Gold fell to 3,372.61 USD/t.oz on July 24, 2025, down 0.45% from the previous day. Over the past month, Gold's price has risen 1.19%, and is up 42.68% 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 July of 2025.

The price of gold per troy ounce increased considerably between 1990 and 2025, despite some fluctuations. A troy ounce is the international common unit of weight used for precious metals and is approximately **** grams. At the end of 2024, a troy ounce of gold cost ******* U.S. dollars. As of * June 2025, it increased considerably to ******** U.S. dollars. Price of – additional information In 2000, the price of gold was at its lowest since 1990, with a troy ounce of gold costing ***** U.S. dollars in that year. Since then, gold prices have been rising and after the economic crisis of 2008, the price of gold rose at higher rates than ever before as the market began to see gold as an increasingly good investment. History has shown, gold is seen as a good investment in times of uncertainty because it can or is thought to function as a good store of value against a declining currency as well as providing protection against inflation. However, unlike other commodities, once gold is mined it does not get used up like other commodities (for example, such as gasoline). So while gold may be a good investment at times, the supply demand argument does not apply to gold. Nonetheless, the demand for gold has been mostly consistent.

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. It is one of the crucial financial assets with a liquid market, intrinsic value, and diversified uses in jewelry, electronics, and for investment purposes. Gold includes both the physical bullion and ETF markets. Mining and refining technological innovations enhance efficiency and sustainability.Gold provides economic stability and security of investments since it is durable, widely accepted, and one that diversifies portfolios. Hence, gold holds a very significant place both in consumer markets and financial systems through its support for industries ranging from luxury goods to technology. 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.

Gold prices in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 600.07 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3352.66 USD per troy ounce in June 2025. | TheGlobalEconomy.com

As of May 2025, the London (morning fixing) price of an ounce of gold cost an average of ******** U.S. dollars, a slight increase compared to the average monthly morning fixing price of ******** U.S. dollars per ounce in the previous month.

London fixing gold price In January 2020, the average price for an ounce of fine gold was ******** U.S. dollars. It increased to ******** U.S. dollars as of April 2022. Although the monthly price for fine gold fluctuates, the average annual price of fine gold is gradually increasing. In 2001, the price for one ounce of gold was *** U.S. dollars, and by 2012 the price had risen to some ***** U.S. dollars. By 2024, the annual average gold price was nearly ***** dollars per ounce. In that year, global gold demand reached ******* metric tons worldwide. Price determinants of fine gold Fine gold is considered to be almost pure gold, where the value of the metal depends on the percentage of fineness. Twenty-four-carat gold is considered fine gold (from 99.9 percent gold by mass and higher). The London Gold Fix acts as a benchmark for the price of gold. The price of gold is set by the members of the London Gold Market Fixing Ltd undertaken by Barclays and its other members. The price is determined twice per business day at 10:30 am and 3:00 pm based on the London bullion market to settle contracts within the bullion market. The price is based on the equilibrium point between supply and demand agreed upon by participating banks. Gold prices must remain flexible, and gold fixing provides an instantaneous price at specified times.

Monthly gold prices in USD since 1833 (sourced from the World Gold Council). The data is derived from historical records compiled by Timothy Green and supplemented by data provided by the World Bank...

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

Explore the complexities of the gold market, influenced by economic data, geopolitical events, currency fluctuations, and interest rate changes. Understand gold's role as a safe-haven asset, its inverse relationship with the U.S. dollar, and the impact of interest rates and central bank actions on its pricing.

In 2024, one troy ounce of gold had an annual average price of ******** U.S. dollars. Gold pricing determinants Gold is a metal that is considered malleable, ductile, and is known for its bright lustrous yellow color. This transition metal is highly valued as a precious metal for its use in coins, jewelry, and in investments. Gold was also once used as a standard for monetary policies between different countries. The price of gold is determined by daily fixings where participants agree to buy or sell at a set price or to maintain the price through supply and demand control. For gold, companies like Barclays Capital, Scotia-Mocatta, Sociétè Générale, HSBC, and Deutsche Bank are members in gold fixing at the London Bullion Market Association.

In 2021, the global gold market decreased by -7.3% to $X for the first time since 2018, thus ending a two-year rising trend. The market value increased at an average annual rate of +3.1% from 2012 to 2021; however, the trend pattern indicated some noticeable fluctuations being recorded in certain years. Over the period under review, the global market reached the maximum level at $X in 2020, and then shrank in the following year.

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: A Brighter Future Ahead?

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.

What happens to gold if CPI increases?

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 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.

Explore the factors affecting gold prices, including inflation, interest rates, supply and demand dynamics, currency fluctuations, and geopolitical events, and learn how these elements influence gold's value in the global market.

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.

Machine Learning Models for Gold Price Prediction

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

Graph and download economic data for Producer Price Index by Commodity: Metals and Metal Products: Gold Ores (WPU10210501) from Jun 1985 to Dec 2021 about ore, gold, metals, commodities, PPI, inflation, price index, indexes, price, 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.

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

Gold prices have surged past $3,300, driven by US-China trade tensions, central bank purchases, and anticipated interest rate cuts, setting new records in the market.

Dataset historical price data for XAU/USD (gold vs USD) from 2004 to Feb 2025, captured across multiple timeframes including 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, weekly, and monthly intervals. Dataset includes Open, High, Low, Close prices, and Volume data.