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.

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.

All-time high price data for Shiba Inu, including the peak value, date achieved, and current comparison metrics.

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

Shiba Inu reached its all-time high price on October 28, 2021.

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...

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!

A list containing 213 different stablecoins reveals that only *** of them had a market cap of over ** billion USD in 2025. The likes of Tether (USDT) and USD Coin (USDC) are tied (pegged) to a "real-world" asset - in their case, the USD - whereas a coin like DAI is connected to the development of other cryptocurrencies. Algorithmic stablecoins have nearly no backing, but use an algorithm that creates more of a particular (crypto) asset within the same blockchain. Stablecoins as a group hit the news frequently in 2022, although only a few featured in a ranking of top 120 cryptocurrencies based on market cap. They are meant to be less volatile than, say, Bitcoin (BTC), Ethereum (ETH), or Shiba Inu (SHIB), or other virtual currencies susceptible to significant price swings. The price of the most well-known stablecoin, Tether (USDT), however, consistently hovers around a single USD as is tied (pegged) to the “real-world” U.S. dollar. As stablecoins are connected to physical currencies, they are not the same as Central Bank-Issued Digital Currencies or CBDC like the digital euro or China's e-CNY - which outright digitalize those same currencies.

Shiba Inu price data for 2025-11-29 including currency, value, high, low, open, close, and percentage difference.

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

16 Top cryptocurrencies in the world (their prices from 2014 till today):

1) Bitcoin https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fb7282c5f05e1699a8cebf16e615130d4%2F1.png?generation=1681234916130546&alt=media" alt=""> 2) Ethereum https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F3782453af1876bd850a5b917d1efe7c3%2F2.png?generation=1681235230452486&alt=media" alt=""> 3) Tether https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Ff272c933f45e47ef6c9f4e26c689eced%2F3.png?generation=1681235244047286&alt=media" alt=""> 4) BNB https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2Fe51401f1583053b3ed4ecdeae598e726%2F4.png?generation=1681235279223075&alt=media" alt=""> 5) USD Coin https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F984ef7081854f76b550a2eaf8dd7278a%2F5.png?generation=1681235293422702&alt=media" alt=""> 6) XRP https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F5da5845f0cb9ed42cdace7e98eb22e30%2F6.png?generation=1681235306291122&alt=media" alt=""> 7) Cardano https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F980d7ea5abd080f36e3bfc2108982e37%2F7.png?generation=1681235319441929&alt=media" alt=""> 8) Dogecoin https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F82821781fba9682ac81571f2217863d2%2F8.png?generation=1681235332486351&alt=media" alt=""> 9) Polygon https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F467f389d8bf6ef09da08a3c28280bd8e%2F9.png?generation=1681235360391108&alt=media" alt=""> 10) Solana https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F26bfae574b7dd26a80fbbf342effc554%2F10.png?generation=1681235373358319&alt=media" alt=""> 11) Polkadot https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F548fbd3829eb6ad0b6f5689359a65a2f%2F11.png?generation=1681235385249856&alt=media" alt=""> 12) Binance USD https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F749a869ae992b2351fecf11bccb0fcb7%2F12.png?generation=1681235398173932&alt=media" alt=""> 13) Litecoin https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F91a4ec18a8850951e228853d1ff4331d%2F13.png?generation=1681235408650631&alt=media" alt=""> 14) Shiba Inu https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F7c8d7a7c38c50a75d6d15afeb5573cca%2F14.png?generation=1681235458023338&alt=media" alt=""> 15) TRON https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F60fc6e4a94f462583b26110cd57df718%2F15.png?generation=1681235471511272&alt=media" alt=""> 16) Avalanche https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10074224%2F52b8bbca95c14be0f35cd9b3272313f0%2F5805.png?generation=1681305637184198&alt=media" alt="">

All-time high price data for Baby Shiba Inu, including the peak value, date achieved, and current comparison metrics.

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

Baby Shiba Inu price data for 2025-11-13 including currency, value, high, low, open, close, and percentage difference.

Baby Shiba Inu price data for 2025-11-16 including currency, value, high, low, open, close, and percentage difference.

Baby Shiba Inu reached its all-time high price on August 24, 2023.

Baby Shiba Inu price data for 2025-11-15 including currency, value, high, low, open, close, and percentage difference.

Baby Shiba Inu price data for 2025-11-27 including currency, value, high, low, open, close, and percentage difference.

Baby Shiba Inu price data for 2025-11-20 including currency, value, high, low, open, close, and percentage difference.