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

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...

Real-Time Cryptocurrency Prices Dataset (Top 200 Coins)

This dataset contains real-time cryptocurrency market data fetched from the Crypto News Mini API (via RapidAPI). The dataset includes detailed price and market information for the top cryptocurrencies, ranked by market capitalization. Each row represents one cryptocurrency with the following attributes:

Features

rank – Global market cap ranking symbol – Trading symbol (e.g., BTC, ETH, SOL) name – Full coin name slug – API-friendly unique identifier id – Internal API ID price – Current price in USD image – Logo image URL market_cap – Total market capitalization in USD change_24h_percent – 24-hour price movement (%)

How This Dataset Was Collected :-

Source: Crypto-News51 Mini Crypto Prices API API Provider: RapidAPI Base Currency: USD Page Size: 20 coins per request Pages scraped: multiple (up to 200 coins total)

Crypto Coin Historical Data (2018-2025)

A dataset containing cryptocurrency historical price data across multiple timeframes. Designed to provide a standardized, easily accessible dataset for cryptocurrency research and algorithmic trading development. This dataset is automatically updated daily using the Binance API, ensuring that it remains current and relevant for users. Last updated on 2025-12-03 00:21:19.

  Usage

from datasets import load_dataset dataset =… See the full description on the dataset page: https://huggingface.co/datasets/linxy/CryptoCoin.

This Dataset contains historical price data for 10 cryptocurrencies spanning from 2021 to 2024, in three different time frames: 1 day, 4 hours, and 1 hour. The data is sourced from the Binance API and stored in CSV (Comma Separated Values) format for easy accessibility and analysis.

Usage

You can use this data for various purposes such as backtesting trading strategies, conducting statistical analysis, or building predictive models related to cryptocurrency markets.

Note

• All timestamps are in UTC timezone.
• Prices are quoted in USDT (Tether).

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Bitcoin Historical Dataset 3M records from 2017-2023

Context:

Bitcoin, the pioneering cryptocurrency, has captured the world's attention as a decentralized digital asset with a fluctuating market value. This dataset offers a comprehensive record of Bitcoin's price evolution, spanning from August 2017 to July 2023. The data has been meticulously collected from the Binance API, with price data captured at one-minute intervals. Each record includes essential information such as the open, high, low, and close prices, alongside associated trading volume. This dataset provides an invaluable resource for those interested in studying Bitcoin's price trends and market dynamics.

Dataset Details:

Total Number of Entries: 3.126.000

Attributes: Timestamp, Open Price, High Price, Low Price, Close Price, Volume , Quote asset volume, Number of trades, Taker buy base asset volume, Taker buy quote asset volume.

Data Type: csv

Size: 133 MB

Date ranges: 2023/08/17 till 2023/07/31

Content:

This dataset provides granular insights into the price history of Bitcoin, allowing users to explore minute-by-minute changes in its market value. The dataset includes attributes such as the open price, high price, low price, close price, trading volume, and the timestamp of each recorded interval. The data is presented in CSV format, making it easily accessible for analysis and visualization.

Inspiration:

The Bitcoin Price Dataset opens up numerous avenues for exploration and analysis, driven by the availability of high-frequency data. Potential research directions include:

Intraday Price Patterns: How do Bitcoin prices vary within a single day? Are there recurring patterns or trends during specific hours? Volatility Analysis: What are the periods of heightened volatility in Bitcoin's price history, and how do they correlate with external events or market developments? Correlation with Events: Can you identify instances where significant price movements coincide with notable events in the cryptocurrency space or broader financial markets? Long-Term Trends: How has the average price of Bitcoin evolved over different years? Are there multi-year trends that stand out? Trading Volume Impact: Is there a relationship between trading volume and price movement? How does trading activity affect short-term price fluctuations?

Acknowledgements:

The dataset has been sourced directly from the Binance API, a prominent cryptocurrency exchange platform. The collaboration with Binance ensures the dataset's accuracy and reliability, offering users a trustworthy foundation for conducting analyses and research related to Bitcoin's price movements.

Licensing:

Users are welcome to utilize this dataset for personal, educational, and research purposes, with attribution to the Binance API as the source of the data.

