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.

Gold prices in , October, 2025 For that commodity indicator, we provide data from January 1960 to October 2025. The average value during that period was 615.3 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 4058.33 USD per troy ounce in October 2025. | TheGlobalEconomy.com

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.

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.

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.

As of June 25, 2024, gold futures contracts to be settled in June 2030 were trading on U.S. markets at around ***** U.S. dollars per troy ounce. This is above the price of ******* U.S. dollars per troy ounce for contracts to be settled in June 2025, indicating that gold traders expect the price of gold to rise over the next five years. Gold futures are contracts that effectively lock in a price for an amount of gold to be purchased at a time in the future, which can then be traded on markets. Futures markets therefore provide an indicator of how investors think a commodities market will develop in the future.

Context

Gold. A precious item with its own duality. In one side, it's a popular investment asset. In another side, it's a commodity. Whether you buy it as an asset or as commodity, the price for gold is always influenced by two things, as similar as other commodities in market: supply and demand. It's not easy to combine many aspects in supply and demand into a single dataset without making it into wall of columns. And also aggregating the data might not easy to do, since the data might not available publicly. But it doesn't mean we can't learn the historical pattern of gold market. At least some gold price historical data are available for public. And we can use that to analyze the market pattern, and, maybe, learn something from them.

Content

This dataset was based on gold price historical data from macrotrends.net. I added one new column, 'Year Range Price', to see how wide the spread of the price annually.

Acknowledgements

The base data for this dataset was retrieved from https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart.

Inspiration

What variable have the biggest correlation with annual Average Closing Price? What information can we see from the graphic? Are there any reasons why the price drop and rise? What happened on those years? Many things can be learn and explore by historical data. Having historical data is like having a kaleidoscope to see the past, learn from them, and use it as information to walk on our future path.

View monthly updates and historical trends for Gold Price. from United Kingdom. Source: World Bank. Track economic data with YCharts analytics.

The average monthly prices for gold increased worldwide between January 2014 and May 2025, although with some fluctuations. In January 2014, the average monthly price for gold worldwide stood at ******** nominal U.S. dollars per troy ounce. Significant jumps in the gold prices were observed, especially in the periods of uncertainty, as the investors tend to see gold as a safe investment option. For instance, the Corona pandemic acted as a shock to the economy, resulting in substantial increases in gold prices in 2020. As of May 2025, gold valued at ******** U.S. dollars per ounce, the highest value reported during this period.

Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on December of 2025.

This dataset contains historical price data for seven essential metals traded on the Multi Commodity Exchange (MCX) India: Gold, Silver, Lead, Zinc, Copper, Nickel, and Aluminum. The data is meticulously collected to support prediction models, trend analysis, and statistical exploration of metal price movements.

The dataset includes: - Daily price data for 7 metals - Open price, high/low values, and closing prices - Data across multiple periods, useful for preliminary exploration, model training, and analysis

Description for each column in the dataset: 1. Date: The date on which the market data was recorded (format: DD-MM-YYYY). 2. Price: The closing price of Copper on the given date, reflecting the last traded price of the day. 3. Open: The opening price of Copper at the start of trading on the given date. 4. High: The highest price Copper reached during the trading day. 5. Low: The lowest price Copper traded at during the day. 6. Vol. (Volume): The total volume of Copper traded on the given day, typically in thousands (K). 7. Change %: The percentage change in the closing price from the previous trading day.

United States - Producer Price Index by Commodity: Metals and Metal Products: Gold Ores was 444.72200 Index Jun 1985=100 in December of 2021, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity: Metals and Metal Products: Gold Ores reached a record high of 515.90000 in August of 2020 and a record low of 78.40000 in April of 2001. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity: Metals and Metal Products: Gold Ores - last updated from the United States Federal Reserve on December of 2025.

