Dogecoin (DOGE) price per day from July 1, 2022 to March 26, 2025 (in U.S. cents)

Daily cryptocurrency data (transaction count, on-chain transaction volume, value of created coins, price, market cap, and exchange volume) in CSV format. The data sample stretches back to December 2013. Daily on-chain transaction volume is calculated as the sum of all transaction outputs belonging to the blocks mined on the given day. “Change” outputs are not included. Transaction count figure doesn’t include coinbase transactions. Zcash figures for on-chain volume and transaction count reflect data collected for transparent transactions only. In the last month, 10.5% (11/18/17) of ZEC transactions were shielded, and these are excluded from the analysis due to their private nature. Thus transaction volume figures in reality are higher than the estimate presented here, and NVT and exchange to transaction value lower. Data on shielded and transparent transactions can be found here and here. Decred data doesn’t include tickets and voting transactions. Monero transaction volume is impossible to calculate due to RingCT which hides transaction amounts.

This dataset provides predicted prices, return on investment (ROI), and sentiment analysis for Binance-Peg Dogecoin over a series of dates. The dataset includes predictions for Binance-Peg Dogecoin's price, with low, average, and high values, as well as ROI figures for each predicted date. This data helps users forecast market trends for Binance-Peg Dogecoin and make informed trading decisions.

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

DOGE WORLD CUP Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa DOGE WORLD CUP sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng DOGE WORLD CUP, tumaas ang halaga nito sa hindi pa naganap na peak sa 2025-03-16, na lumampas sa -- USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni DOGE WORLD CUP, na karaniwang tinutukoy bilang "DOGE WORLD CUP all-time low", ay naganap noong 2025-03-16. Kung ang isa ay bumili ng DOGE WORLD CUP sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na 0%. Sa pamamagitan ng disenyo, ang 100B DOGE WORLD CUP ay malilikha. Sa ngayon, ang circulating supply ng DOGE WORLD CUP ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang DOGE WORLD CUP dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

This is a submission for Challenge #24 by Desights User

Click here for Challenge Details Note: This submission is in REVIEW state and is only accessible by Challenge Reviewers. So you might get errors when you try to download this asset directly from Ocean Market.

Submission Description

The cryptocurrency is not just a new form of value store and exchange, it is a revolution of its own. Beginning with its use to provide peer-to-peer payment network (or digital money) like Bitcoin, today’s cryptocurrency, or crypto for short, have evolved way beyond its humble start. Underlying the crypto world, there lies amazing technology called Blockchain. In simple term, blockchain is a decentralized and shared digital ledger that records transactions transparently and immutably across nodes in the network. Today’s Crypto community has slowly turned into industry of its own introducing a whole spectrum of enigmatic pattern, trends, and economic framework. In this report we will explore the trend, correlations, and dynamics related to 20 selected Crypto projects to derived insights and build models that predict the future of crypto. Key Findings: Our exploratory data analysis (EDA) underlines the span and general pattern of the Google Trend and Price related data. The data being analyzed span from the earliest entry on 2014-09-17 up to the latest on 2024-04-07. Time series decomposition was performed to extract trend, seasonal cycle, and residuals that made up the Google Interest Trend data. Analysis on the time-series decomposition help us distinguish cluster (a) with projects on the rise such as Solana, SingularityNet, Fetch.ai, and Ocean Protocol; and cluster (b) containing old project such as Dogecoin, Litecoin Filecoin, Tezos that are facing stagnant/downfall trend. Based on the Google Trends’s Correlation across projects we characterize Highly correlated projects cluster with correlation of about >0.8, and up to 0.92 with Bitcoin-Ethereum-Chainlink-Litecoin-Monero as the prominent group members. By introducing additional Google Trend data to understand Crypto Narrative, we worked toward building interpretable Event/Entity driving the market sentiment to explain our decomposed Time-series data. Based on Lag Characteristics in Correlation of Google Trend and Price/Trade Volume we highlight the tendency for the correlation to accumulate at longer lag time. Using NeuralProphet Framework we build forecasting models for Google Trend and Token Price for all 20 projects investigated here. We deployed these models to predict Trend and Price for all 20 projects for the following 52 weeks (up until April 2025). The developed models performed extraordinarily well with the R^2 value for most fall between the range of 0.75-0.88, while the highest goes up to 0.919. We highlight the correlation between Bitcoin, Ethereum, Ocean, with the rest of other projects. Ocean and Bitcoin, also Ethereum and Solana are the most correlated, both with correlation value of 0.89. The Kucoin’s KCS token is the least correlated with both Ocean and Bitcoin (0.31), while with Ethereum, Filecoin have the least correlation (0.41).

Conclusion This investigative study presents a thorough data analysis and exploration of correlations, time-lag characteristics, and time-series decomposition concerning Google Trends and token prices for 20 selected crypto/blockchain projects. By decomposing the time-series data, we have identified several clusters of crypto projects that is moving up in popularity such as Fetch.ai, SingularityNet, Solana, Ocean and some others that are stuck or in downfall trend, such as Dogecoin and Litecoin. Our analysis also includes a detailed exploration of various factors that contribute to understanding the data better, such as the incorporation of event-driven trends that explain outlier spikes in the residual data from our decomposed time-series.

In addition to our in-depth analysis, we build strong mini-library of forecasting models for predicting the Google Trend as well as price for the upcoming year with R^2 score that goes as high as 0.88 for most cases. Moreover, in order to demonstrate the utility of our exploratory data analysis tools and pipeline in full we also include all the results and analysis output produced in this work.

Looking ahead, we plan to expand our developed forecasting models and the presented data into a "CryptoForecast MiniApp." This application, based on the Streamlit package, will be hosted on a decentralized cloud (Akash) and connected to the Ocean marketplace and Predictoor, enhancing accessibility and utility for users interested in real-time data for Google Trends and Crypto Token Price forecasts.

Download figures for crypto wallets in 2024 were much closer to the all-time high reached in the middle of 2021 than in other years. This after comparing download figures from several apps, including Coinbase, Blockchain.com, Metamask, Trust, and Binance, among others. Whilst the ten most popular cryptocurrency wallets registered roughly 2.2 million downloads in December 2020, one month later it had grown to over 5.6 million. It is around this time the price of Bitcoin grew significantly, and other cryptocurrencies such as Dogecoin attracted more and more general interest.