Dogecoin trading volume did not reach until significant value until 2021, when public endorsements and certain events led to significant investor interest. The biggest recorded volume occurred in **************, two days after Tesla CEO Elon Musk posted - and eventually deleted - a cryptic tweet that said "Doge Barking at the Moon", along with a picture of the 1926 painting with the same name from Spanish artist Joan Miró. Many people saw this message as yet another endorsement for the cryptocurrency, as it looked like a reference to "to the moon" - originally a catch phrase from Reddit group WallStreetBets for the GameStop stock and eventually became associated with Dogecoin. This because around this time, the Dogecoin price development exceeded that of Bitcoin.

Detailed Token trading volume metrics and analytics for Dogecoin, including historical data and trends.

Although the number of transactions in Dogecoin increased in early 2021, there were roughly ****** of these on a single day. This figure is significantly lower when compared to the transaction volumes of other cryptocurrencies. Between January 28 and January 29, Dogecoin's value grew by around *** percent to ******** U.S. dollars after comments from Tesla CEO Elon Musk. The digital coin quickly grew to become the most talked-about cryptocurrency available.

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!

Dogecoin's market cap grew ******** in January 2021 after tweets from Tesla CEO Elon Musk, and has kept on growing since. By February 2021, the market cap of the cryptocurrency based on the famous internet meme had already *************. Compared to both the Bitcoin market capitalization as well as the Ethereum market cap, Dogecoin was not as popular.

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.

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

By 2035, the Dogecoin (DOGE) Market is estimated to expand to USD 804.4 Billion, showcasing a robust CAGR of 24.3% between 2025 and 2035, starting from a valuation of USD 73.5 Billion as of December 12, 2024.

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.

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

This dataset was collected from CoinGecko public API https://www.coingecko.com/api/documentation with the currency of usd. The json file includes marketcap, price and 24hr volume, and the csv file is a result after data wrangling process.

Daily data (00:00 UTC)

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.

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

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.

Blockchain data query: Daily Trade Volume for Meme Coins i.e. PEPE, WIF, SHIB, and DOGE (Past 7 days)

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 comprehensive dataset offers a thorough and meticulous analysis of Dogecoin transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

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.

The market cap of the top 10 stablecoin initially multiplied over time, reaching a combined value of over ****** billion USD in September 2025. Note this value does not include TerraUSD (UST), the algorithmic stablecoin tied to the LUNA crypto which declined severely in May 2022. Up to then, estimates reveal that the market cap had more than tripled within five months - likely following growing interest worldwide in cryptocurrencies, after sudden price spikes in a coin like Dogecoin (DOGE). Stability above all, or what does a stablecoin do? Stablecoins are cryptocurrencies - like the commonly known Bitcoin (BTC) and Ethereum (ETH) - but their value is determined differently. Whilst the price of Bitcoin mainly follows supply - how many coins are being mined or are available to purchase - and demand - how many investors want to buy the coin - stablecoins are synthetically connected to the price of an altogether different asset. Tether's USDT, for instance, is connected to the price development of the U.S. dollar (USD): if the U.S. dollar falls in the FX market, so does the USDT. Compare this to the "regular" price history of a cryptocurrency like Ripple (XRP) and stablecoins reveal themselves to be a relatively less volatile digital currency to either use or invest in than their counterparts in the free market. A test ground for digital payments This stability of these particular cryptocurrencies is important for two areas in digital payments that do not prefer volatility. For instance, these coins are a popular choice within the world of Decentralized Finance or DeFi - an online financial market without the supervision of central bank that relies on cryptocurrencies for payments and loans. Because of that reliance, it is a market that can rapidly change in size due to price fluctuations or changing transaction fees of certain cryptocurrencies - something that is less likely to occur when using stablecoins. Additionally, stablecoins are considered the inspiration for so-called CBDC or Central Bank Digital Currencies - such as China's e-CNY currency or the "digital euro" that is being researched in the EU-27. In terms of how advanced countries worldwide are into researching their own cryptocurrency, China ranked third in 2020, behind Cambodia, and The Bahamas.

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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...