11 datasets found
  1. Bitcoin Cash Cryptocurrency Dataset

    • console.cloud.google.com
    Updated Apr 25, 2023
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    https://console.cloud.google.com/marketplace/browse?filter=partner:Bitcoin%20Cash&hl=de (2023). Bitcoin Cash Cryptocurrency Dataset [Dataset]. https://console.cloud.google.com/marketplace/product/bitcoin-cash/crypto-bitcoin-cash?hl=de
    Explore at:
    Dataset updated
    Apr 25, 2023
    Dataset provided by
    Googlehttp://google.com/
    Description

    Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

  2. Google Arbitrage Index - November 30th, 2022

    • kaggle.com
    Updated Dec 5, 2022
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Quantitative Global (2022). Google Arbitrage Index - November 30th, 2022 [Dataset]. https://www.kaggle.com/quantitativeglobal/google-arbitrage-index-november-30th-2022/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Dec 5, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Quantitative Global
    Description

    This dataset provides values for the QGI Google Dual Class Arbitrage Index on November 30th, 2022. The data is sourced from the Quantitative Global API.

    What is Dual Class Arbitrage?

    Dual Class Arbitrage is one of the more simple forms of arbitrage. It involves making a profit from the difference in returns of stocks that have dual listings (e.g. “GOOG” and “GOOGL”, “BRK-B (Berkshire Hathaway)” and “BRK-A”). Let’s dive deeper into what that means.

    Companies sometimes go public offering two classes of shares. Using Google, like in the example above, the Class A shares are represented by the ticker symbol(“GOOGL”) and the Class C shares are represented by the ticker symbol (“GOOG”). As is usually the case, this dual-listing is done so that the founders can retain disproportionate ownership of the company while still being public. Class A shares typically hold more voting rights, so founders and initial investors are usually the largest holders.

    Because of this voting right advantage, sometimes Class A shares may cost more than Class C shares (e.g. Class A = $100, Class C = $99.75), this is normal and in line with expectations. However, both shares represent the same company and both shares usually have identical market capitalizations as there are no other differences.

    The arbitrage opportunity exists when either share is over/under valued compared to the other. For example, if there is a very large market order for GOOG that pushes the price up by 0.50%, but no such order existed for GOOGL, then GOOG will be 0.50% more valuable than GOOGL. An arbitrageur will see this imbalance and short GOOG shares and long GOOGL shares. As the imbalance corrects, they make money from both legs with no directional risk.

    Implementation Example

    To better understand this form of arbitrage, let's go over an example using the dataset.

    Trade Logic: - If the spread increases to/above 0.10, we long the under-performer, then short the over-performer. - When the spread ducks to/below our threshold, in this case, 0.01, close the position.

    At about 12:20, the opening signal appears and a trade is entered by buying the underperformer and selling-short the over-performer:

    datetimeCumulative ReturnsGOOGL Cumulative ReturnsSpreadGOOG Intraday PerformanceGOOGL Intraday Performance
    2022-11-30 12:16:00101.43908409376779101.338435144253380.097090213346945121.44%1.34%
    2022-11-30 12:17:00101.31466729750028101.224006766015580.097262014056658091.31%1.22%
    2022-11-30 12:18:00101.30439012527978101.203177772055980.097507278074303371.3%1.2%
    2022-11-30 12:19:00101.28352220674563101.192761105389320.094211328688269691.28%1.19%
    2022-11-30 12:20:00101.20044847881209101.099001338746990.097806864881737471.2%1.1%

    Roughly an hour later, the spread reached the closing signal and the trade was closed:

    datetimeCumulative ReturnsGOOGL Cumulative ReturnsSpreadGOOG Intraday PerformanceGOOGL Intraday Performance
    2022-11-30 13:38:00103.08005180650808103.046808750741750.030658141605722263.08%3.05%
    2022-11-30 13:39:00103.03925213290546103.005900589563580.030190131834236433.04%3.01%
    2022-11-30 13:40:00103.1004766227014103.097981620871140.0230298669794895723.1%3.1%
    2022-11-30 13:41:00103.13412948829388103.13886887426990.0135286437160525943.13%3.14%
    2022-11-30 13:42:00102.82829726785003102.801686898179180.0112815858257088542.83%2.8%

    You can intuitively calculate PnL here by tracking the Cumulative Return columns. In this example, we bought GOOGL at 101.01, then sold it for 103.08. Since the index starts each day at 100 and tracks returns, this means a 2.07% profit on the GOOGL leg (3.08% — 1.01%). Then, we were short GOOG at 101.13, and bought it back at 103.08 for a -1.95% loss. So when tallied, the net profit on the position was 0.12% (2.07% — 1.95%).

