2 datasets found
  1. Solana Price History (SOL-USD)

    • kaggle.com
    Updated May 8, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Gokberk Kozak (2024). Solana Price History (SOL-USD) [Dataset]. https://www.kaggle.com/datasets/gokberkkozak/solana-price-history-sol-usd
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 8, 2024
    Dataset provided by
    Kaggle
    Authors
    Gokberk Kozak
    Description

    This dataset contains the price movements of the SOL cryptocurrency over the last four years. The data has been collected through the Yahoo Finance API. The dataset consists of the following columns:

    DATE: Date and time the price information pertains to.
    OPEN: Opening price on the specified date.
    HIGH: Highest price reached on the specified date.
    LOW: Lowest price reached on the specified date.
    CLOSE: Closing price on the specified date.
    VOLUME: Volume of transactions that occurred on the specified date.
    

    This dataset can be utilized to analyze recent price movements of the SOL cryptocurrency, identify trends, and make future price predictions. It can be used for various purposes including financial analysis, training machine learning models, and understanding market trends.

  2. Integrated Cryptocurrency Historical Data for a Predictive Data-Driven...

    • cryptodata.center
    Updated Dec 4, 2024
    + more versions
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    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

  3. 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
Gokberk Kozak (2024). Solana Price History (SOL-USD) [Dataset]. https://www.kaggle.com/datasets/gokberkkozak/solana-price-history-sol-usd
Organization logo

Solana Price History (SOL-USD)

Discover Solana's wild ride from 2020 to 2024 now! 💰

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 8, 2024
Dataset provided by
Kaggle
Authors
Gokberk Kozak
Description

This dataset contains the price movements of the SOL cryptocurrency over the last four years. The data has been collected through the Yahoo Finance API. The dataset consists of the following columns:

DATE: Date and time the price information pertains to.
OPEN: Opening price on the specified date.
HIGH: Highest price reached on the specified date.
LOW: Lowest price reached on the specified date.
CLOSE: Closing price on the specified date.
VOLUME: Volume of transactions that occurred on the specified date.

This dataset can be utilized to analyze recent price movements of the SOL cryptocurrency, identify trends, and make future price predictions. It can be used for various purposes including financial analysis, training machine learning models, and understanding market trends.

Search
Clear search
Close search
Google apps
Main menu