Uranium fell to 76.20 USD/Lbs on September 5, 2025, down 0.65% from the previous day. Over the past month, Uranium's price has risen 5.32%, but it is still 4.69% 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 September of 2025.

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

View monthly updates and historical trends for Uranium Spot Price. Source: International Monetary Fund. Track economic data with YCharts analytics.

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.

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

Snapshot img

Uranium Market Size 2025-2029

The uranium market size is forecast to increase by USD 2.18 billion at a CAGR of 8.2% between 2024 and 2029.

The market is characterized by the rising adoption of uranium in nuclear weapons and nuclear reactors, presenting significant growth opportunities. This is due to the escalating reliance on renewable energy, and the rise in uranium mining initiatives. Uranium's role as a primary fuel source in nuclear energy generation continues to expand, driven by the increasing demand for clean energy and the depletion of conventional energy resources. However, the market faces substantial challenges due to the high initial and production costs of uranium. These costs, coupled with the volatility in uranium prices, pose significant challenges for market participants.
Additionally, investments in research and development of advanced nuclear technologies, such as small modular reactors and nuclear fusion, could offer potential solutions to the high production costs and supply constraints, positioning these companies at the forefront of the evolving market landscape. To capitalize on the growth opportunities and navigate these challenges effectively, companies must focus on optimizing production costs, exploring alternative sources of uranium, and collaborating with industry peers to share best practices and resources. The market is witnessing significant growth due to the increasing adoption of uranium in nuclear weaponry and nuclear reactors.

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

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is characterized by a complex interplay of factors, including nuclear emergency response, fusion power research, and nuclear weapons proliferation and disarmament. Small modular reactors and advanced reactors are gaining traction as solutions for nuclear energy security, while radioactive tracer and isotope production are essential in various industries, from agriculture to medical imaging. Nuclear fuel reprocessing and spent fuel management are critical aspects of nuclear arms control and non-proliferation efforts. Breeder reactors and nuclear forensics contribute to nuclear security, while radiation therapy, protection, and nuclear medicine imaging advance healthcare applications.
Nuclear energy sustainability is a pressing concern, with the need for effective radioactive waste storage and transportation solutions. The Nuclear Security Summit underscores the importance of addressing nuclear terrorism risks. Nuclear magnetic resonance is a versatile technology with applications in various sectors, from materials science to medical research. Additionally, the production cost of uranium and the prices in the market significantly influence the profitability of nuclear power plants.

How is this Uranium Industry segmented?

The uranium industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Energy
  Military
  Others


Source

  Primary
  Secondary


Application

  Industrial counterweights
  Radiation shielding
  Medical isotopes


Geography

  North America

    US
    Canada
    Mexico


  Europe

    Germany
    Russia
    Ukraine


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By End-user Insights

The energy segment is estimated to witness significant growth during the forecast period. Uranium plays a crucial role in nuclear power generation, supplying fuel for electricity production in power plants around the world. The global shift towards cleaner energy sources and the rising awareness of carbon footprint reduction have fueled the demand for nuclear power. Nuclear power economics have gained significance, leading to increased investment in uranium production and conversion to uranium hexafluoride for enrichment. Uranium mining continues to be a critical aspect of the industry, with safety, regulation, and sustainability being key considerations. Nuclear power plants require stringent safety measures, including radiation detection and shielding, to ensure reliable operation. Nuclear fuel services provide essential support, from fabrication and licensing to decommissioning and waste management.

Uranium oxide is used in fuel assemblies, while uranium metal is essential for nuclear engineering and innovation. Nuclear power infrastructure development, including construction and technology advancements, continues to drive market growth. Despite the challenges of nuclear power regulation and the presence of nuclear weapons, the industry remains committed to nuclear power safety and security. Uranium enrichment and

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!

Global demand for uranium is forecast to reach *** 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.

Nuclear Energy Index rose to 40.68 USD on September 5, 2025, up 0.87% from the previous day. Over the past month, Nuclear Energy Index's price has fallen 1.48%, but it is still 75.72% higher than a year ago, 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.

According to Cognitive Market Research, the global Enriched Uranium market size will be USD 13214.5 million in 2024. It will expand at a compound annual growth rate (CAGR) of 4.00% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 5285.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 2.2% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 3964.35 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 3039.34 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.0% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 660.73 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.4% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 264.29 million in 2024 and will grow at a compound annual growth rate (CAGR) of 3.7% from 2024 to 2031.
The UHF Technology is the dominant segment in the Enriched Uranium Market due to its superior range and reliability in communication and tracking systems

Market Dynamics of Enriched Uranium Market

Key Drivers for Enriched Uranium Market

Rising Demand for Clean and Sustainable Energy to Boost Market Growth: The growing focus on reducing greenhouse gas emissions and achieving carbon neutrality is significantly driving the demand for enriched uranium. Nuclear power, which relies on enriched uranium, is recognized as a reliable and clean energy source with minimal carbon emissions compared to fossil fuels. Many countries are shifting their energy mix towards nuclear energy to meet international climate goals and rising energy demands. This transition is further fueled by increasing investments in nuclear power plants, particularly in regions like Asia-Pacific and Europe, where energy security and sustainability are paramount concerns. For instance, In July 2021, Orano SA announced a strategic partnership with the French Alternative Energies and Atomic Energy Commission (CEA) to collaborate on the development of new technologies for the decommissioning of nuclear facilities and the management of radioactive waste

Technological Advancements in Uranium Enrichment Processes to Drive Market Growth: Technological innovations in uranium enrichment methods are enhancing efficiency, reducing production costs, and increasing the output of enriched uranium. Advancements like centrifuge technology and laser isotope separation are enabling more precise and cost-effective enrichment processes, driving the market forward. These technological improvements are not only benefiting existing nuclear power facilities but also encouraging new investments in uranium enrichment facilities. As a result, companies and governments are better equipped to meet the growing demand for enriched uranium, ensuring long-term energy supply security while maintaining operational cost-efficiency.

Key Restraints for Enriched Uranium Market

Stringent Regulations and Safety Concerns, will Limit Market Growth: The enriched uranium market faces challenges due to stringent regulations and safety concerns surrounding nuclear energy. Governments and international organizations impose rigorous safety standards and non-proliferation protocols to prevent misuse and ensure the safe handling, transportation, and storage of enriched uranium. Compliance with these regulations often leads to high operational costs and lengthy approval processes for nuclear power projects. Moreover, public concerns about nuclear accidents, radioactive waste management, and environmental risks further hinder market growth. These factors collectively slow down the adoption of nuclear energy, limiting the expansion of the enriched uranium market.

Key Trends for Enriched Uranium Market

Transition to High-Assay Low-Enriched Uranium (HALEU): Innovative reactor designs, including small modular reactors (SMRs), necessitate HALEU (enriched between 5% and 20%). This shift is increasing the demand for elevated enrichment levels, thereby generating new prospects for market participants.

Growing Collaborations and Strategic Partnerships: Businesses and governmental entities are establishing joint ventures to secure uranium ...

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 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

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.

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

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

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.

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