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Silver rose to 36.11 USD/t.oz on July 2, 2025, up 0.21% from the previous day. Over the past month, Silver's price has risen 4.60%, and is up 18.28% 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.

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.

During the last quarter of 2024, the silver prices in the USA reached 31.3 USD/toz (H2 2024 Avg Price) in December. As per the silver price chart, prices surged during the latter half of 2024, largely influenced by the Federal Reserve’s interest rate cuts, which diminished the dollar. This shift made silver an attractive investment, particularly as a lower-cost alternative to gold.

Explore IMARC’s newly published report, titled “Silver Prices, Trend, Chart, Demand, Market Analysis, News, Historical and Forecast Data Report 2025 Edition,” offers an in-depth analysis of silver pricing, covering an analysis of global and regional market trends and the critical factors driving these price movements.

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

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.

Silver Standard Resources stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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

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

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.

Silver Prices: 110 years of historical data from 1915 to 2025.

MAG Silver stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

📈 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.

📈 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.

Chile Average Price: Silver: COMEX data was reported at 32.226 USD/Troy oz in 14 May 2025. This records a decrease from the previous number of 32.388 USD/Troy oz for 12 May 2025. Chile Average Price: Silver: COMEX data is updated daily, averaging 21.240 USD/Troy oz from Jan 2005 (Median) to 14 May 2025, with 5127 observations. The data reached an all-time high of 48.580 USD/Troy oz in 29 Apr 2011 and a record low of 11.740 USD/Troy oz in 18 Mar 2020. Chile Average Price: Silver: COMEX data remains active status in CEIC and is reported by Chilean Copper Commission. The data is categorized under Global Database’s Chile – Table CL.P001: Average Price: Metals. [COVID-19-IMPACT]

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

Germany Commodity Price: Precious Metals: Silver data was reported at 32.585 USD/Troy oz in 15 May 2025. This records an increase from the previous number of 32.161 USD/Troy oz for 14 May 2025. Germany Commodity Price: Precious Metals: Silver data is updated daily, averaging 19.573 USD/Troy oz from Jan 2013 (Median) to 15 May 2025, with 3135 observations. The data reached an all-time high of 34.800 USD/Troy oz in 22 Oct 2024 and a record low of 11.859 USD/Troy oz in 18 Mar 2020. Germany Commodity Price: Precious Metals: Silver data remains active status in CEIC and is reported by Deutsche Börse Group. The data is categorized under Global Database’s Germany – Table DE.P: Commodity Prices. [COVID-19-IMPACT]

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

Spot Price: Central Bank of Oman: Gold per Ounce: Buy data was reported at 1,229.680 OMR in 18 May 2025. This records an increase from the previous number of 1,208.530 OMR for 15 May 2025. Spot Price: Central Bank of Oman: Gold per Ounce: Buy data is updated daily, averaging 532.042 OMR from Dec 2007 (Median) to 18 May 2025, with 3830 observations. The data reached an all-time high of 1,337.450 OMR in 22 Apr 2025 and a record low of 0.105 OMR in 02 Jan 2022. Spot Price: Central Bank of Oman: Gold per Ounce: Buy data remains active status in CEIC and is reported by Central Bank of Oman. The data is categorized under World Trend Plus’s Commodity Market – Table OM.P001: Gold and Silver Spot Price. [COVID-19-IMPACT]