Gold fell to 4,023.41 USD/t.oz on October 27, 2025, down 2.15% from the previous day. Over the past month, Gold's price has risen 4.96%, and is up 46.60% 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 October 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.

The Gold – Sample Data dataset captures structured insights into how sentiment, macroeconomic drivers, and market events influence gold prices. Covering multiple themes such as monetary policy, institutional buying, consumer demand, and supply dynamics, the dataset provides a transparent view of narrative flows that act as leading indicators for price direction. For the period 10–17 May 2025, the dataset highlights: Bearish sentiment from U.S. dollar strength and rising mining output. Bullish sentiment from central bank reserve purchases, jewellery demand recovery, and safe-haven flows amid geopolitical tensions. Policy influence with the Federal Reserve’s rate decisions directly impacting gold’s relative attractiveness. Each entry records timestamped events, directional sentiment (up/down), topic classification, and narrative detail, allowing systematic traders and analysts to test correlations between sentiment shifts and subsequent gold price action. This data helps quants and commodity desks integrate structured sentiment into models, evaluate thematic drivers of gold volatility, and identify predictive signals ahead of market moves.

As of June 25, 2024, gold futures contracts to be settled in June 2030 were trading on U.S. markets at around 2,795 U.S. dollars per troy ounce. This is above the price of 2,482.6 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.

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 12 November 2021.

--- 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 ---

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.

Historical AI model predictions and analysis for Gold (spot) stock across multiple timeframes and confidence levels

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.

Gunvor is expanding its precious metals division into physical bullion, capitalizing on a historic gold price rally and surging market volumes, by hiring key traders in major financial hubs.

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.

View weekly updates and historical trends for COMEX Gold Futures Swap Dealers Long Positions. Source: US Commodity Futures Trading Commission. Track econo…

View weekly updates and historical trends for COMEX Gold Combined Open Interest. Source: US Commodity Futures Trading Commission. Track economic data with…

View weekly updates and historical trends for COMEX Gold Futures Managed Money Short Positions. Source: US Commodity Futures Trading Commission. Track eco…

This dataset contains historical price data for seven essential metals traded on the Multi Commodity Exchange (MCX) India: Gold, Silver, Lead, Zinc, Copper, Nickel, and Aluminum. The data is meticulously collected to support prediction models, trend analysis, and statistical exploration of metal price movements.

The dataset includes: - Daily price data for 7 metals - Open price, high/low values, and closing prices - Data across multiple periods, useful for preliminary exploration, model training, and analysis

Description for each column in the dataset: 1. Date: The date on which the market data was recorded (format: DD-MM-YYYY). 2. Price: The closing price of Copper on the given date, reflecting the last traded price of the day. 3. Open: The opening price of Copper at the start of trading on the given date. 4. High: The highest price Copper reached during the trading day. 5. Low: The lowest price Copper traded at during the day. 6. Vol. (Volume): The total volume of Copper traded on the given day, typically in thousands (K). 7. Change %: The percentage change in the closing price from the previous trading day.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

Silver fell to 47.51 USD/t.oz on October 27, 2025, down 2.22% from the previous day. Over the past month, Silver's price has risen 1.26%, and is up 40.95% 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 October of 2025.

View weekly updates and historical trends for COMEX Gold Combined Managed Money Spread Positions. Source: US Commodity Futures Trading Commission. Track e…

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

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.