Uranium fell to 77.80 USD/Lbs on July 2, 2025, down 0.32% from the previous day. Over the past month, Uranium's price has risen 8.21%, but it is still 9.17% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on July of 2025.

Graph and download economic data for Global price of Uranium (PURANUSDM) from Jan 1990 to Apr 2025 about uranium, World, and price.

In December 2024, the global average price per pound of uranium stood at roughly 60.22 U.S. dollars. Uranium prices peaked in June 2007, when it reached 136.22 U.S. dollars per pound. The average annual price of uranium in 2023 was 48.99 U.S. dollars per pound. Global uranium production Uranium is a heavy metal, and it is most commonly used as a nuclear fuel. Nevertheless, due to its high density, it is also used in the manufacturing of yacht keels and as a material for radiation shielding. Over the past 50 years, Kazakhstan and Uzbekistan together dominated uranium production worldwide. Uranium in the future Since uranium is used in the nuclear energy sector, demand has been constantly growing within the last years. Furthermore, the global recoverable resources of uranium increased between 2015 and 2021. Even though this may appear as sufficient to fulfill the increasing need for uranium, it was forecast that by 2035 the uranium demand will largely outpace the supply of this important metal.

Nuclear Energy Index rose to 37.52 USD on July 2, 2025, up 0.54% from the previous day. Over the past month, Nuclear Energy Index's price has risen 14.39%, and is up 26.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Nuclear Energy Index.

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.

Uranium Energy (YCA): The Sun's Yellow Cake, or Just a Fool's Gold Investment?

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

United States - Producer Price Index by Industry: Other Metal Ore Mining: Other Metal Ores, Including Uranium was 2094.53800 Index Dec 2003=100 in July of 2023, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Other Metal Ore Mining: Other Metal Ores, Including Uranium reached a record high of 2762.31300 in May of 2022 and a record low of 100.00000 in December of 2003. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Other Metal Ore Mining: Other Metal Ores, Including Uranium - last updated from the United States Federal Reserve on June of 2025.

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.

Uranium Royalty Corp. (UROY): Will the King of Nuclear Energy Power its Way to Success?

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

United States PPI: Mining: EO: MO: OM: Primary Products: Oths incl Uranium data was reported at 2,094.538 Dec2003=100 in Jul 2023. This records a decrease from the previous number of 2,334.358 Dec2003=100 for Jun 2023. United States PPI: Mining: EO: MO: OM: Primary Products: Oths incl Uranium data is updated monthly, averaging 534.100 Dec2003=100 from Dec 2003 (Median) to Jul 2023, with 229 observations. The data reached an all-time high of 2,762.313 Dec2003=100 in May 2022 and a record low of 100.000 Dec2003=100 in Dec 2003. United States PPI: Mining: EO: MO: OM: Primary Products: Oths incl Uranium data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.I: Producer Price Index: by Industry: Logging and Mining.

Global demand for uranium was forecasted to reach 240 million pounds of U3O8 by 2035. While demand will be growing constantly, supply of uranium was expected to drop over time. It was forecasted that new assets will be required to fill that supply gap.

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.

Yellow Cake (YCA) - Uranium's Bright Future: A Nuclear Option for Your Portfolio?

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.

Energy Fuels Sees Uranium Demand Boosting Shares. (UUUU)

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

Slovakia PPI: incl Excise: Industry: MQ: MO: Non Ferrous Metal Ores excl Uranium and Thorium data was reported at 105.900 Dec1995=100 in Dec 2001. This records a decrease from the previous number of 106.400 Dec1995=100 for Nov 2001. Slovakia PPI: incl Excise: Industry: MQ: MO: Non Ferrous Metal Ores excl Uranium and Thorium data is updated monthly, averaging 79.600 Dec1995=100 from Jan 1998 (Median) to Dec 2001, with 48 observations. The data reached an all-time high of 111.000 Dec1995=100 in Oct 2001 and a record low of 46.300 Dec1995=100 in Dec 1998. Slovakia PPI: incl Excise: Industry: MQ: MO: Non Ferrous Metal Ores excl Uranium and Thorium data remains active status in CEIC and is reported by Statistical Office of the Slovak Republic. The data is categorized under Global Database’s Slovakia – Table SK.I016: Producer Price Index: Dec1995=100.

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.

UEC Uranium Energy Corp. Common Stock

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.

EL8 ELEVATE URANIUM LTD

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.

(AURA) Aura Energy: Uranium Powerhouse or Fading Hope?

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

Studies of marine terraces and their fossils can yield important information about sea level history, tectonic uplift rates, and paleozoogeography. The marine terrace record on Santa Rosa Island, California is complex. Two prominent low-elevation terraces appear to record the ~80 ka (MIS 5a) and ~120 ka (MIS 5e) high-sea stands, based on U-series dating of fossil corals, but interpretations are tentative because of clear indications of open-system behavior with respect to U-series nuclides. Nevertheless, low uplift rates are implied by a preferred interpretation of the ages. It is inferred that low late Pleistocene uplift rates, combined with glacial isostatic adjustment (GIA) processes likely resulted in reoccupation of the ~120 ka 2nd terrace during the ~100 ka (MIS 5c) high-sea stand. Study of a high-elevation marine terrace on the western part of Santa Rosa Island also shows evidence of fossil mixing. Strontium isotope ages of fossil mollusks indicate an age range of ~500 ...

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.

Berkeley Energia (BKY) Stock Forecast: Uranium Boom on the Horizon?

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.

Berkeley Energia (BKY) Uranium Boom: Riding the Nuclear Wave

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

生产者价格指数（PPI）：采矿业：EO：MO：OM：incl Uranium在07-01-2023达2,094.538Dec2003=100，相较于06-01-2023的2,334.358Dec2003=100有所下降。生产者价格指数（PPI）：采矿业：EO：MO：OM：incl Uranium数据按月更新，12-01-2003至07-01-2023期间平均值为534.100Dec2003=100，共229份观测结果。该数据的历史最高值出现于05-01-2022，达2,762.313Dec2003=100，而历史最低值则出现于12-01-2003，为100.000Dec2003=100。CEIC提供的生产者价格指数（PPI）：采矿业：EO：MO：OM：incl Uranium数据处于定期更新的状态，数据来源于U.S. Bureau of Labor Statistics，数据归类于全球数据库的美国 – Table US.I: Producer Price Index: by Industry: Logging and Mining。

生产者价格指数（PPI）：包括消费税：工业：MQ：MO：有色金属矿石，不包括铀和钍在12-01-2001达105.9001995年12月=100，相较于11-01-2001的106.4001995年12月=100有所下降。生产者价格指数（PPI）：包括消费税：工业：MQ：MO：有色金属矿石，不包括铀和钍数据按月更新，01-01-1998至12-01-2001期间平均值为79.6001995年12月=100，共48份观测结果。该数据的历史最高值出现于10-01-2001，达111.0001995年12月=100，而历史最低值则出现于12-01-1998，为46.3001995年12月=100。CEIC提供的生产者价格指数（PPI）：包括消费税：工业：MQ：MO：有色金属矿石，不包括铀和钍数据处于定期更新的状态，数据来源于Statisticky urad Slovenskej republiky，数据归类于Global Database的斯洛伐克 – 表 SK.I016：生产者价格指数：1995年12月=100。