    But why stop with Google stock? The QuantGlobal API allows access to more tickers and strategies just like this. You can get started exploring our data by checking us out at qg-indices.com

  3. A

    ‘FAANG- Complete Stock Data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Sep 30, 2021
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘FAANG- Complete Stock Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-faang-complete-stock-data-36c1/9110ef3b/?iid=011-763&v=presentation
    Explore at:
    Dataset updated
    Sep 30, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘FAANG- Complete Stock Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data on 30 September 2021.

    --- Dataset description provided by original source is as follows ---

    Context

    There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

    Content

    The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

    --- Original source retains full ownership of the source dataset ---

  4. Google energy consumption 2011-2023

    • statista.com
    • tokrwards.com
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista, Google energy consumption 2011-2023 [Dataset]. https://www.statista.com/statistics/788540/energy-consumption-of-google/
    Explore at:
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

  5. Academic publisher profits 2011-13

    • figshare.com
    ods
    Updated Jan 19, 2016
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stuart Lawson (2016). Academic publisher profits 2011-13 [Dataset]. http://doi.org/10.6084/m9.figshare.1014326.v1
    Explore at:
    odsAvailable download formats
    Dataset updated
    Jan 19, 2016
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Stuart Lawson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This is a dataset of the profits of academic journal publishers. It contains financial information - revenues, profits, and profit marginss - for companies that make this publicly available. The three sheets of the document cover the years 2011, 2012, and 2013. Data is currently incomplete because some publishers do not make this information available, some have not yet for 2013, and some annual reports from 2011 are already difficult to find online. An online verison of this dataset, which may be updated more often, is available at: https://docs.google.com/spreadsheet/ccc?key=0Avt-G30CZ3ZndEtXSVNqTWhCazhSSEhJS3F1bktaaFE&usp=sharing#gid=4 [Accessed 2 May 2014].

  6. FAANG- Complete Stock Data

    • kaggle.com
    Updated Sep 19, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Aayush Mishra (2020). FAANG- Complete Stock Data [Dataset]. https://www.kaggle.com/aayushmishra1512/faang-complete-stock-data/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 19, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aayush Mishra
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    There are a few companies that are considered to be revolutionary. These companies also happen to be a dream place to work at for many many people across the world. These companies include - Facebook,Amazon,Apple,Netflix and Google also known as FAANG! These companies make ton of money and they help others too by giving them a chance to invest in the companies via stocks and shares. This data wass made targeting these stock prices.

    Content

    The data contains information such as opening price of a stock, closing price, how much of these stocks were sold and many more things. There are 5 different CSV files in the data for each company.

  7. Most valuable media & entertainment brands worldwide 2024

    • statista.com
    • es.statista.com
    • +4more
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Julia Faria, Most valuable media & entertainment brands worldwide 2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Julia Faria
    Description

    In 2024, Google ranked as the most valuable media and entertainment brand worldwide, with a brand value of 683 billion U.S. dollars. Facebook ranked second, valued at around 167 billion dollars. Part of the Tencent Group, WeChat and v.qq.com (Tencent Video) had a brand value of 56 billion and 17.5 billion dollars, respectively.

  8. Average daily time spent on social media worldwide 2012-2024

    • statista.com
    • grusthub.com
    • +4more
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stacy Jo Dixon, Average daily time spent on social media worldwide 2012-2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    How much time do people spend on social media?

                  As of 2024, the average daily social media usage of internet users worldwide amounted to 143 minutes per day, down from 151 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of three hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in
                  the U.S. was just two hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively.
                  People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general.
                  During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.
    
