Memecoin Shiba Inu saw its price grow by more than 300 percent within one and the same month, marking a trading fury reminiscent of Dogecoin in early 2021. Indeed, the SHIB coin ranked as one of the biggest crypto in the world based on 24h trading volume in October 2021 - with trading activity being almost as high as that of Ethereum. Originally starting out as a fun token based on the famous Japanese dog breed, the digital currency grabbed mainstream attention in 2021 probably due to its low barrier of entry: Even after its price spikes, Shiba Inu was one of the cheapest coins available, especially when compared to the price of 99 other cryptocurrencies. Combine this with survey information that most consumers in the U.S. and the UK invest in crypto either for growth or fun, and Shiba Inu is probably seen by many as a relatively simple, "quick win" investment. As of May 19, 2025, the price of one million Shiba Inu is 14.08.

This dataset contains the predicted prices of the asset Shiba inu on Base over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Somewhat resembling developments of Dogecoin in early 2021, the market cap of Shiba Inu (SHIB) more than tripled in only a few weeks. This rapid growth especially occurred since September 2021, with the cryptocurrency breaking the ** billion U.S. dollar barrier multiple in the following month. Whilst no exact reason was given why the memecoin grew so fast during this time, the digital currency did become available on Coinbase, one of the world's most well-known crypto exchanges, on September 17. This move, and the fact that consumers in the U.S. and the UK overall tend to invest in crypto for fun or as a potential growth investment, might explain the popularity of the dog-themed coin. Indeed, Shiba Inu ranked as the number ** cryptocurrency in the world based on market cap in late October 2021 - closely behind DeFi-affilated like Uniswap (UNI) and Terra (LUNA).

This dataset contains the predicted prices of the asset Wrapped Shiba Inu (Universal) over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Content

Historical data of SHIBA INU

Date : Date of observation Open : Opening price on the given day High : Highest price on the given day Low : Lowest price on the given day Close : Closing price on the given day Volume : Volume of transactions on the given day Market Cap : Market capitalization

Acknowledgements

Found all the historical data from website: https://coinmarketcap.com/

This dataset contains the predicted prices of the asset Pepe Shiba Inu over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

What is Shiba Inu?

Shiba Inu (ticker: SHIB), also known informally as Shiba Token, is a decentralized cryptocurrency created in August 2020 by an anonymous person or persons known as "Ryoshi". Shiba Inu (柴犬) is a Japanese term for a breed of dog; it is the dog that is depicted in Dogecoin's symbol, which itself was originally a joke cryptocurrency based on the Doge meme. Shiba Inu was created in August 2020, dubbing itself the 'dogecoin killer'. On 13 May, Vitalik Buterin donated more than 50 trillion SHIB (worth over $1 billion at the time) to the India COVID-Crypto Relief Fund. The cryptocurrency notably surged in early October 2021. Initially up over 55% after rumors of the launch of a decentralized exchange, its value continued rising with an increase of 240% over the week.

Data Description

This dataset provides the history of daily prices of Shiba Inu. All the column descriptions are provided. Currency is USD

Find my notebook : Advanced EDA & Data Wrangling - Crypto Market Data where I cover the full EDA and advanced data wrangling to get beautiful dataset ready for analysis.

Find my Deep Reinforcement Learning v1 notebook: "https://www.kaggle.com/code/franoisgeorgesjulien/deep-reinforcement-learning-for-trading">Deep Reinforcement Learning for Trading

Find my Quant Analysis notebook:"https://www.kaggle.com/code/franoisgeorgesjulien/quant-analysis-visualization-btc-v1">💎 Quant Analysis & Visualization | BTC V1

Dataset Presentation:

This dataset provides a comprehensive collection of hourly price data for 34 major cryptocurrencies, covering a time span from January 2017 to the present day. The dataset includes Open, High, Low, Close, Volume (OHLCV), and the number of trades for each cryptocurrency for each hour (row).

Making it a valuable resource for cryptocurrency market analysis, research, and trading strategies. Whether you are interested in historical trends or real-time market dynamics, this dataset offers insights into the price movements of a diverse range of cryptocurrencies.

This is a pure gold mine, for all kind of analysis and predictive models. The granularity of the dataset offers a wide range of possibilities. Have Fun!

