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

Gold Reserves in the United States remained unchanged at 8133.46 Tonnes in the third quarter of 2025 from 8133.46 Tonnes in the second quarter of 2025. This dataset provides - United States Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Information on the amount of gold that is available across various U.S. Treasury-maintained locations, as well as data on the weight and book value of these gold reserves.

The U.S. Treasury-Owned Gold dataset provides the amount of gold that is available across various U.S. Treasury-maintained locations. The data shows whether the gold is held in deep storage or working stock, that is, available to the U.S. Mint as raw material for the creation of congressionally authorized coins. The dataset includes the weight of gold in troy ounces (a measurement unit still used today for precious metals and gunpowder) and the book value in dollars. The book value is not the market value, but instead represents the total number of troy ounces multiplied by a value established by law ($42.222), set in 1973.

Context

Historically, gold had been used as a form of currency in various parts of the world including the USA. In present times, precious metals like gold are held with central banks of all countries to guarantee re-payment of foreign debts, and also to control inflation which results in reflecting the financial strength of the country. Recently, emerging world economies, such as China, Russia, and India have been big buyers of gold, whereas the USA, SoUSA, South Africa, and Australia are among the big seller of gold.

Forecasting rise and fall in the daily gold rates can help investors to decide when to buy (or sell) the commodity. But Gold prices are dependent on many factors such as prices of other precious metals, prices of crude oil, stock exchange performance, Bonds prices, currency exchange rates, etc.

The challenge of this project is to accurately predict the future adjusted closing price of Gold ETF across a given period of time in the future. The problem is a regression problem, because the output value which is the adjusted closing price in this project is continuous value.

Content

Data for this study is collected from November 18th 2011 to January 1st 2019 from various sources. The data has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The dataset has 1718 rows in total and 80 columns in total. Data for attributes, such as Oil Price, Standard and Poor’s (S&P) 500 index, Dow Jones Index US Bond rates (10 years), Euro USD exchange rates, prices of precious metals Silver and Platinum and other metals such as Palladium and Rhodium, prices of US Dollar Index, Eldorado Gold Corporation and Gold Miners ETF were gathered.

The historical data of Gold ETF fetched from Yahoo finance has 7 columns, Date, Open, High, Low, Close, Adjusted Close, and Volume, the difference between Adjusted Close and Close is that the closing price of a stock is the price of that stock at the close of the trading day. Whereas the adjusted closing price takes into account factors such as dividends, stock splits, and new stock offerings to determine a value. So, Adjusted Close is the outcome variable which is the value you have to predict.

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Acknowledgements

The data is collected from Yahoo finance.

Inspiration

Can you predict Gold prices accurately using traditional machine learning algorithms

Gold fell to 4,199.97 USD/t.oz on December 2, 2025, down 0.75% from the previous day. Over the past month, Gold's price has risen 4.93%, and is up 58.92% 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 December of 2025.

Please find my Tableau viz for this dataset here: https://public.tableau.com/app/profile/jamie.collins5558/viz/CentralBankReserves/Dashboard1 Feel free to copy, or use as a template/inspiration for your own visualisations.

This dataset provides a comprehensive snapshot of central bank reserves, including foreign exchange (FX) reserves, total reserves, and gold holdings, for 165 countries. It includes detailed metrics such as gold reserves in tonnes and millions (USD), the percentage of total reserves held in gold, and the 20-year change in gold holdings. The dataset also categorises countries by region and economic grouping (e.g., high income, upper middle income, lower middle income, low income), offering a valuable resource for analysing global financial trends, reserve management strategies, and the role of gold in national economies.

• Country: The name of the country (e.g., Afghanistan, United States of America).
• Region: The geographical region of the country (e.g., Central Asia, Western Europe, Latin America & Caribbean).
• Economic grouping: The World Bank income classification of the country (e.g., High income, Upper middle income, - - -- Lower middle income, Low income).
• FX Reserves: Foreign exchange reserves in millions of USD (e.g., 68448.33 for Algeria). Some values are marked as "AWAITED" where data is unavailable.
• Total Reserves: Total reserves (including FX and gold) in millions of USD (e.g., 83007.11 for Algeria). Some values are marked as "AWAITED."
• Gold Reserves Tonnes: Gold reserves held by the central bank in metric tonnes (e.g., 173.56 for Algeria). Some values are marked as "AWAITED."
• Gold Reserves Millions: The value of gold reserves in millions of USD (e.g., 14558.78 for Algeria). Some values are marked as "AWAITED."
• Holdings %: The percentage of total reserves held in gold (e.g., 17.54 for Algeria). Some values are marked as "AWAITED."
• 20yr change: The change in gold holdings (in tonnes) over the past 20 years (e.g., -0.09 for Algeria). Positive values indicate an increase, while negative values indicate a decrease.

