Gold rose to 3,754.06 USD/t.oz on September 26, 2025, up 0.11% from the previous day. Over the past month, Gold's price has risen 10.48%, and is up 41.22% 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 September 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.

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.

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.

Gold spot and futures prices, technical support/resistance levels, market sentiment and historical trends.

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.

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

Gold prices hit record highs as investors turn to the precious metal amid global trade tensions and economic uncertainty. Learn about the factors driving this surge and future predictions.

Gold prices have reached record highs, driven by trade tensions and tariffs, positioning gold as a key safe-haven asset amid economic uncertainties.

EGPB

About Dataset This benchmark dataset consisting 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 •… See the full description on the dataset page: https://huggingface.co/datasets/Farah42/EGPBD-An-Event-based-Gold-Prices-Benchmark-Dataset.

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

In 2025, the price of platinum is forecast to hover around ***** U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to ***** U.S. dollars. Precious metals Precious metals are counted among the most valuable commodities worldwide. The most well known such metals are gold, silver and the platinum group metals. A precious metal can be used as an industrial commodity or as an investment. The major areas of application include the following sectors: technology, car-making, industrial manufacturing and jewelry making. Furthermore, gold and silver are used as coinage metals, and gold reserves are held by the central banks of many countries worldwide in order to store value or for use as a redemption medium. The idea behind this procedure is that gold reserves will help secure and stabilize the countries’ respective currencies. At ***** tons, the United States is the country with the most extensive stock of gold. It is kept in an underground vault at the New York Federal Reserve Bank. Russia, the United States, Canada, South Africa and China are the main producers of precious metals. Silver is the most abundant of the metals, followed by gold and palladium. Barrick Gold is the world’s largest gold mining company. The Toronto-based firm produced some **** million ounces of gold in 2020. The leading silver producers include Mexico-based Fresnillo, Poland’s KGHM Polska Miedž and the mining giant Glencore. Anglo Platinum and Impala are the key mining companies to produce platinum group metals. In 2023, Silver prices are expected to settle at around **** U.S. dollars per troy ounce. It is expected to remain the precious metal with the lowest value per ounce. The price of gold is forecast to drop to around ***** U.S. dollars per ounce, making it the most expensive precious metal in 2023.

Content

Historical daily prices of gold and silver since 1962 to now. Price per ounce in USD.

Acknowledgements

Data obtained from LBMA

Inspiration

You try different things on this dataset:

• predict the future price of gold and silver
• study the correlation between gold and silver
• combine with other datasets such as stocks and crypto currencies datasets to get insightsLBMA

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

Gold prices soared over 3% amid escalating US-China trade tensions, driven by new tariffs and market volatility. The precious metal continues to be a top-performing investment, bolstered by strong safe-haven demand and central bank buying.

This dataset contains the predicted prices of the asset $GOLD over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Brazil Futures: Gold: Turnover: Value data was reported at 24,109.000 BRL mn in Jun 2019. This records an increase from the previous number of 18,537.000 BRL mn for May 2019. Brazil Futures: Gold: Turnover: Value data is updated monthly, averaging 183.777 BRL mn from Jan 1995 (Median) to Jun 2019, with 294 observations. The data reached an all-time high of 494,616.000 BRL mn in Jul 2012 and a record low of 8.889 BRL mn in Dec 2001. Brazil Futures: Gold: Turnover: Value data remains active status in CEIC and is reported by B3 S.A.. The data is categorized under Brazil Premium Database’s Financial Market – Table BR.ZB005: B3: Futures: Gold.

This dataset contains the predicted prices of Gram Gold for the upcoming years based on user-defined projections.

The global plain gold jewelry market, encompassing 24K, 22K, and 18K gold variations, across online and offline retail channels, is experiencing robust growth. While precise market size figures for 2025 are unavailable, leveraging the provided CAGR (let's assume a conservative 5% for illustration) and a hypothetical 2019 market size of $50 billion, a 2025 market size of approximately $64 billion can be reasonably inferred. This growth is fueled by several key factors. Firstly, gold's enduring appeal as a safe-haven asset and its cultural significance in various regions drive consistent demand. Secondly, evolving consumer preferences, including a rising interest in minimalist designs and sustainable sourcing, are positively impacting the market. The online segment is witnessing rapid expansion due to increased e-commerce penetration and the convenience it offers. Major players like Chow Tai Fook, Tanishq, and others are leveraging online platforms to expand their reach and cater to a broader customer base. However, market growth faces certain challenges. Economic fluctuations, particularly inflation and recessionary pressures, can impact consumer spending on non-essential items like jewelry. Furthermore, fluctuating gold prices directly influence market dynamics and consumer purchasing decisions. The market is segmented geographically, with Asia-Pacific (particularly India and China) remaining a dominant region due to strong cultural ties to gold and significant consumer purchasing power. North America and Europe also exhibit considerable demand, driven by both investment and adornment motivations. The segmentation by karat (24K, 22K, 18K) reflects varying preferences for purity and color, offering diverse product options to meet consumer needs. Future growth will depend on managing price volatility, adapting to changing consumer preferences, and strategically navigating the evolving retail landscape. The competitive landscape is marked by a mix of established global players and regional brands. Large jewelry conglomerates benefit from established supply chains and strong brand recognition. However, smaller, specialized brands are gaining traction by offering unique designs and catering to niche market segments. Success in this market will require a combination of strong brand building, efficient supply chain management, and the ability to adapt quickly to shifting consumer preferences and macroeconomic conditions. The continued rise of e-commerce and the increasing importance of sustainable practices are likely to further shape the market landscape in the coming years. Understanding regional nuances and tailoring marketing strategies accordingly will be crucial for long-term market success.

Gold prices surged to a historic high as investors bet on Fed rate cuts, with analysts forecasting further gains driven by economic uncertainty and strong demand.