Uranium fell to 76.90 USD/Lbs on October 20, 2025, down 1.41% from the previous day. Over the past month, Uranium's price has fallen 1.28%, and is down 6.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Uranium - values, historical data, forecasts and news - updated on October of 2025.

The average annual price for one pound of uranium was ******U.S. dollars in 2024. This is the highest annual average since 2007, and comes in the wake of greater fuel demand as the global economy began recovering from the coronavirus pandemic as well as the energy crisis.

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

In June 2025, the global average price per pound of uranium stood at roughly 59.58 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 2024 was 69.69 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.

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

Index Time Series for Global X Uranium Index ETF. The frequency of the observation is daily. Moving average series are also typically included. NA

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 Corp. (UEC) Outlook Positive Amidst Energy Transition

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

Index Time Series for Sprott Junior Uranium Miners ETF. The frequency of the observation is daily. Moving average series are also typically included. The fund will, under normal circumstances, invest at least 80% of its total assets in securities of the index. The index is designed to track the performance of companies that derive at least 50% of their revenue and/or assets from (i) mining, exploration, development, and production of uranium; (ii) earning uranium royalties; and/or (iii) supplying uranium. The index generally consists of from 30 to 40 constituents. The fund is non-diversified.

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

Southern Ocean sediments reveal a spike in authigenic uranium 127,000 years ago, within the last interglacial, reflecting decreased oxygenation of deep water by Antarctic Bottom Water (AABW). Unlike ice age reductions in AABW, the interglacial stagnation event appears decoupled from open ocean conditions and may have resulted from coastal freshening due to mass loss from the Antarctic ice sheet. AABW reduction coincided with increased North Atlantic Deep Water (NADW) formation, and the subsequent reinvigoration in AABW coincided with reduced NADW formation. Thus, alternation of deep water formation between the Antarctic and the North Atlantic, believed to characterize ice ages, apparently also occurs in warm climates.

Isotopic compositions of uranium (234U and 238U) and thorium (230Th and 232Th) were measured in metalliferous sediments from the western flank of the East Pacific Rise at 21°-22°S, in the area of hydrothermal activity and massive sulfide accumulation at the axis of the EPR. Concentration of 232Th (on the carbonate-free base) is consistent with composition of mafic extrusive rocks; isotope ratios 232Th/238U and 234U/238U indicate that about 70% of uranium passes into sediments from sea water with hydrothermal iron hydroxide. Mean sedimentation rates are calculated for seven cores by the nonequilibrium 230Th method with use of the constant concentration model. Flux of 230Th to bottom sediments is calculated and its mean value is used to determine sedimentation rate in four other cores. The constant flux model is used to calculate change of sedimentation rate with depth for seven cores over time interval of 100-300 ky. Sedimentation rates varied not much (0.3-0.6 cm/ky). The greatest changes occurred in two cores: one located near massive sulfide structures, and another near the spreading axis. Determinations of mean rates by the radiocarbon method and the nonequilibrium thorium method are in good agreement.

A rapid procedure for Io (Th230) dating of sediments with accumulation rates in the range of several cm/1000 years is described. Studying of large sample populations with very small Io-excess activity is possible as the counting time (around 1500 min/sample) are 2 to 5 times shorter than with the standard Io-excess method. Improved sensitivity of the Io-excess measurement is achieved by:1) extraction ( ~90 %) of the authigenic Io-excess with EDTA, with minor leaching ( ~30 %) of the allogenic Th232 and Io-supported,2) processing samples as large as 10 g or more.The procedure was applied to sediments from the Caribbean (V 12-122) and from the Ionian Sea (M22_48 and M17_17). In the case of the standard core V 12-122 our results are in good agreement with previous time-consuming Io determinations. The resulting average accumulation rates of 2.0 ± 0.3 cm/1000 years for the Ionian Sea cores are close to the average derived from magnetic reversal studies of a nearby core.

Index Time Series for Global X Uranium UCITS ETF. The frequency of the observation is daily. Moving average series are also typically included.

High resolution 230Thex and 10Be and biogenic barium profiles were measured at three sediment gravity cores (length 605-850 cm) from the Weddell Sea continental margin. Applying the 230Thex dating method, average sedimentation rates of 3 cm/kyr for the two cores from the South Orkney Slope and of 2.4 cm/kyr for the core from the eastern Weddell Sea were determined and compared to delta18O and lithostratigraphic results. Strong variations in the radionuclide concentrations in the sediments resembling the glacial/interglacial pattern of the delta18O stratigraphy and the 10Be stratigraphy of high northern latitudes were used for establishing a chronostratigraphy. Biogenic Ba shows a pattern similar to the radionuclide profiles, suggesting that both records were influenced by increased paleoproductivity at the beginning of the interglacials. However, 230Thex0 fluxes (0 stands for initial) exceeding production by up to a factor of 4 suggest that sediment redistribution processes, linked to variations in bottom water current velocity, played the major role in controlling the radionuclide and biogenic barium deposition during isotope stages 5e and 1. The correction for sediment focusing makes the 'true' vertical paleoproductivity rates, deduced from the fluxes of proxy tracers like biogenic barium, much lower than previously estimated. Very low 230Thex0 concentrations and fluxes during isotope stage 6 were probably caused by rapid deposition of older, resedimented material, delivered to the Weddell Sea continental slopes by the grounded ice shelves and contemporaneous erosion of particles originating from the water column.

We have determined convergence rates of the Australia plate with the New Hebrides Island Arc using the chronostratigraphy of Bougainville Guyot, drilled at Site 831, Ocean Drilling Program Leg 134. The convergence rate at the New Hebrides Island Arc is the vectorial sum of convergence rates between the Australia and Pacific plates (8.8 cm/yr at Espiritu Santo Island) and the opening rate of the North Fiji Basin. We assume that the relative motion of the Australia and Pacific plates is unchanging on the 1.5 m.y. time scale and that any changes of rate occurred in the North Fiji Basin. Convergence rates can be calculated because we know the distances at which carbonate sedimentation would cease and resume as the Bougainville Guyot emerged and submerged during its crossing of the outer rise flexure west of the New Hebrides Island Arc. From 1.42 to 0.393 Ma, Bougainville Guyot was subaerially exposed as it moved approximately 177 km across the outer rise and no sediment was deposited. The mean convergence rate during this time interval was 17.2 +/- 7 cm/yr, as determined from strontium-isotope and uranium-series ages of the last carbonates before emergence and the first carbonates deposited after submergence. The Australia plate has converged approximately 52 km with the New Hebrides Island Arc at a mean rate of 13.2 +/- 1 cm/yr since 0.393 +/- .011 Ma when Bougainville Guyot re-submerged and carbonate sedimentation resumed. This age is based on a precise mass-spectrometric 230Th age measurement and is reliable because the uranium isotopic composition of the sample indicates no diagenetic alteration. The change in convergence rates from 17.2 to 13.2 cm/yr indicates a significant change in the opening rate of the North Fiji Basin. However, this conclusion depends on the age of initial opening of the North Fiji Basin. If the North Fiji Basin began to open at 10 Ma, then the average opening rate at Espiritu Santo Island has been 6 cm/yr. If opening began at 12 Ma, then the average rate had to be 5 cm/yr. Because the relative motion between the Australia and Pacific plates is 8.8 cm/yr, the net convergence rate at the central New Hebrides Island Arc must have averaged 13.8 to 14.8 cm/yr. Younger dates of initial opening would require higher average convergence rates. […]