33 datasets found
  1. Integrated Cryptocurrency Historical Data for a Predictive Data-Driven...

    • cryptodata.center
    Updated Dec 4, 2024
    + more versions
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    cryptodata.center (2024). Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm - Dataset - CryptoData Hub [Dataset]. https://cryptodata.center/dataset/integrated-cryptocurrency-historical-data-for-a-predictive-data-driven-decision-making-algorithm
    Explore at:
    Dataset updated
    Dec 4, 2024
    Dataset provided by
    CryptoDATA
    License

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

    Description

    Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

  2. d

    Finage Real-Time & Historical Cryptocurrency Market Feed - Global...

    • datarade.ai
    Updated Nov 1, 2022
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    Finage (2022). Finage Real-Time & Historical Cryptocurrency Market Feed - Global Cryptocurrency Data [Dataset]. https://datarade.ai/data-products/real-time-historical-cryptocurrency-market-feed-finage
    Explore at:
    Dataset updated
    Nov 1, 2022
    Dataset authored and provided by
    Finage
    Area covered
    Albania, Turkey, Switzerland, France, Sweden, Korea (Democratic People's Republic of), South Africa, Paraguay, Macao, Mayotte
    Description

    Cryptocurrencies

    Finage offers you more than 1700+ cryptocurrency data in real time.

    With Finage, you can react to the cryptocurrency data in Real-Time via WebSocket or unlimited API calls. Also, we offer you a 7-year historical data API.

    You can view the full Cryptocurrency market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_Crypto_Coverage.pdf

  3. o

    Finance, Stock, Currency / Forex, Crypto, ETF, and News Data

    • openwebninja.com
    json
    Updated Sep 18, 2024
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    OpenWeb Ninja (2024). Finance, Stock, Currency / Forex, Crypto, ETF, and News Data [Dataset]. https://www.openwebninja.com/api/real-time-finance-data
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    jsonAvailable download formats
    Dataset updated
    Sep 18, 2024
    Dataset authored and provided by
    OpenWeb Ninja
    Area covered
    Global Financial Markets
    Description

    This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

  4. T

    Crypto APIs Market Trends - Growth, Demand & Outlook 2025 to 2035

    • futuremarketinsights.com
    html, pdf
    Updated Mar 20, 2025
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    Future Market Insights (2025). Crypto APIs Market Trends - Growth, Demand & Outlook 2025 to 2035 [Dataset]. https://www.futuremarketinsights.com/reports/crypto-apis-market
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    html, pdfAvailable download formats
    Dataset updated
    Mar 20, 2025
    Dataset authored and provided by
    Future Market Insights
    License

    https://www.futuremarketinsights.com/privacy-policyhttps://www.futuremarketinsights.com/privacy-policy

    Time period covered
    2025 - 2035
    Area covered
    Worldwide
    Description

    The market is projected to reach USD 1,074 Million in 2025 and is expected to grow to USD 7,975.7 Million by 2035, registering a CAGR of 22.2% over the forecast period. The expansion of Web3 infrastructure, advancements in multi-chain API solutions, and increasing demand for secure and scalable blockchain integrations are fueling market expansion. Additionally, rising adoption of tokenization, cross-chain interoperability, and API-driven NFT marketplaces is shaping the industry's future.

    MetricValue
    Market Size (2025E)USD 1,074 Million
    Market Value (2035F)USD 7,975.7 Million
    CAGR (2025 to 2035)22.2%

    Country-wise Insights

    CountryCAGR (2025 to 2035)
    USA22.5%
    CountryCAGR (2025 to 2035)
    UK21.8%
    RegionCAGR (2025 to 2035)
    European Union (EU)22.2%
    CountryCAGR (2025 to 2035)
    Japan22.4%
    CountryCAGR (2025 to 2035)
    South Korea22.7%

    Competitive Outlook

    Company NameEstimated Market Share (%)
    Coinbase Cloud18-22%
    Binance API12-16%
    Chainalysis10-14%
    Alchemy8-12%
    CryptoAPIs6-10%
    Other Companies (combined)30-40%
  5. Cryptocurrency extra data - Litecoin

    • kaggle.com
    Updated Jan 20, 2022
    + more versions
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    Yam Peleg (2022). Cryptocurrency extra data - Litecoin [Dataset]. http://doi.org/10.34740/kaggle/dsv/3066229
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 20, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yam Peleg
    Description

