How many cryptocurrencies are there? In short, there were over ***** as of August 2025, although there were many more digital coins in the early months of 2022. Note, however, that a large portion of cryptocurrencies might not be that significant. There are other estimates of roughly ****** cryptocurrencies existing, but most of these are either inactive or discontinued. Due to how open the creation process of a cryptocurrency is, it is relatively easy to make one. Indeed, the top 20 cryptocurrencies make up nearly ** percent of the total market. Why are there thousands of cryptocurrencies? Any private individual or company that knows how to write a program on a blockchain can technically create a cryptocurrency. That blockchain can be an existing one. Ethereum and Binance Smart Chain are popular blockchain platforms for such ends, including smart contracts within Decentralized Finance (DeFi). The ease of crypto creation allows some individuals to find solutions to real-world payment problems while others hope to make a quick profit. This explains why some crypto lack utility. Meme coins such as Dogecoin - named after a Japanese dog species - are an infamous example, with Dogecoin's creator coming out and stating the coin started as a joke. The many types of cryptocurrency Meme coins are but one group of cryptocurrencies. Other types include altcoins, utility tokens, governance tokens, and stablecoins. Altcoins are often measured against Bitcoin, as this refers to all crypto that followed Bitcoin - the first digital currency ever created. Utility tokens and governance tokens are somewhat connected to NFTs and the metaverse. A specific example is the MANA cryptocurrency, which allows real estate purchases in the Decentraland metaverse. Stablecoins refer to the likes of Tether, which are pegged to a real-world asset like the U.S. dollar. Such coins are meant to be less volatile than regular cryptocurrency.

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 June 25, 2025, the cumulative market cap of cryptocurrencies reached a value of ******.

About this dataset As cryptocurrency markets have gained prominence, individuals and organizations have shown an increased fascination with crafting automated trading strategies. The creation of algorithmic trading approaches, though, necessitates rigorous backtesting to ascertain their profitability. Consequently, the cornerstone of any triumphant algorithmic trading strategy lies in the availability of meticulously detailed historical trading data. This dataset will provide you a deeper understanding of working with this type of financial security, it provides you with open, high, low, close (OHLC) information, recorded at 1-hour intervals (not very high-velocity data), encompassing a multitude of cryptocurrency pairs. This data resource is invaluable for those seeking to devise and refine automated trading systems, data analysis, or predictions.

Content This dataset contains the historical trading data (OHLC) of 14 crypto securities at 1 1-hour resolution. The source of this data is Coindesk. The data in the CSV files is refined and cleaned for easier interpretation.

The data is free to use.

The dataset used in this research is a historical record of Bitcoin, Ethereum, and Litecoin’s daily trading activity, containing essential financial metrics for each date. This sample includes the following columns: Date: The specific day of each recorded entry, showing a continuous timeline. Open: The price of currencies at the start of the trading day. High: The highest price of currencies reached during the day. Low: The lowest price of currencies traded throughout the day. Close: The closing price of the currencies at the end of the trading day. Volume: The total trading volume, indicating the number of currencies traded that day in units. Market Cap: The total market capitalization of currencies, calculated as the total supply multiplied by the closing price.

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.

Financial Tweets - Cryptocurrency

This dataset is part of the scraped financial tweets that I collected from a variety of financial influencers on Twitter, all the datasets can be found here:

Crypto: https://huggingface.co/datasets/StephanAkkerman/financial-tweets-crypto Stocks (and forex): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-stocks Other (Tweet without cash tags): https://huggingface.co/datasets/StephanAkkerman/financial-tweets-other

  Data… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/financial-tweets-crypto.

Bitcoin is a crypto currency leveraging blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing tree of blocks. Each block contains a number of transactions. To learn more, read the Bitcoin Wiki . This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program. The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. To further interoperate with Ethereum and ERC-20 token transactions, we also created some views that abstract the blockchain ledger to be presented as a double-entry accounting ledger. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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 Crypto Market Forecasts are Provided in Terms of Value (USD).

