The NYSE Energy Index tracks the performance of the equity components on the New York Stock Exchange that offer goods and services in the energy industry by market capitalization. Between January 2004 and February 2025, the index fluctuated significantly and reached its highest value in May 2008, with a value of 19,469.84, whereas it reached its lowest value in October 2020, at 4,912.48 index points. As of February 2025, the value of the NYSE Energy Index amounted to 13,620.84 index points.

United States New York Stock Exchange: Index: S&P 500 Energy Sector data was reported at 617.470 NA in Apr 2025. This records a decrease from the previous number of 715.750 NA for Mar 2025. United States New York Stock Exchange: Index: S&P 500 Energy Sector data is updated monthly, averaging 534.950 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 727.630 NA in Jun 2014 and a record low of 216.820 NA in Oct 2020. United States New York Stock Exchange: Index: S&P 500 Energy Sector data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

Nuclear Energy Index fell to 38.14 USD on June 27, 2025, down 1.62% from the previous day. Over the past month, Nuclear Energy Index's price has risen 14.95%, and is up 31.97% 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.

China Shanghai Stock Exchange: Index: Sector Index: Energy data was reported at 1,764.600 NA in Apr 2025. This records a decrease from the previous number of 1,831.110 NA for Mar 2025. China Shanghai Stock Exchange: Index: Sector Index: Energy data is updated monthly, averaging 1,603.230 NA from Dec 2014 (Median) to Apr 2025, with 125 observations. The data reached an all-time high of 2,496.950 NA in Apr 2015 and a record low of 969.670 NA in Jun 2020. China Shanghai Stock Exchange: Index: Sector Index: Energy data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s China – Table CN.EDI.SE: Shanghai Stock Exchange: Monthly.

Solar Energy Index fell to 33.66 USD on June 27, 2025, down 1.89% from the previous day. Over the past month, Solar Energy Index's price has risen 4.60%, but it is still 16.68% lower 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 Solar Energy Index.

Wind Energy Index fell to 18.28 USD on June 27, 2025, down 0.27% from the previous day. Over the past month, Wind Energy Index's price has risen 9.86%, and is up 12.77% 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 Wind Energy Index.

United States New York Stock Exchange: Index: S&P Energy Select Sector data was reported at 847.570 NA in Apr 2025. This records a decrease from the previous number of 984.570 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Energy Select Sector data is updated monthly, averaging 735.960 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 1,005.670 NA in Jun 2014 and a record low of 300.330 NA in Oct 2020. United States New York Stock Exchange: Index: S&P Energy Select Sector data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

The NYSE U.S. Market Energy Sector Index tracks the performance of the U.S. domiciled equity components listed on the U.S. stock exchanges that offer goods and services in the energy sector. The statistics shows the monthly development of the NYSE U.S. Market Energy Sector Index from December 2015 to June 2023. During that time, the index fluctuated significantly and reached its lowest point of all time in March 2020, with a value of 1,001.37. As of June 2023, the NYSE U.S. Market Energy Index stood at 2,962.5.

Prices for Solar Energy Index including live quotes, historical charts and news. Solar Energy Index was last updated by Trading Economics this June 12 of 2025.

United States New York Stock Exchange: Index: Energy Index data was reported at 12,462.527 NA in Apr 2025. This records a decrease from the previous number of 14,207.149 NA for Mar 2025. United States New York Stock Exchange: Index: Energy Index data is updated monthly, averaging 11,215.500 NA from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 16,068.000 NA in Aug 2014 and a record low of 4,912.500 NA in Oct 2020. United States New York Stock Exchange: Index: Energy Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Monthly.

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.

NuScale Power (SMR): Redefining Nuclear Energy?

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

Prices for Nuclear Energy Index including live quotes, historical charts and news. Nuclear Energy Index was last updated by Trading Economics this June 28 of 2025.