    Context:

    This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

    Introduction

    This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

    The Data

    For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

    
    1. **timestamp** - A timestamp for the minute covered by the row.
    2. **Asset_ID** - An ID code for the cryptoasset.
    3. **Count** - The number of trades that took place this minute.
    4. **Open** - The USD price at the beginning of the minute.
    5. **High** - The highest USD price during the minute.
    6. **Low** - The lowest USD price during the minute.
    7. **Close** - The USD price at the end of the minute.
    8. **Volume** - The number of cryptoasset u units traded during the minute.
    9. **VWAP** - The volume-weighted average price for the minute.
    10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
    11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
    12. **Asset_Name** - Human readable Asset name.
    

    Indexing

    The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

    Usage Example

    The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

    Baseline Example Notebooks:

    These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

    Loose-ends:

    This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

    • VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
    • Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
    • Filtering: No filtration of 0 volume data is taken place.

    Example Visualisations

    Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

    Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

    License

    This data is being collected automatically from the crypto exchange Binance.

  6. Cryptocurrency extra data - Ethereum Classic

    • kaggle.com
    zip
    Updated Nov 26, 2021
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    Yam Peleg (2021). Cryptocurrency extra data - Ethereum Classic [Dataset]. https://www.kaggle.com/yamqwe/cryptocurrency-extra-data-ethereum-classic
    Explore at:
    zip(1259751691 bytes)Available download formats
    Dataset updated
    Nov 26, 2021
    Authors
    Yam Peleg
    Description

    Context:

    This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

    Introduction

    This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

    The Data

    For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

    
    1. **timestamp** - A timestamp for the minute covered by the row.
    2. **Asset_ID** - An ID code for the cryptoasset.
    3. **Count** - The number of trades that took place this minute.
    4. **Open** - The USD price at the beginning of the minute.
    5. **High** - The highest USD price during the minute.
    6. **Low** - The lowest USD price during the minute.
    7. **Close** - The USD price at the end of the minute.
    8. **Volume** - The number of cryptoasset u units traded during the minute.
    9. **VWAP** - The volume-weighted average price for the minute.
    10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
    11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
    12. **Asset_Name** - Human readable Asset name.
    

    Indexing

    The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

    Usage Example

    The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

    Baseline Example Notebooks:

    These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

    Loose-ends:

    This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

    • VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
    • Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
    • Filtering: No filtration of 0 volume data is taken place.

    Example Visualisations

    Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

    Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

    License

    This data is being collected automatically from the crypto exchange Binance.

  7. d

    Crypto Real-Time Price Data (VWAP) | +65 DEX & CEX | 10,000 Cryptocurrency...

    • datarade.ai
    .json
    Updated Apr 11, 2025
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    Blocksize (2025). Crypto Real-Time Price Data (VWAP) | +65 DEX & CEX | 10,000 Cryptocurrency Tickers | No Rate Limits [Dataset]. https://datarade.ai/data-products/crypto-real-time-price-data-vwap-65-dex-cex-10-000-blocksize
    Explore at:
    .jsonAvailable download formats
    Dataset updated
    Apr 11, 2025
    Dataset authored and provided by
    Blocksize
    Area covered
    Bosnia and Herzegovina, El Salvador, Cabo Verde, Turkey, Saint Helena, French Polynesia, Lithuania, Albania, Tokelau, Sri Lanka
    Description

    Access our data for free: https://matrix.blocksize.capital/auth/open/sign-up

    Blocksize’s Real-Time VWAP feed delivers ultra-low-latency, institutional-grade crypto market data, purpose-built for execution, risk management, and on-chain applications. Delivered exclusively via WebSocket APIs with no usage limits, the feed offers sub-second updates and a flat, transparent pricing model — making it ideal for high-frequency trading platforms, DeFi protocols, and data aggregators alike.

    Our VWAP feed goes beyond raw exchange pricing. Data is sourced from a broad network of centralized and decentralized exchanges and dynamically aggregated using volume-weighted methodologies to reflect real trading activity. Each data point is calculated from validated trade events, ensuring that prices represent true market value rather than isolated or illiquid trades.

