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.

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.

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

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).

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.

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.

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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?

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 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.

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 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

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

Index Time Series for DB Gold Double Long ETN. The frequency of the observation is daily. Moving average series are also typically included. The index is intended to reflect changes in the market value of certain gold futures contracts and is comprised of a single unfunded gold futures contract.

Index Time Series for MiraeAsset TIGER Gold Futures ETF(H). The frequency of the observation is daily. Moving average series are also typically included.

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.

Copper rose to 5.19 USD/Lbs on December 2, 2025, up 0.35% from the previous day. Over the past month, Copper's price has risen 3.22%, and is up 25.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on December of 2025.

Index Time Series for Samsung KODEX Gold Futures Special Asset ETF Hedged. The frequency of the observation is daily. Moving average series are also typically included.

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

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.

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.