Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% 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 December of 2025.

Explore the dynamics of the gold market, including the factors affecting its price fluctuations and various investment options like ETFs and physical gold. Learn how geopolitical events and economic indicators influence gold as a safe-haven asset.

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

Among the five largest gold mining companies, Goldfields — headquartered in Johannesburg, South Africa-saw the largest growth in its share price recently. As of June 2025, the company had reached ****** index points. While all the top five mining companies experienced significant increase in their share prices over this period despite some fluctuations, Barrick Mining Corporation had the least increase when compared to others, with an index point of ******.

Dataset

This dataset was created by Yug Kaushik

Released under CC BY-NC-SA 4.0

Contents

Gold Fields 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

This data set provides a comprehensive record of daily gold prices from January 19, 2014 to January 22, 2024. The data is provided by Nasdaq and includes key financial metrics for each trading day. . The dataset consists of the following columns:

• Date: A unique date for each trading day recorded.
• Close: The closing price of gold on the relevant date.
• Volume: Gold trading volume on the relevant date.
• Open: The opening price of gold on the relevant date.
• High: The highest recorded price of gold during the trading day.
• Low: The lowest price recorded for gold in the trading day. This data set is valuable for researchers, analysts, and data enthusiasts who want to understand historical trends, patterns, and changes in gold prices over time. Analysts can use this database to perform a variety of analyses, including price forecasting, trend spotting, and market behavior studies.

Possible conditions: - Time Series Analysis: Explore trends and patterns in gold prices over a given period. - Advanced Modeling: Build models to predict future gold prices based on historical data. - Trading Strategy Development: Develop and reverse trade strategies using the given price and volume information. - Market Sentiment Analysis: Analyze the impact of market events on gold prices and assess market sentiment. - Statistical Analysis: Perform tests and statistical analysis to gain insight into the characteristics of gold price movements.

Description: Users are advised to verify the accuracy and reliability of the information and to be aware of the limitations and biases inherent in financial databases. In addition, it is important to consider external factors such as economic indicators, geopolitical events, and market sentiment when using databases for analysis and use.

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.

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 Uncertain Future

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

Data set column

1. Date - mm/dd/yyyy
2. SPX - is a free-float weighted measurement stock market index of the 500 largest companies listed on stock exchanges in the United States.
3. GLD - Gold Price
4. USO - United States Oil Fund
5. SLV - Silver Price
6. EUR/USD - currency pair quotation of the Euro against the US

Explore the potential of investing in the gold stock market for portfolio diversification, inflation hedging, and growth opportunities. Understand the risks and benefits of large-cap miners vs. junior exploration firms, and learn how to navigate geopolitical and regulatory factors, gold price fluctuations, and investment strategies like gold ETFs.

This dataset can be used to predict the stock market. The data is extracted from MT5 terminal integrated in python.

The datasets include the minute by minute fluctuations of Gold and Silver prices over from 1st of January 2023 to 17th April 2025. The data can be used to train models for seasonality or a minute-by-minute approach.

The data has 7 columns:

• Time: The minute in which the event or movement occurred (Can be converted to Datetype with pd.to_datatime)
• Open Price
• High Price
• Low Price
• Close Price (The Feature to Predict if desired)
• Tick Volume: The Volume at the in which the event or movement occurred
• EMA: Exponential Moving Average (Technical Estimator for Risk management in the predictions and overall better performance)
• OBV: On-Balance Volume (Technical Estimator for Risk management in the predictions and overall better performance)

Two datasets are used;

Achilles Data Gold-Silver: with 1,416,340 rows to predict Gold, Silver and other Metals. Achilles Data Gold: with 708,264 rows to predict Gold, Silver and other Metals.

You may find the paper of our implementation here: https://doi.org/10.48550/arXiv.2410.21291

Graph and download economic data for Book Value of U.S. Mint Held Treasury Gold Working Stock: All Locations: Coins, Blanks, Miscellaneous (MHTGWSALV) from Jan 2012 to Oct 2025 about coins, miscellaneous, gold, stocks, accounting, reserves, Treasury, 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: A Beacon for 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

Stock Price Time Series for Aya Gold & Silver Inc. Aya Gold & Silver Inc., together with its subsidiaries, engages in the exploration, evaluation, and development of precious metals projects in Morocco. The company primarily explores for gold and silver deposits. Its flagship project holds 100% interests in the Zgounder property located approximately 260 kms east of Agadir in the Proterozoic Siroua Massif of the Anti-Atlas Range, Morocco covering an area of approximately 350 square kilometers. The company was incorporated in 2007 and is based in Mount Royal, Canada.

Stock Price Time Series for Centerra Gold Inc. Centerra Gold Inc. engages in the acquisition, exploration, development, and operation of gold and copper properties in North America, Turkey, and internationally. It also explores for molybdenum deposits. The company's principal assets include the Mount Milligan gold-copper mine located in British Columbia, Canada; and the Öksüt gold mine located in Türkiye. It also owns the Kemess project in British Columbia, Canada; the Goldfield District project in Nevada, United States; owns and operates a Molybdenum BU, which includes the Langeloth metallurgical processing facility that operates in Pennsylvania; and the Thompson Creek Mine in Idaho; and 75% owned the Endako Mine in British Columbia, Canada. Centerra Gold Inc. was incorporated in 2002 and is headquartered in Toronto, Canada.

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.

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