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 rose to 3,476.40 USD/t.oz on September 1, 2025, up 0.79% from the previous day. Over the past month, Gold's price has risen 3.03%, and is up 39.21% 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 comprehensive dataset offers a decade's worth of insights into gold price trends, spanning from 2013 to 2023. It meticulously captures the daily opening and closing prices, highs and lows, along with trading volume for each day. Such a wealth of information can be instrumental for those seeking to analyze or visualize market dynamics over this ten-year period. All data was sourced from the authoritative platform: Investing.com Gold Historical Data

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).

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.

This Dataset contains Historical Price of Gold in Indian Commodity Market . The data has been collected from https://in.investing.com/commodities/ using web scrapping . The script can be customized to suit the needs (like customizing frequency interval , commodity type etc ) Link to web scrapping script - https://github.com/Pritam3355/web_scrapping/blob/master/stock_price.py

Column contains - Date, Price ,Open , High ,Low ,Volume ,Chg% these columns can be sorted first in the website then use the url in script to download the data according to your need

Gold prices in , July, 2025 For that commodity indicator, we provide data from January 1960 to July 2025. The average value during that period was 603.55 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3352.66 USD per troy ounce in June 2025. | TheGlobalEconomy.com

Bullion Price: Monthly Average: Mumbai: Gold: Standard data was reported at 84,995.000 INR/10 g in Feb 2025. This records an increase from the previous number of 79,079.000 INR/10 g for Jan 2025. Bullion Price: Monthly Average: Mumbai: Gold: Standard data is updated monthly, averaging 9,691.000 INR/10 g from Apr 1990 (Median) to Feb 2025, with 419 observations. The data reached an all-time high of 84,995.000 INR/10 g in Feb 2025 and a record low of 3,285.000 INR/10 g in Jul 1990. Bullion Price: Monthly Average: Mumbai: Gold: Standard data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.PG002: Memo Items: Bullion Price.

Content

Daily gold prices (2014-01-01 to 2025-01-06)

Acknowledgements

Raw Data Source: MCX Market This data frame is pre-processed to time series analysis and forecasting

Inspiration

Forecast, Predict Prices, Time Series Forecasting

Note

Prices are in INR (Indian Rupee) for 10gm of Gold 24K.

Gold Prices in this dataset makes no guarantee or warranty on the accuracy or completeness of the data provided.

In 2016, the global imports of gold totaled 10K tons, reducing by -7.1% against the previous year figure. Overall, it indicated a prominent expansion from 2007 to 2016: the total imports volume incr...

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.

Is Gold Still the Safe Haven? S&P GSCI Gold Index

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.

Barrick Gold (GOLD) - Digging for Gains: A Golden Opportunity?

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

Silver fell to 40.69 USD/t.oz on September 2, 2025, down 0.09% from the previous day. Over the past month, Silver's price has risen 8.74%, and is up 45.03% 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 September of 2025.

Context

Content

Ticker Description 0 GC=F Gold 1 SI=F Silver 2 CL=F Crude Oil 3 ^GSPC S&P500 4 PL=F Platinum 5 HG=F Copper 6 DX=F Dollar Index 7 ^VIX Volatility Index 8 EEM MSCI EM ETF 9 EURUSD=X Euro USD 10 ^N100 Euronext100 11 ^IXIC Nasdaq 12 ^BSESN Bse sensex 13 ^NSEI Nifty 50 14 ^DJI Dow

In 2016, the global imports of gold totaled 10K tons, reducing by -7.1% against the previous year figure. Overall, it indicated a prominent expansion from 2007 to 2016: the total imports volume incr...

Snapshot img

India Jewelry Market Size 2025-2029

The India jewelry market size is forecast to increase by USD 25.6 billion, at a CAGR of 5.9% between 2024 and 2029.

Major Market Trends & Insights

By Type - Gold segment was valued at USD 46.20 billion in 2022
By Distribution Channel - Specialist retailers segment accounted for the largest market revenue share in 2022

Market Size & Forecast

Market Opportunities: USD 64.43 billion
Market Future Opportunities: USD 25.60 billion 
CAGR : 5.9%

Market Summary

The market is a significant player in the global industry, with a substantial contribution to the world's total production and exports. According to industry reports, India's jewelry market size was valued at around USD 35 billion in 2020, representing a notable share of the global market. The sector's growth can be attributed to several factors, including the country's rich heritage in jewelry crafting, a large consumer base, and increasing demand for gold and precious stones. In recent years, there has been a noticeable shift in consumer preferences towards online sales channels. The online the market is projected to grow at a steady pace, driven by factors such as convenience, affordability, and the growing acceptance of digital transactions.
Despite this trend, traditional brick-and-mortar stores continue to dominate the market, accounting for the majority of sales. Gold remains the most popular metal in the Indian jewelry market, with demand driven by cultural traditions, investment purposes, and fashion trends. However, other precious metals and stones, such as silver, diamonds, and precious gems, also hold considerable market share. The Indian jewelry industry faces various challenges, including increasing raw material costs, intense competition, and changing consumer preferences. To stay competitive, players in the market are focusing on innovation, product diversification, and digital transformation. Despite these challenges, the future looks promising for the Indian jewelry market, with opportunities for growth in both domestic and international markets.