    To maintain pricing integrity, Blocksize applies anomaly detection and statistical filtering to exclude outlier trades and unreliable exchanges. These include real-time checks for extreme price deviations and cross-venue consistency tests. Even during volatile conditions, the system adapts with smart rules for handling market gaps, low liquidity windows, or delayed exchange responses.

    Our infrastructure supports adaptive, liquidity-sensitive timeframes, requiring sufficient trading activity to ensure meaningful pricing. In rare cases where markets are thin or inactive, fallback logic ensures continuity by using the most recent validated data — while clearly flagging the conditions to users.

    With 99.9% uptime, GDPR-compliant hosting, and a roadmap aligned with institutional and DeFi client demands, Blocksize's Real-Time VWAP feed enables fast, reliable integration of crypto pricing data across products, dashboards, or smart contract systems.

    Our Customers:

    • Oracles & DeFi Protocols and Applications
    • Asset & Fund Managers investing in digital assets
    • Asset Custodians storing digital assets
    • Banks, Brokers with crypto offering
    • Traditional Data Providers planning to extend their offering to digital assets
    • Information Provider platforms

    Questions? Reach out to our qualified data team.

    PII Statement: Our datasets does not include personal, pseudonymized, or sensitive user data

  8. Database of influencers' tweets in cryptocurrency (2021-2023)

    • cryptodata.center
    • data.mendeley.com
    Updated Dec 4, 2024
    + more versions
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    cryptodata.center (2024). Database of influencers' tweets in cryptocurrency (2021-2023) [Dataset]. https://cryptodata.center/dataset/https-data-mendeley-com-datasets-8fbdhh72gs-5
    Explore at:
    Dataset updated
    Dec 4, 2024
    Dataset provided by
    CryptoDATA
    License

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

    Description

    Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

  9. m

    Comments on Telegram channels related to cryptocurrencies along with...

    • data.mendeley.com
    Updated Mar 8, 2024
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    kia jahanbin (2024). Comments on Telegram channels related to cryptocurrencies along with sentiments [Dataset]. http://doi.org/10.17632/3733zt5bs6.1
    Explore at:
    Dataset updated
    Mar 8, 2024
    Authors
    kia jahanbin
    License

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

    Description

    Through Telegram API, the authors collected this database over four months ago. These data are Telegram's comments of over eight professional Telegram channels about cryptocurrencies from December 2023 to March 2024. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets or Telegram's comments on one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity considerably. This database has a main table with eight columns. The columns of the main table are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Furthermore, we have added Python code to extract Telegram's comments. We used the RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer-based HDRB model(https://ieeexplore.ieee.org/document/10292644) for sentiment analysis.

  10. Digital Currencies Data | 5000 crypto pairs & 150 exchanges

    • datarade.ai
    .json, .csv
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    Twelve Data, Digital Currencies Data | 5000 crypto pairs & 150 exchanges [Dataset]. https://datarade.ai/data-products/digital-currencies-data-5000-crypto-pairs-150-exchanges-twelve-data
    Explore at:
    .json, .csvAvailable download formats
    Dataset authored and provided by
    Twelve Data
    Area covered
    Bonaire, Antigua and Barbuda, Morocco, Denmark, Mauritius, Poland, San Marino, Colombia, Turkey, Niue
    Description

    Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

    At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

    We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

  11. Bitcoin Market and Mining Dataset with Energy and Efficiency Metrics

    • zenodo.org
    csv
    Updated Jun 20, 2025
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    Giorgio Morico; Giovanni De Gasperis; Giovanni De Gasperis; Giorgio Morico (2025). Bitcoin Market and Mining Dataset with Energy and Efficiency Metrics [Dataset]. http://doi.org/10.5281/zenodo.15705316
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 20, 2025
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Giorgio Morico; Giovanni De Gasperis; Giovanni De Gasperis; Giorgio Morico
    License

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

    Time period covered
    Jun 20, 2025
    Description

    This dataset was manually constructed by aggregating and engineering multiple categories of Bitcoin-related data from different sources. It includes:

    - Market data (e.g., Open, Close, Volume, Fees, Transactions) collected from [Mempool](https://mempool.space/it/)
    - Energy data (e.g., estimated power consumption, efficiency metrics) retrieved via the [Electricity Maps API](https://portal.electricitymaps.com/docs/getting-started#geolocation)
    - Technical financial indicators (e.g., EMA, MACD, RSI, VWAP, TRIX, Bollinger Bands Width) computed using the [FinTa](https://github.com/peerchemist/finta) Python library
    - Derived metrics (e.g., momentum, volatility, normalization functions) calculated with custom Python functions

    The dataset is structured with one row per day and includes both raw values and engineered features. It offers a detailed overview of Bitcoin's economic, market, and energy dimensions.

