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.

SHIB is the main token of the Shiba Inu ecosystem, bringing the power of a decentralized, community-led currency to millions across the globe. Since its inception in late 2020, the Ethereum-based SHIB token has grown to become a worldwide phenomenon, and is now accepted as a form of payment at hundreds of locations, either directly or through third-party intermediaries.

This dataset is having historical data of Shiba Inu from 2020 to 2023. Dataset columns:- 1. Open 2. High 3. Low 4. Close 5. Volume 6. Market Cap 7. Timestamp

Please UPVOTE

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).

Shiba Inu trading volume accelerated significantly in October 2021, as the *** trade volume grew from roughly *********** U.S. dollars into double digits. Whilst the cryptocurrency based on the famous Japanese dog breed already had a trading peak early in ********, ************ could be seen as the breakthrough month for the coin as it became one of the most valuable cryptocurrencies available. The meme-inspired digital currency saw a major price increase during this month, as it invoked a similar sentiment as to early 2021 when Dogecoin became a popular cryptocurrency to invest in.

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/

Daily historical price data for Shiba Inu including high, low, open, close, and percentage difference over the most recent 24 days.

Shiba Inu (SHIB) is a token that aspires to be an Ethereum-based alternative to Dogecoin (DOGE), the popular memecoin. Unlike Bitcoin, which is designed to be scarce, SHIB is intentionally abundant — with a circulating supply of one quadrillion. The Shiba Inu Token ecosystem supports projects such as an NFT art incubator and the development of a decentralized exchange called Shibaswap. https://imgsrv2.voi.id/MlbLCz0b9dyLZJje0kL68S7_c8IL0bO07MsdqsOnpPc/auto/1200/675/sm/1/bG9jYWw6Ly8vcHVibGlzaGVycy81NTM1My8yMDIxMDUzMTEyNDctbWFpbi5jcm9wcGVkXzE2MjI0NDcxMDAuanBn.jpg" alt="Shiba">

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

Historical price and volatility data for Shiba Inu in Vietnamese Dong across different time periods.

Historical price and volatility data for Shiba Inu in US Dollar across different time periods.

Historical price and volatility data for Shiba Inu in Taiwan New Dollar across different time periods.

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!

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

Historical price and volatility data for Shiba Inu in UKrainian Hryvnia across different time periods.

Daily historical price data for Baby Shiba Inu including high, low, open, close, and percentage difference over the most recent 24 days.

Prices for Shiba Inu - 為替レート including live quotes, historical charts and news. Shiba Inu - 為替レート was last updated by Trading Economics this December 2 of 2025.

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...

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