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

Image attribute : Image by Freepik

Norway Index: Oslo Bors Stock Exchange: Energy data was reported at 887.130 29Dec1995=100 in Nov 2018. This records a decrease from the previous number of 977.780 29Dec1995=100 for Oct 2018. Norway Index: Oslo Bors Stock Exchange: Energy data is updated monthly, averaging 561.870 29Dec1995=100 from May 2001 (Median) to Nov 2018, with 211 observations. The data reached an all-time high of 1,053.200 29Dec1995=100 in Sep 2018 and a record low of 116.260 29Dec1995=100 in Feb 2003. Norway Index: Oslo Bors Stock Exchange: Energy data remains active status in CEIC and is reported by Oslo Stock Exchange. The data is categorized under Global Database’s Norway – Table NO.Z001: Oslo Stock Exchange: Index.

Prices for Wind Energy Index including live quotes, historical charts and news. Wind Energy Index was last updated by Trading Economics this June 28 of 2025.

As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

While the global coronavirus (COVID-19) pandemic caused all major stock market indices to fall sharply in March 2020, both the extent of the decline at this time, and the shape of the subsequent recovery, have varied greatly. For example, on March 15, 2020, major European markets and traditional stocks in the United States had shed around 40 percent of their value compared to January 5, 2020. However, Asian markets and the NASDAQ Composite Index only shed around 20 to 25 percent of their value. A similar story can be seen with the post-coronavirus recovery. As of November 14, 2021 the NASDAQ composite index value was around 65 percent higher than in January 2020, while most other markets were only between 20 and 40 percent higher.

Why did the NASDAQ recover the quickest?

Based in New York City, the NASDAQ is famously considered a proxy for the technology industry as many of the world’s largest technology industries choose to list there. And it just so happens that technology was the sector to perform the best during the coronavirus pandemic. Accordingly, many of the largest companies who benefitted the most from the pandemic such as Amazon, PayPal and Netflix, are listed on the NADSAQ, helping it to recover the fastest of the major stock exchanges worldwide.

Which markets suffered the most?

The energy sector was the worst hit by the global COVID-19 pandemic. In particular, oil companies share prices suffered large declines over 2020 as demand for oil plummeted while workers found themselves no longer needing to commute, and the tourism industry ground to a halt. In addition, overall share prices in two major stock exchanges – the London Stock Exchange (as represented by the FTSE 100 index) and Hong Kong (as represented by the Hang Seng index) – have notably recovered slower than other major exchanges. However, in both these, the underlying issue behind the slower recovery likely has more to do with political events unrelated to the coronavirus than it does with the pandemic – namely Brexit and general political unrest, respectively.

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.

DJ Commodity Energy index: Analysts Predict Volatility Ahead

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

Sri Lanka CSE: Index: Power & Energy data was reported at 110.110 NA in Oct 2018. This records a decrease from the previous number of 110.410 NA for Sep 2018. Sri Lanka CSE: Index: Power & Energy data is updated monthly, averaging 122.250 NA from Dec 2004 (Median) to Oct 2018, with 167 observations. The data reached an all-time high of 196.200 NA in Jan 2005 and a record low of 67.200 NA in Dec 2008. Sri Lanka CSE: Index: Power & Energy data remains active status in CEIC and is reported by Colombo Stock Exchange. The data is categorized under Global Database’s Sri Lanka – Table LK.Z001: Colombo Stock Exchange: Index.

This data is made up of daily stock prices and commodities' futures of a range of variables including NASDAQ clean focused price index, ARCA technology price index, Brent oil futures, Henry hub natural gas futures, Newcastle coal futures, carbon emission futures and green information technology stock price. The dataset supports empirical analysis which examines the volatility of clean energy stock returns (CERs) given the aggregate influence of energy security elements (ESEs) internal to CERs and the individuals influences of a range of exogenous variables including oil futures, natural gas futures, coal futures, carbon emission futures and green information technology stock price.