Gold rose to 4,533.64 USD/t.oz on March 27, 2026, up 3.51% from the previous day. Over the past month, Gold's price has fallen 14.82%, but it is still 46.99% 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 March of 2026.

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.

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

Context: This dataset contains the gold commodity news annotated into various dimensions including information such as past movements and expected directionality in prices, asset comparison and other general information that the news is referring to

Content: The dataset contains 12 columns.

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

Context

This is a news dataset for the commodity market where we have manually annotated 10,000+ 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.

As of December 1st, 2025, gold futures contracts to be settled in June 2031 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 December 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.

The purpose of this study was to look at the cointegration, spillover impact, and lead-lag connection for returns across spot price and the futures price of products gold and silver. Everyday spot and futures prices of Gold and Silver, which were heavily exchanged on the Multi Commodity Exchange (MCX) throughout 2019-2020, were used to compile the data for this study. The return series are considered for testing the spillover effect between the series through GARCH (1,1) Model. The series are checked for stationarity before using the GARCH (1,1) model. The ADF (Augmented Dickey Fuller) test was used to examine the return series' stationarity. The Johansen cointegration and Granger causality tests were used to validating the cointegration and lead-lag connection between futures and spot pricing for chosen bullion commodities after the return series was confirmed stationary. The analysis helps to achieve the following Hypotheses: H01: There is no stationarity in the dataset for the chosen bullion commodities. H02: Cointegration between futures and spot prices is not present in the market for the chosen bullion commodities. H03: The futures price is not influenced by the spot price for the chosen bullion commodities. H04: The Spot price is not influenced by the Futures price for the chosen bullion commodities

The finding provides a new view of the series cointegration, lead-lag, and spillover effect. The spot and futures prices of gold and silver are considered for price discovery. It tells derivatives traders that gold and silver are preferable assets for hedgers and speculators to diversify their portfolios.

Gold prices in , February, 2026 For that commodity indicator, we provide data from January 1960 to February 2026. The average value during that period was 635.09 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 5019.97 USD per troy ounce in February 2026. | TheGlobalEconomy.com

Gold is a primary global commodity used as a hedge against inflation and currency devaluation. This dataset combines long-term historical benchmarks from the World Bank with recent high-frequency market data to provide a continuous view of gold prices from 1995 through early 2026.

Content

The dataset consists of a single CSV file containing:

Date: The timestamp for the observation (Daily or Monthly).

Gold_Price_USD_YF: Market closing price in USD (via Yahoo Finance).

Gold_Price_WB_Monthly: Global benchmark price per troy ounce (via World Bank).

Data Sources

World Bank (wbdata): Historical global commodity "Pink Sheet" data.

Yahoo Finance (yfinance): Daily market spot and futures prices (Ticker: GC=F). https://finance.yahoo.com/quote/GC=F/history/

License

Data Files (CC BY 4.0): You are free to share and adapt this data as long as credit is given to the original sources (World Bank and Yahoo Finance).

Column Metadata

Assigning descriptions to individual columns is critical for a 10.0 usability score. | Column Name | Description | | :--- | :--- | | Date | The date of record in YYYY-MM-DD format. | | Gold_Price_USD_YF | The daily/monthly average closing price of Gold Futures in USD. | | Gold_Price_WB_Monthly | The monthly global average price of gold per troy ounce (World Bank benchmark). |

Dataset Citations:

World Bank (Primary source for 1995-2000): https://www.worldbank.org/en/research/commodity-markets

Yahoo Finance (Primary source for 2000-2026): https://finance.yahoo.com/quote/GC=F/history

Eurostat (Economic Indicators): https://ec.europa.eu/eurostat/data/database

FAOSTAT (Price Indices): https://www.fao.org/faostat/en/#data/PP

Keywords & Tags:

Finance, Commodities, Economics, Time Series Analysis, Global, Gold Prices, Historical Gold Prices, Monthly Gold Prices, World Bank, Yahoo Finance

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

CN: Commodity Trading Market over 100 M Yuan: Operating Area: Gold, Jeweller, Jade Market data was reported at 1,864.003 sq m th in 2023. This records a decrease from the previous number of 1,911.003 sq m th for 2022. CN: Commodity Trading Market over 100 M Yuan: Operating Area: Gold, Jeweller, Jade Market data is updated yearly, averaging 1,904.001 sq m th from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 2,556.300 sq m th in 2017 and a record low of 497.300 sq m th in 2008. CN: Commodity Trading Market over 100 M Yuan: Operating Area: 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: Operating Area.

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.

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

View weekly updates and historical trends for COMEX Gold Futures Total Reportable Long Positions. Source: US Commodity Futures Trading Commission. Track e…

CN: Commodity Trading Market over 100 M Yuan: Number of Booth: Gold, Jeweller, Jade Market data was reported at 17,926.000 Unit in 2023. This records an increase from the previous number of 15,622.000 Unit for 2022. CN: Commodity Trading Market over 100 M Yuan: Number of Booth: Gold, Jeweller, Jade Market data is updated yearly, averaging 16,182.000 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 22,017.000 Unit in 2019 and a record low of 6,688.000 Unit in 2008. CN: Commodity Trading Market over 100 M Yuan: Number of Booth: 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: Number of Booth.

This comprehensive dataset contains 10 years of historical gold futures prices (2016-2026) sourced from Yahoo Finance, along with advanced machine learning predictions extending through December 2026. Perfect for financial analysis, time series forecasting, and machine learning enthusiasts.

Keywords: gold price prediction, time series forecasting, XGBoost, financial analysis, commodity trading, investment analysis, machine learning finance, gold futures, technical analysis, price forecasting

Historical Data Columns:** - Date (index) - Open, High, Low, Close prices (USD) - Volume - Adjusted Close Machine Learning Projects - Time series forecasting - Regression modeling - Feature engineering practice - Model comparison studies

✅ Financial Analysis - Investment strategy backtesting - Risk assessment - Trend analysis - Portfolio optimization

✅ Educational Purposes - Learning technical analysis - Understanding commodity markets - Practicing data visualization - Exploring EDA techniques

Model Performance

🤖 XGBoost Regression Model: - R² Score: 0.99+ (99%+ variance explained) - MAPE: <1% (prediction error) - RMSE: ~$15-20 (typical error range) - Training Set: 2,000+ samples - Test Set: 500+ samples

Data Source & Citation

Primary Source: Yahoo Finance (yfinance Python API) Ticker Symbol: GC=F (Gold Futures - COMEX) API: https://pypi.org/project/yfinance/ License: Educational and research purposes

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

China Commodity Trading Market over 100 M Yuan: Number of Market: Gold, Jeweller, Jade Market data was reported at 17.000 Unit in 2023. This records a decrease from the previous number of 19.000 Unit for 2022. China Commodity Trading Market over 100 M Yuan: Number of Market: Gold, Jeweller, Jade Market data is updated yearly, averaging 22.500 Unit from Dec 2008 (Median) to 2023, with 16 observations. The data reached an all-time high of 28.000 Unit in 2012 and a record low of 15.000 Unit in 2009. China Commodity Trading Market over 100 M Yuan: Number of Market: 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: Number of Market.

CN: 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. CN: 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. CN: 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.

Discover the booming precious metal trading platform market! This in-depth analysis reveals a $15 billion market in 2025, projected to grow at an 8% CAGR through 2033. Learn about key drivers, trends, and top companies shaping this dynamic sector. Explore market segmentation, regional insights, and future opportunities.