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.

This data set provides a comprehensive record of daily gold prices from January 19, 2014 to January 22, 2024. The data is provided by Nasdaq and includes key financial metrics for each trading day. . The dataset consists of the following columns:

• Date: A unique date for each trading day recorded.
• Close: The closing price of gold on the relevant date.
• Volume: Gold trading volume on the relevant date.
• Open: The opening price of gold on the relevant date.
• High: The highest recorded price of gold during the trading day.
• Low: The lowest price recorded for gold in the trading day. This data set is valuable for researchers, analysts, and data enthusiasts who want to understand historical trends, patterns, and changes in gold prices over time. Analysts can use this database to perform a variety of analyses, including price forecasting, trend spotting, and market behavior studies.

Possible conditions: - Time Series Analysis: Explore trends and patterns in gold prices over a given period. - Advanced Modeling: Build models to predict future gold prices based on historical data. - Trading Strategy Development: Develop and reverse trade strategies using the given price and volume information. - Market Sentiment Analysis: Analyze the impact of market events on gold prices and assess market sentiment. - Statistical Analysis: Perform tests and statistical analysis to gain insight into the characteristics of gold price movements.

Description: Users are advised to verify the accuracy and reliability of the information and to be aware of the limitations and biases inherent in financial databases. In addition, it is important to consider external factors such as economic indicators, geopolitical events, and market sentiment when using databases for analysis and use.

United States Index: Philadelphia Stock Exchange: Gold and Silver data was reported at 64.460 Jan1979=100 in Nov 2018. This records an increase from the previous number of 64.280 Jan1979=100 for Oct 2018. United States Index: Philadelphia Stock Exchange: Gold and Silver data is updated monthly, averaging 94.040 Jan1979=100 from Jan 1980 (Median) to Nov 2018, with 467 observations. The data reached an all-time high of 226.580 Jan1979=100 in Dec 2010 and a record low of 43.870 Jan1979=100 in Oct 2000. United States Index: Philadelphia Stock Exchange: Gold and Silver data remains active status in CEIC and is reported by Philadelphia Stock Exchange. The data is categorized under Global Database’s United States – Table US.Z014: Philadelphia Stock Exchange: Indexes.

This dataset can be used to predict the stock market. The data is extracted from MT5 terminal integrated in python.

The datasets include the minute by minute fluctuations of Gold and Silver prices over from 1st of January 2023 to 17th April 2025. The data can be used to train models for seasonality or a minute-by-minute approach.

The data has 7 columns:

• Time: The minute in which the event or movement occurred (Can be converted to Datetype with pd.to_datatime)
• Open Price
• High Price
• Low Price
• Close Price (The Feature to Predict if desired)
• Tick Volume: The Volume at the in which the event or movement occurred
• EMA: Exponential Moving Average (Technical Estimator for Risk management in the predictions and overall better performance)
• OBV: On-Balance Volume (Technical Estimator for Risk management in the predictions and overall better performance)

Two datasets are used;

Achilles Data Gold-Silver: with 1,416,340 rows to predict Gold, Silver and other Metals. Achilles Data Gold: with 708,264 rows to predict Gold, Silver and other Metals.

You may find the paper of our implementation here: https://doi.org/10.48550/arXiv.2410.21291

The size of the Gold Market was valued at USD 3.2 Trillion in 2023 and is projected to reach USD 4.5 Trillion by 2032, with an expected CAGR of 7.38% during the forecast period. Recent developments include: March 2023: Pan American Silver Corporation acquired all the issued and outstanding common shares of Yamana Gold Inc., as part of the arrangement, which includes its mines and increased the geographical operations of the company in Latin America., February 2023: Barrick Gold, the world's second-biggest gold producer, announced a 10% increase in attributable proved and probable gold mineral reserves to 76 million ounces net of depletion in 2022 while maintaining current reserves.. Key drivers for this market are: Demand for Gold in the form of Jewelry and Long-term Savings, Increasing Consumption in High-End Electronics Applications; Other Drivers. Potential restraints include: Declining Ore Grades and Other Technical Challenges, Other Restraints. Notable trends are: Jewelry Segment to Dominate the Demand.

Gold Fields 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 >>

China Warehouse Stock: Shanghai Future Exchange: Gold data was reported at 106.644 Ton in 27 Mar 2026. This records a decrease from the previous number of 106.743 Ton for 26 Mar 2026. China Warehouse Stock: Shanghai Future Exchange: Gold data is updated daily, averaging 1.530 Ton from Oct 2008 (Median) to 27 Mar 2026, with 4247 observations. The data reached an all-time high of 106.845 Ton in 20 Mar 2026 and a record low of 0.090 Ton in 30 May 2011. China Warehouse Stock: Shanghai Future Exchange: Gold data remains active status in CEIC and is reported by Shanghai Futures Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZB: Shanghai Futures Exchange: Commodity Futures: Stock.