Ready to Use - Cleaned and arranged dataset less than 0.015% of missing data hour: crypto_data.csv

First Draft - Before External Sources Merge (to cover missing data points): crypto_force.csv

Original dataset merged from all individual token datasets: cryptotoken_full.csv

crypto_data.csv & cryptotoken_full.csv highly challenging wrangling situations: - fix 'Date' formats and inconsistencies - find missing hours and isolate them for each token - import external data source containing targeted missing hours and merge dataframes to fill missing rows

see notebook 'Advanced EDA & Data Wrangling - Crypto Market Data' to follow along and have a look at the EDA, wrangling and cleaning process.

Date Range: From 2017-08-17 04:00:00 to 2023-10-19 23:00:00

Date Format: YYYY-MM-DD HH-MM-SS (raw data to be converted to datetime)

Data Source: Binance API (some missing rows filled using Kraken & Poloniex market data)

Crypto Token in the dataset (also available as independent dataset): - 1INCH - AAVE - ADA (Cardano) - ALGO (Algorand) - ATOM (Cosmos) - AVAX (Avalanche) - BAL (Balancer) - BCH (Bitcoin Cash) - BNB (Binance Coin) - BTC (Bitcoin) - COMP (Compound) - CRV (Curve DAO Token) - DENT - DOGE (Dogecoin) - DOT (Polkadot) - DYDX - ETC (Ethereum Classic) - ETH (Ethereum) - FIL (Filecoin) - HBAR (Hedera Hashgraph) - ICP (Internet Computer) - LINK (Chainlink) - LTC (Litecoin) - MATIC (Polygon) - MKR (Maker) - RVN (Ravencoin) - SHIB (Shiba Inu) - SOL (Solana) - SUSHI (SushiSwap) - TRX (Tron) - UNI (Uniswap) - VET (VeChain) - XLM (Stellar) - XMR (Monero)

Date column presents some inconsistencies that need to be cleaned before formatting to datetime: - For column 'Symbol' and 'ETCUSDT' = '23-07-27': it is missing all hours (no data, no hourly rows for this day). I fixed it by using the only one row available for that day and duplicated the values for each hour. Can be fixed using this code:

start_timestamp = pd.Timestamp('2023-07-27 00:00:00')
end_timestamp = pd.Timestamp('2023-07-27 23:00:00')

hourly_timestamps = pd.date_range(start=start_timestamp, end=end_timestamp, freq='H')

hourly_data = {
  'Date': hourly_timestamps,
  'Symbol': 'ETCUSDT',
  'Open': 18.29,
  'High': 18.3,
  'Low': 18.17,
  'Close': 18.22,
  'Volume USDT': 127468,
  'tradecount': 623,
  'Token': 'ETC'
}

hourly_df = pd.DataFrame(hourly_data)
df = pd.concat([df, hourly_df], ignore_index=True)

df = df.drop(550341)

• Some rows for 'Date' have extra digits '.000' '.874' etc.. instead of the right format YYYY-MM-DD HH-MM-SS. To clean it you can use the following code:

# Count the occurrences of the pattern '.xxx' in the 'Date' column
count_occurrences_before = df['Date'].str.count(r'\.\d{3}')
print("Occurrences before cleaning:", count_occurrences_before.sum()) 

# Remove '.xxx' pattern from the 'Date' column
df['Date'] = df['Date'].str.replace(r'\.\d{3}', '', regex=True)

# Count the occurrences of the pattern '.xxx' in the 'Date' column after cleaning
count_occurrences_after = df['Date'].str.count(r'\.\d{3}')
print("Occurrences after cleaning:", count_occurrences_after.sum()) 

**Disclaimer: Any individual or entity choosing to engage in market analysis, develop predictive models, or utilize data for trading purposes must do so at their own discretion and risk. It is important to understand that trading involves potential financial loss, and decisions made in the financial mar...

Batay sa propesyonal na teknikal na pagsusuri at mga AI model, nagbibigay kami ng eksaktong datos sa pagtataya ng presyo para sa Shiba Inu sa 2025-11-25. Kasama rito ang multi-scenario analysis (bullish, base, bearish), pagtatasa ng panganib, teknikal na indikasyon, at mga inaasahang galaw ng merkado upang matulungan ang mga mamumuhunan sa paggawa ng matalinong desisyon sa trading at pagbuo ng matatag na estratehiya sa pamumuhunan.

This dataset contains the predicted prices of the asset Shiba Doge Inu over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Introduction

Dogecoin is an open source peer-to-peer digital currency, favored by Shiba Inus worldwide. It is qualitatively more fun while being technically nearly identical to its close relative Bitcoin. This dataset contains its historical stock price in USD on a daily frequency starting from 17 September 2014.

