Stay informed with real-time charts of international precious metal prices. Monitor spot prices for Silver in USD, GBP, and EUR. Access live updates here >>

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.

The price of an ounce of silver increased sharply in 2021, rising around 17 percent from January 28 to February 1. The cause of this increase is attributed to retail investors mobilized via social media with the intention of causing losses to professional investors, similar to the rise in the stock price of video game retailer GameStop, and the stock price of cinema operator AMC several days beforehand. As of midnight July 18, 2023, the price of silver was trading at 24.9 U.S. dollars per troy ounce.

View monthly updates and historical trends for Silver Price. from United States. Source: World Bank. Track economic data with YCharts analytics.

Continuously updated Monex bid/ask prices for Silver spot and common bullion products.

Silver Price - Historical chart and current data through 2025.

Silver Prices - Historical chart and current data through 2025.

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

Context

Silver is one of the world's most closely watched commodities, serving as a key indicator of economic health, a hedge against inflation, and a cornerstone of financial markets. Access to clean, reliable, and long-term historical data is essential for analysts, investors, and data scientists looking to understand its behavior, forecast future trends, and build robust financial models.

This dataset provides a comprehensive and daily-updated record of silver prices, specifically sourced from the Silver Futures (SI=F) market, which is the standard for long-term historical analysis.

Content

This dataset contains daily price information for Silver Futures (SI=F) in a clean, tabular format. Each row represents a single trading day and includes the following columns:

Date: The date of the trading session (YYYY-MM-DD).

Open: The price at which silver first traded for the day in USD.

High: The highest price reached during the trading day in USD.

Low: The lowest price reached during the trading day in USD.

Close: The closing price at the end of the trading day in USD.

Volume: The total number of futures contracts traded during the day.

Learn where to find real-time silver spot prices, factors influencing its volatility, and investing tips, including viewing silver as a safe-haven asset.

Explore the dynamic market of silver, a key metal in industry and investment. Understand how factors like economic conditions, demand-supply dynamics, and geopolitical events impact silver prices, along with its essential role in electronics and solar technologies for a sustainable future.

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

Content

Historical daily prices of gold and silver since 1962 to now. Price per ounce in USD.

Acknowledgements

Data obtained from LBMA

Inspiration

You try different things on this dataset:

• predict the future price of gold and silver
• study the correlation between gold and silver
• combine with other datasets such as stocks and crypto currencies datasets to get insightsLBMA

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.

Silver's Uncertain Future?

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

Current spot price plus 1-month and 1-year forecasts for Silver as published on ChAI Predict.

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

Graph and download economic data for Credit Suisse NASDAQ Silver FLOWS106 Price Index (NASDAQQSLVO) from 2013-02-28 to 2025-11-06 about silver, NASDAQ, credits, price index, indexes, price, and USA.

This statistic depicts the average monthly prices for silver worldwide from January 2014 through June 2025. In June 2025, the average monthly price for silver worldwide stood at ***** nominal U.S. dollars per troy ounce.

This dataset contains daily historical data of major financial instruments and indexes from January 1, 2015, to August 15, 2025 . It includes the following columns:

SPX – S&P 500 Index daily closing prices.

GLD – SPDR Gold Shares ETF daily adjusted closing prices.

USO – United States Oil Fund ETF daily adjusted closing prices.

SLV – iShares Silver Trust ETF daily adjusted closing prices.

EUR/USD – Daily Euro to US Dollar exchange rate.

The data was collected from Yahoo Finance using the yfinance Python library. The dataset is intended for research, analysis, and educational purposes.

Gold–Silver Pair Trading Dataset and Visualization

Author: Vineet Kumar Mittal Version: 1.0 Date: November 2025 DOI: https://doi.org/10.5281/zenodo.17537028

Overview

This repository contains the full dataset and visual analytics used in the study: "Gold–Silver Pair Trading: Mean Reversion Strategy Using Machine Learning."

It includes: - Historical gold and silver futures data (raw) - Processed dataset with spreads, ratios, and Z-scores - Key analysis charts (hedge ratio, spread, equity curve, etc.) - Reproducibility and licensing information

Contents

1. Main Analytical Dataset

File: gold_silver_live_panel.csv
Description:
Processed data containing gold/silver prices, ratio, spread, rolling statistics, and Z-scores used for the study's analysis and backtesting.

Column Definitions: See below for detailed description of each field included in gold_silver_live_panel.csv.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F30232042%2F4b0cf366a892c352a775411296a97f02%2FScreenshot%202025-11-08%20083140.jpg?generation=1762608723135200&alt=media" alt="">

2. Raw Historical Data

Files: - Gold Futures Historical Data_2015_2025.csv
- Silver Futures Historical Data_2015_2025.csv

Description:
Raw daily closing prices used to compute the ratio, spread, and other derived features.
Data sourced from Investing.com (continuous Gold and Silver Futures contracts).

Covers 2015–2025, daily frequency (excluding weekends and market holidays).

3. Visualization Outputs (Figures)

Please use below DOI to see all the figures and data.

DOI: https://doi.org/10.5281/zenodo.17537028

4. Citation

If you use this dataset, please cite:

Mittal, V. K. (2025). Gold Silver Pair Trading - Mean Reversion Strategy Using Machine Learning (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17537028

5. License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to use, distribute, and build upon this dataset for academic and non-commercial research purposes, provided proper attribution is given.

6. Contact

For queries, collaborations, or extended analysis: Vineet Kumar Mittal