    This dataset is suitable for use in machine learning models, time series forecasting, and the study of relationships between Bitcoin market behavior and its underlying energy consumption patterns.

  12. MarketData for MarketPredict RESTFul API including News and Market Data

    • figshare.com
    xlsx
    Updated Jun 9, 2021
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    ForexPredict BoEC (2021). MarketData for MarketPredict RESTFul API including News and Market Data [Dataset]. http://doi.org/10.6084/m9.figshare.14754966.v1
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jun 9, 2021
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    ForexPredict BoEC
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    About 3 years of news and market data in FOREX and CryptoCurrencies Markets.

  13. Cryptocurrency extra data - Maker

    • kaggle.com
    Updated Jan 20, 2022
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    Yam Peleg (2022). Cryptocurrency extra data - Maker [Dataset]. http://doi.org/10.34740/kaggle/dsv/3067075
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 20, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yam Peleg
    Description

    Context:

    This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

    Introduction

    This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

    The Data

    For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

    
    1. **timestamp** - A timestamp for the minute covered by the row.
    2. **Asset_ID** - An ID code for the cryptoasset.
    3. **Count** - The number of trades that took place this minute.
    4. **Open** - The USD price at the beginning of the minute.
    5. **High** - The highest USD price during the minute.
    6. **Low** - The lowest USD price during the minute.
    7. **Close** - The USD price at the end of the minute.
    8. **Volume** - The number of cryptoasset u units traded during the minute.
    9. **VWAP** - The volume-weighted average price for the minute.
    10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
    11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
    12. **Asset_Name** - Human readable Asset name.
    

    Indexing

    The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

    Usage Example

    The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

    Baseline Example Notebooks:

    These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

    Loose-ends:

    This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

    • VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
    • Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
    • Filtering: No filtration of 0 volume data is taken place.

    Example Visualisations

    Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

    Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

    License

    This data is being collected automatically from the crypto exchange Binance.

  14. f

    Data from: 3MEthTaskforce: Multi-source Multi-level Multi-token Ethereum...

    • auckland.figshare.com
    zip
    Updated Jan 15, 2025
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    Haoyuan Li; Mengxiao Zhang; Maoyuan Li; Jianzheng Li; Shuangyan Deng; Zijian Zhang; Jiamou Liu (2025). 3MEthTaskforce: Multi-source Multi-level Multi-token Ethereum Data Platform [Dataset]. http://doi.org/10.17608/k6.auckland.28208411.v2
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jan 15, 2025
    Dataset provided by
    The University of Auckland
    Authors
    Haoyuan Li; Mengxiao Zhang; Maoyuan Li; Jianzheng Li; Shuangyan Deng; Zijian Zhang; Jiamou Liu
    License

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

    Description

    3MEth Dataset OverviewSection 1: Token TransactionsThis section provides 303 million transaction records from 3,880 tokens and 35 million users on the Ethereum blockchain. The data is stored in 3,880 CSV files, each representing a specific token. Each transaction includes the following information:Sender and receiver wallet addresses: Enables network analysis and user behavior studies.Token address: Links transactions to specific tokens for token-specific analysis.Transaction value: Reflects the number of tokens transferred, essential for liquidity studies.Blockchain timestamp: Captures transaction timing for temporal analysis.Apart from the large dataset, we also provide a smaller CSV file containing 267,242 transaction records from 29,164 wallet addresses. This smaller dataset involves a total of 1,194 tokens, covering the time period September 2016 to November 2023. This detailed transaction data is critical for studying user behavior, liquidity patterns, and tasks such as link prediction and fraud detection.Section 2: Token InformationThis section offers metadata for 3,880 tokens, stored in corresponding CSV files. Each file contains:Timestamp: Marks the time of data update.Token price: Useful for price prediction and volatility studies.Market capitalization: Reflects the token's market size and dominance.24-hour trading volume: Indicates liquidity and trading activity.Section 3: Global Market IndicesThis section provides macro-level data to contextualize token transactions, stored in separate CSV files. Key indicators include:Bitcoin dominance: Tracks Bitcoin's share of the cryptocurrency market.Total market capitalization: Measures the overall market's value, with breakdowns by token type.Stablecoin market capitalization: Highlights stablecoin liquidity and stability.24-hour trading volume: A key measure of market activity.These indices are essential for integrating global market trends into predictive models for volatility and risk-adjusted returns.Section 4: Textual IndicesThis section contains sentiment data from Reddit's Ethereum community, covering 7,800 top posts from 2014 to 2024. Each post includes:Post score (net upvotes): Reflects engagement and sentiment strength.Timestamp: Aligns sentiment with price movements.Number of comments: Gauges sentiment intensity.Sentiment indices: Sentiment scores computed using methods detailed in the data preprocessing section.The full Reddit textual dataset is available upon request; please contact us for access. Alternatively our open-source repository includes a tool to guide users in collecting Reddit data. Researchers are encouraged to apply for a Reddit API Key and adhere to Reddit's policies. This data is valuable for understanding social dynamics in the market and enhancing sentiment analysis models that can explain market movements and improve behavioral predictions.

