Price: Provides price information for gold products listed on the KRX Gold Market. ③ Emissions Price: Searches for price information for emission products listed on the Carbon Emissions Market.

Spain Gold Price: EUR data was reported at 34.010 EUR/g in Oct 2018. This records an increase from the previous number of 33.060 EUR/g for Sep 2018. Spain Gold Price: EUR data is updated monthly, averaging 20.085 EUR/g from Jan 1999 (Median) to Oct 2018, with 238 observations. The data reached an all-time high of 43.590 EUR/g in Sep 2012 and a record low of 7.780 EUR/g in Aug 1999. Spain Gold Price: EUR data remains active status in CEIC and is reported by Bank of Spain. The data is categorized under Global Database’s Spain – Table ES.P006: Crude Oil and Gold Price.

Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil. Data comes from the International Monetary Fund (IMF).All rights are reservedDataDataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).LicenseThe IMF grants permission to visit its Sites and to download and copy information, documents, and materials from the Sites for personal, noncommercial usage only, without any right to resell or redistribute or to compile or create derivative works, subject to these Terms and Conditions of Usage and also subject to more specific restrictions that may apply to particular information within the Sites. Any rights not expressly granted herein are reserved.For more information please visit: Copyright and Usage.

Real-time gold price data updated every 5 minutes

Sunflower Oil rose to 1,359 INR/10 kg on September 1, 2025, up 0.07% from the previous day. Over the past month, Sunflower Oil's price has risen 5.43%, and is up 45.13% 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 Sunflower Oil.

EGPB

About Dataset This benchmark dataset consisting of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset. Key variables & Features include: • Previous gold prices • Future gold prices with predictions for one day, one week, and one month • Oil prices •… See the full description on the dataset page: https://huggingface.co/datasets/Farah42/EGPBD-An-Event-based-Gold-Prices-Benchmark-Dataset.

The dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

Prices of Bitcoin, US stock indices, Gold and crude oil

Context

Time series of major commodity prices and indices including iron, cooper, wheat, gold, oil

Content

Dataset contains Monthly prices for 53 commodities and 10 indexes, starting from 1980 to 2016, Last updated on march 17, 2016. The reference year for indexes are 2005 (meaning the value of indexes are 100 and all other values are relative to that year).

Inspiration

This is a challenging dataset with a fair share of NaN values. Some really good potential for EDA and also Time Series Analysis!

This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market's short-term interest rate along with the Vasicek model and joint radiation term structure model., , , # Short-term interest rate estimates based on futures markets

Abstract: This data is the month-end data of the time series from January 2009 to March 2023 for four commodities such as gold soybean crude oil and natural gas. These time series data can be used to estimate the market short-term interest rate together with the Vasicek model and the joint radiation term structure model

Usage: The data in Table 1 and Table 2 can be read into the established interest rate estimation model code using python to estimate the short-term interest rate

Data structure: month-end time series data; The xlsx tables mainly include Table 1 and Table 2

Source: Bloomberg Data Terminal

Specific variable definition:

• The gold futures price is the futures price data from the end of January 2009 to the end of March 2023, in ounces per dollar
• Soybean futures prices are futures price data from the end of January 2009 to the end of March 2023, in tons per dollar
• Natural gas futures price...

The global oil-soluble gold nanoparticles market was valued at USD 1.2 billion in 2025 and is expected to grow at a CAGR of 7.5% from 2025 to 2033, reaching an estimated value of USD 2.0 billion by 2033. The market growth is primarily driven by the rising demand for oil-soluble gold nanoparticles in life science applications, particularly in drug delivery and diagnostics. The increasing adoption of oil-soluble gold nanoparticles in industrial applications, such as electronics, catalysis, and optics, is further contributing to the market growth. However, factors such as fluctuations in the price of gold and regulatory challenges related to the use of nanoparticles can restrain the market's growth. Key players in the market include Nanopartz, Nanocs, SkySpring Nanomaterials, American Elements, and BBI Solutions, among others.

Context

Content

Ticker Description 0 GC=F Gold 1 SI=F Silver 2 CL=F Crude Oil 3 ^GSPC S&P500 4 PL=F Platinum 5 HG=F Copper 6 DX=F Dollar Index 7 ^VIX Volatility Index 8 EEM MSCI EM ETF 9 EURUSD=X Euro USD 10 ^N100 Euronext100 11 ^IXIC Nasdaq 12 ^BSESN Bse sensex 13 ^NSEI Nifty 50 14 ^DJI Dow

This dataset provides comprehensive end-of-day (EoD) pricing data for commodities options and futures, offering insights across a variety of currencies. It caters to traders, analysts, and institutions involved in commodity markets, providing critical data for hedging, risk management, and market analysis.

Key features of the dataset include:

End-of-Day Prices: Daily closing prices for a broad range of commodities options and futures. Commodities Coverage: Includes key commodity sectors such as energy (oil, natural gas), metals (gold, silver), agriculture (wheat, corn), and more. Multi-Currency Data: Pricing information is available in various currencies, allowing for global market analysis and cross-currency comparisons. Trading Volume & Open Interest: Data on the number of contracts traded and outstanding positions for market activity insights.

This dataset is essential for those tracking the commodities market, providing actionable data for strategy development, risk management, and financial decision-making.

Choose reference data from EDI and you will benefit from:

• A global data vendor offering affordable pricing structure.
• Fully customized data set to precisely fit your requirements.
• Flexible enterprise data licence options, we sell data, we do not rent data.
• Services from a company whose on-going commitment is to provide quality reference data solutions.

Correlations between implied volatilities, full sample period.

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.

MV Oil: Black Gold or Fool's Gold? (MVO)

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 Bitcoin price and social economic factors that influence Bitcoin price.