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

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 were used in this study are as follows: [1] finance.yahoo.com/ [2] coinmarketcap.com/historical/ [3] cryptodatadownload.com/ [4] kaggle.com/philmohun/cryptocurrency-financial-data [5] kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] min-api.cryptocompare.com/ [9] p.nomics.com/cryptocurrency-bitcoin-api [10] coinapi.io/ [11] coingecko.com/en/api [12] cryptowat.ch/ [13] alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: aimaghsoodi.github.io/CLUSMCDA-R-Package/ github.com/Aimaghsoodi/CLUS-MCDA-II github.com/azadkavian/CLUS-MCDA UPDATE: This version also have the datapoint predictions using the Prophet Forecasting Model.