Key Statistics Countries Covered: 165 - Regions Represented: Includes Central Asia, Western Europe, Latin America & Caribbean, Middle East & North Africa, Sub-Saharan Africa, South East Asia, East Asia, South Asia, Australasia / Oceania, and North America. - Economic Groupings: High income (e.g., United States, Japan), Upper middle income (e.g., Brazil, China), Lower middle income (e.g., India, Egypt), and Low income (e.g., Afghanistan, Haiti). - Largest Gold Reserves: The United States holds the largest gold reserves at 8,133.46 tonnes, valued at $682,276.85 million, accounting for 74.97% of its total reserves. - Highest Gold Holdings %: Bolivia has the highest percentage of reserves in gold at 95.59%, despite holding only 22.53 tonnes. - Largest 20-Year Increase in Gold: The Russian Federation increased its gold holdings by 1,945.79 tonnes over 20 years, followed by China with a 1,684.55-tonne increase. Potential Use Cases

This dataset is ideal for a variety of analytical and research purposes, including:

• Economic Analysis: Investigate the relationship between a country’s economic grouping and its reserve composition, particularly the reliance on gold versus foreign exchange.
• Financial Stability Studies: Analyse how countries with higher gold holdings percentages (e.g., Bolivia, Uzbekistan) manage financial stability compared to those with lower percentages (e.g., Chile, South Korea).
• Historical Trends: Use the 20-year change in gold holdings to study trends in reserve management strategies, such as China and Russia’s significant increases in gold reserves.
• Geopolitical Insights: Explore how regions like Central Asia (e.g., Kazakhstan, Uzbekistan) or Middle East & North Africa (e.g., Qatar, Saudi Arabia) differ in their reserve strategies, potentially reflecting geopolitical priorities.
• Data Visualisation: Create maps, bar charts, or scatter plots to visualise global gold reserves, regional differences, or the correlation between income levels and gold holdings. Notes for Users
• Missing Data: Some countries have "AWAITED" in place of numerical values for FX reserves, total reserves, gold reserves, and holdings percentages. Users may need to handle these missing values (e.g., by excluding them, imputing values, or sourcing additional data).
• Gold Valuation: The "Gold Reserves Millions" column reflects the value of gold reserves in USD, based on the gold price as of 2024. Users should note that gold prices fluctuate, and historical comparisons may require adjustment for price changes.
• 20-Year Change: The "20yr change" column provides the change in gold holdings in tonnes from 2005 to 2025. Negative values indicate a reduction in gold reserves (e.g., Switzerland reduced by 314.35 tonnes), while positive values indicate an increase (e.g., India increased by 521.31 tonnes).

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The raw data that is used in this dataset is the basic OHLC time series dataset for a gold market of the last 20 years collected and verified from different exchanges. This dataset contains over 8677 daily candle prices (rows) and in order to make it wealthy, extra datasets were merged with it to provide more details to each data frame. The sub-datasets contain historical economic information such as interest rates, inflation rates, and others that are highly related and affecting the gold market movement.

Raw dataset:

Time Range: 1988-08-01 to 2023-11-10 Number of data entries: 4050 Number of features: 4 (open, high, low, close OHLC daily candle price)

What are done to prepare this dataset : 1. Starting Exploratory Data Analysis (EDA) for all the raw datasets. 2. Find and fill in missing days. 3. Merge all the datasets into one master dataset based on the time index. 4. Verify the merge process. 5. Check and remove Duplicates. 6. Check and fill in missing values. 7. Including the basic technical indicators and price moving averages. 8. Outliers Inspection and treatment by different methods. 9. Adding targets. 10. Feature Analysis to identify the importance of each feature. 11. Final check.

