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.

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

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

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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

Step into the opulent world of Iranian gold with our captivating dataset, where each gleaming entry tells a tale of market movements and economic shifts.🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙🪙

This treasure trove of data holds the key to unlocking the secrets of Iran's precious metal, providing a comprehensive record of its price fluctuations over time. With meticulous precision, we have captured the following details for each golden moment:

Date: Embark on a chronological journey through Iran's gold market, from its humble beginnings to its present-day grandeur. Open: Witness the dawn of each trading day as the market sets the stage for the day's drama. Close: As the sun sets on the trading floor, discover the final resting point of gold's value, reflecting the day's triumphs and tribulations. Max: Reach for the stars as we uncover the highest peaks of gold's ascent, marking moments of market euphoria and insatiable demand. Min: Delve into the depths of gold's valleys, revealing the troughs of market sentiment and periods of uncertainty. Our dataset is a golden opportunity to delve into the fascinating dynamics of Iran's gold market, empowering you to:

Track the ebb and flow of Iranian gold prices over time

Identify patterns and trends that shape the market's trajectory Uncover the factors that influence gold's value in Iran Gain insights into the economic and geopolitical forces that impact the gold market Enrich your understanding of Iran's financial landscape and its role in the global gold trade Prepare to be captivated by the allure of Iranian gold as you navigate our dataset, a true gem that unlocks the secrets of this precious metal's journey through history.

Context

Gold. A precious item with its own duality. In one side, it's a popular investment asset. In another side, it's a commodity. Whether you buy it as an asset or as commodity, the price for gold is always influenced by two things, as similar as other commodities in market: supply and demand. It's not easy to combine many aspects in supply and demand into a single dataset without making it into wall of columns. And also aggregating the data might not easy to do, since the data might not available publicly. But it doesn't mean we can't learn the historical pattern of gold market. At least some gold price historical data are available for public. And we can use that to analyze the market pattern, and, maybe, learn something from them.

Content

This dataset was based on gold price historical data from macrotrends.net. I added one new column, 'Year Range Price', to see how wide the spread of the price annually.

Acknowledgements

The base data for this dataset was retrieved from https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart.

Inspiration

What variable have the biggest correlation with annual Average Closing Price? What information can we see from the graphic? Are there any reasons why the price drop and rise? What happened on those years? Many things can be learn and explore by historical data. Having historical data is like having a kaleidoscope to see the past, learn from them, and use it as information to walk on our future path.

Gold Prices - Historical chart and current data through 2025.

Gold prices in , October, 2025 For that commodity indicator, we provide data from January 1960 to October 2025. The average value during that period was 615.3 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 4058.33 USD per troy ounce in October 2025. | TheGlobalEconomy.com

Context

Working on the prediction , trend of the gold prices for mining planning.

Content

Dataset for now just content the open , close and high price in a day. The price which price start in the gold market (open) , the final price of the day (close) and the highest price (high).

Inspiration

What will make a better prediction a ARIMA model or Neural network?

The dataset contains year- and month-wise compiled data on the average prices of Gold (Standard) and Silver at Mumbai in India

Note: Standard Gold means Gold having fineness 995 parts per thousand (%) and above

Stock Price Time Series for Aya Gold & Silver Inc. Aya Gold & Silver Inc., together with its subsidiaries, engages in the exploration, evaluation, and development of precious metals projects in Morocco. The company primarily explores for gold and silver deposits. Its flagship project holds 100% interests in the Zgounder property located approximately 260 kms east of Agadir in the Proterozoic Siroua Massif of the Anti-Atlas Range, Morocco covering an area of approximately 350 square kilometers. The company was incorporated in 2007 and is based in Mount Royal, Canada.

Silver fell to 57.28 USD/t.oz on December 2, 2025, down 1.22% from the previous day. Over the past month, Silver's price has risen 19.11%, and is up 84.81% 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 December of 2025.

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.

Dataset of historical annual silver prices from 1970 to 2022, including significant events and acts that impacted silver prices.

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The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

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Dataset

This dataset was created by Nguyễn Văn Phong

Contents

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.