Graph and download economic data for Producer Price Index by Commodity: Miscellaneous Products: Jewelry, Gold and Platinum (WPU15940222) from Dec 2011 to Sep 2025 about platinum, jewelry, miscellaneous, gold, commodities, PPI, inflation, price index, indexes, price, and USA.

Between January 1971 and May 2025, gold had average annual returns of **** percent, which was only slightly more than the return of commodities, with an annual average of around eight percent. The annual return of gold was over ** percent in 2024. What is the total global demand for gold? The global demand for gold remains robust owing to its historical importance, financial stability, and cultural appeal. During economic uncertainty, investors look for a safe haven, while emerging markets fuel jewelry demand. A distinct contrast transpired during COVID-19, when the global demand for gold experienced a sharp decline in 2020 owing to a reduction in consumer spending. However, the subsequent years saw an increase in demand for the precious metal. How much gold is produced worldwide? The production of gold depends mainly on geological formations, market demand, and the cost of production. These factors have a significant impact on the discovery, extraction, and economic viability of gold mining operations worldwide. In 2024, the worldwide production of gold was expected to reach *** million ounces, and it is anticipated that the rate of growth will increase as exploration technologies improve, gold prices rise, and mining practices improve.

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

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The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average

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

Context

Historically, gold had been used as a form of currency in various parts of the world including the USA. In present times, precious metals like gold are held with central banks of all countries to guarantee re-payment of foreign debts, and also to control inflation which results in reflecting the financial strength of the country. Recently, emerging world economies, such as China, Russia, and India have been big buyers of gold, whereas the USA, SoUSA, South Africa, and Australia are among the big seller of gold.

Forecasting rise and fall in the daily gold rates can help investors to decide when to buy (or sell) the commodity. But Gold prices are dependent on many factors such as prices of other precious metals, prices of crude oil, stock exchange performance, Bonds prices, currency exchange rates, etc.

The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value.

Content

Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The historical data of Gold ETF fetched from Yahoo finance has 7 columns, Date, Open, High, Low, Close, Adjusted Close, and Volume, the difference between Adjusted Close and Close is that the closing price of a stock is the price of that stock at the close of the trading day. Whereas the adjusted closing price takes into account factors such as dividends, stock splits, and new stock offerings to determine a value. So, Adjusted Close is the outcome variable which is the value you have to predict.

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Acknowledgements

The data is collected from Yahoo finance.

Inspiration

Can you predict Gold prices accurately using traditional machine learning algorithms

Belgium Gold Price Per Kg: EUR data was reported at 115,180.000 EUR in 26 Nov 2025. This records an increase from the previous number of 114,645.000 EUR for 25 Nov 2025. Belgium Gold Price Per Kg: EUR data is updated daily, averaging 34,090.000 EUR from Jan 1999 (Median) to 26 Nov 2025, with 6722 observations. The data reached an all-time high of 118,645.000 EUR in 17 Oct 2025 and a record low of 28,055.000 EUR in 31 Dec 2013. Belgium Gold Price Per Kg: EUR data remains active status in CEIC and is reported by National Bank of Belgium. The data is categorized under World Trend Plus’s Commodity Market – Table BE.P: Gold Price: National Bank of Belgium. [COVID-19-IMPACT]

Argentina Commodities Prices Index: Incl Gold: USD data was reported at 126.258 Dec1995=100 in Mar 2016. This records an increase from the previous number of 124.770 Dec1995=100 for Feb 2016. Argentina Commodities Prices Index: Incl Gold: USD data is updated monthly, averaging 109.496 Dec1995=100 from Jan 1996 (Median) to Mar 2016, with 243 observations. The data reached an all-time high of 233.577 Dec1995=100 in Sep 2012 and a record low of 62.274 Dec1995=100 in Feb 1999. Argentina Commodities Prices Index: Incl Gold: USD data remains active status in CEIC and is reported by Central Bank of Argentina. The data is categorized under Global Database’s Argentina – Table AR.I031: Commodities Prices Index. Replacement series ID: 376239727