Hope you enjoy this dataset as much as I enjoyed putting it together. Can't wait to see what you can come up with :)

Content

The dataset has one csv file for each of the top 50 crypto coins by Market Capitalization.

Price history is available on a daily basis from Jan 1, 2015.

Column Information

• date : date of observation - the price is taken at 00:00:00 hours
• price : Price at the given date and time
• total_volume : volume of transactions on the given day
• market_cap : Market capitalization in USD

Acknowledgements

This data is taken from CoinGecko API and so please check with their terms of usage for using it in your projects.

Photo Credit:

Photo by Pierre Borthiry - Peiobty on Unsplash

Context

The past two months were crazy in the crypto market. The goal is to allow analyze correlations between Bitcoin and other Crypto Currencies in order to do smarter day-trading.

Content

This data set was updated every 15 min using Coin Market Cap API and includes the top 100 coins market cap, price in USD and price in BTC. Every row has its update time in EST Time zone

Acknowledgements

Coin Market Cap API

Inspiration

Who are the followers and leaders in the crypto market? When BTC goes down - what coins should be bought and when? When it goes up - which coins start to rise following it but still giving us enough time to buy them?

This dataset combines daily Bitcoin market data, U.S. Treasury holdings, and Google News–based sentiment scores. Sentiment is generated using CryptoBERT (https://huggingface.co/kk08/CryptoBERT) and offset by one day (row K corresponds to day K‑1).

Data sources & tools: - Bitcoin: Coinbase via ccxt - Treasury data: U.S. Treasury API - Sentiment: google_news_api with queries covering: Bitcoin, Ethereum, Binance Coin, Web3, Binance, Coinbase, market events, Crypto Twitter, Reddit cryptocurrency, and crypto market news

Coverage: - Daily rows from Dec 1, 2022 to Nov 8, 2025 - Columns include price/volume (open, high, low, close, volume), Treasury series, and weighted_sentiment (weighted average of title and summary of article sentiments)

Use cases: - Time series forecasting for Bitcoin - Studying relationships between financial indicators and sentiment - Feature engineering for ML models

🚀 Title: One-Hour High-Frequency Crypto Snapshot – Top 250 Coins, ~75K Ticks

📝 Overview

This dataset captures live market snapshots every 12 seconds for the top 250 cryptocurrencies, all fetched over a one-hour period using the CoinGecko Demo API. Perfect for real-time trend tracking, volatility analysis, and comparison across major coins.

📊 Schema Summary

🎯 Use Cases

• Visualize real-time price evolution for Bitcoin, Ethereum, and other major coins
• Compute rolling averages and short-term volatility
• Perform coin-to-coin comparisons (price dynamics, volume trends)
• Explore volume-price correlations, and flag anomaly detection
• Build heatmaps, live dashboards, or time-series models

📌 Attribution & Licensing

Data collected via CoinGecko API—**Data powered by CoinGecko**

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I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1h Data: Raw 1h historical data from multiple exchanges, covering the entire trading history of BTCUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken BTCUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/OVOyF5A.png" alt="BTCUSD Dataset Summary">

https://i.imgur.com/6hxG2G3.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)

The "Cryptocurrency Market Data from Coin Market Capital API" dataset provides comprehensive and up-to-date information on a wide range of cryptocurrencies. As the name suggests, the dataset revolves around the dynamic world of digital currencies and has been meticulously collected by utilizing the Coin Market Capital API.

Context

I started collecting data from Coin Market Cap of all crypto currencies and token on a 12 hours cycle. Plan to upload it every weekend. If you need it before for your analysis please PM me.

Content

All available crypto currency data from coin market cap - Symbol, Rank, Price USD, Price BTC, Market Cap, Date, Time.

Updated every 12 hours - update time is in EST.

Acknowledgements

Thanks coinmarketcap.com for the API access

Inspiration

What correlations are there? Is there a low-risk portfolio? What are the signals before crypto rise or crushes?

Dataset Overview Canonical on-chain token reference for fungible and non-fungible assets, providing unified structure and lineage for every recognized contract. Each row represents a unique token or collection, traceable to its genesis event and ABI-decoded metadata.

