Gold rose to 3,362.51 USD/t.oz on August 1, 2025, up 2.25% from the previous day. Over the past month, Gold's price has risen 0.15%, and is up 37.65% 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 August of 2025.

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

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 ---

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).

Analysis of ‘Gold Prices’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/tunguz/gold-prices on 13 February 2022.

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

Context

Historical Annual and Monthly Gold Prices.

Content

Historical Annual and Monthly Gold Prices. Retrieved from DataHub, and expected to be updated annually.

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

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19238395%2F16f18b685654bc1f07d8d614bcea2e13%2FScreenshot%202024-06-25%20141846.png?generation=1719305359168334&alt=media" alt="">

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?

This document contains statistical data and analysis of global gold demand and prices from 2010 to 2024, presented by Dojipedia, a website focused on Forex investment information. The data is organized quarterly and includes various categories of gold demand such as jewelry fabrication, technology use, investment, and central bank purchases. It also provides the LBMA gold price in US dollars per ounce for each quarter.The document highlights significant events that influenced gold prices and demand during this period. These events include major economic crises, geopolitical tensions, and market shifts. For instance, it mentions the European debt crisis in 2010, the U.S. credit rating downgrade in 2011, the Federal Reserve's quantitative easing tapering signals in 2013, and the COVID-19 pandemic's impact starting in 2020.The data shows how gold demand and prices often increase during times of economic uncertainty or political instability, as investors view gold as a safe-haven asset. For example, gold prices reached record highs in 2024 amid global economic and geopolitical uncertainties.Dojipedia presents itself as a platform with five years of Forex market investment experience. The site offers free educational content on technical analysis methods such as Elliott Wave, ICT Trading, and Smart Money Concept. It also mentions plans to publish free books on technical analysis.The document includes a disclaimer stating that the information provided is for general purposes only and not financial advice. It warns about the high risks associated with investing in financial markets like CFDs, Forex, cryptocurrencies, and gold. The disclaimer emphasizes that leveraged products may not be suitable for all investors due to the high risk to capital.Overall, this document serves as a comprehensive resource for those interested in gold market trends and their relationship to global economic events over the past decade and a half.

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 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

Dataset Card for Sentiment Analysis of Commodity News (Gold)

This is a news dataset for the commodity market which has been manually annotated for 10,000+ news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021). The dataset was curated by Ankur Sinha and Tanmay Khandait and is detailed in their paper "Impact of News on the Commodity Market: Dataset and Results." It is currently published by the authors on… See the full description on the dataset page: https://huggingface.co/datasets/SaguaroCapital/sentiment-analysis-in-commodity-market-gold.

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!*

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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

Analysis of ‘Gold Historical Datasets’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nward7/gold-historical-datasets on 28 January 2022.

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

Content

This contains data files of gold historical data (USD).

Date : Date of observation Price: The official price of the given day, month, or year Open : Opening price on the given day, month, or year High : Highest price on the given day, month, or year Low : Lowest price on the given day, month, or year Volume : Volume of transactions on the given day, month, or year Change %: Percent change of the previous and current (day, month, or year) price.

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

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.

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. It is one of the crucial financial assets with a liquid market, intrinsic value, and diversified uses in jewelry, electronics, and for investment purposes. Gold includes both the physical bullion and ETF markets. Mining and refining technological innovations enhance efficiency and sustainability.Gold provides economic stability and security of investments since it is durable, widely accepted, and one that diversifies portfolios. Hence, gold holds a very significant place both in consumer markets and financial systems through its support for industries ranging from luxury goods to technology. 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.

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.

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

Analysis of ‘Sentiment Analysis of Commodity News (Gold)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ankurzing/sentiment-analysis-in-commodity-market-gold on 14 February 2022.

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

Context

This is a news dataset for the commodity market where we have manually annotated 11,412 news headlines across multiple dimensions into various classes. The dataset has been sampled from a period of 20+ years (2000-2021).

Content

The dataset has been collected from various news sources and annotated by three human annotators who were subject experts. Each news headline was evaluated on various dimensions, for instance - if a headline is a price related news then what is the direction of price movements it is talking about; whether the news headline is talking about the past or future; whether the news item is talking about asset comparison; etc.

Acknowledgements

Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." In Future of Information and Communication Conference, pp. 589-601. Springer, Cham, 2021.

https://arxiv.org/abs/2009.04202 Sinha, Ankur, and Tanmay Khandait. "Impact of News on the Commodity Market: Dataset and Results." arXiv preprint arXiv:2009.04202 (2020)

We would like to acknowledge the financial support provided by the India Gold Policy Centre (IGPC).

Inspiration

Commodity prices are known to be quite volatile. Machine learning models that understand the commodity news well, will be able to provide an additional input to the short-term and long-term price forecasting models. The dataset will also be useful in creating news-based indicators for commodities.

Apart from researchers and practitioners working in the area of news analytics for commodities, the dataset will also be useful for researchers looking to evaluate their models on classification problems in the context of text-analytics. Some of the classes in the dataset are highly imbalanced and may pose challenges to the machine learning algorithms.

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

Dataset

This dataset was created by Nguyễn Văn Phong

Contents