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.

From the Texas Work Group on Blockchain Matters, this is the report and proposed master plan to expand the blockchain industry in Texas in compliance with House Bill 1576, passed by the 87th Texas Legislature. This report examines the current blockchain industry in Texas, reviews the state’s current academic, educational, and workforce needs required to grow the industry, and identifies areas for economic growth and development opportunities presented by blockchain technology. The report contains legislative and policy recommendations aimed at encouraging the industry’s expansion and establishing regulatory and legal clarity to establish Texas as a leader in the blockchain technology and cryptocurrency space.

CryptoSentiment is a dataset, which contains sentiment information about cryptocurrency assets, gathered by various online sources, and analyzed by FinBERT sentiment extractor. More specifically, we provide a publicly available dataset containing fine-grained sentiment analysis data (minute-basis) about cryptocurrency market collected by different online sources. CryptoSentiment dataset includes 235,907 sentiment scores for 14 different cryptocurrencies gathered from various online sources such as news articles and social media.

Crypto Trading and Technical Indicators

Understanding the Market Dynamics of 600 Popular Cryptocurrencies

By [source]

About this dataset

This dataset provides an unprecedented overview of the crypto industry, offering comprehensive market analysis of more than 600 well-known cryptocurrencies. The data contained in this dataset is extremely up-to-date, ranging from trading statuses, price movements and volatility levels to technical indicators and market capitalization. Perfect for those interested in cryptocurrency trading, technical analysis or investing, this data can be used to facilitate informed decisions and fulfill respective requirements.
The 35 columns present in this dataset enable users to gain a greater understanding into price movements and distinguish between various levels of volatility. It also allows users to analyze oscillator ratings for each crypto asset listed within for accurate risk management. Banks, investors, data analysts as well as crypto exchanges could all benefit from utilizing this powerful dataset; its ability to provide a top level summary into the entire crypto industry has earned it a valuable place among the cryptocurrency community worldwide

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How to use the dataset

This dataset provides comprehensive market analysis of more than 600 popular cryptocurrencies, including trading prices, volatility, technical indicators, and market capitalization. In this guide, we'll cover what kind of information you can obtain from the dataset, how to use it effectively to gain insight into the crypto industry, and how to analyze the results in order to make informed decisions regarding cryptocurrency trading.

The dataset consists of 35 columns that are divided into two main categories: Market Information and Technical Indicators. The Market Information section contains data about each cryptocurrency's price performance – including change percentages for 1 week/1 month/3 months/6 months/1 year – as well as its fully diluted market capitalization (FD Mkt Cap), traded volume (Traded Vol), last trading price in USD (Last_y), available coins (Avail Coins), total coins created with a max supply(Total Coins) and its respective rating out of 5 stars by moving averages(Moving Averages Rating). The Technical Indicators section includes data pertaining to oscillator ratings (Oscillators Rating) such as Average Directional Index (ADX), Awesome Oscillator(AO), Average True Range(ATR) , Commodity Channel Index20(CCI20) etc., moving averages such as Simple Moving Average 20 days /50 days / 200 days (SMA20/ SMA50 / SMA200) , Bollinger Bands upper & lower limit lines comprised of standard deviations known as BB Up & BB Low respectively , Momentum(MOM ), Relative Strength Index14 day time frame indicator denoted by RSI14 , Macd level & signal line along with Stochitic %K &%D indicators.

With all that knowledge now let’s take a look at some tips on how you can analyse this data effectively. For example: finding safety ranks based on volatility scores or locatingcryptocurrencies whose MACD line has recently crossed over its signal line thus giving buy signals or vice versa giving sell signals also mining through various time frames using multiple technical indicators like ADX +CCI20+RSI14 etc can help spot potential trends which may be indicative for a particular currency . Also moving averages assessments over several time periods might be useful for calculating trend based values in order for possible bullish or bearish orientations . Furthermore when examining long term trends across multiple currencies it might be suitable carry out simple comparisons between certain columns from one currency against

Research Ideas

• Utilizing the price movements and technical indicators, investors can analyze the different crypto industry trends and develop strategies to apply them in their portfolios.
• Governmental institutions and banks can use this dataset to monitor the industry’s activity from country to country, helping create regulatory policies when necessary.
• Crypto exchanges can design algorithms based on this data set which will assist with detecting any manipulation or malicious activities in trades occurring in their platform

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - Y...

