Gold rose to 4,602.85 USD/t.oz on April 29, 2026, up 0.15% from the previous day. Over the past month, Gold's price has risen 1.94%, and is up 40.64% 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 April of 2026.

Dataset Card for Sentiment Analysis of Commodity News (Gold)

This is a news dataset for the commodity market which has been manually annotated for 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021). The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in their paper "Impact of News on the Commodity Market: Dataset and Results." It is currently published by the authors on… See the full description on the dataset page: https://huggingface.co/datasets/SaguaroCapital/sentiment-analysis-in-commodity-market-gold.

Gold is a primary global commodity used as a hedge against inflation and currency devaluation. This dataset combines long-term historical benchmarks from the World Bank with recent high-frequency market data to provide a continuous view of gold prices from 1995 through early 2026.

Content

The dataset consists of a single CSV file containing:

Date: The timestamp for the observation (Daily or Monthly).

Gold_Price_USD_YF: Market closing price in USD (via Yahoo Finance).

Gold_Price_WB_Monthly: Global benchmark price per troy ounce (via World Bank).

Data Sources

World Bank (wbdata): Historical global commodity "Pink Sheet" data.

Yahoo Finance (yfinance): Daily market spot and futures prices (Ticker: GC=F). https://finance.yahoo.com/quote/GC=F/history/

License

Data Files (CC BY 4.0): You are free to share and adapt this data as long as credit is given to the original sources (World Bank and Yahoo Finance).

Column Metadata

Assigning descriptions to individual columns is critical for a 10.0 usability score. | Column Name | Description | | :--- | :--- | | Date | The date of record in YYYY-MM-DD format. | | Gold_Price_USD_YF | The daily/monthly average closing price of Gold Futures in USD. | | Gold_Price_WB_Monthly | The monthly global average price of gold per troy ounce (World Bank benchmark). |

Dataset Citations:

World Bank (Primary source for 1995-2000): https://www.worldbank.org/en/research/commodity-markets

Yahoo Finance (Primary source for 2000-2026): https://finance.yahoo.com/quote/GC=F/history

Eurostat (Economic Indicators): https://ec.europa.eu/eurostat/data/database

FAOSTAT (Price Indices): https://www.fao.org/faostat/en/#data/PP

Keywords & Tags:

Finance, Commodities, Economics, Time Series Analysis, Global, Gold Prices, Historical Gold Prices, Monthly Gold Prices, World Bank, Yahoo Finance

About Dataset

This dataset contains historical gold price data combined with news and article-based indicators for each date. It is designed for financial analysis, time series forecasting, and machine learning applications.

The dataset integrates gold price movements with external news signals to help analyze how economic events and market sentiment influence price trends.

Dataset Features

• Date – Timeline of observations
• Gold_Price – Historical gold price in USD per troy ounce
• Article1 to Article5 – Indicators representing the presence of relevant financial or economic news on each date

The article columns help capture external information that may impact gold price behavior.

Data Cleaning and Processing

The dataset has been carefully preprocessed to ensure consistency and usability:

• Missing gold price values have been filled using recent historical trends (last 4 values approximation)
• Weekend data (Saturday and Sunday) has been filled with neutral indicators to maintain sequence consistency
• The dataset is structured to preserve continuity in time series data

Use Cases

This dataset can be used for:

• Gold price prediction and forecasting
• Time series analysis
• Machine learning and regression modeling
• News-based or sentiment-driven analysis
• Financial and macroeconomic research

Purpose

Gold prices are influenced by multiple factors such as economic conditions, investor sentiment, and global events. By combining price data with news-related features, this dataset provides a strong foundation for building more realistic and context-aware predictive models.

Gold reference prices published on the World Gold Council Goldhub data page, including reference prices from the London Bullion Market Association and Shanghai Gold Exchange. The dataset covers a range of frequencies including daily, weekly, monthly and annual observations, with historical coverage back to 2015 or earlier where available. Prices are quoted in currency units per troy ounce unless otherwise stated. Updated weekly.

This statistic depicts the average annual prices for gold from 2014 to 2024 with a forecast until 2026. In 2024, the average price for gold stood at 2,388 U.S. dollars per troy ounce, the highest value recorded throughout the period considered. In 2026, the average gold price is expected to increase, reaching 3,200 U.S. dollars per troy ounce.