Copper rose to 5.19 USD/Lbs on December 2, 2025, up 0.35% from the previous day. Over the past month, Copper's price has risen 3.22%, and is up 25.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on December of 2025.

Dollar-Rial-Toman Live Price Dataset

A comprehensive, daily-updated dataset of US Dollar to Iranian Rial exchange rates (USD/IRR) with historical data from November 2011 to present. This dataset is ideal for financial analysis, economic research, forecasting, and machine learning projects.

Dataset Overview

• Time Period: November 26, 2011 - Present (continuously updated)
• Total Records: 3,648+ daily price points
• Data Source: TGJU.org (Tehran Gold & Jewelry Union)
• Update Frequency: Daily (automated via GitHub Actions)
• Format: CSV with proper date formatting and integer price structure

Data Structure

The CSV file contains the following columns:

Download the Data

• Dataset on Kaggle: Kaggle: kooroshkz/Dollar-Rial-Toman-Live-Price-Dataset
• Dataset on Github: GitHub: kooroshkz/Dollar-Rial-Toman-Live-Price-Dataset

View Scraper and workflow source on GitHub

This live dataset, scraper source code and workflow is available on GitHub where you can explore, download, and use it directly.

Documentation & Charts

"https://kooroshkz.github.io/Dollar-Rial-Toman-Live-Price-Dataset/" target="_blank"> https://raw.githubusercontent.com/kooroshkz/Dollar-Rial-Toman-Live-Price-Dataset/main/assets/img/IntractiveChart.png">

Interactive charts and dataset overview are available at:
kooroshkz.github.io/Dollar-Rial-Toman-Live-Price-Dataset

Loading in Python

import pandas as pd

# Load dataset
df = pd.read_csv('data/Dollar_Rial_Price_Dataset.csv')

# Convert date column to datetime
df['Gregorian Date'] = pd.to_datetime(df['Gregorian Date'], format='%Y/%m/%d')

# Price columns are already integers
price_columns = ['Open Price', 'Low Price', 'High Price', 'Close Price']
print(df[price_columns].dtypes) # All should be int64

Direct Load in Python

# pip install kagglehub[hf-datasets]
import kagglehub

df = kagglehub.load_dataset(
  "kooroshkz/dollar-rial-toman-live-price-dataset",
  adapter="huggingface",
  file_path="Dollar_Rial_Price_Dataset.csv",
  pandas_kwargs={"parse_dates": ["Gregorian Date"]}
)

print(df.head())

Loading in R

# Load dataset
data <- read.csv("data/Dollar_Rial_Price_Dataset.csv", stringsAsFactors = FALSE)

# Convert date column
data$Gregorian.Date <- as.Date(data$Gregorian.Date, format = "%Y/%m/%d")

# View structure
str(data)

Data Quality & Updates

• Validation: All price data undergoes validation checks for accuracy
• Automated Updates: Dataset is automatically updated daily at 8:00 AM UTC
• Data Integrity: Built-in duplicate prevention and format validation
• Historical Consistency: Maintains consistent formatting across all time periods
• Integer Prices: All price values stored as integers for precise calculations

Technical Implementation

This dataset is maintained using an automated web scraping system that:

• Monitors TGJU.org for new exchange rate data
• Validates and processes new records
• Maintains data consistency and prevents duplicates
• Automatically commits updates to the repository

Contributing

If you find data inconsistencies or have suggestions for improvements, please open an issue in the GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use this dataset in your research or projects, please cite:

Dollar-Rial-Toman Live Price Dataset
Author: Koorosh Komeili Zadeh
Source: https://github.com/kooroshkz/Dollar-Rial-Toman-Live-Price-Dataset
Data Source: TGJU.org (Tehran Gold & Jewelry Union)
Date Range: November 2011 - Present

Keywords

USD to Rial dataset, Dollar to Toman dataset, Iran exchange rate CSV, USD/IRR daily price, foreign exchange Iran dataset, TGJU data, time series currency dataset

Disclaimer

This ...

About this Dataset

This is an email list of music industry professionals nationwide. The list has 11,097 contacts. It contains their full name and email.

Category

Lead Generation

Keywords

music,music industry,music business,Music Email List,Email List

Row Count

11097

Price

$11099.00