The CAPIVIX Index gives crypto traders something traditional markets have long relied on - a clear measure of expected market volatility. Think of it as the VIX for Bitcoin and Ethereum, showing what the market anticipates for price swings over the next 30 days.

This crypto volatility index tracks market sentiment for BTC/USD and ETH/USD pairs by analyzing options data from major derivatives exchanges. When CAPIVIX rises, it signals increased uncertainty and potential turbulence ahead. When it falls, markets are expecting calmer conditions.

What makes CAPIVIX valuable is its methodology - we've adapted the widely-trusted VIX calculation approach to work specifically with cryptocurrency options. This gives you a standardized way to gauge market anxiety or confidence across different market conditions.

The index updates continuously throughout trading hours, incorporating real-time options pricing to reflect the market's evolving risk perception. For traders and investors looking to understand market sentiment beyond price movements alone, CAPIVIX provides that crucial additional dimension of market intelligence.

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade 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 ◦ Bitcoin Price Data

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

Whether you're hedging positions, timing entries and exits, or just wanting to better understand market psychology, our Bitcoin and Ethereum volatility data offers valuable insights into what the market collectively expects in the weeks ahead.

Analysis of ‘Crypto Fear and Greed Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/adelsondias/crypto-fear-and-greed-index on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Crypto Fear and Greed Index

Each day, the website https://alternative.me/crypto/fear-and-greed-index/ publishes this index based on analysis of emotions and sentiments from different sources crunched into one simple number: The Fear & Greed Index for Bitcoin and other large cryptocurrencies.

Why Measure Fear and Greed?

The crypto market behaviour is very emotional. People tend to get greedy when the market is rising which results in FOMO (Fear of missing out). Also, people often sell their coins in irrational reaction of seeing red numbers. With our Fear and Greed Index, we try to save you from your own emotional overreactions. There are two simple assumptions:

• Extreme fear can be a sign that investors are too worried. That could be a buying opportunity.
• When Investors are getting too greedy, that means the market is due for a correction.

Therefore, we analyze the current sentiment of the Bitcoin market and crunch the numbers into a simple meter from 0 to 100. Zero means "Extreme Fear", while 100 means "Extreme Greed". See below for further information on our data sources.

Data Sources

We are gathering data from the five following sources. Each data point is valued the same as the day before in order to visualize a meaningful progress in sentiment change of the crypto market.

First of all, the current index is for bitcoin only (we offer separate indices for large alt coins soon), because a big part of it is the volatility of the coin price.

But let’s list all the different factors we’re including in the current index:

Volatility (25 %)

We’re measuring the current volatility and max. drawdowns of bitcoin and compare it with the corresponding average values of the last 30 days and 90 days. We argue that an unusual rise in volatility is a sign of a fearful market.

Market Momentum/Volume (25%)

Also, we’re measuring the current volume and market momentum (again in comparison with the last 30/90 day average values) and put those two values together. Generally, when we see high buying volumes in a positive market on a daily basis, we conclude that the market acts overly greedy / too bullish.

Social Media (15%)

While our reddit sentiment analysis is still not in the live index (we’re still experimenting some market-related key words in the text processing algorithm), our twitter analysis is running. There, we gather and count posts on various hashtags for each coin (publicly, we show only those for Bitcoin) and check how fast and how many interactions they receive in certain time frames). A unusual high interaction rate results in a grown public interest in the coin and in our eyes, corresponds to a greedy market behaviour.

Surveys (15%) currently paused

Together with strawpoll.com (disclaimer: we own this site, too), quite a large public polling platform, we’re conducting weekly crypto polls and ask people how they see the market. Usually, we’re seeing 2,000 - 3,000 votes on each poll, so we do get a picture of the sentiment of a group of crypto investors. We don’t give those results too much attention, but it was quite useful in the beginning of our studies. You can see some recent results here.

Dominance (10%)

The dominance of a coin resembles the market cap share of the whole crypto market. Especially for Bitcoin, we think that a rise in Bitcoin dominance is caused by a fear of (and thus a reduction of) too speculative alt-coin investments, since Bitcoin is becoming more and more the safe haven of crypto. On the other side, when Bitcoin dominance shrinks, people are getting more greedy by investing in more risky alt-coins, dreaming of their chance in next big bull run. Anyhow, analyzing the dominance for a coin other than Bitcoin, you could argue the other way round, since more interest in an alt-coin may conclude a bullish/greedy behaviour for that specific coin.

Trends (10%)

We pull Google Trends data for various Bitcoin related search queries and crunch those numbers, especially the change of search volumes as well as recommended other currently popular searches. For example, if you check Google Trends for "Bitcoin", you can’t get much information from the search volume. But currently, you can see that there is currently a +1,550% rise of the query „bitcoin price manipulation“ in the box of related search queries (as of 05/29/2018). This is clearly a sign of fear in the market, and we use that for our index.