The Cryptocurrency Market Report Segments the Industry by Transaction Purpose (Payments & Remittances, Trading and Investment Transfers, Decentralized Finance (DeFi) Protocol Flows, and More), by User Type (Retail and Institutional), by Cryptocurrency (BTC, ETH, Ripple, and More), and by Geography (North America, South America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

Dataset Card for Cryptonews articles with price momentum labels

  Dataset Summary

The dataset was gathered from two prominent sources in the cryptocurrency industry: Cryptonews.com and Binance.com. The aim of the dataset was to evaluate the impact of news on crypto price movements. As we know, news events such as regulatory changes, technological advancements, and major partnerships can have a significant impact on the price of cryptocurrencies. By analyzing the data… See the full description on the dataset page: https://huggingface.co/datasets/SahandNZ/cryptonews-articles-with-price-momentum-labels.

Our extensive historical database captures every significant market movement, from the earliest Bitcoin trades through today's crypto ecosystem, across 350+ global exchanges.

This rich historical dataset serves multiple critical functions: from enabling sophisticated strategy backtesting and long-term trend analysis to supporting academic research and trading pattern identification. Whether analyzing market volatility, studying price correlations, or conducting deep market research, our historical data provides the reliable foundation needed for meaningful cryptocurrency market analysis.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume - Full Cryptocurrency Investor Data

Technical Excellence: - 99,9% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our commitment to data quality and technical excellence makes us the trusted choice for cryptocurrency market data needs.

The cryptocurrency market surpassed the barrier of $100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1469 cryptocurrencies introduced between April 2013 and May 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.

Context

Since the launch of Bitcoin in 2008, hundreds of similar projects based on the blockchain technology have emerged. We call these cryptocurrencies (also coins or cryptos in the Internet slang). Some are extremely valuable nowadays, and others may have the potential to become extremely valuable in the future1. In fact, on the 6th of December of 2017, Bitcoin has a market capitalization above $200 billion.

Content

The cryptocurrency market is exceptionally volatile2 and any money you put in might disappear into thin air. Cryptocurrencies mentioned here might be scams similar to Ponzi Schemes or have many other issues (overvaluation, technical, etc.). Please do not mistake this for investment advice. *

2 Update on March 2020: Well, it turned out to be volatile indeed :D

That said, let's get to business. We will start with a CSV we conveniently downloaded on the 6th of December of 2017 using the coinmarketcap API (NOTE: The public API went private in 2020 and is no longer available) named datasets/coinmarketcap_06122017.csv.

Acknowledgements

Data set is from DataCamp

Description

This dataset, titled "Cryptocurrency Market Sentiment & Prediction," is a synthetic collection of real-time crypto market data designed for advanced analysis and predictive modeling. It captures a comprehensive range of features including price movements, social sentiment, news impact, and trading patterns for 10 major cryptocurrencies. Tailored for data scientists and analysts, this dataset is ideal for exploring market volatility, sentiment analysis, and price prediction, particularly in the context of significant events like the Bitcoin halving in 2024 and increasing institutional adoption.

Key Features Overview: - Price Movements: Tracks current prices and 24-hour price change percentages to reflect market dynamics. - Social Sentiment: Measures sentiment scores from social media platforms, ranging from -1 (negative) to 1 (positive), to gauge public perception. - News Sentiment and Impact: Evaluates sentiment from news sources and quantifies their potential impact on market behavior. - Trading Patterns: Includes data on 24-hour trading volumes and market capitalization, crucial for understanding market activity. - Technical Indicators: Features metrics like the Relative Strength Index (RSI), volatility index, and fear/greed index for in-depth technical analysis. - Prediction Confidence: Provides a confidence score for predictive models, aiding in assessing forecast reliability.

Purpose and Applications: - Perfect for machine learning tasks such as price prediction, sentiment-price correlation studies, and volatility classification. - Supports time series analysis for forecasting price movements and identifying volatility clusters. - Valuable for research into the influence of social media and news on cryptocurrency markets, especially during high-impact events.