After data preparation and feature engineering:

Time Range: 1999-12-30 to 2023-10-01

Number of data entries: 8677

Number of featuers: 28

Features list: open, high, low, close (OHLC daily candle price) dxy_open, dxy_close, dxy_high, dxy_low, fred_fedfunds, usintr, usiryy (Ecnomic inducators) RSI, MACD, MACD_signal, MACD_hist, ADX, CCI (Technical indicators) ROC SMA_10, SMA_20, EMA_10, EMA_20, SMA_50, EMA_50, SMA_100, SMA_200, EMA_100, EMA_200 (Moving avrages)

Targets List: next_1_day_price next_3_day_price next_7_day_price next_30_day_price next_1_day_Price_Change next_3_day_Price_Change next_7_day_Price_Change next_30_day_Price_Change next_30_day_Price_Change next_1_day_price_direction( Up, Same ,Down) next_3_day_price_direction( Up, Same ,Down) next_7_day_price_direction( Up, Same ,Down) next_30_day_price_direction( Up, Same ,Down)

Abbreviations of Features: dxy = US Dollar Index fred_fedfunds= Effective Federal Funds Rate usintr= US Interest Rate usiryy= US Inflation Rate YOY RSI= Relative Strength Index MACD= Moving Average Convergence Divergence ADX= Avrerage Directional Index CCI=Commodity Channel Index ROC= Rate of Change SMA= Simple Moving Average EMA= Exponential Moving Average

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Gold Bar, WA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 26,663, while the mean income for the highest quintile (20% of households with the highest income) is 174,051. This indicates that the top earners earn 7 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 207,743, which is 119.36% higher compared to the highest quintile, and 779.14% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Bar median household income. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gold Bar. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gold Bar. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gold Bar, the median household income stands at $107,656 for householders within the 45 to 64 years age group, followed by $87,448 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $52,647.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Bar median household income by age. You can refer the same here

Exports of Gold, Nonmonetary in the United States increased to 2852 USD Million in February from 2751 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Gold, Nonmonetary.

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

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.

(i) Imports of goods and services comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, nonmonetary gold, and services. Data are in current U.S. dollars. (ii) Trade value total Import/Export Value in thousands of US Dollars current value. Imports is gross imports. Quantity (kg) (iii) world (iv) https://wits.worldbank.org/methodology.html

This table contains 6 series, with data starting from 1999 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Type of reserve (6 items: Total, Canada's official international reserves; Convertible foreign currencies, United States dollars;Convertible foreign currencies, other than United States; Gold; ...).

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.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gold Hill. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gold Hill. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gold Hill, householders within the under 25 years age group have the highest median household income at $76,250, followed by those in the 25 to 44 years age group with an income of $71,161. Meanwhile householders within the 45 to 64 years age group report the second lowest median household income of $38,750. Notably, householders within the 65 years and over age group, had the lowest median household income at $36,304.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Hill median household income by age. You can refer the same here

(i) Exports of goods and services comprise all transactions between residents of a country and the rest of the world involving a change of ownership from residents to nonresidents of general merchandise, net exports of goods under merchanting, nonmonetary gold, and services. Data are in current U.S. dollars. (ii) Trade value total Import/Export Value in thousands of US Dollars current value. Exports is gross exports. Quantity (kg) (iii) world (iv) https://wits.worldbank.org/methodology.html

The dataset contains year-, month- and day-wise historically compiled data from the year 2001 to till date on the value of India's foreign exchange reserves such as Gold, Special Drawing Rights (SDRs) and other Assets, along with its Reserve Tranche Position in International Monetary Fund (IMF)

Notes : 1) Foreign Currency Assets exclude investment in foreign currency denominated bonds issued by IIFC (UK), SDRs transferred by Government of India to RBI and foreign currency received under SAARC SWAP arrangement. Foreign currency assets in US dollar take into account appreciation/depreciation of non-US currencies (such as Euro, Sterling, Yen, Australian Dollar, etc.) held in reserves. Foreign exchange holdings are converted into rupees at rupee-US dollar RBI holding rates.

2) Data on SDR includes SDRs 3,082.5 million allocated under general allocation and SDRs 214.6 million allocated under special allocation by the IMF done on August 28, 2009 and September 9, 2009, respectively.

3) Gold data Include Rupees 31463 crore(US $ 6,699 million) reflecting the purchase of 200 metric tonnes of gold from IMF on November 3, 2009.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Gold Bar. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Gold Bar, the median income for all workers aged 15 years and older, regardless of work hours, was $49,429 for males and $39,764 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Gold Bar. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Gold Bar.

- Full-time workers, aged 15 years and older: In Gold Bar, among full-time, year-round workers aged 15 years and older, males earned a median income of $72,800, while females earned $66,417, resulting in a 9% gender pay gap among full-time workers. This illustrates that women earn 91 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Gold Bar.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Gold Bar.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Bar median household income by race. You can refer the same here