Gold fell to 3,376.98 USD/t.oz on August 27, 2025, down 0.49% from the previous day. Over the past month, Gold's price has risen 1.88%, and is up 34.64% 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 August 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.

Dataset historical price data for XAU/USD (gold vs USD) from 2004 to Feb 2025, captured across multiple timeframes including 5-minute, 15-minute, 30-minute, 1-hour, 4-hour, daily, weekly, and monthly intervals. Dataset includes Open, High, Low, Close prices, and Volume data.

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

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.

Monthly gold prices in USD since 1833 (sourced from the World Gold Council). The data is derived from historical records compiled by Timothy Green and supplemented by data provided by the World Bank...

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.

Machine Learning Models for Gold Price Prediction

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

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

Silver fell to 38.22 USD/t.oz on August 27, 2025, down 1.02% from the previous day. Over the past month, Silver's price has risen 0.12%, and is up 30.96% compared to the same time last year, 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 August of 2025.

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

The dataset was collected for the period spanning between 01/07/2019 and 31/12/2022.The historical Twitter volume were retrieved using ‘‘Bitcoin’’ (case insensitive) as the keyword from bitinfocharts.com. Google search volume was retrieved using library Gtrends. 2000 tweets per day using 4 times interval were crawled by employing Twitter API with the keyword “Bitcoin. The daily closing prices of Bitcoin, oil price, gold price, and U.S stock market indexes (S&P 500, NASDAQ, and Dow Jones Industrial Average) were collected using R libraries either Quantmod or Quandl.

The table presents the correlation among illiquidity series and volatility series for all financial markets. The sample runs from January 1, 2010 to March 22, 2021.

This dataset provides comprehensive end-of-day (EoD) pricing data for commodities options and futures, offering insights across a variety of currencies. It caters to traders, analysts, and institutions involved in commodity markets, providing critical data for hedging, risk management, and market analysis.

Key features of the dataset include:

End-of-Day Prices: Daily closing prices for a broad range of commodities options and futures. Commodities Coverage: Includes key commodity sectors such as energy (oil, natural gas), metals (gold, silver), agriculture (wheat, corn), and more. Multi-Currency Data: Pricing information is available in various currencies, allowing for global market analysis and cross-currency comparisons. Trading Volume & Open Interest: Data on the number of contracts traded and outstanding positions for market activity insights.

This dataset is essential for those tracking the commodities market, providing actionable data for strategy development, risk management, and financial decision-making.

Choose reference data from EDI and you will benefit from:

• A global data vendor offering affordable pricing structure.
• Fully customized data set to precisely fit your requirements.
• Flexible enterprise data licence options, we sell data, we do not rent data.
• Services from a company whose on-going commitment is to provide quality reference data solutions.

Description The Import/Export Price Index (End Use) for Nonmonetary Gold refers to a measure used to track changes in the prices of imported nonmonetary gold. Nonmonetary gold refers to gold that is not used as a medium of exchange or currency but rather for purposes such as jewelry, industrial applications, or investment.

The Import/Export Price Index tracks the changes in the prices paid for goods and services purchased/exported from other countries.

By focusing specifically on nonmonetary gold, this index provides insights into the cost fluctuations of imported/Exported gold for various end uses, such as jewelry making, industrial processes, or investment purposes.

Monitoring the Gold Price Index for Nonmonetary Gold can be useful for businesses, investors, policymakers, and economists to understand trends in the international gold market, gauge inflationary pressures, and make informed decisions related to trade, investment, and monetary policy.

Files IQ12260.csv --> Export Price Index IR14270.csv --> Import Price Index

Citation U.S. Bureau of Labor Statistics, Import Price Index (End Use): Nonmonetary Gold [IR14270], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IR14270, February 29, 2024.

U.S. Bureau of Labor Statistics, Export Price Index (End Use): Nonmonetary Gold [IQ12260], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IQ12260, February 29, 2024.

Gold Reserves in Nepal remained unchanged at 7.99 Tonnes in the second quarter of 2024 from 7.99 Tonnes in the first quarter of 2024. This dataset provides - Nepal Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In the context of the COVID-19’s outbreak and its implications for the financial sector, this study analyses the aspect of hedging and safe-haven under pandemic. Drawing on the daily data from 02 August 2019 to 17 April 2020, our key findings suggest that the contagious effects in financial assets’ returns significantly increased under COVID-19, indicating exacerbated market risk. The connectedness spiked in the middle of March, consistent with lockdown timings in major economies. The effect became severe with the WHO’s declaration of a pandemic, confirming negative news effects. The return connectedness suggests that COVID-19 has been a catalyst of contagious effects on the financial markets. The crude oil and the government bonds are however not as much affected by the spillovers as their endogenous innovation. In term of spillovers, we do find the safe-haven function of Gold and Bitcoin. Comparatively, the safe-haven effectiveness of Bitcoin is unstable over the pandemic. Whereas, GOLD is the most promising hedge and safe-haven asset, as it remains robust during the current crisis of COVID-19 and thus exhibits superiority over Bitcoin and Tether. Our findings are useful for investors, portfolio managers and policymakers interested in spillovers and safe havens during the current pandemic.

Returns of the indices used in the article "To hedge or not to hedge? Cryptocurrencies, gold and oil against stock market risk" (Echaust K, Just M, Kliber A). The data cover the period from January 2nd, 2020, to December 30th, 2022 and includes stock market indices of six countries (France– CAC 40, Germany – DAX, the United Kingdom – FTSE 100, the United States – S&P 500, Japan – Nikkei 225, and Hong Kong – Hang Seng)

Currently, the stock market is attractive, and it is challenging to develop an efficient investment model with high accuracy due to changes in the values of the shares for political, economic, and social reasons. This paper presents an innovative proposal for a short-term, automatic investment model to reduce capital loss during trading, applying a reinforcement learning (RL) model. On the other hand, we propose an adaptable data window structure to enhance the learning and accuracy of investment agents in three foreign exchange markets: crude oil, gold, and the Euro. In addition, the RL model employs an actor-critic neural network with rectified linear unit (ReLU) neurons to generate specialized investment agents, enabling more efficient trading, minimizing investment losses across different time periods, and reducing the model's learning time. The proposed RL model obtained a reduction average loss of 0.03% in Euro, 0.25% in Gold, and 0.13% in Crude Oil in the test phase with varying initial conditions.

Other-Long-Term-Assets Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.