Dataset Scope: - Covers a simulated 30-day period, offering a snapshot of market behavior under varying conditions. - Focuses on major cryptocurrencies including Bitcoin, Ethereum, Cardano, Solana, and others, ensuring relevance to current market trends.

Dataset Structure Table:

Dataset Statistics Table:

This dataset is a powerful resource for machine learning projects, sentiment analysis, and crypto market research, providing a robust foundation for AI/ML model development and testing.

Korean Companies’ Cryptocurrency Data provides important information to analyze and predict trends in the rapidly changing cryptocurrency market. This data includes transaction history, price fluctuations, market sentiment, and exchange data. Collected from various sources such as major cryptocurrency exchanges, blockchain networks, and news sources, it helps investors predict market trends, manage risks, and discover investment opportunities, which are crucial for evaluating the value of Korean companies involved in the cryptocurrency industry.

Comprehensive dataset for the Layer 1 blockchain narrative, including sector-wide price performance, trading volume, volume to market cap ratios, and correlation data for tokens like BTC, ETH, SOL, and others.

Through Telegram API, the authors collected this database over four months ago. These data are Telegram's comments of over eight professional Telegram channels about cryptocurrencies from December 2023 to March 2024. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets or Telegram's comments on one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity considerably. This database has a main table with eight columns. The columns of the main table are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Furthermore, we have added Python code to extract Telegram's comments. We used the RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer-based HDRB model(https://ieeexplore.ieee.org/document/10292644) for sentiment analysis.

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

This dataset provides comprehensive access to financial market data from Google Finance in real-time. Get detailed information on stocks, market quotes, trends, ETFs, international exchanges, forex, crypto, and related news. Perfect for financial applications, trading platforms, and market analysis tools. The dataset is delivered in a JSON format via REST API.

Crypto & Stock Tweets

  Overview

This dataset is a combination of publically available financial tweets.

  Datset Size

Stock Tweets: 2,624,314 Crypto Tweets: 5,748,725 Bitcoin Tweets: 4,820,915

  Sources

This dataset is a combination of data from various reputable sources, each contributing a unique perspective on financial tweets:

Stock Market Tweets Data: 923,673 rows of stock tweets Stock Market Tweets: 1,700,641 rows of stock tweets Crypto Tweets:… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/crypto-stock-tweets.

This dataset provides insights into public opinion regarding Bitcoin, derived from comments posted on the /r/Bitcoin subreddit during June 2022 [1, 2]. It is designed to help users track current trends and developments within the cryptocurrency world [2]. The data includes the actual body text of the comments, alongside their assigned sentiment, making it a valuable resource for understanding the evolving landscape of Bitcoin [1, 2].

Columns

The dataset includes several key columns for each comment: * type: Describes the type of post, stored as a String [1-3]. * subreddit.name: The name of the subreddit, which is "/r/Bitcoin" in this case, stored as a String [1-3]. * subreddit.nsfw: Indicates whether the subreddit is Not Safe For Work (NSFW), a Boolean value [1-4]. The sources indicate that almost all entries (170,032 out of 170,036) are marked as 'false' for NSFW [4]. * created_utc: The timestamp when the post was created, allowing for chronological analysis [1-8]. * permalink: The permanent link to the original post or comment on Reddit, a String [1-3]. * score: The score of the post, an Integer value, typically reflecting upvotes or downvotes [1, 2]. * body: The main text content of the comment, stored as a String [1-3]. Notably, about 7% of comments are "[removed]" and 3% are "[deleted]" [8]. * sentiment: The assigned sentiment of the post, a String. This column also appears to have numerical values ranging from -1.00 (most negative) to 1.00 (most positive), with detailed label counts across various ranges [1, 3, 8-10]. A significant portion of comments, 32,903, fall into the -0.04 to 0.00 sentiment range [9].