Chains and Coverage ETH, BSC, Base, Arbitrum, Unichain, Avalanche, Polygon, Celo, Linea, Optimism (others on request). Full history from chain genesis; reorg-aware real-time ingestion and updates. Includes both native coins (ETH, BNB, AVAX, etc.) and token contracts (ERC-20, ERC-721, ERC-1155, ERC-4626, custom standards).

Schema List the columns exactly as delivered. • contract_address BYTEA - PK; 20-byte contract address • block_number BIGINT - first block where the token was recognized • block_time TIMESTAMPTZ - UTC timestamp when the block was mined • tx_index INTEGER - tx index for that event • log_index INTEGER - log index for that event • name TEXT - asset name (from ABI or native coin registry) • symbol TEXT - token symbol or ticker • decimals SMALLINT - number of decimal places for fungible tokens (NULL for NFTs) • metadata_uri TEXT - optional field for NFT metadata base URI (if applicable) • _tracing_id BYTEA - deterministic row-level hash • _parent_tracing_ids BYTEA[] - hash(es) of immediate parent rows in the derivation graph • _genesis_tracing_ids BYTEA[] - hash(es) of original sources (genesis of the derivation path) • _created_at TIMESTAMPTZ - Record creation timestamp. • _updated_at TIMESTAMPTZ - Record last update timestamp

Notes • Use encode(contract_address,'hex') for hex presentation. • Metadata for each token type is retrieved deterministically via ABI decoding or registry sources. • If the ABI read was unsuccessful, the token is not present in this table.

Lineage Every row has a verifiable path back to the originating raw events via the lineage triple and tracing graph: • _tracing_id - this row’s identity • _parent_tracing_ids - immediate sources • _genesis_tracing_ids - original on-chain sources This supports audits and exact reprocessing to source transactions/logs/function calls.

Common Use Cases • Canonical token registry for normalization across DeFi datasets • Symbol, name, decimals lookups for accurate unit scaling in analytics • Cross-chain asset identity resolution • Foundation for NFT, LP token, and vault datasets • Integration layer for pricing engines, wallets, and indexers

Quality • Each row includes a cryptographic hash linking back to raw on-chain events for auditability. • Tick-level resolution for precision. • Reorg-aware ingestion ensuring data integrity. • Complete backfills to chain genesis for consistency.

About & Content

• This dataset contains all ERC20 transfers of $PEPE from inception until 2023 May 8.
• Each transfer contains information about the address of the sender and receiver as well the amount of $PEPE token that was transferred.
• Data is in ascending format (oldest => newest).

Data was obtained by downloading and parsing ERC20 transfer event logs using endpoint https://syve.readme.io/reference/event-logs from https://www.syve.ai/data-api

Inspiration

• $PEPE is a meme coin that blew up in price in an incredibly short amount of time.
• This makes it a case study of a successful meme coin for which public on-chain data can be studied for patterns that could have predicted it's success.
• The data is also great for discovering wallets that were able to purchase $PEPE early.

Final remarks

Any question? Feel free to send me a DM on Twitter: https://twitter.com/martkiro

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Short Description

Here you are a dataset containing the top 100 coins by their total volume across all markets during 9th January of 2021. The prices are in USD dollars. If you need a different or specific cryptocurrency data, open a new Discussion and I will try to do my best.

Content

This dataset was obtained thanks to cryptocompare API. You can see that it contains data about the top 100 coins by volume market during moreover 2020 (it contains a little bit of 2019 and 2021 years also). The dataset has 10 columns: 1. datetime: the date in which the coin had that price and volume. 2. low: the lowwst price of that day in USD dollars. 3. high: the highest price of that day in USD dollars. 4. open: the price when that day started in the markets in USD dollars. 5. close: the price when that day closed in the markets in USD dollars. 6. volumefrom: the quantity of that coin that was traded in that day. 7. volumeto: the quantity of that coin that was traded in that day in USD dollars. 8. cryptocurrency: the symbol of the cryptocurrency. 9. image_url: the image url containing the cryptocurrency logo. 10. coin_name: the full name of the cryptocurrency.

To get the data I used this endpoint and to get the top 100 coins list I used this one.

The code I used to generate can be found in my personal github (I would really appreciate any contribution, follow or star 😃). Feel free to contact me (you can find my email in my github) if you need something about the data.

Acknowledgements

Part of the code used to obtain the data was inspired in this fabulous analsys from Roman Orac.