For more information refer to https://dogecoin.com/

Credits

Image Credits: Unsplash - claybanks

Context

Dogecoin is a cryptocurrency created by software engineers Billy Markus and Jackson Palmer, who decided to create a payment system as a "joke", making fun of the wild speculation in cryptocurrencies at the time. It is considered both the first "meme coin", and, more specifically, the first "dog coin". Despite its satirical nature, some consider it a legitimate investment prospect. Dogecoin features the face of the Shiba Inu dog from the "doge" meme as its logo and namesake. It was introduced on December 6, 2013, and quickly developed its own online community, reaching a market capitalization of over $85 billion on May 5, 2021.

Content

This dataset contains 1018 text files with comma-separated values, with each file representing historical data for each company. The data in each file contains daily stock data for the company from when it became public to present. There are 7 columns: - Date - Open - High - Low - Close - Adjusted close price for splits and dividend and/or capital gain distributions - Volume

Acknowledgements

Thanks to Yahoo Finance!

Este conjunto de dados contém os preços previstos do ativo Shiba Inu On Base nos próximos 16 anos. Estes dados são calculados inicialmente com uma taxa de crescimento anual padrão de 5%. Após o carregamento da página, um controle deslizante permite que o usuário ajuste a taxa de acordo com suas próprias projeções, positivas ou negativas. A taxa de crescimento máxima ajustável é de 100%, e a mínima é de -100%.

Dieser Datensatz enthält die prognostizierten Preise der Kryptowährung Shiba inu on Base für die nächsten 16 Jahre. Diese Daten werden anfangs mit einer standardmäßigen jährlichen Wachstumsrate von 5 Prozent berechnet und verfügen nach dem Laden der Seite über eine gleitende Skalenkomponente, mit der der Benutzer die Wachstumsrate weiter an seine eigenen positiven oder negativen Prognosen anpassen kann. Die maximal anpassbare positive Wachstumsrate beträgt 100 Prozent und die minimal anpassbare Wachstumsrate beträgt -100 Prozent.

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

Este conjunto de datos contiene los precios pronosticados del activo Shiba Inu On Base para los próximos 16 años. Estos datos se calculan inicialmente con una tasa de crecimiento anual predeterminada del 5 %, y después de cargar la página, incluyen un componente de escala móvil donde el usuario puede ajustar la tasa de crecimiento según sus propias proyecciones, ya sean positivas o negativas. La tasa máxima de crecimiento ajustable positivo es del 100 %, y la tasa mínima de crecimiento ajustable es del -100 %.

该数据集包含未来 16 年 Shiba Inu On Base 资产的预测价格。这些数据最初使用默认的 5% 年增长率进行计算，页面加载后，用户可通过滑动比例组件根据自己的正面或负面预测进一步调整增长率。最大可调正增长率为 100%，最小可调增长率为 -100%。

此資料集包含資產 Shiba Inu On Base 在未來 16 年的預測價格。此數據最初是使用預設的 5% 年增長獎勵率計算的，頁面加載後，它具有滑動比例組件，使用者可以根據自己的正面或負面預測進一步調整增長獎勵率。最高可調正獎勵率為 100%，最低可調獎勵率為 -100%。

Este conjunto de datos contiene los precios previstos del activo Wrapped Shiba Inu (Universal) para los próximos 16 años. Estos datos se calculan inicialmente utilizando un porcentaje de crecimiento anual predeterminado del 5%, y, una vez cargada la página, se muestra un componente de escala móvil en el que el usuario puede ajustar aún más el porcentaje de crecimiento según sus propias previsiones, ya sean positivas o negativas. El porcentaje de crecimiento ajustable positivo máximo es del 100%, y el porcentaje de crecimiento ajustable mínimo es del -100%.

Este conjunto de dados contém os preços previstos do ativo Shiba Inu but Japanese nos próximos 16 anos. Estes dados são calculados inicialmente utilizando uma taxa de crescimento anual padrão de 5% e, após o carregamento da página, apresentam um componente de escala deslizante onde o utilizador pode ajustar a taxa de crescimento de acordo com as suas próprias projeções, sejam elas positivas ou negativas. A taxa máxima de crescimento ajustável positiva é de 100%, e a taxa mínima de crescimento ajustável é de -100%.