There's a story behind every dataset and here's your opportunity to share yours.

Copyright disclaimer

This dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

This published version is an unofficial copy of their data, which can be also collected using their API (e.g., GET https://api.alternative.me/fng/?limit=10&format=csv&date_format=us).

--- Original source retains full ownership of the source dataset ---

Context

This Dataset is being collected Two Sources 1. Yahoo Finance 2. Alternative.me

Content

This dataset specifically includes daily closing prices of Bitcoin, as well as daily volumes of Bitcoin, and the Fear and Greed Index values for the overall crypto market. This dataset presents a unique opportunity for researchers and analysts to explore the relationship between the prices and volumes of Bitcoin, as well as the sentiment of the overall crypto market. By conducting thorough analysis of this dataset, researchers and analysts can gain valuable insights into the behavior and trends of the cryptocurrency market. This includes examining the daily closing prices and volumes of Bitcoin, as well as the Fear and Greed Index values for the overall crypto market. Through comprehensive analysis, potential patterns, trends, and correlations between price movements, trading volumes, and market sentiment can be identified. These insights can inform investment strategies and decision-making, providing a more nuanced understanding of the dynamics of the cryptocurrency market. This data presents a unique opportunity for researchers and analysts to uncover valuable information that can contribute to a deeper understanding of the cryptocurrency market and its potential implications for investment decision-making.

Data Collection Strategy

The data collection strategy for this dataset involves gathering daily market closing prices and volume data of Bitcoin and collection daily crypto market fear and greed index.

Measurement of Fear and Greed Index

To understand the methodology behind measuring the Fear and Greed Index, please refer to the official link at https://alternative.me/crypto/fear-and-greed-index/

Copyright Disclaimer

A part of this dataset is produced and maintained by the administrators of https://alternative.me/crypto/fear-and-greed-index/.

CoinAPI provides crypto market indices including VWAP and PRIMKT data for accurate price discovery. Get real-time and historical crypto index information to establish reliable market references. Our indices help traders identify true market values across digital asset exchanges.

In 2023, a country ranking that estimates crypto adoption based on transaction volume placed Malaysia in the top 30 in the world. Nevertheless, Malaysia fell slightly behind when it comes to retail centralized service value. Meanwhile, Malaysia was in the 40th place based on its P2P exchange trade volume. Peer-to-peer (P2P) crypto exchanges are a type of crypto exchange that let users trade cryptocurrencies with one another without the influence of a mediator, such as banks or other regulatory bodies.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin Index Revolutionize Cryptocurrency?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In 2023, a country ranking that estimates crypto adoption based on transaction volume placed Indonesia in the top ten of the world. Indonesia ranked ******* in the world when it comes to retail value received from DeFi protocols or consumers who were buying certain DeFi protocols. In comparison, Indonesia was in the **** place based on its P2P exchange trade volume. Peer-to-peer (P2P) crypto exchanges are a type of crypto exchange that let users trade cryptocurrencies with one another without the influence of a mediator, such as banks or other regulatory bodies.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin index redefine the crypto markets?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Is the S&P Bitcoin Index the Future of Crypto Investment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Between 2020 and 2023, a country ranking that estimates crypto adoption based on transaction volume consistently placed the U.S. in the top 10 of the world. The figure for 2022, especially, stands out as it broke a declining trend in 2021 and was likely caused by the change of the methodology to now include Decentralized Finance (DeFi) in the index. For example, the United States ranked second in the world when it comes to on-chain retail value received from DeFi protocols - or consumers who were buying certain DeFi protocols. This may refer to the growing use of OpenSea and other Web3 wallets within the U.S. particularly in the first months of 2022.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will the S&P Bitcoin Index Usher in a New Era of Cryptocurrency Adoption?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Bitcoin Pulse is a curated dataset combining hourly crypto, macroeconomic, and sentiment indicators to help researchers and developers forecast Bitcoin price movements.

It brings together a wide range of features from:

🟢 Crypto markets: BTC, ETH, SOL, DOGE, and more

📈 Global indices: NASDAQ, S&P500, DAX, and others

🧠 Sentiment & psychology: Fear & Greed Index, Google Trends, BTC dominance

💹 Derivatives signals: Open interest, volatility metrics

⏱️ Hourly frequency, fully filled, aligned, and ready for time series modeling

Graph and download economic data for Coinbase Index (DISCONTINUED) from 2015-01-01 to 2020-05-26 about cryptocurrency, indexes, and USA.