  15. W

    Web Screen Scraping Tools Report

    • marketresearchforecast.com
    doc, pdf, ppt
    Updated Mar 9, 2025
    + more versions
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    Market Research Forecast (2025). Web Screen Scraping Tools Report [Dataset]. https://www.marketresearchforecast.com/reports/web-screen-scraping-tools-31399
    Explore at:
    ppt, pdf, docAvailable download formats
    Dataset updated
    Mar 9, 2025
    Dataset authored and provided by
    Market Research Forecast
    License

    https://www.marketresearchforecast.com/privacy-policyhttps://www.marketresearchforecast.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The web screen scraping tools market, valued at $2831.7 million in 2025, is projected to experience robust growth, driven by the escalating demand for real-time data across diverse sectors. The market's Compound Annual Growth Rate (CAGR) of 4.6% from 2025 to 2033 indicates a steady expansion, fueled primarily by the increasing adoption of data-driven decision-making in e-commerce, investment analysis, and the burgeoning cryptocurrency industry. The "Pay-to-Use" segment currently dominates, reflecting businesses' preference for reliable, feature-rich solutions. However, the "Free-to-Use" segment shows promising growth potential, particularly among smaller businesses and individual developers seeking cost-effective data extraction solutions. Geographic growth is expected to be broad, with North America and Europe maintaining significant market share, while the Asia-Pacific region presents considerable untapped potential due to increasing digitalization and e-commerce adoption. Competitive pressures amongst established players like Import.io, Scrapinghub, and Apify are driving innovation and improvements in ease-of-use, data accuracy, and scalability. The market faces challenges related to legal and ethical concerns surrounding data scraping, as well as the ongoing evolution of website structures that can render scraping tools ineffective, necessitating constant updates and adaptations. The sustained growth trajectory of the web screen scraping tools market is anticipated to continue due to several factors. Firstly, the increasing complexity of data management across various sectors necessitates efficient data acquisition tools. Secondly, the expansion of e-commerce and the growth of the global digital economy fuels demand for accurate, up-to-date product information and market intelligence. Thirdly, the rise of big data analytics and the associated need for large datasets will continue to propel the adoption of web screen scraping solutions. The evolving regulatory landscape regarding data scraping will necessitate solutions that emphasize ethical and compliant data acquisition practices. This will drive innovation within the industry towards more responsible and robust web scraping tools that cater to the needs of businesses while respecting data privacy and copyright regulations. This will also favor the development of specialized tools optimized for specific sectors such as finance and e-commerce, rather than universal solutions.

  16. A

    ‘Crypto Fear and Greed Index’ analyzed by Analyst-2

    • analyst-2.ai
    Updated May 28, 2018
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2018). ‘Crypto Fear and Greed Index’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-crypto-fear-and-greed-index-e01d/63c3ed46/?iid=001-519&v=presentation
    Explore at:
    Dataset updated
    May 28, 2018
    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 ‘Crypto Fear and Greed Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adelsondias/crypto-fear-and-greed-index on 13 February 2022.

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

    Crypto Fear and Greed Index

    Each day, the website https://alternative.me/crypto/fear-and-greed-index/ publishes this index based on analysis of emotions and sentiments from different sources crunched into one simple number: The Fear & Greed Index for Bitcoin and other large cryptocurrencies.

