Gold rose to 4,760.48 USD/t.oz on April 22, 2026, up 0.85% from the previous day. Over the past month, Gold's price has risen 7.99%, and is up 43.55% 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 April of 2026.

MetaData

GOLD Stock Price Data (2000-2024)

Dataset Description

This dataset contains historical stock price data for GOLD from 2000 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into GOLD's stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of GOLD stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of GOLD stock at the end of each trading day.

High: The highest price reached by GOLD stock during the trading day.

Low: The lowest price reached by GOLD stock during the trading day.

Open: The price of GOLD stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

Gold Fields stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Between January 2018 and June 2024, the price of shares in the SPDR Gold Shares ETF (also known as the SPDR Gold Trust) increased, reaching over *** dollars per share. The SPDR Gold Shares ETF is one of the largest precious metal ETF by assets under management and one of the ten largest holders of gold in the world - meaning that each share is effectively ownership of a share of gold bullion. Shares are bought and sold on the New York Stock Exchange, while much of the ETF's gold is held in London.

Centerra Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Context

GOLD

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Content

Geography: World

Time period: Nov 2004- August 2025

Unit of analysis: Gold Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

Among the five largest gold mining companies, Goldfields — headquartered in Johannesburg, South Africa-saw the largest growth in its share price recently. As of June 2025, the company had reached ****** index points. While all the top five mining companies experienced significant increase in their share prices over this period despite some fluctuations, Barrick Mining Corporation had the least increase when compared to others, with an index point of ******.

Lundin Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Context

SPDR Gold Shares (GLD) This fund buys gold bullion. The only time it sells gold is to pay expenses and honor redemptions​. Because of the ownership of bullion, this fund is extremely sensitive to the price of gold and will follow gold price trends closely.

One upside to owning gold bars is that no one can loan or borrow them. Another upside is that each share of this fund represents more gold than shares in other funds that do not buy physical gold. However, the downside is taxes. The Internal Revenue Service (IRS) considers gold a collectible, and taxes on long-term gains are high. (For more, see: The Most Affordable Way to Buy Gold: Physical Gold or ETFs?)

Content

Fund overview: CategoryCommodities Precious Metals Fund familySPDR State Street Global Advisors

Acknowledgements

Yahoo Finance

Inspiration

Dataset will be helpful for people who are looking to start playing the Time Series Analysis. What always got my attention was, when Dollar goes down DowJones and Nasdaq goes up and vice-versa. Can this dataset be used for creating a Causal Model?

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.

IAU:TSX i-80 Gold Corp.

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 dataset contains the predicted prices of the asset SPDR Gold Shares Defichain over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Live price, performance charts, and AI-powered analysis for SPDR Gold Shares (GLD) ETF. Updated daily by StoxPulse.

Eldorado Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Dataset

This dataset was created by Ragul V L

Released under MIT

Contents

New Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Gold reference prices published on the World Gold Council Goldhub data page, including reference prices from the London Bullion Market Association and Shanghai Gold Exchange. The dataset covers a range of frequencies including daily, weekly, monthly and annual observations, with historical coverage back to 2015 or earlier where available. Prices are quoted in currency units per troy ounce unless otherwise stated. Updated weekly.

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 Price Outlook: S&P GSCI Gold index Faces Volatility Amidst Economic Uncertainties

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

Barrick Gold stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Gold prices updated in real-time. Track the gold spot price in GBP, USD, EUR, JPY, AUD, CAD & CHF >>

Overview

This dataset contains historical market data for the SPDR Gold Shares ETF (GLD), widely used as a proxy for gold price movements in financial modeling and quantitative research.

To support experimentation and model robustness analysis, the dataset has been augmented with 210 synthetic time-series samples generated using statistically consistent distribution-aware methods.

This makes the dataset suitable for:

Time-series forecasting

Financial regression modeling

Quantitative finance experiments

Machine learning benchmarking

Educational and academic projects

Objective

The dataset enables the development of predictive models to forecast gold price behavior using structured financial features.

Common tasks include:

Predicting GLD closing price

Volatility modeling

Feature importance analysis

Time-series cross-validation studies

Financial ML experimentation

Synthetic Data Transparency

The additional 210 rows were generated using:

Normal distribution sampling (μ, σ) estimated from original numeric features

Value clipping to preserve realistic financial bounds

Sequential date extension (for time-series continuity)

Fixed random seed for reproducibility

⚠️ Important: Synthetic samples are intended for experimentation and modeling practice only. They should not be used for real-world trading decisions or financial backtesting.