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

As of May 2025, the London (morning fixing) price of an ounce of gold cost an average of ******** U.S. dollars, a slight increase compared to the average monthly morning fixing price of ******** U.S. dollars per ounce in the previous month.

London fixing gold price In January 2020, the average price for an ounce of fine gold was ******** U.S. dollars. It increased to ******** U.S. dollars as of April 2022. Although the monthly price for fine gold fluctuates, the average annual price of fine gold is gradually increasing. In 2001, the price for one ounce of gold was *** U.S. dollars, and by 2012 the price had risen to some ***** U.S. dollars. By 2024, the annual average gold price was nearly ***** dollars per ounce. In that year, global gold demand reached ******* metric tons worldwide. Price determinants of fine gold Fine gold is considered to be almost pure gold, where the value of the metal depends on the percentage of fineness. Twenty-four-carat gold is considered fine gold (from 99.9 percent gold by mass and higher). The London Gold Fix acts as a benchmark for the price of gold. The price of gold is set by the members of the London Gold Market Fixing Ltd undertaken by Barclays and its other members. The price is determined twice per business day at 10:30 am and 3:00 pm based on the London bullion market to settle contracts within the bullion market. The price is based on the equilibrium point between supply and demand agreed upon by participating banks. Gold prices must remain flexible, and gold fixing provides an instantaneous price at specified times.

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

Graph and download economic data for CBOE Gold ETF Volatility Index (GVZCLS) from 2008-06-03 to 2026-03-25 about ETF, VIX, volatility, gold, stock market, and USA.

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 ******.

شرح الكود علي اليوتيوب https://yt6.pics.ee/56xmxb

About Dataset Data Overview: This data file is a Comma separated value file format with 2290 rows and 7 columns. It contains 5 columns which are numerical in datatype and one column in Date format. Clearly the data shows value of the variables SPX,GLD,USO,SLV,EUR/USD against the dates in the date column.

Data consists of various GLD (gold) prices for several days in the period of 10 years [Date- MM/DD/YYYY].

SPX - The Standard and Poor's 500, or simply the S&P 500, is a stock market index tracking the performance of 500 large companies listed on stock exchanges in the United States. GLD - SPDR Gold Shares is part of the SPDR family of exchange-traded funds (ETF) managed and marketed by State Street Global Advisors. USO - The United States Oil Fund ® LP (USO) is an exchange-traded security whose shares may be purchased and sold on the NYSE Arca. SLV - The iShares Silver Trust (SLV) is an exchange traded fund (ETF) that tracks the price performance of the underlying holdings in the LMBA Silver Price. EUR/USD - The Currency Pair EUR/USD is the shortened term for the euro against U.S. dollar pair, or cross for the currencies of the European Union (EU) and the United States (USD). The value of the EUR/USD pair is quoted as 1 euro per x U.S. dollars. For example, if the pair is trading at 1.50, it means it takes 1.5 U.S. dollars to buy 1 euro.

Context

These datasets can be used in predicting the Bitcoin price movement with respect to the Gold, Oil, and the Stock market. There are separate datasets for Oil, Bitcoin, and Gold, if one wants to work on a particular thing at a time.

Content

• bitcoin_raw: The historic trade data of Bitcoin
• gold_df: Historic data of Gold price movement
• oil_price_raw: Contains the historic Oil price movements
• raw_v2_dataset: Close prices of Bitcoin, Gold, Oil, SP500 along with the VIX and other features
• vix_current: VIX historic values

Acknowledgements

• The base dataset is from Kaggle : https://www.kaggle.com/wojtekbonicki/bitcoin-data
• Bitcoin historic raw data is from Coindesk portal
• Oil historic raw data has been acquired from Yahoo Finance
• VIX close dataset is from CBOE
• Gold historic data is from World Gold Council (GoldHub)

Inspiration

The paper one may take inspiration from : Rama Malladi et al. (2019), "Predicting Bitcoin Return And Volatility Using Gold And The Stock Market"

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

The price of gold per troy ounce increased considerably between 1990 and 2025, despite some fluctuations. A troy ounce is the international common unit of weight used for precious metals and is approximately **** grams. At the end of 2024, a troy ounce of gold cost ******* U.S. dollars. As of * June 2025, it increased considerably to ******** U.S. dollars. Price of – additional information In 2000, the price of gold was at its lowest since 1990, with a troy ounce of gold costing ***** U.S. dollars in that year. Since then, gold prices have been rising and after the economic crisis of 2008, the price of gold rose at higher rates than ever before as the market began to see gold as an increasingly good investment. History has shown, gold is seen as a good investment in times of uncertainty because it can or is thought to function as a good store of value against a declining currency as well as providing protection against inflation. However, unlike other commodities, once gold is mined it does not get used up like other commodities (for example, such as gasoline). So while gold may be a good investment at times, the supply demand argument does not apply to gold. Nonetheless, the demand for gold has been mostly consistent.

gold price dataset for a stock market analysis. Reference from Quandl https://www.quandl.com/

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

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: A Beacon of Precious Metal Value?

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