Gold rose to 3,320.86 USD/t.oz on July 1, 2025, up 0.53% from the previous day. Over the past month, Gold's price has fallen 1.80%, but it is still 42.51% higher than a year ago, 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 July 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.

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.

Machine Learning Models for Gold Price Prediction

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

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.

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).

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.

What happens to gold if CPI increases?

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

Goldman Sachs raises its year-end gold price target to $3,700 due to economic uncertainties and strong demand. UBS also revises its forecast to $3,500, highlighting gold's status as a secure investment.

In 2021, the Myanmar's gold market decreased by -48.3% to $X for the first time since 2018, thus ending a two-year rising trend. Over the period under review, consumption saw a abrupt slump. Over the period under review, the market hit record highs at $X in 2015; however, from 2016 to 2021, consumption failed to regain momentum.

In 2025, the price of platinum is forecast to hover around 1,150 U.S. dollars per troy ounce. Meanwhile, the cost of per troy ounce of gold is expected to amount to 1,700 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 8,100 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 five 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 23.5 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 1,663 U.S. dollars per ounce, making it the most expensive precious metal in 2023.

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.

Predictions: S&P GSCI Gold index is expected to continue its upward trend in the near term, driven by safe-haven demand amid ongoing geopolitical uncertainties and concerns about global economic growth. The index may face some resistance at higher levels, but it is likely to break through and reach new highs. Risks: The main risks to the S&P GSCI Gold index's upward trend include a significant improvement in the global economic outlook, a sharp decline in geopolitical tensions, and a shift in investor sentiment towards riskier assets. A prolonged period of high inflation could also pose a risk to the index, as investors may seek alternative safe-haven assets such as bonds.

The average gold price increased by 1.7% to $1800 per troy ounce in 2021. This year, it was forecast to ease, but rising political uncertainty may reverse the forecast.

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.

How does stagflation affect gold prices?

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: 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

Context

Gold, the yellow shiny metal, has been the fancy of mankind since ages. From making jewelry to being used as an investment, gold covers a huge spectrum of use cases. Gold, like other metals, is also traded on the commodities indexes across the world. For better understanding time series in a real-world scenario, we will work with gold prices collected historically and predict its future value.

Content

Metals such as gold have been traded for years across the world. Prices of gold are determined and used for trading the metal on commodity exchanges on a daily basis using a variety of factors. Using this daily price-level information only, our task is to predict future price of gold.

Data

For the purpose of implementing time series forecasting technique , i will utilize gold pricing from Quandl. Quandl is a platform for financial, economic, and alternative datasets. To access publicly shared datasets on Quandl, we can use the pandas-datareader library as well as quandl (library from Quandl itself). The following snippet shows a quick one-liner to get your hands on gold pricing information since 1970s.

import quandl gold_df = quandl.get("BUNDESBANK/BBK01_WT5511")

The time series is univariate with date and time feature

Starter Kernel(s)

-Start with Fundamentals: TSA & Box-Jenkins Methods

This notebook is an overview of TSA and traditional methods

Acknowledgements

For this dataset and tasks, i will depend upon Quandl. The premier source for financial, economic, and alternative datasets, serving investment professionals. Quandl’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

Inspiration

• Forecast gold price

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

#

MORE DATASETs ...

Analysis of ‘Daily Gold Price (2015-2021) Time Series’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nisargchodavadiya/daily-gold-price-20152021-time-series on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Content

Daily gold prices (2014-01-01 to 2021-12-29)

Acknowledgements

Raw Data Source: https://in.investing.com/commodities/gold-mini This data frame is preprocessed to time series analysis and forecasting

Inspiration

Forecast, Predict Prices, Time Series Forecasting

Note

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

--- Original source retains full ownership of the source dataset ---

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.

Gold Target Market Outlook

The global gold target market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach around USD 3.7 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.3% during the forecast period. This steady growth is driven by various factors including increasing geopolitical uncertainties, inflation hedging characteristics of gold, and rising demand across different applications. The intrinsic value and limited supply of gold continue to make it a safe haven investment in times of economic volatility, further solidifying its role in diverse portfolios worldwide.

One of the significant growth factors driving the gold target market is the persistent demand for gold as a hedge against inflation and currency devaluation. In the face of fluctuating global economies and the ongoing volatility in currency markets, investors often turn to gold as a means to preserve wealth. The metalÂ’s ability to maintain its value over time makes it an attractive asset, especially in regions experiencing high inflation rates. Moreover, central banks continue to increase their gold reserves as part of their monetary policy strategies, thereby fueling demand in this market segment.

Another crucial factor contributing to the growth of the gold market is the expanding middle class and rising disposable incomes, particularly in developing economies. As incomes rise, so does the demand for luxury items, including gold jewelry. Countries like India and China, which have deep-rooted cultural affinities with gold, are witnessing significant increases in gold consumption for both investment and ornamental purposes. This cultural significance, combined with economic growth, has positioned the Asia Pacific region as a major consumer of gold, bolstering the market's global expansion.

Technological advancements and innovations in gold mining and refining processes are also propelling market growth. Modern techniques and equipment have improved the efficiency of gold extraction and processing, reducing costs and increasing output. Additionally, the development of new financial products like gold-backed exchange-traded funds (ETFs) has made gold investments more accessible to a broader range of investors. The convenience and flexibility of these products have attracted both retail and institutional investors, further driving market demand.

The emergence of Edible Gold Beverage is an intriguing development in the gold market, blending luxury with culinary innovation. This unique product taps into the growing trend of gourmet experiences, where consumers seek novel and opulent ways to indulge. Edible gold, known for its non-toxic and inert properties, is increasingly being used to enhance beverages, offering a visually stunning and luxurious appeal. This trend is particularly popular in high-end restaurants and events, where presentation and exclusivity are paramount. The incorporation of gold into beverages not only elevates the sensory experience but also aligns with the cultural significance of gold as a symbol of wealth and celebration. As consumer preferences evolve towards unique and extravagant experiences, the Edible Gold Beverage market is poised for growth, attracting both connoisseurs and curious consumers alike.

Regionally, Asia Pacific dominates the gold target market, accounting for a significant share due to its large population, cultural affinity for gold, and increasing economic power. North America and Europe follow with substantial market contributions, driven by investment demand and industrial applications. The Middle East, with its strong cultural and economic ties to gold, also presents a lucrative market, while Latin America is emerging as a notable player due to its rich natural gold reserves and growing investments in mining infrastructure.

Product Type Analysis

The segmentation of the gold market by product type includes bullion, coins, jewelry, and exchange-traded funds (ETFs). Gold bullion, comprising bars and ingots, represents a significant portion of the market due to its traditional use as a store of value and its appeal to both retail and institutional investors. As a tangible asset, bullion is favored for its purity and weight, often considered the most direct way to hold gold. The demand for bullion remains robust amidst economic uncertainties, with investors seeking security against market fluctuations and geopolitical tensions.

Coins are

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 Rush: Price of Gold Set to Hit $2,100 by Year-End

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