Distribution

This dataset focuses on comments from the /r/Bitcoin subreddit from June 2022 [1, 2]. It contains approximately 170,035 unique comment entries [4]. The timestamps for created_utc are distributed across June 2022, with varying numbers of comments per time interval, for example, 12,392 comments were recorded between 1655544958.04 and 1655596797.80 [6]. The sentiment analysis is detailed across numerous bins, showing a wide spread of positive, negative, and neutral sentiments [8-10].

Usage

This dataset is ideal for data science and analytics [2]. Potential uses include: * Tracking cryptocurrency trends: Staying up-to-date with the latest developments in Bitcoin [2]. * Sentiment analysis: Analysing public opinion and sentiment towards Bitcoin over time [1]. * Natural Language Processing (NLP) research: Utilising the comment body text for linguistic analysis [2]. * Market research: Understanding community discussions and concerns related to Bitcoin. * Time-series analysis: Observing how sentiment and discussion volume change over the month of June 2022.

Coverage

The dataset covers content from the Reddit /r/Bitcoin subreddit [1, 2]. * Time Range: Specifically the month of June 2022 [1, 2]. * Geographic Scope: While Reddit is global, the specific geographic origin of users is not detailed in the dataset columns. However, it can be considered a global snapshot of online discussion [11]. * Demographic Scope: Reflects the opinions and discussions of Reddit users who actively participate in the /r/Bitcoin subreddit.

License

CC0

Who Can Use It

• Data Scientists and Analysts: For conducting sentiment analysis, trend tracking, and NLP projects [2].
• Researchers: Studying online communities, cryptocurrency market dynamics, and public discourse.
• Cryptocurrency Enthusiasts and Investors: To gain insights into community perception and market sentiment.
• Developers: To train and test NLP models related to financial or cryptocurrency text.

Dataset Name Suggestions

• Bitcoin Subreddit Comments: June 2022 Sentiment Analysis
• Reddit r/Bitcoin Public Opinion Data (June 2022)
• Cryptocurrency Discussion Sentiment Dataset

Attributes

Original Data Source:Viral Fads and Cryptocurrency

This dataset was manually constructed by aggregating and engineering multiple categories of Bitcoin-related data from different sources. It includes:

- Market data (e.g., Open, Close, Volume, Fees, Transactions) collected from [Mempool](https://mempool.space/it/)
- Energy data (e.g., estimated power consumption, efficiency metrics) retrieved via the [Electricity Maps API](https://portal.electricitymaps.com/docs/getting-started#geolocation)
- Technical financial indicators (e.g., EMA, MACD, RSI, VWAP, TRIX, Bollinger Bands Width) computed using the [FinTa](https://github.com/peerchemist/finta) Python library
- Derived metrics (e.g., momentum, volatility, normalization functions) calculated with custom Python functions

The dataset is structured with one row per day and includes both raw values and engineered features. It offers a detailed overview of Bitcoin's economic, market, and energy dimensions.

This dataset is suitable for use in machine learning models, time series forecasting, and the study of relationships between Bitcoin market behavior and its underlying energy consumption patterns.

When you need to analyze crypto market history, batch processing often beats streaming APIs. That's why we built the Flat Files S3 API - giving analysts and researchers direct access to structured historical cryptocurrency data without the integration complexity of traditional APIs.

Pull comprehensive historical data across 800+ cryptocurrencies and their trading pairs, delivered in clean, ready-to-use CSV formats that drop straight into your analysis tools. Whether you're building backtest environments, training machine learning models, or running complex market studies, our flat file approach gives you the flexibility to work with massive datasets efficiently.

Why work with us?

Market Coverage & Data Types: - Comprehensive historical data since 2010 (for chosen assets) - Comprehensive order book snapshots and updates - Trade-by-trade data

Technical Excellence: - 99,9% uptime guarantee - Standardized data format across exchanges - Flexible Integration - Detailed documentation - Scalable Architecture

CoinAPI serves hundreds of institutions worldwide, from trading firms and hedge funds to research organizations and technology providers. Our S3 delivery method easily integrates with your existing workflows, offering familiar access patterns, reliable downloads, and straightforward automation for your data team. Our commitment to data quality and technical excellence, combined with accessible delivery options, makes us the trusted choice for institutions that demand both comprehensive historical data and real-time market intelligence