    Why Measure Fear and Greed?

    The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

    • Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
    • When Investors are getting too greedy, that means the market is due for a correction.

    Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

    Data Sources

    We are gathering data from the five following sources. Each data point is valued the same as the day before in order to visualize a meaningful progress in sentiment change of the crypto market.

    First of all, the current index is for bitcoin only (we offer separate indices for large alt coins soon), because a big part of it is the volatility of the coin price.

    But let’s list all the different factors we’re including in the current index:

    Volatility (25 %)

    We’re measuring the current volatility and max. drawdowns of bitcoin and compare it with the corresponding average values of the last 30 days and 90 days. We argue that an unusual rise in volatility is a sign of a fearful market.

    Market Momentum/Volume (25%)

    Also, we’re measuring the current volume and market momentum (again in comparison with the last 30/90 day average values) and put those two values together. Generally, when we see high buying volumes in a positive market on a daily basis, we conclude that the market acts overly greedy / too bullish.

    Social Media (15%)

    While our reddit sentiment analysis is still not in the live index (we’re still experimenting some market-related key words in the text processing algorithm), our twitter analysis is running. There, we gather and count posts on various hashtags for each coin (publicly, we show only those for Bitcoin) and check how fast and how many interactions they receive in certain time frames). A unusual high interaction rate results in a grown public interest in the coin and in our eyes, corresponds to a greedy market behaviour.

    Surveys (15%) currently paused

    Together with strawpoll.com (disclaimer: we own this site, too), quite a large public polling platform, we’re conducting weekly crypto polls and ask people how they see the market. Usually, we’re seeing 2,000 - 3,000 votes on each poll, so we do get a picture of the sentiment of a group of crypto investors. We don’t give those results too much attention, but it was quite useful in the beginning of our studies. You can see some recent results here.

    Dominance (10%)

    The dominance of a coin resembles the market cap share of the whole crypto market. Especially for Bitcoin, we think that a rise in Bitcoin dominance is caused by a fear of (and thus a reduction of) too speculative alt-coin investments, since Bitcoin is becoming more and more the safe haven of crypto. On the other side, when Bitcoin dominance shrinks, people are getting more greedy by investing in more risky alt-coins, dreaming of their chance in next big bull run. Anyhow, analyzing the dominance for a coin other than Bitcoin, you could argue the other way round, since more interest in an alt-coin may conclude a bullish/greedy behaviour for that specific coin.

    Trends (10%)

    We pull Google Trends data for various Bitcoin related search queries and crunch those numbers, especially the change of search volumes as well as recommended other currently popular searches. For example, if you check Google Trends for "Bitcoin", you can’t get much information from the search volume. But currently, you can see that there is currently a +1,550% rise of the query „bitcoin price manipulation“ in the box of related search queries (as of 05/29/2018). This is clearly a sign of fear in the market, and we use that for our index.

    There's a story behind every dataset and here's your opportunity to share yours.

    Copyright disclaimer

    This dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

    This published version is an unofficial copy of their data, which can be also collected using their API (e.g., GET https://api.alternative.me/fng/?limit=10&format=csv&date_format=us).

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

  17. Crypto Wallet Market Analysis North America, Europe, APAC, South America,...

    • technavio.com
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    Technavio, Crypto Wallet Market Analysis North America, Europe, APAC, South America, Middle East and Africa - US, Canada, China, Germany, UK, The Netherlands, India, France, Italy, Japan - Size and Forecast 2025-2029 [Dataset]. https://www.technavio.com/report/crypto-wallet-market-industry-analysis
    Explore at:
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    United Kingdom, Canada, United States, Global
    Description

    Snapshot img

    Crypto Wallet Market Size 2025-2029

    The crypto wallet market size is forecast to increase by USD 631.2 million, at a CAGR of 20.6% between 2024 and 2029.

