Silver rose to 38.37 USD/t.oz on July 11, 2025, up 3.65% from the previous day. Over the past month, Silver's price has risen 5.59%, and is up 24.68% 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 July of 2025.

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 >>

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

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.

Silver prices in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 9.59 USD per troy ounce with a minimum of 0.91 USD per troy ounce in January 1960 and a maximum of 42.7 USD per troy ounce in April 2011. | TheGlobalEconomy.com

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.

Predicting Silver Prices with Machine Learning: Promising Approaches and Considerations

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

In 2024, the average nominal price of silver in India was ****** Indian rupees for one kilogram, which was an increase of over ****** rupees from the previous year, and the highest figure during the period of consideration.

This statistic depicts the average monthly prices for silver worldwide from January 2014 through January 2025. In January 2025, the average monthly price for silver worldwide stood at 30.41 nominal U.S. dollars per troy ounce.

Get the latest insights on price movement and trend analysis of Silver in different regions across the world (Asia, Europe, North America, Latin America, and the Middle East Africa).

According to Cognitive Market Research, the global silver bullion market size is USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD XX million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD XX million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
The US had the most significant global silver bullion market revenue share in 2024.

Market Dynamics of Silver Bullion Market

Key Drivers of Silver Bullion Market

Increasing Demand for Safe Haven Investments

The increasing wish for safe haven investments is driving the market for silver bullion to continue growing. Investors look for assets that deliver stability and wealth preservation throughout difficult economic, geopolitical, and market situations. Due to its inherent worth and historical importance as a wealth vault, silver is drawing more and more attention from investors trying to diversify their holdings and protect themselves from inflation and currency depreciation. The COVID-19 pandemic's aftereffects, trade disputes, and geopolitical tensions have all contributed to the current state of the global economy, which has raised investor anxieties and increased demand for silver bullion. Concerns about possible inflationary pressures are developing as governments execute large stimulus programs and central banks adopt loose monetary policies; this is pushing investors into physical assets like silver.

Increasing Industrial Applications Will Promote Market Expansion

The market for silver bullion is also expected to rise significantly due to the growing number of industrial uses. Due to its special qualities, which include its high conductivity, malleability, and resistance to corrosion, silver is used in a wide range of industries, including electronics, healthcare, automotive, and renewable energy. The industrial demand for silver is anticipated to grow in the upcoming years due to technological developments and advancements boosting demand in developing applications including solar panels, electric vehicles, and 5G technology. Silver's industrial demand is further bolstered by its antibacterial characteristics, which render it increasingly desirable in therapeutic applications. The market for silver bullion is expected to increase steadily as long as industries keep innovating and creating new goods that need silver. Investors who are eager to profit from the growing industrial need for this precious metal will be drawn to this market.

Restraint Factors Of Silver Bullion Market

Volatility in Precious Metal Prices will hinder market growth.

The price volatility of precious metals can have a substantial impact on the development of the silver bullion market. The price of silver can vary due to changes in currency values, geopolitical tensions, and global economic conditions. Investors get indeterminate as a result of these swings, which could make them unwilling to buy silver bullion. Investors who bought silver at higher prices may lose money as a result of abrupt price reductions, which could affect market liquidity and confidence. Businesses that use silver as a raw resource, such as manufacturers, face difficulties due to the unpredictable nature of silver pricing. Businesses may find it challenging to correctly manage expenses and plan production schedules in the face of shifting silver prices. Price variations can disrupt the supply chain, as suppliers and buyers are driving the changing market conditions.

Market participants may use hedging techniques or look for alternate investments to lessen the impact of price volatility, which could result in money being taken out of...

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

📈 Daily Historical Stock Price Data for Americas Gold and Silver Corporation (2003–2025)

A clean, ready-to-use dataset containing daily stock prices for Americas Gold and Silver Corporation from 2003-10-27 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Americas Gold and Silver Corporation Ticker Symbol: USAS Date Range: 2003-10-27 to 2025-05-28 Frequency:… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-americas-gold-and-silver-corporation-20032025.

Historical dataset of daily Brent (Europe) crude oil prices over the last ten years. Values shown are daily closing prices.

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.

S&P GSCI Silver Index: A Reliable Benchmark for Silver Prices?

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

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

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

📈 Daily Historical Stock Price Data for Discovery Silver Corp. (2014–2025)

A clean, ready-to-use dataset containing daily stock prices for Discovery Silver Corp. from 2014-10-24 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: Discovery Silver Corp. Ticker Symbol: DSV.TO Date Range: 2014-10-24 to 2025-05-28 Frequency: Daily Total Records: 2658 rows (one per… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-discovery-silver-corp-20142025.

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

📈 Daily Historical Stock Price Data for First Majestic Silver Corp. (2006–2025)

A clean, ready-to-use dataset containing daily stock prices for First Majestic Silver Corp. from 2006-11-28 to 2025-05-28. This dataset is ideal for use in financial analysis, algorithmic trading, machine learning, and academic research.

  🗂️ Dataset Overview

Company: First Majestic Silver Corp. Ticker Symbol: AG Date Range: 2006-11-28 to 2025-05-28 Frequency: Daily Total Records: 4653 rows… See the full description on the dataset page: https://huggingface.co/datasets/khaledxbenali/daily-historical-stock-price-data-for-first-majestic-silver-corp-20062025.

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