Gold fell to 4,723.07 USD/t.oz on April 13, 2026, down 0.60% from the previous day. Over the past month, Gold's price has fallen 5.67%, but it is still 47.02% 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 April 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 provides 5 sec interval historical data for Gold (MCX India). It includes Open, High, Low, Close, and Volume (OHLCV) for the period of 2024 to early 2026.

Source: Data fetched via Angel One SmartAPI. Context: Perfect for algorithmic traders and data scientists wanting to study the Indian commodity market or test intraday trading strategies. Timezone: Indian Standard Time (IST).

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

Gold prices in , March, 2026 For that commodity indicator, we provide data from January 1960 to March 2026. The average value during that period was 640.39 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

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

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.

Daily gold premium/discount data for China and India markets

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

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.

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. A precious item with its own duality. In one side, it's a popular investment asset. In another side, it's a commodity. Whether you buy it as an asset or as commodity, the price for gold is always influenced by two things, as similar as other commodities in market: supply and demand. It's not easy to combine many aspects in supply and demand into a single dataset without making it into wall of columns. And also aggregating the data might not easy to do, since the data might not available publicly. But it doesn't mean we can't learn the historical pattern of gold market. At least some gold price historical data are available for public. And we can use that to analyze the market pattern, and, maybe, learn something from them.

Content

This dataset was based on gold price historical data from macrotrends.net. I added one new column, 'Year Range Price', to see how wide the spread of the price annually.

Acknowledgements

The base data for this dataset was retrieved from https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart.

Inspiration

What variable have the biggest correlation with annual Average Closing Price? What information can we see from the graphic? Are there any reasons why the price drop and rise? What happened on those years? Many things can be learn and explore by historical data. Having historical data is like having a kaleidoscope to see the past, learn from them, and use it as information to walk on our future path.

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

Silver fell to 73.97 USD/t.oz on April 9, 2026, down 0.20% from the previous day. Over the past month, Silver's price has fallen 16.18%, but it is still 137.20% 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 April of 2026.

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.

The table presents the individual unit root test results for the illiquidity and volatility of each market. Illiquidity is measured using the Amihud measure for each financial market. The sample runs from January 1, 2010 to March 22, 2021.

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.

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.