This document contains statistical data and analysis of global gold demand and prices from 2010 to 2024, presented by Dojipedia, a website focused on Forex investment information. The data is organized quarterly and includes various categories of gold demand such as jewelry fabrication, technology use, investment, and central bank purchases. It also provides the LBMA gold price in US dollars per ounce for each quarter.The document highlights significant events that influenced gold prices and demand during this period. These events include major economic crises, geopolitical tensions, and market shifts. For instance, it mentions the European debt crisis in 2010, the U.S. credit rating downgrade in 2011, the Federal Reserve's quantitative easing tapering signals in 2013, and the COVID-19 pandemic's impact starting in 2020.The data shows how gold demand and prices often increase during times of economic uncertainty or political instability, as investors view gold as a safe-haven asset. For example, gold prices reached record highs in 2024 amid global economic and geopolitical uncertainties.Dojipedia presents itself as a platform with five years of Forex market investment experience. The site offers free educational content on technical analysis methods such as Elliott Wave, ICT Trading, and Smart Money Concept. It also mentions plans to publish free books on technical analysis.The document includes a disclaimer stating that the information provided is for general purposes only and not financial advice. It warns about the high risks associated with investing in financial markets like CFDs, Forex, cryptocurrencies, and gold. The disclaimer emphasizes that leveraged products may not be suitable for all investors due to the high risk to capital.Overall, this document serves as a comprehensive resource for those interested in gold market trends and their relationship to global economic events over the past decade and a half.

Gold prices updated in real-time. Track the gold spot price in GBP, USD, EUR, JPY, AUD, CAD & CHF >>

Gold Fields stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The dataset shows average price in domestic and foreign markets of gold and silver

Note: The data sources are: 1. Gold and Silver Prices in Mumbai: Business Standard, Business Line, or The Economic Times (Mumbai) , IBJA Website. 2. Gold Price in London: LBMA. 3. Silver Price in New York:Thomson Reuters.

Gold spot prices

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.

Gold Price Outlook: S&P GSCI Gold index Faces Volatility Amidst Economic Uncertainties

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

Historical and live gold price data with up to 5 years of price history, technical indicators, and session-level change tracking.

Explore short and medium-term Gold price forecast analysis and check long-term Gold predictions for 2026, 2030, and beyond.

This dataset contains monthly gold prices from 1950-01 to 2020-07. Gold is a precious metal that has been used as a store of value and a medium of exchange for thousands of years, and is still widely traded in financial markets today. The gold price is influenced by a variety of factors, including global economic conditions, geopolitical events, and supply and demand dynamics.

The dataset includes a total of 847 data points, with each row representing the gold price for a particular month. The data was sourced from the World Gold Council and is in USD per troy ounce.

This dataset can be used for a variety of applications, including financial analysis, time series forecasting, and machine learning modeling. Potential use cases include predicting future gold prices based on historical trends, analyzing the relationship between gold prices and other economic indicators, and developing trading strategies for gold-related assets.

Data Source: World Gold Council

The size of the Gold Market was valued at USD 3.2 Trillion in 2023 and is projected to reach USD 4.5 Trillion by 2032, with an expected CAGR of 7.38% during the forecast period. Recent developments include: March 2023: Pan American Silver Corporation acquired all the issued and outstanding common shares of Yamana Gold Inc., as part of the arrangement, which includes its mines and increased the geographical operations of the company in Latin America., February 2023: Barrick Gold, the world's second-biggest gold producer, announced a 10% increase in attributable proved and probable gold mineral reserves to 76 million ounces net of depletion in 2022 while maintaining current reserves.. Key drivers for this market are: Demand for Gold in the form of Jewelry and Long-term Savings, Increasing Consumption in High-End Electronics Applications; Other Drivers. Potential restraints include: Declining Ore Grades and Other Technical Challenges, Other Restraints. Notable trends are: Jewelry Segment to Dominate the Demand.

The dataset shows average price in domestic and foreign markets of gold and silver

Note: 1. The data source for this Table for the period till 1999-2000 are Bombay Bullion Association and Press Trust of India. For the period 2000-01 onwards, the data sources are (i) Business Standard/ Business Line and Economic Times, Mumbai/IBJA Website for gold and silver price in Mumbai and LBMA for gold price in London and (ii) Thomson Reuters for silver price in New York. 2. Data provided in this Table for the period 1979-80 to 1999-2000 and 2000-01 to 2020-21 may not be strictly comparable due to different sources of information.

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.

Philadelphia Gold and Silver Index: A Beacon of Precious Metal Value?

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

Centerra Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

Philadelphia Gold and Silver Index: The Future of Precious Metals?

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