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.

Monthly and long-term gold price data (US$/toz): historical series and analyst forecasts curated by FocusEconomics.

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 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.

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Gold Price Prediction in INR (2004-2024)🇮🇳 - Can You Forecast the Future? ✨

Data Description:

1. Time Period: October 2004 - December 2024 (20 years) - Long-term Trend Analysis!
2. Frequency: Monthly Average Gold Price (INR) - Uncover Seasonality!
3. Source: Financial Service Website (verified) ✅ - Reliable Data for Accurate Predictions!

Potential Applications:

• Time Series Analysis: Analyze trends and seasonality in gold prices. - Unravel the Gold Market Mystery! ️‍♀️
• Gold Price Prediction: Forecast future gold prices using ARIMA, SARIMA, fbProphet, etc. - Predict the Next Big Move!

Key Points:

• Long-Term Trend: Explore the evolution of gold prices over two decades. - See How Gold Has Changed Over Time! ⏳
• Prediction Models: Evaluate the effectiveness of different time series forecasting models. - Find the Best Tool for the Job!
• INR Focus: Gain insights specific to the Indian gold market. 🇮🇳 - Understand the Indian Gold Market Landscape! ️

⚠️ Note:

• Past performance does not guarantee future results.

Bonus:

This dataset allows you to explore the fascinating world of gold price prediction in the Indian market. Challenge yourself! Can you develop a model that outperforms the rest?

Get the latest insights on price movement and trend analysis of Gold in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

Explore the intricate dance between gold prices and key economic events across major global players – Canada, Japan, USA, Russia, European Union, and China. This comprehensive dataset spans from January 2019 to December 2023, offering a nuanced analysis of how economic news from these influential regions impacts the ever-volatile gold market. Delve into the ebb and flow of financial landscapes, uncovering trends, correlations, and invaluable insights for strategic decision-making in the dynamic world of investments.

Historical Gold Price Dataset:

• Day: The day of the week when the data was recorded.
• Date: The specific date corresponding to the recorded gold price.
• Hour: The time of day when the gold price was recorded.
• Country: The country associated with the economic event or news affecting gold prices.
• Event: The economic event or news that potentially influenced gold prices.
• Actual: The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

** Economic Calendar Dataset**:

• Day: The day of the week when the economic event is scheduled.
• Date: The specific date when the economic event is expected to occur.
• Hour: The time of day when the economic event is scheduled.
• Country: The country associated with the economic event.
• Event: The specific economic event or news scheduled to take place.
• Actual The actual reported value or figure related to the economic event.
• Previous: The previously reported value or figure for the same economic event.
• Consensus: The consensus forecast or expected value for the economic event.
• Forecast: The forecasted value or figure for the economic event.

In 2021, the U.S. gold market increased by 16% to $X, rising for the fourth year in a row after three years of decline. Overall, consumption recorded buoyant growth. Over the period under review, the market reached the maximum level in 2021 and is expected to retain growth in years to come.

After three years of decline, the Indian gold market increased by 162% to $X in 2021. In general, consumption showed a relatively flat trend pattern. As a result, consumption attained the peak level and is likely to continue growth in the immediate term.

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

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.

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

The Kuwaiti gold market soared to $X in 2021, jumping by 142% against the previous year. This figure reflects the total revenues of producers and importers (excluding logistics costs, retail marketing costs, and retailers' margins, which will be included in the final consumer price). In general, consumption showed prominent growth. As a result, consumption reached the peak level and is likely to continue growth in the immediate term.

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

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

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 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

This dataset contains daily financial data from 2015 to 2025, including gold prices and related market indicators. It includes the following fields: date – trading date SPX – S&P 500 index value GLD – gold price ISO – international stock index SLV – silver price EUR/USD – USD exchange rate The dataset can be useful for time-series analysis, forecasting, and studying correlations between gold and global markets.

The Sri Lankan gold market was finally on the rise to reach $X in 2021, after three years of decline. Over the period under review, consumption saw a noticeable increase. Gold consumption peaked at $X in 2017; however, from 2018 to 2021, consumption remained at a lower figure.

In 2021, the Qatari gold market was finally on the rise to reach $X for the first time since 2018, thus ending a two-year declining trend. Over the period under review, consumption showed a pronounced expansion. As a result, consumption attained the peak level and is likely to continue growth in the immediate term.