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

Cryptocurrencies

Finage offers you more than 1700+ cryptocurrency data in real time.

With Finage, you can react to the cryptocurrency data in Real-Time via WebSocket or unlimited API calls. Also, we offer you a 7-year historical data API.

You can view the full Cryptocurrency market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_Crypto_Coverage.pdf

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...

The market is projected to reach USD 1,074 Million in 2025 and is expected to grow to USD 7,975.7 Million by 2035, registering a CAGR of 22.2% over the forecast period. The expansion of Web3 infrastructure, advancements in multi-chain API solutions, and increasing demand for secure and scalable blockchain integrations are fueling market expansion. Additionally, rising adoption of tokenization, cross-chain interoperability, and API-driven NFT marketplaces is shaping the industry's future.

Metric	Value
Market Size (2025E)	USD 1,074 Million
Market Value (2035F)	USD 7,975.7 Million
CAGR (2025 to 2035)	22.2%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	22.5%

Country	CAGR (2025 to 2035)
UK	21.8%

Region	CAGR (2025 to 2035)
European Union (EU)	22.2%

Country	CAGR (2025 to 2035)
Japan	22.4%

Country	CAGR (2025 to 2035)
South Korea	22.7%

Competitive Outlook

Company Name	Estimated Market Share (%)
Coinbase Cloud	18-22%
Binance API	12-16%
Chainalysis	10-14%
Alchemy	8-12%
CryptoAPIs	6-10%
Other Companies (combined)	30-40%

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)

Developing secure software is essential for protecting passwords and other sensitive data. Despite the abundance of cryptographic libraries available to developers, prior work has shown that developers often unknowingly misuse the provided Application Programming Interfaces (APIs), resulting in serious security vulnerabilities. Eclipse CogniCrypt is an IDE plugin that aims at helping developers use cryptographic APIs more easily and securely by providing three main functionalities: (1) it provides a use-case oriented view of cryptographic APIs and guides the developer through their configuration, (2) it generates the code needed to accomplish the chosen use case based on the selected choices, and (3) it continuously analyzes the developer’s code to ensure that no API misuses are introduced later. However, so far the effectiveness of CogniCrypt was never empirically evaluated. In this work, we fill this gap through a controlled experiment with 24 Java developers. We evaluate the tool’s effectiveness in reducing API misuses and saving developer time. The results show that CogniCrypt significantly improves code security and also speeds up development for cryptograph-related tasks. The feedback received during the study suggests that developers particularly appreciate CogniCrypt’s code generation. Its static-analysis is valued for keeping the code up-to-date. Yet, the further integration of generated code into a developer’s project still presents a major challenge. Nonetheless, our results show that CogniCrypt effectively helps application developers produce more secure code.

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).

Crypto Data Platform Market Outlook

According to our latest research, the global Crypto Data Platform market size reached USD 1.85 billion in 2024, reflecting robust adoption across institutional and retail segments. The market is expected to expand at a CAGR of 18.2% during the forecast period, with revenues projected to reach USD 9.25 billion by 2033. This growth is primarily fueled by the increasing demand for real-time data analytics, advanced trading solutions, and regulatory compliance tools in the rapidly evolving cryptocurrency industry. The surge in digital asset adoption, coupled with heightened institutional participation and technological advancements, is driving the need for comprehensive, scalable, and secure crypto data platforms worldwide.

A significant growth factor for the Crypto Data Platform market is the exponential rise in crypto trading volumes and the proliferation of digital assets. As institutional investors, hedge funds, and family offices continue to increase their exposure to cryptocurrencies, the requirement for accurate, timely, and actionable data has become paramount. Crypto data platforms are now pivotal in providing market participants with historical and real-time price feeds, blockchain analytics, on-chain indicators, and sentiment analysis. These platforms also enable seamless integration with trading systems and portfolio management tools, empowering users to make informed investment decisions. The ongoing innovation in decentralized finance (DeFi) and the emergence of new digital asset classes further intensify the demand for robust data solutions, positioning crypto data platforms as a critical infrastructure layer in the digital economy.

Another key driver is the growing emphasis on regulatory compliance and risk management across the crypto ecosystem. As governments and regulatory bodies worldwide introduce stricter frameworks for anti-money laundering (AML), know-your-customer (KYC), and market surveillance, enterprises and exchanges are increasingly leveraging crypto data platforms to ensure adherence to these mandates. These platforms offer advanced compliance modules, transaction monitoring, and risk analytics, enabling stakeholders to mitigate operational and reputational risks. The integration of artificial intelligence (AI) and machine learning (ML) into these solutions further enhances their capability to detect anomalies, prevent fraud, and deliver predictive insights, thereby fostering trust and transparency in the market.

The rapid advancement in cloud computing, API-driven architectures, and interoperability standards is also propelling the Crypto Data Platform market forward. As digital asset markets operate around the clock and across geographies, there is a pressing need for scalable, resilient, and highly available data infrastructure. Cloud-based deployment models facilitate seamless access to vast datasets, while API integrations enable real-time connectivity with trading platforms, wallets, and external data sources. This technological evolution is enabling both established financial institutions and emerging fintech startups to harness the power of crypto data without significant upfront investments in hardware or IT resources. As a result, the market is witnessing accelerated product innovation, ecosystem collaboration, and the entry of new players offering specialized data services.

