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

Bitcoin dominance steadily declined in April 2024 to below ** percent, amid rumors of central banks halting or potentially lowering interest rates in the future. Within the crypto world, this so-called "dominance" ratio is one of the oldest and most investigated metrics available. It measures the coin's market cap relative to the overall crypto market — effectively showing how strong Bitcoin compared to all the other cryptocurrencies that are not BTC, called "altcoins". Why dominance matters is because market caps of any crypto can change relatively quickly, either due to sudden price changes or a change of recorded trading volume. Essentially, the figure somewhat resembles a trading sentiment, revealing whether Bitcoin investors are responding to certain events or whether Bitcoin is losing out on functions offered by, for example, stablecoins or NFT tokens. "Dominance" criticism: Ethereum and stablecoin The interpretation of the Bitcoin metric is not without its criticism. When first conceived, Bitcoin was the first cryptocurrency to be created and had a substantial market share within all cryptocurrencies? The overall share of stablecoins, such as Tether, as well as Ethereum increasingly start to resemble that of Bitcoin, however. Some analysts argue against this comparison. For one, they point towards the large influence of trading activity between Bitcoin and Ethereum in the dominance metric. Second, they argue that stablecoins can be traded in for Bitcoin and Ethereum, essentially showing how much investors are willing to engage with "regular" cryptocurrency. A rally around Bitcoin in late 2023? By December 2023, the Bitcoin price reached roughly 41,000 U.S. dollars — the first time in 20 months such a value was reached. A weaker U.S. dollar, speculation on decreasing interest rates, and a potential Bitcoin ETF approval are believed to be at the heart of this price increase. Whether this will hold in 2024 is unclear: The monthly interest rate from the U.S. Fed is speculated to decrease in 2024, despite a vow of "higher for longer". In December 2023, the thought of decreasing interest rates and the potential of a Bitcoin ETF fuelled market sentiment towards riskier assets.

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).

This dataset supports the research article titled "A Hybrid SVR-Based Framework for Cryptocurrency Price Forecasting and Strategy Backtesting". It includes historical price data, technical indicator features, and visualization-ready data used in the study for four major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC).

The dataset consists of:

• Raw historical price data (CSV/Excel)

• Technical indicators used as input features for SVR modeling (e.g., MA, RSI, KD, MACD, Bollinger Bands, etc.)

• Data behind charts in the manuscript (e.g., predicted vs. actual prices, strategy-specific outputs)

All data are provided in CSV format with accompanying documentation for reproducibility. This repository is meant to ensure transparency, facilitate replication, and encourage future research based on SVR and crypto trading strategies.