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

The dataset consists in cryptocurrency prices, sp500, epu and google trends statistics at daily frequency, as well as the matlab codes used for the analyses.

Bitcoin (BTC) price again reached an all-time high in 2025, as values exceeded over 107,000 USD in June 2025. That particular price hike was connected to the approval of Bitcoin ETFs in the United States, whilst previous hikes in 2021 were due to events involving Tesla and Coinbase, respectively. Tesla’s announcement in March 2021 that it had acquired 1.5 billion U.S. dollars’ worth of the digital coin, for example, as well as the IPO of the U.S.’ biggest crypto exchange fueled mass interest. The market was noticeably different by the end of 2022, however, with Bitcoin prices reaching roughly 94,315.98 as of May 4, 2025, after another crypto exchange, FTX, filed for bankruptcy. Is the world running out of Bitcoin? Unlike fiat currency like the U.S. dollar - as the Federal Reserve can simply decide to print more banknotes - Bitcoin’s supply is finite: BTC has a maximum supply embedded in its design, of which roughly 89 percent had been reached in April 2021. It is believed that Bitcoin will run out by 2040, despite more powerful mining equipment. This is because mining becomes exponentially more difficult and power-hungry every four years, a part of Bitcoin’s original design. Because of this, a Bitcoin mining transaction could equal the energy consumption of a small country in 2021. Bitcoin’s price outlook: a potential bubble? Cryptocurrencies have few metrics available that allow for forecasting, if only because it is rumored that only a few cryptocurrency holders own a large portion of available supply. These large holders - referred to as “whales” - are said to make up of two percent of anonymous ownership accounts, whilst owning roughly 92 percent of BTC. On top of this, most people who use cryptocurrency-related services worldwide are retail clients rather than institutional investors. This means outlooks on whether Bitcoin prices will fall or grow are difficult to measure, as movements from one large whale already having a significant impact on this market.

Consumers from countries in Africa, Asia, and South America were most likely to be an owner of cryptocurrencies, such as Bitcoin, in 2025. This conclusion can be reached after combining ** different surveys from the Statista's Consumer Insights over the course of that year. Nearly one out of three respondents to Statista's survey in Nigeria, for instance, mentioned they either owned or use a digital coin, rather than *** out of 100 respondents in the United States. This is a significant change from a list that looks at the Bitcoin (BTC) trading volume in ** countries: There, the United States and Russia were said to have traded the highest amounts of this particular virtual coin. Nevertheless, African and Latin American countries are noticeable entries in that list too. Daily use, or an investment tool? The survey asked whether consumers either owned or used cryptocurrencies but does not specify their exact use or purpose. Some countries, however, are more likely to use digital currencies on a day-to-day basis. Nigeria increasingly uses mobile money operations to either pay in stores or to send money to family and friends. Polish consumers could buy several types of products with a cryptocurrency in 2019. Opposed to this is the country of Vietnam: Here, the use of Bitcoin and other cryptocurrencies as a payment method is forbidden. Owning some form of cryptocurrency in Vietnam as an investment is allowed, however. Which countries are more likely to invest in cryptocurrencies? Professional investors looking for a cryptocurrency-themed ETF were more often found in Europe than in the United or China, according to a survey in early 2020. Most of the largest crypto hedge fund managers with a location in Europe in 2020, were either from the United Kingdom or Switzerland - the country with the highest cryptocurrency adoption rate in Europe according to Statista's Global Consumer Survey. Whether this had changed by 2025 was not yet clear.

In the year 2023, Vietnam occupied the third position in the Crypto Adoption Index, out of a total of 154 nations worldwide. Within the five metrics of this ranking, the nation achieved the second position for on-chain value received and the third position for on-chain retail value received.

It is estimated that the cumulative market cap of cryptocurrencies increased in early 2023 after the downfall in November 2022 due to FTX. That value declined in the summer of 2023, however, as international uncertainty grew over a potential recession. Bitcoin's market cap comprised the majority of the overall market capitalization. What is market cap? Market capitalization is a financial measure typically used for publicly traded firms, computed by multiplying the share price by the number of outstanding shares. However, cryptocurrency analysts calculate it as the price of the virtual currencies times the number of coins in the market. This gives cryptocurrency investors an idea of the overall market size, and watching the evolution of the measure tells how much money is flowing in or out of each cryptocurrency. Cryptocurrency as an investment The price of Bitcoin has been erratic, and most other cryptocurrencies follow its larger price swings. This volatility attracts investors who hope to buy when the price is low and sell at its peak, turning a profit. However, this does little for price stability. As such, few firms accept payment in cryptocurrencies. As of May 21, 2025, the cumulative market cap of cryptocurrencies reached a value of *******.

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.