Gold fell to 3,324.13 USD/t.oz on August 22, 2025, down 0.46% from the previous day. Over the past month, Gold's price has fallen 1.88%, but it is still 32.46% 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 August of 2025.

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.

As of the end of April 2024, boerse.de Gold was the best-performing gold exchange-traded commodity (ETC) worldwide. EUWAX Gold followed closely behind in second place, providing an annual return of ***** percent by the month of April.

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 ---

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.

China Commodity Trading Market over 100 M Yuan: Number of Booth by Category: Gold, Silver and Jewellery data was reported at 27,519.000 Unit in 2023. This records an increase from the previous number of 25,194.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Booth by Category: Gold, Silver and Jewellery data is updated yearly, averaging 24,582.500 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 33,707.000 Unit in 2019 and a record low of 9,428.000 Unit in 2008. China Commodity Trading Market over 100 M Yuan: Number of Booth by Category: Gold, Silver and Jewellery data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Number of Booth by Category.

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

In 2024, the rate of return on gold was 26.62 percent, making gold the leading commodity based on return rate in that year. Natural resources, like any other investment, exhibit a wide range of fluctuations over time.

Silver rose to 37.90 USD/t.oz on August 20, 2025, up 1.35% from the previous day. Over the past month, Silver's price has fallen 2.63%, but it is still 28.02% higher than a year ago, 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 August of 2025.

China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Gold, Jeweller, Jade Market data was reported at 2.965 RMB bn in 2023. This records a decrease from the previous number of 3.631 RMB bn for 2022. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Gold, Jeweller, Jade Market data is updated yearly, averaging 4.601 RMB bn from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 7.689 RMB bn in 2019 and a record low of 1.717 RMB bn in 2009. China Commodity Trading Market over 100 M Yuan: Turnover: Retail: Gold, Jeweller, Jade Market data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Wholesale, Retail and Catering Sector – Table CN.RJA: Commodity Trading Market over 100 Million Yuan: Turnover: Retail.

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

The global precious metal trading platform market is experiencing robust growth, driven by increasing investor interest in gold, silver, platinum, and palladium as safe-haven assets and diversification tools. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth is fueled by several key factors. Technological advancements, including the rise of mobile trading apps and sophisticated charting tools, are making precious metal trading more accessible to a wider range of investors. Furthermore, the increasing volatility in global financial markets is prompting investors to seek refuge in precious metals, bolstering demand for platforms facilitating their trading. Regulatory changes aiming to improve market transparency and investor protection are also indirectly supporting market expansion. However, challenges remain, including potential regulatory hurdles in specific regions and the inherent risks associated with volatile commodity markets. The market is segmented by platform type (web-based, mobile-based), trading style (spot, futures, options), and investor type (retail, institutional). Key players like GAIN Global Markets Inc., AxiTrader Limited, LMAX Global, IG Group, and CMC Markets are vying for market share through innovation, strategic partnerships, and expansion into new geographic markets. Competition is intense, forcing providers to continuously enhance their offerings and improve customer experience to retain a competitive edge. The forecast period of 2025-2033 presents significant opportunities for expansion, particularly in emerging markets with growing retail investor bases. The continued growth of the precious metal trading platform market is projected to be influenced by several ongoing trends. The increasing adoption of artificial intelligence (AI) and machine learning (ML) for algorithmic trading and risk management is expected to further enhance the efficiency and sophistication of trading platforms. The integration of blockchain technology for improved security and transparency is also gaining traction. However, potential restraints include cybersecurity threats, the need for robust compliance frameworks, and the ongoing evolution of investor preferences which necessitate platform adaptation. The expanding availability of educational resources and improved investor awareness about precious metals trading is expected to positively impact market growth. Furthermore, strategic mergers and acquisitions within the industry are likely to reshape the competitive landscape. Geographic expansion into underpenetrated regions, coupled with the development of tailored products to meet the specific needs of diverse investor segments, will be crucial for achieving sustained growth in the coming years.

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

The global precious metal trading platform market, valued at $3.863 billion in 2025, is projected to experience robust growth, driven by increasing investor interest in gold, silver, platinum, and palladium as safe haven assets and inflation hedges. The market's Compound Annual Growth Rate (CAGR) of 5.6% from 2019 to 2033 indicates a steady expansion, fueled by technological advancements such as improved online trading platforms, mobile accessibility, and the integration of AI-driven analytical tools. The rise of retail investors and the growing adoption of sophisticated trading strategies further contribute to market expansion. Increased regulatory scrutiny and cybersecurity concerns, however, pose potential restraints to growth. Market segmentation is likely dominated by platform types (e.g., web-based, mobile, desktop), trading styles (e.g., spot, futures), and investor demographics (e.g., retail, institutional). Key players like GAIN Global Markets, AxiTrader, LMAX Global, IG Group, and CMC Markets are vying for market share through competitive pricing, advanced features, and strong customer support. Geographic distribution is expected to be influenced by economic conditions and investor sentiment in major regions like North America, Europe, and Asia-Pacific. The forecast period (2025-2033) will likely see increased competition and consolidation as companies strive to enhance their offerings and cater to the evolving needs of traders. The market's sustained growth relies on several factors. The volatility of traditional financial markets consistently pushes investors toward precious metals. The ongoing development of user-friendly platforms with advanced charting, analytics, and educational resources further broadens the appeal to both experienced and novice traders. Moreover, the expansion of the market into emerging economies presents significant opportunities for growth. However, maintaining trust through robust security measures and complying with evolving regulatory frameworks are critical for long-term success. The presence of established players along with a growing number of smaller, niche platforms suggests a dynamic competitive landscape with continued innovation in technology and service offerings driving market expansion.

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

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 table presents the mean, standard deviation (SD) for the illiquidity and volatility of each commodity market as well as the stock market. Illiquidity is measured using the Amihud measure for each market. The sample runs from January 1, 2010 to March 22, 2021.

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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Discover all the exchange data, pricing and fundamentals, and research findings you'll need from the commodities metals market with LSEG's data options.

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.