Uranium rose to 71.75 USD/Lbs on July 11, 2025, up 0.35% from the previous day. Over the past month, Uranium's price has risen 2.87%, but it is still 16.72% 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.

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Uranium Mining Market Size 2023-2027

The uranium mining market size is forecast to increase by 3490.06 t at a CAGR of 1.39% between 2022 and 2027.

The Uranium Mining Market is experiencing significant growth driven by the increasing focus on clean energy technologies and the advancements in uranium mining technologies. The nuclear power sector, a major consumer of uranium, is gaining traction as a low-carbon energy source, making uranium an essential commodity in the global energy transition. However, the market is not without challenges. Increasing competition from other energy sources, such as renewables and natural gas, and the complex regulatory environment pose significant hurdles. Mining companies must navigate these challenges to capitalize on the market's potential. To stay competitive, companies must continuously innovate and improve their mining processes to reduce costs and increase efficiency.
Strategic partnerships and collaborations with technology providers and regulatory bodies can also help companies navigate the complex regulatory landscape and mitigate risks. Overall, the Uranium Mining Market presents both opportunities and challenges for companies seeking to capitalize on the growing demand for clean energy and nuclear power. Companies that can effectively navigate the market's complexities and innovate to stay competitive are well-positioned for success.

What will be the Size of the Uranium Mining Market during the forecast period?

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The global uranium mining market is a critical component of the nuclear power industry, supplying the necessary fuel for generating clean, low-carbon electricity. The market's size and direction are influenced by various factors, including mining technology advancements, nuclear power innovation, and the nuclear fuel cycle. Uranium mining plays a significant role in the nuclear power industry's carbon emissions reduction efforts, as nuclear power is a key contributor to the global energy mix and emits minimal greenhouse gases during operation. Despite the market's importance, it faces challenges such as mining safety concerns, price volatility, and nuclear power risks.
Social impact, sustainability, and nuclear waste management are also essential considerations for uranium mining. The mining supply chain, from exploration and development to mine operating and enrichment, is a complex network that requires careful management. Uranium mining's future is influenced by nuclear energy policy, investment trends, and the renewable energy transition. Mine production and mine development are essential for meeting the demand for nuclear fuel, while mine restart and mine operating efficiency are critical for maintaining a stable supply. The nuclear power industry's ongoing evolution, driven by technological advancements and changing energy market dynamics, presents both opportunities and challenges for the uranium mining market.

How is this Uranium Mining Industry segmented?

The uranium mining industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD t' for the period 2023-2027, as well as historical data from 2017-2021 for the following segments.

Method

  ISL
  Underground and open pit


Technique

  Dynamic leaching
  Heap leaching


Deposit Type

  Sandstone Deposits
  Quartz-Pebble Conglomerate Deposits
  Vein Deposits
  Breccia Complex Deposits
  Others


Product

  Uranium Ore
  Yellowcake (U308)


End-Use

  Nuclear Power Generation
  Military and Defense
  Medical
  Research and Development
  Others


Geography

  APAC

    Australia


  Middle East and Africa
  North America

    Canada


  Europe
  South America

    Brazil

By Method Insights

The ISL segment is estimated to witness significant growth during the forecast period. Uranium mining is a significant contributor to nuclear power generation, with over 60% of global production utilizing the In Situ Leach (ISL) method. Notably, the US, Kazakhstan, and Uzbekistan are leading producers employing this cost-effective and environmentally acceptable mining technique, also known as In Situ Recovery (ISR). Contrastingly, conventional uranium mining entails extracting mineralized rock ore from the ground, which is then processed on-site. ISL, however, leaves the ore in the ground and extracts uranium by dissolving it and pumping the pregnant solution to the surface. Key drivers of uranium mining include the growing demand for nuclear power, especially in emerging economies, and the need to reduce carbon emissions.

Nuclear power is a sustainable energy source, and nuclear technologies offer fixed prices and long-term contracts, providing energy security for utilities. Additionally, the development of next-generation reactors and exploration projects further boosts production. Environmental goals and subsidies also i

Nuclear Energy Index rose to 37.72 USD on July 11, 2025, up 1.75% from the previous day. Over the past month, Nuclear Energy Index's price has risen 4.31%, and is up 20.90% 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.

Australia Production Volume by Major Mineral Commodity: Uranium data was reported at 5,797.253 Metric Ton in 2024. This records an increase from the previous number of 5,408.723 Metric Ton for 2023. Australia Production Volume by Major Mineral Commodity: Uranium data is updated yearly, averaging 7,138.198 Metric Ton from Jun 1990 (Median) to 2024, with 35 observations. The data reached an all-time high of 10,965.517 Metric Ton in 2005 and a record low of 2,621.668 Metric Ton in 1995. Australia Production Volume by Major Mineral Commodity: Uranium data remains active status in CEIC and is reported by Department of Industry, Science and Resources. The data is categorized under Global Database’s Australia – Table AU.WB001: Production Volume.