    The market is experiencing significant growth, driven by the increasing popularity of digital currencies and the expanding availability of crypto wallets. The inclination towards digital currencies, offering benefits such as decentralization, anonymity, and faster transactions, is fueling market expansion. However, challenges persist, with misuse and security attacks posing significant obstacles to widespread adoption. As the crypto market continues to evolve, it presents both opportunities and risks for businesses. Companies seeking to capitalize on this market can focus on enhancing security measures, ensuring user-friendly interfaces, and expanding their offerings to cater to diverse user needs. Navigating the challenges requires continuous innovation and a commitment to addressing security concerns, ensuring trust and confidence among users. In summary, the market is characterized by robust growth, driven by the shift towards digital currencies, while grappling with challenges related to security and misuse. Companies must seize opportunities to provide secure, user-friendly solutions to capitalize on this dynamic market.

    What will be the Size of the Crypto Wallet 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.
    Request Free SampleThe market continues to evolve, with various types of wallets catering to diverse user needs. Hardware wallets provide offline storage for private keys, enhancing security. Non-fungible tokens (NFTs) integration enables users to store and manage unique digital assets. Multi-currency wallets support various cryptocurrencies, while decentralized applications (dApps) integration offers seamless access to decentralized finance (DeFi) services. Transaction history and asset management are essential features for effective portfolio tracking. Wallet providers offer biometric authentication for enhanced security, while open-source wallets ensure transparency and community-driven development. Development kits (SDKs) enable customizable wallet solutions, catering to specific business requirements. Regulatory compliance is crucial, with Anti-Money Laundering (AML) and Know Your Customer (KYC) integrations becoming standard. Network fees, transaction fees, and gas fees are ongoing considerations for users, necessitating efficient wallet management. Security audits, import/export functions, and backup and restore capabilities are essential for maintaining wallet security. Cross-chain compatibility, seed phrases, staking rewards, and smart contract integrations are emerging trends, offering users more flexibility and opportunities. User experience (UX) and privacy coins prioritize user privacy and convenience. Payment gateways, merchant services, and wallet integrations facilitate seamless transactions. Key management and wallet recovery solutions ensure users maintain control over their assets. Threshold signatures and multi-signature wallets offer enhanced security through collective approval mechanisms. Blockchain integration, cryptocurrency exchange integration, and API integrations streamline user experience. The market's continuous dynamism underscores the importance of staying informed and adaptable to evolving trends and user needs.

    How is this Crypto Wallet Industry segmented?

    The crypto wallet 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. ProductSoftware-basedHardware-basedOSAndroidiOSOthersApplicationTradingPeer-to-peer paymentsRemittanceOthersEnd-userIndividualCommercialGeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyThe NetherlandsUKAPACChinaIndiaJapanRest of World (ROW)

    By Product Insights

    The software-based segment is estimated to witness significant growth during the forecast period.Crypto wallets serve as essential digital vaults for managing various cryptocurrencies and non-fungible tokens (NFTs). These wallets offer users the ability to purchase, swap, lend, and earn digital assets, contributing to the growing recognition of cryptocurrencies as a liquid and broadly held asset class. Software-based wallets, including desktop applications and browser extensions, facilitate transactions online, making them known as hot wallets. Multi-currency wallets support multiple cryptocurrencies and tokens, while decentralized applications (dApps) enable users to access various DeFi services. Wallet providers offer additional features like transaction history, biometric authentication, and user interfaces tailored to individual preferences. Asset manage

  18. d

    Development activity data for 2,000 cryptocurrencies

    • datarade.ai
    .json, .sql
    Updated Mar 19, 2025
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    Contora Inc. (2025). Development activity data for 2,000 cryptocurrencies [Dataset]. https://datarade.ai/data-products/contora-s-development-activity-data-on-7-700-cryptocurrencies-contora-inc
    Explore at:
    .json, .sqlAvailable download formats
    Dataset updated
    Mar 19, 2025
    Dataset authored and provided by
    Contora Inc.
    Area covered
    Hungary, Honduras, Estonia, Gibraltar, Mexico, Poland, Isle of Man, Finland, Norway, Netherlands
    Description

    We are monitoring open-source repositories of 2,000 major crypto coins and tokens to understand the development activity, see which projects gain developers' community, which were abundant.

    The main fields are the number of source code contributors and the number of code commits. The dataset has 10+ years of history, and the data is updated daily.

    The daily number of source code contributors and commits gives answers to such questions as: - Which crypto projects on the market are developing most actively now? - Which projects are idle or abundant by their developers' community? - Which projects attract more and more developers?

    Such data helps estimate risks of long-term investing into a variety of alt-coins, it is valuable for Crypto VCs, Crypto Hedge Funds, blockchain infrastructure startups.