What will be the size of the India Jewelry Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The Indian jewelry market exhibits a significant presence in the global industry, accounting for a substantial market share. Currently, the market experiences a steady expansion, with approximately 12% of the global jewelry demand being met from India. Looking forward, the market is projected to witness a continuous growth trajectory, with future expectations indicating a potential increase of around 15%. A notable comparison reveals that India's jewelry market outperforms several other industries in terms of growth. For instance, the market for jewelry in India has grown at a faster pace compared to the global average growth rate of the jewelry industry over the past five years.
This trend is driven by various factors, including increasing consumer disposable income, growing urbanization, and changing fashion trends. Moreover, the Indian jewelry market's diverse product offerings, ranging from traditional to contemporary designs, cater to a broad consumer base, further contributing to its growth. Additionally, the market's competitive landscape is characterized by numerous small and medium-sized enterprises, ensuring a vibrant and dynamic business environment.

How is this India Jewelry Market segmented?

The jewelry in India industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Gold
  Diamond
  Others


Distribution Channel

  Specialist retailers
  Online


End-user

  Women
  Men


Product Type

  Necklaces and chains
  Earrings
  Others


Geography

  APAC

    India

By Type Insights

The gold segment is estimated to witness significant growth during the forecast period.

The Indian jewelry market, with a focus on legal compliance, is a significant sector driven by the continuous demand for gold jewelry. According to recent market trends, gold jewelry demand has experienced a 15% increase, with an estimated 20% of the population purchasing gold for various reasons, including cultural significance and investment purposes. In the near future, industry experts anticipate a 12% expansion in the gold jewelry market due to increasing disposable income and changing consumer preferences. Beyond gold, the Indian jewelry industry is evolving with the adoption of advanced technologies and sustainable practices. For instance, jewelry insurance policies, wholesale distribution, and customer relationship management have become essential components of the market.

Pricing strategies and marketing techniques are also undergoing tra

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: What's the Forecast?

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

According to Cognitive Market Research, The Global Gold Mining market size is USD 202515.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 3.80% from 2023 to 2030.

The demand for Gold Mining is rising due to the technological advancements in exploration and extraction and rising demand for gold in various industries.
Demand for Hardrock (LODE) mining remains higher in the Gold Mining market.
The Investment category held the highest Gold Mining market revenue share in 2023.
North American Gold Mining will continue to lead, whereas the Asia Pacific Gold Mining market will experience the most substantial growth until 2030.

How did COVID–19 impact the Gold Mining market?

The COVID-19 pandemic had a multifaceted impact on the Gold Mining market. Initially, the global economic uncertainty and financial market volatility triggered a surge in demand for gold as a safe-haven asset, driving up gold prices. However, the pandemic also disrupted mining operations worldwide due to lockdowns, supply chain interruptions, and workforce limitations. The implementation of social distancing measures and health protocols led to operational slowdowns and, in some cases, temporary halts in gold mining activities. Additionally, travel restrictions hindered exploration and development projects. Despite these challenges, the resilience of gold as a safe investment during uncertain times sustained market interest.

MARKET DYNAMICS: KEY DRIVERS

Global Economic Conditions and Gold Prices to Provide Viable Market Output

The Gold Mining market is significantly influenced by global economic conditions and the prevailing prices of gold. Economic uncertainties, geopolitical tensions, and inflation concerns often drive investors towards gold as a safe-haven asset. Consequently, higher demand for gold results in increased exploration, production, and investment in the gold mining sector. Fluctuations in gold prices directly impact the profitability of gold mining operations, influencing production decisions and exploration activities.

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.

Growing population with high income and demand for gold jewellery to propel market growth

Technological Advancements in Exploration to Propel Market Growth

One key driver in the Gold Mining market is the continuous advancement of technology in exploration methods. Innovative technologies, such as remote sensing, geophysical surveys, and advanced drilling techniques, enhance the efficiency and precision of gold exploration. These technological advancements not only contribute to the discovery of new gold deposits but also improve the accuracy of resource estimation. The integration of artificial intelligence and data analytics further enhances decision-making processes, allowing gold mining companies to optimize exploration efforts, reduce exploration risks, and maximize the discovery of economically viable gold reserves.

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..

Environmental and Regulatory Challenges to Restrict Market Growth

Environmental and regulatory challenges emerge as significant restraints in the Gold Mining market. Stringent environmental regulations and increased scrutiny on the impact of mining activities on ecosystems pose hurdles for gold mining companies. Compliance with environmental standards necessitates sophisticated waste management and reclamation practices, adding operational complexities and costs. Additionally, securing permits for exploration and mining activities becomes a prolonged process, causing delays.

The Gold Mining market refers to the sector of the mining industry dedicated to the exploration, extraction, refining, and commercialization of gold. Gold mining involves various processes, from prospecting and geological assessments to the extraction of gold-bearing ores and the subsequent processing of extracted materials to obtain refined gold. Market is fuled by technological advancements in exploration and extraction and rising demand for gold ...

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: Analysts Bullish on Future Prospects.

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.

Is the S&P GSCI Gold Index Signaling a Shift in 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