If I were to boil the thesis down to a few bullets, I’d say: Uranium is an essential input for nuclear reactors with no substitute. Following the Fukushima disaster, there was a massive supply glut as reactors were taken offline due to safety concerns Now a supply crunch is looming, with a current market deficit of ~40m lbs Nuclear power plants usually contract uranium supplies several years out before their inventory gets run down. Due to the oversupply coming out of the previous cycle, however, they have been purchasing additional supply needs in the spot market instead of contracting years in advance. 13f filings indicate that the power plants’ coverage rates (contracted lbs of uranium supply / lbs of uranium required) are beginning to trend below 100%, indicating utilities have less locked-in supply than they need to keep running their reactors, at a time when market supply is tightening (note utilities typically look to maintain coverage ratios well above 100% to ensure no unforeseen shortfalls) Global demand for uranium is increasing, with ~56 new reactors under construction an a further 99 in planning currently. Nuclear power currently generates ~10% of the world’s electricity but with the closure of coal and fossil fuel power plants due to ESG considerations, nuclear energy is increasingly being seen as the only viable way to make up up the lost energy capacity. Putting all of this together, a fundamental supply/demand imbalance for an essential commodity with price insensitive buyers and ESG tailwinds makes the bull case extremely compelling. But a picture is worth a thousand words, so some historic charts probably best provide a sense of the future upside expected in the next cycle. Using the data of form 8k, at the peak of the previous uranium bull market in 2007 (when there was no supply deficit) the uranium spot price reached ~$136/lb after a run up from ~$15/share at the start of 2004 (~9x increase). Today the current price is ~$42/lb with the view that the price will reach new highs in this coming cycle: Many uranium investors, based on the majority of form 10q, focus on the miners rather than the commodity as being the way to play the new uranium bull market, as these are more levered to price increases in the underlying commodity. The share price for Canadian-based Cameco Corporation (CCO / CCJ, the second largest uranium producer in the world) increased from USD $3/share to $55/share ( ~18x bagger) during the previous bull market from ~2004 – 2007: While Cameco’s performance was impressive, it was not the biggest winner during the previous uranium bull market. Australian miner Paladin Energy ($PALAF) went from AUD $0.01 to AUD $10.70 (~1000x! ) between late 2003 and the market peak in Q1 2007, according to their stock price in Google Sheets: Similar multibagger returns for uranium stocks will be seen again if a new bull market in uranium materializes in the coming 2-3 years when utilities’ uranium supply falls to inoperable levels & they begin contracting again for new supplies. Based on SEC form 4, Paladin in particular is expected to be big winner in any new bull market, as it operates one of the lowest cost uranium mines in the world, the Langer Heinrich mine in Namibia, which was a fully producing mine before being idled in the last bear market. As such, it is a ready-to-go miner rather than a speculative prospect, and so is in a position to immediately capitalise on an uptick in uranium prices and a new contracting cycle with utilities. Given the extent of the structural supply/demand imbalance (which again wasn’t present during the previous bull market) combined with utilities likely becoming forced purchasers of uranium at almost any price, market commentators are forecasting the uranium spot price to reach highs of up to $150/lb, thus enabling the producers to contract at price levels 3x+ the current spot price, driving a massive increase in profitability and cash flows. With some very interesting dynamics and the sprott uranium trust acting as a catalyst, I think the uranium market has the potential to offer a really unique and asymmetric return over the next 2 years. To reproduce this analysis, use this guide on how to get stock price in Excel. You will also need high-quality stock data, I recommend you check out Finnhub Stock Api Cheers!

Value represents the benchmark prices which are representative of the global market. They are determined by the largest exporter of a given commodity. Prices are period averages in nominal U.S. dollars.

Copyright © 2016, International Monetary Fund. Reprinted with permission. Complete terms of use and contact details are available at http://www.imf.org/external/terms.htm.

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

Kazakhstan Production Value by Commodity: Non Ferrous Ore excl Uranium and Thorium Ore data was reported at 944,368.000 KZT mn in 2017. This records an increase from the previous number of 794,365.000 KZT mn for 2016. Kazakhstan Production Value by Commodity: Non Ferrous Ore excl Uranium and Thorium Ore data is updated yearly, averaging 229,266.500 KZT mn from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 944,368.000 KZT mn in 2017 and a record low of 15,859.000 KZT mn in 1998. Kazakhstan Production Value by Commodity: Non Ferrous Ore excl Uranium and Thorium Ore data remains active status in CEIC and is reported by The Agency of Statistics of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.S002: Sectorial Financial Survey: Mining.

Locations and known contained resource for mineral deposits. Data sourced from the Mineral Deposits database and separated into commodities for display within SARIG. The Mineral Deposits database contains records of some 11,000 separate South... Locations and known contained resource for mineral deposits. Data sourced from the Mineral Deposits database and separated into commodities for display within SARIG. The Mineral Deposits database contains records of some 11,000 separate South Australian mines and mineral deposits and can be searched by deposit name, mineral district, commodity, mapsheet or coordinates.

United States Imports: cif: Uranium or Thorium Ores & Concentrates data was reported at 8.368 USD mn in Jan 2025. This records an increase from the previous number of 7.457 USD mn for Dec 2024. United States Imports: cif: Uranium or Thorium Ores & Concentrates data is updated monthly, averaging 24.483 USD mn from Jan 2002 (Median) to Jan 2025, with 216 observations. The data reached an all-time high of 164.023 USD mn in Jan 2009 and a record low of 0.004 USD mn in Dec 2020. United States Imports: cif: Uranium or Thorium Ores & Concentrates data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.JA129: Imports: by Commodity: 4 Digit HS Code.

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

This data collection contains energy commodity production statistics for approximately 200 United Nations reporting countries for the years 1970-1979. In this file, each record refers to an individual reporting country and the quantity of its various transactions (e.g., production, imports, exports, bunkers, additions to stocks, and capacity) for a given energy commodity in a given year. Only annual data are included. The 70 types of commodities reported include solid fuels (e.g., coal, peat, and charcoal), liquid fuels (e.g., crude petroleum, gasoline, and kerosene), gases, uranium, and both industrial and public types of geothermal, hydro, and nuclear generated electricity. Information is also included on the population (in thousands) of the reporting country, the quantity of the commodity per transaction, and the date of the transaction. Supplementary data not contained in this data collection are in the introduction and footnotes of the individual tables published in the YEARBOOK OF WORLD ENERGY STATISTICS, 1979.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

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

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

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