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.

This dataset is about books. It has 1 row and is filtered where the book is Trading in gold : how to buy, sell and profit in the market. It features 7 columns including author, publication date, language, and book publisher.

This dataset contains monthly gold prices from 1950-01 to 2020-07. Gold is a precious metal that has been used as a store of value and a medium of exchange for thousands of years, and is still widely traded in financial markets today. The gold price is influenced by a variety of factors, including global economic conditions, geopolitical events, and supply and demand dynamics.

The dataset includes a total of 847 data points, with each row representing the gold price for a particular month. The data was sourced from the World Gold Council and is in USD per troy ounce.

This dataset can be used for a variety of applications, including financial analysis, time series forecasting, and machine learning modeling. Potential use cases include predicting future gold prices based on historical trends, analyzing the relationship between gold prices and other economic indicators, and developing trading strategies for gold-related assets.

Data Source: World Gold Council

This dataset provides values for GOLD RES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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 is about book subjects. It has 2 rows and is filtered where the books is Gold futures : facts & figures : trading strategies & tactics. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Dataset of historical annual gold prices from 1970 to 2024, including significant events and acts that impacted gold prices.

Description for Kaggle Project

Title: Gold Price Prediction

Subtitle: Analysis and Forecasting Using Gold Price Data from Kaggle's goldstock.csv

Description This project aims to analyze and forecast gold prices using a comprehensive dataset spanning from January 19, 2014, to January 22, 2024. The dataset, sourced from Kaggle, includes daily gold prices with key financial metrics such as opening and closing prices, trading volume, and the highest and lowest prices recorded each trading day. Through this project, we perform time series analysis, develop predictive models, formulate and backtest trading strategies, and conduct market sentiment and statistical analyses.

Upload an Image - Choose a relevant image such as a graph of gold price trends, a gold bar, or an illustrative image related to financial data analysis.

Datasets - Source: Kaggle - File: goldstock.csv

Context, Sources, and Inspiration -Context: Understanding the dynamics of gold prices is crucial for investors and financial analysts. This project provides insights into historical price trends and equips users with tools to predict future prices. - Sources: The dataset is sourced from Kaggle and contains historical gold price data obtained from Nasdaq. Inspiration: The inspiration behind this project is to enable researchers, analysts, and data enthusiasts to make informed decisions, develop trading strategies, and contribute to a broader understanding of market behavior.

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.

This dataset is about books. It has 1 row and is filtered where the book is Akan weights and the gold trade. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides values for GOLD RESERVESWHY GOLD IS IMPORTANT reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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

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

Permutable AI’s Silver Intelligence dataset (XAG) provides deep insights into silver’s dual role as both a precious and industrial metal. The system captures industrial demand drivers such as solar panels, EVs, and 5G technology, alongside monetary policy and geopolitical influences. With story signal analysis, the dataset identifies new narratives and evolving sentiment that impact silver prices. Historical datasets and the Co-Pilot API enable systematic traders and analysts to model silver’s performance across macroeconomic cycles.

DAKOTA GOLD CORP stock trading activity and institutional ownership changes

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.

Project developed by Giovanni Federico (Università di Pisa) and Antonio Tena Junguito (Universidad Carlos III de Madrid). This data base outlines changes in world trade from 1800 to 2016. Dataset: Gold Coast - Ghana

Permutable AI’s Copper Intelligence dataset delivers structured analytics on global copper markets, tracking mine production, industrial demand, and trade flows. By processing global news, the system highlights supply disruptions, such as Chilean mine strikes, and demand-side drivers tied to infrastructure and energy transition. Advanced story signal detection captures new developments, sentiment shifts, and persistent narratives affecting copper prices. Historical datasets and millisecond-latency API access make this dataset essential for traders and analysts monitoring one of the most critical industrial metals.

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including UTXO, STXO, WAL, CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

Imports of Gold, Nonmonetary Sitc in the United States decreased to 783 USD Million in February from 978 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Gold, Nonmonetary Sitc.