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!

The price of the cryptocurrency based on the famous internet meme broke its price decline in early November 2022, as people started buying the coin after FTX's collapse. This rally only lasted for a few days, however, as a Dogecoin was worth roughly 0.16 U.S. dollars on November 16, 2025. This is a different development than in 2021, when the crypto became very popular in a short amount of time. Between January 28 and January 29, 2021, Dogecoin's value grew by around 216 percent to 0.023535 U.S. dollars after comments from Tesla CEO Elon Musk. The digital coin quickly grew to become the most talked-about cryptocurrency available, not necessarily for its price - the prices of Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and several other virtual currencies were much higher than those of DOGE - but for its growth.

Dataset Presentation:

This dataset provides collection of H4 intervals price data for DOGECOIN. The dataset includes many advanced technical indicators.

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 and behaviours.

Date Range: From 2023-05-20 00:00:00 to 2023-11-02 12:00:00

Date Format: YYYY-MM-DD HH-MM-SS

Data Source: Binance API

Features:

• date: (YYYY-MM-DD HH-MM-SS). The specific day on which the data was recorded.
• open: The price at which a security first trades upon the opening of an exchange on a trading day.
• high: The highest price of the security during the trading day.
• low: The lowest price of the security during the trading day.
• close: The final price at which the security trades during the trading day.
• volume: The number of shares or contracts traded for the security on that day.
• tradecount: The number of individual trades conducted for the security on that day.
• hour: The specific hour of the trading day.
• day: The day of the week.
• month: The month of the year.
• volatility: A statistical measure of the dispersion of returns for the security.
• hc_change: The change between the high and close prices.
• lc_change: The change between the low and close prices.
• price_change: The change in price from the previous trading day to the current trading day.
• CMO: Chande Momentum Oscillator, a technical momentum indicator.
• Z-Score: A statistical measurement of a score's relationship to the mean in a group of scores.
• day_of_month: The specific day within the month.
• is_weekend: A binary indicator if the day is a weekend (1 for Yes, 0 for No).
• close_lag_1: The closing price from one day prior.
• volume_lag_1: The trading volume from one day prior.
• price_change_lag_1: The price change from one day prior.
• volume_lag_2: The trading volume from two days prior.
• price_change_lag_2: The price change from two days prior.
• volume_lag_3: The trading volume from three days prior.
• price_change_lag_3: The price change from three days prior.
• close_rolling_mean_3: The average closing price over the last 3 days.
• close_rolling_std_3: The standard deviation of the closing price over the last 3 days.
• volume_rolling_mean_3: The average trading volume over the last 3 days.
• volume_rolling_std_3: The standard deviation of the trading volume over the last 3 days.
• volume_price_interaction: An interaction term between volume and price, often used to capture combined effects.
• close_rolling_mean_5: The average closing price over the last 5 days.
• close_rolling_mean_10: The average closing price over the last 10 days.
• close_rolling_mean_20: The average closing price over the last 20 days.
• close_rolling_std_5: The standard deviation of the closing price over the last 5 days.
• close_rolling_std_10: The standard deviation of the closing price over the last 10 days.
• close_rolling_std_20: The standard deviation of the closing price over the last 20 days.

These features can be used in financial analysis, especially in the context of time series forecasting and algorithmic trading strategies.

This dataset is about cryptos per day. It has 1 row and is filtered where the crypto is Dogecoin and the date is the 8th of May 2025. It features 3 columns: date, and highest price.

This dataset contains the predicted prices of the asset Top Doge 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.

Context

Dogecoin (DOGE) is a cryptocurrency . Users are able to generate DOGE through the process of mining. Dogecoin has a current supply of 130,358,670,858.74646. The last known price of Dogecoin is 0.21878838 USD and is up 2.68 over the last 24 hours. It is currently trading on 375 active market(s) with $2,994,599,100.91 traded over the last 24 hours. More information can be found at http://dogecoin.com/.

Content

Two csv file are present in dataset section a. First one contains daily based data of Dogecoin and have approx. 1462 rows in dataset. b. Second one contains weekly based data of Dogecoin and have approx. 211 rows in dataset.

Dataset is from 24-Aug-2017 to 24-Aug-2021.

Attributes

• Date :- Date of trading
• Open :- Price from the first transaction of a trading day
• High :- Maximum price in a trading day
• Low :- Minimum price in a trading day
• Close:- Price from the last transaction of a trading day
• Adj Close :- Closing price adjusted to reflect the value after accounting for any corporate actions
• Volume :- Number of units traded in a day

Acknowledgements

This is updated dataset of Dogecoin which is downloaded from yahoo finance. Feel free to download this dataset.

Inspiration

He who serves the most, reaps the most...... by- Jim Rohn

Please do like this dataset.

This dataset contains the predicted prices of the asset Dogecoin 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.

This dataset contains the predicted prices of the asset DOGECOIN X 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.

This dataset contains the predicted prices of the asset Rich DogeCoin 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.

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

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...

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

Content

This dataset contain day wise price, open, high, low, vol., %change of dogecoin cryptocurrency.

Data Sources

https://in.investing.com/

Inspiration

How a cryptocurrency started as a joke, surged to greater heights becoming top 5 among other cryptocurrency?

Context

As cryptocurrency is gaining mainstream attention, this year the news of Elon Musk hosting the SNL made huge news in the crypto community. The coin who people relate to elon the most is dogecoin, he even tweets about dogecoin and posts memes about it.

Content

The Columns in the dataset are: Date: DD/MM/YYYY format Open: Opening price of the coin in that particular date Close: Closing price of the coin in that particular date High: Highest price of the coin in that particular date Low: Lowest price of the coin in that particular date

Acknowledgements

I downloaded the data from a website called Marketwatch, where we can see the price of stocks, cryptocurrency and other financial information.

Stock Market Analysis of Dogecoin Cryptocurrency from it's Founding / Listing Years which is 2014 to 2022.

Data Dictionary

About Dogecoin DOGE

Dogecoin : DOGE 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. It is the current shirt sponsor of Watford Football Club.

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This dataset contains two main parts:

1) Elon Musk's Tweets from the year 2021: This part of the dataset provides a comprehensive collection of tweets made by Elon Musk (@elonmusk) during the year 2021. Each record includes the date and time of the tweet (in UTC), the unique tweet ID, the text of the tweet. This data can be used for sentiment analysis, natural language processing tasks, or to study the correlation between public figures' social media activity and market movements.

2) Dogecoin (DOGE) Price Data for 2021: This part of the dataset includes the minutes price data for Dogecoin cryptocurrency throughout the year 2021 using UTC time zone. This data is useful for time-series analysis, market trend prediction, and studying market volatility. These were obtained using Binance API and in particular the klines.

Together, this dataset offers a unique opportunity to explore potential correlations between Elon Musk's Twitter activity and Dogecoin price movements during 2021. It could serve as a basis for studying the impact of influential individuals' social media activity on cryptocurrency prices, developing trading strategies, or as a unique case study in the broader field of socio-economic dynamics in the age of social media.

This dataset contains the predicted prices of the asset Chinese Dogecoin 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 Wrapped Dogecoin 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.

This dataset contains the predicted prices of the asset @DOGE 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.