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.

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.

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...

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.

Delving into the realm of Dogecoin inputs, this dataset provides a broad and detailed view. Highlighting the intricate workings of the blockchain system, it stands as a continuously updated, invaluable asset in the ever-changing landscape of blockchain. This resource is a wellspring of information, suitable for various users. Whether you're a financial professional examining input dynamics, a researcher navigating the subtleties of input structures, or a blockchain enthusiast keen on understanding this technology's core components, this dataset is tailored for your exploration.

For any additional information or questions about this input dataset, please reach out to us at info@blockchair.com. Our devoted team is on standby to assist, ensuring you gain the utmost value from the data presented.

This dataset dives deep into the intricacies of Dogecoin blocks, presenting a comprehensive and holistic perspective. It meticulously captures unique attributes inherent to block datasets, such as block size, the number of transactions, and miner rewards, offering a window into the complex mechanics of the blockchain ecosystem. Updated in real-time, this dataset stands as a testament to the ever-evolving world of blockchain, making it an invaluable resource for a broad spectrum of users. Whether you're a financial expert analyzing block dynamics, a researcher delving into the subtleties of block configurations, or a blockchain aficionado eager to grasp the foundational elements, this dataset is designed with you in mind.

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 provides a detailed look at the top 10 cryptocurrencies from 2011 to 2023. While not all of the included cryptocurrencies were in existence in 2011, the data begins at this point to provide a comprehensive historical view of the cryptocurrency market. The dataset includes daily market capitalization, trading volume, price, and other key metrics for each cryptocurrency. The top 10 cryptocurrencies in this dataset include Bitcoin, Ethereum, Binance Coin, Cardano, Dogecoin, XRP, Solana, Polkadot, USD Coin, and Terra. It is important to note that this dataset is not empty and provides meaningful information, even for the earlier years when not all of the included cryptocurrencies were yet in existence. This dataset is useful for anyone interested in analyzing trends in the cryptocurrency market over time or investigating the performance of individual cryptocurrencies. The data was collected from a variety of sources and is updated regularly to ensure accuracy and completeness.