Regionally, North America continues to dominate the Crypto Data Platform market, accounting for the largest revenue share in 2024. The region’s leadership is underpinned by the presence of major crypto exchanges, institutional investors, and a mature regulatory landscape. Europe and Asia Pacific are also witnessing rapid adoption, driven by progressive regulatory initiatives, growing fintech ecosystems, and increasing retail investor participation. Latin America and the Middle East & Africa are emerging as promising markets, supported by rising digital asset adoption and government-led blockchain initiatives. However, regional disparities in regulatory clarity, technological infrastructure, and capital market maturity present both opportunities and challenges for market participants.

Component Analysis

The Crypto Data Platform market by component is segmented into Solutions and Services, each playing a vital role in the industry’s value chain. Solutions encompass the core software platforms that aggregate, normali

REST API access to thousands of currency pairs, cryptocurrencies and commodities. 100,000 requests/day - €50/month. Real-time quotes and max. available history for all cryptos, currencies and commodities!

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 :)

REST-API Zugang zu tausenden Währungspaaren, Cryptocurrencies und Rohstoffen. 100.000 Anfragen/Tag. Realtime Kurse und max. available Historie für alle Cryptos, Währungen und Rohstoffe!

Comprehensive Web3 dataset covering 927M+ active users, 1.8B+ wallets, 23M+ web2-wallet links, 82M+ dapp events, 8M+ smart contracts across 300+ blockchains

We monitor a number of mentions and their sentiment on Reddit, Twitter, and Telegram for the top 100 major crypto coins by liquidity.

Designed for quants and algorithmic traders, our real-time data stream provides you with an in-depth look at the social movements around cryptocurrencies and tokens.

Stay informed on the quantity and content of discussions, social buzz, and sentiment around any crypto/web3 project with our razor-sharp data. Social Pulse won't let you miss a beat in the fast-paced world of crypto trading.

REST API access in JSON format for over 50,000 stocks, ETFs, funds and indices. Historical price data with up to 100 years history of stocks, funds, ETFs, crypto-currencies and bonds from over 50 exchanges (XETRA, Frankfurt Stock Exchange, London, New York) worldwide!

Context

In a quest to find the best exchange for both traders and blockchain projects, I am scouring the internet to lump all exchange data into one place!

Content

You can read more from their exchange report here

And here is a Tableua summary table (note, doesn't contain 0-score exchanges, need to use API to get that data) https://www.cryptocompare.com/external/research/exchange-ranking/

Now, let's dig into the categories a bit:

-Legal Score: multi-dimensional score that includes things like KYC procedures, insurance against losses, sanction compliance, etc. -Investment Score: Where does investment come from? Large/Small VC? Amount? -Team Score: Exchange age, team credentials, etc -DataProvision Score: API Response time, granularity of candlestick data, API availability/limits, etc -TradeMonitoring Score: Internal/external trade surveillance? -MarketQuality Score: average spread, liquidity, natural trading behavior, etc. -Security Score: SSL, cold wallets, hacks, etc -NegativeReport Penalty: deducts 5% from any exchanges with negative reports (flash crash, privacy breach, etc) -One Star: number of 1-star ratings -Two Star: number of 2-star ratings -Three Star: ... -Four Star: ... -Five Star: ... -Percent One Star: % 1-star ratings -Percent Two Star: ... -Percent Three Star: ... -Percent Four Star: ... -Percent FIve Star: ... -Avg Star: Average star rating of exchange -[Other columns]: Not so sure about the vategorical columns. Couldn't find any info on the website. They are too unbalanced anyway so likely not useful

Acknowledgements

Thanks to CryptoCompare for their free API where you can access the data here: https://min-api.cryptocompare.com/data/exchanges/general

And a special shoutout to my friend for creating this awesome Google Sheets Add-On that makes connecting API's and getting data a breeze: https://mixedanalytics.com/api-connector/

Cover Photo https://unsplash.com/photos/DfjJMVhwH_8

Inspiration

What factors make for a good exchange?

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.

Study results and scripts to obtain the results for our paper "Python Crypto Misuses in the Wild" [@akwick @gh0st42 @Breitfelder @miramezini]The archives in this folder contains the following:- evaluations.tar.gz contains the evaluation folder from the GitHub project linked in References. - tools.tar.gz contains the tools folder from the GitHub project linked in References.- repos-py-with-dep-only-src-files.zip contains the source files and their dependencies of the Python projects analyzed.- repos-micropy-with-dep-only-src-files.zip contains the sources files and their depedencies of the MicroPython projects analyzed.

Blockchain data query: API Query Ethereum

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

This dataset contains information about bitcoin prices at hourly intervals. It cover between 2019-09 to 2023-05. I get this data with using Binance API. Here are the features of dataset:

• open_time: Kline Open time in unix time format
• open: Open Price
• high: High Price
• low: Low Price
• close: Close Price
• volume: Volume
• close_time: Kline Close time in unix time format
• quote_volume: Quote Asset Volume
• count: Number of Trades
• taker_buy_volume: Taker buy quote asset volume during this period
• taker_buy_quote_volume: Taker buy base asset volume during this period
• ignore : Ignore(you can drop this feature)