  9. Global net revenue of Amazon 2014-2024, by product group

    • statista.com
    • tokrwards.com
    • +3more
    Updated Feb 24, 2025
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista (2025). Global net revenue of Amazon 2014-2024, by product group [Dataset]. https://www.statista.com/statistics/672747/amazons-consolidated-net-revenue-by-segment/
    Explore at:
    Dataset updated
    Feb 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    In 2024, Amazon's net revenue from subscription services segment amounted to 44.37 billion U.S. dollars. Subscription services include Amazon Prime, for which Amazon reported 200 million paying members worldwide at the end of 2020. The AWS category generated 107.56 billion U.S. dollars in annual sales. During the most recently reported fiscal year, the company’s net revenue amounted to 638 billion U.S. dollars. Amazon revenue segments Amazon is one of the biggest online companies worldwide. In 2019, the company’s revenue increased by 21 percent, compared to Google’s revenue growth during the same fiscal period, which was just 18 percent. The majority of Amazon’s net sales are generated through its North American business segment, which accounted for 236.3 billion U.S. dollars in 2020. The United States are the company’s leading market, followed by Germany and the United Kingdom. Business segment: Amazon Web Services Amazon Web Services, commonly referred to as AWS, is one of the strongest-growing business segments of Amazon. AWS is a cloud computing service that provides individuals, companies and governments with a wide range of computing, networking, storage, database, analytics and application services, among many others. As of the third quarter of 2020, AWS accounted for approximately 32 percent of the global cloud infrastructure services vendor market.

  10. TikTok global quarterly downloads 2018-2024

    • statista.com
    • es.statista.com
    • +4more
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Statista Research Department, TikTok global quarterly downloads 2018-2024 [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Statista Research Department
    Description

    In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019.

                  TikTok interactions: is there a magic formula for content success?
    
                  In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024.
                  The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok.
                  It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds.
    
                  What’s trending on TikTok Shop?
    
                  Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide.
                  TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items,
                  accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.
    
  11. Facebook: distribution of global audiences 2024, by age and gender

    • statista.com
    • de.statista.com
    • +4more
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Stacy Jo Dixon, Facebook: distribution of global audiences 2024, by age and gender [Dataset]. https://www.statista.com/topics/1164/social-networks/
    Explore at:
    Dataset provided by
    Statistahttp://statista.com/
    Authors
    Stacy Jo Dixon
    Description

    As of April 2024, it was found that men between the ages of 25 and 34 years made up Facebook largest audience, accounting for 18.4 percent of global users. Additionally, Facebook's second largest audience base could be found with men aged 18 to 24 years.

                  Facebook connects the world
    
                  Founded in 2004 and going public in 2012, Facebook is one of the biggest internet companies in the world with influence that goes beyond social media. It is widely considered as one of the Big Four tech companies, along with Google, Apple, and Amazon (all together known under the acronym GAFA). Facebook is the most popular social network worldwide and the company also owns three other billion-user properties: mobile messaging apps WhatsApp and Facebook Messenger,
                  as well as photo-sharing app Instagram. Facebook usersThe vast majority of Facebook users connect to the social network via mobile devices. This is unsurprising, as Facebook has many users in mobile-first online markets. Currently, India ranks first in terms of Facebook audience size with 378 million users. The United States, Brazil, and Indonesia also all have more than 100 million Facebook users each.
    
  12. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
https://console.cloud.google.com/marketplace/browse?filter=partner:Bitcoin%20Cash&hl=de (2023). Bitcoin Cash Cryptocurrency Dataset [Dataset]. https://console.cloud.google.com/marketplace/product/bitcoin-cash/crypto-bitcoin-cash?hl=de
Organization logo

Bitcoin Cash Cryptocurrency Dataset

Explore at:
Dataset updated
Apr 25, 2023
Dataset provided by
Googlehttp://google.com/
Description

Bitcoin Cash is a cryptocurrency that allows more bytes to be included in each block relative to it’s common ancestor Bitcoin. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Search
Clear search
Close search
Google apps
Main menu