  19. W

    Wallet as a Service (WaaS) Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jul 24, 2025
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    Data Insights Market (2025). Wallet as a Service (WaaS) Report [Dataset]. https://www.datainsightsmarket.com/reports/wallet-as-a-service-waas-510957
    Explore at:
    doc, pdf, pptAvailable download formats
    Dataset updated
    Jul 24, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The Wallet as a Service (WaaS) market is experiencing rapid growth, driven by the increasing adoption of cryptocurrencies and decentralized applications (dApps). The surging demand for secure and user-friendly digital asset management solutions is fueling this expansion. While precise market sizing data isn't provided, considering the prominent players listed (Coinbase, Fireblocks, etc.) and the burgeoning Web3 ecosystem, a reasonable estimation places the 2025 market size at approximately $2 billion. A Compound Annual Growth Rate (CAGR) of 30% from 2025 to 2033, reflecting optimistic yet realistic market expansion based on technological advancements and wider cryptocurrency adoption, is plausible. Key drivers include the simplification of cryptocurrency access for both individuals and businesses, the rise of institutional investment in digital assets, and the expanding need for robust security features to mitigate risks associated with self-custody. Market trends indicate a shift towards modular and customizable WaaS solutions, integration with decentralized finance (DeFi) platforms, and increased focus on regulatory compliance. Restraints include regulatory uncertainty in various jurisdictions, the inherent volatility of the cryptocurrency market, and ongoing security concerns related to digital asset custody. Segmentation likely includes solutions for individuals, businesses, and institutions, categorized by features like security levels, customization options, and supported cryptocurrencies. The competitive landscape is dynamic, with established players like Coinbase and Fireblocks vying for market share alongside emerging innovative companies like Thirdweb and Web3Auth.
    The forecast period (2025-2033) suggests significant expansion for the WaaS market. The CAGR of 30% reflects a strong growth trajectory fueled by factors like the maturation of the DeFi ecosystem, increasing institutional adoption, and advancements in blockchain technology. While several factors might temper this growth, the overall outlook remains positive. By 2033, based on the projected CAGR, the market could reach a substantial size, likely exceeding $10 billion. This estimation acknowledges inherent market volatility but underlines the significant potential of WaaS to become a critical infrastructure component within the evolving digital asset landscape. The key to success for WaaS providers will be to balance security, user experience, regulatory compliance, and adaptability to the constantly changing needs of the cryptocurrency market.

  20. Cryptocurrency extra data - TRON

    • kaggle.com
    Updated Jan 20, 2022
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    Yam Peleg (2022). Cryptocurrency extra data - TRON [Dataset]. http://doi.org/10.34740/kaggle/dsv/3066485
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 20, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Yam Peleg
    Description

    Context:

    This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

    Introduction

    This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

    The Data

    For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

    
    1. **timestamp** - A timestamp for the minute covered by the row.
    2. **Asset_ID** - An ID code for the cryptoasset.
    3. **Count** - The number of trades that took place this minute.
    4. **Open** - The USD price at the beginning of the minute.
    5. **High** - The highest USD price during the minute.
    6. **Low** - The lowest USD price during the minute.
    7. **Close** - The USD price at the end of the minute.
    8. **Volume** - The number of cryptoasset u units traded during the minute.
    9. **VWAP** - The volume-weighted average price for the minute.
    10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
    11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
    12. **Asset_Name** - Human readable Asset name.
    

    Indexing

    The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

    Usage Example

    The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

    Baseline Example Notebooks:

    These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

    Loose-ends:

    This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

    • VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
    • Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
    • Filtering: No filtration of 0 volume data is taken place.

    Example Visualisations

    Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

    Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

    License

    This data is being collected automatically from the crypto exchange Binance.

Share
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Close
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cryptodata.center (2024). Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm - Dataset - CryptoData Hub [Dataset]. https://cryptodata.center/dataset/integrated-cryptocurrency-historical-data-for-a-predictive-data-driven-decision-making-algorithm
Organization logo

Integrated Cryptocurrency Historical Data for a Predictive Data-Driven Decision-Making Algorithm - Dataset - CryptoData Hub

Explore at:
Dataset updated
Dec 4, 2024
Dataset provided by
CryptoDATA
License

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

Description

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

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