Investment cannot be separated from the level of return and risk inherent in assets. Today, investment instruments are not only stocks, currencies, bonds, deposits, savings and others. The beginning of Bitcoin’s emergence as a pioneer of Cryptocurrency was in 2009. Crypto assets are emerging rapidly and are accompanied by an increase in the number of transactions each period. The growth in the market capitalization value of crypto assets has also grown significantly. During COVID-19, many investments, such as stocks, experienced a decline due to market uncertainty. The results of this study prove that with the existence of COVID-19, the crypto market is not affected. Crypto is an attraction characterized by a high degree of fluctuation, and there is no limit to transactions in the open market 24 hours to trade. The Cryptocurrency market is currently a market that can provide short-term benefits to risk-taking investors, while the market in other investment instruments is declining. 78% of the value capitalization of the top 200 cryptocurrencies is represented by the top 9 cryptos used as samples in this study. So that if there is a decrease in these 9 cryptos, it will also have an impact on the overall capitalization value of crypto in the market. The future development of Cryptocurrencies will no longer be digital assets traded with many speculators who can control prices, it can even be digital money that can be used worldwide without any transaction fees and is controlled on a blockchain system. (2023-01-12)

Crypto Charts

This dataset is a collection of a sample of images from tweets that I scraped using my Discord bot that keeps track of financial influencers on Twitter. The data consists mainly of images that are cryptocurrency charts. This dataset can be used for a wide variety of tasks, such as image classification or feature extraction.

  FinTwit Charts Collection

This dataset is part of a larger collection of datasets, scraped from Twitter and labeled by a human (me).… See the full description on the dataset page: https://huggingface.co/datasets/StephanAkkerman/crypto-charts.

Bitcoin's role within the overall cryptocurrency market picked up in 2024, whilst Ethereum lost terrain to currencies like Solana. This according to a metric that compares a coin's market cap relative to the overall crypto market called "dominance". This ratio shows how strong, for example, Bitcoin is compared to all the other cryptocurrencies. A comparison between Bitcoin and multiple other coins reveals that the shape of the crypto market has changed dramatically over time. Bitcoin typically has a dominance of over ** percent, so the interest for analysts lies more in whether Bitcoin's market share has gone up or down when compared to altcoins.

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

Context

Ethereum Classic is an open-source, public, blockchain-based distributed computing platform featuring smart contract (scripting) functionality. It provides a decentralized Turing-complete virtual machine, the Ethereum Virtual Machine (EVM), which can execute scripts using an international network of public nodes. Ethereum Classic and Ethereum have a value token called "ether", which can be transferred between participants, stored in a cryptocurrency wallet and is used to compensate participant nodes for computations performed in the Ethereum Platform.

Ethereum Classic came into existence when some members of the Ethereum community rejected the DAO hard fork on the grounds of "immutability", the principle that the blockchain cannot be changed, and decided to keep using the unforked version of Ethereum. Till this day, Etherum Classic runs the original Ethereum chain.

Content

In this dataset, you will have access to Ethereum Classic (ETC) historical block data along with transactions and traces. You can access the data from BigQuery in your notebook with bigquery-public-data.crypto_ethereum_classic dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.crypto_ethereum_classic.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset wouldn't be possible without the help of Allen Day, Evgeny Medvedev and Yaz Khoury. This dataset uses Blockchain ETL. Special thanks to ETC community member @donsyang for the banner image.

Inspiration

One of the main questions we wanted to answer was the Gini coefficient of ETC data. We also wanted to analyze the DAO Smart Contract before and after the DAO Hack and the resulting Hardfork. We also wanted to analyze the network during the famous 51% attack and see what sort of patterns we can spot about the attacker.

Tether is a stablecoin cryptocurrency that is hosted on the Ethereum and Bitcoin blockchains, among others. Its tokens are issued by the Hong Kong company Tether Limited, which in turn is controlled by the owners of Bitfinex. Wikipedia

The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

Datasets

Become a Data Provider

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

Reddit: /r/CryptoCurrency

Posts, Scores, Comment Counts and Creation Timestamps

By Reddit [source]

About this dataset

This dataset contains detailed information on posts, scores and comments from the Reddit subreddit ‘CryptoCurrency’ - a fascinating online community devoted to discussion and analysis of the latest developments in blockchain investments, digital currencies, and other associated topics. Dive into the data to see what ultimate insights cryptocurrency enthusiasts are offering each other - their post titles, scores (the net upvotes a post has received), comment counts, created dates and timestamps are all laid out here for easy exploration. By taking advantage of this unique snapshot into crypto discussions and trends you can gain a better understanding not only of what topics have been popular over time but also how they're being discussed across this passionate community. Are there particular trends or patterns that emerge? It's up to you to uncover them!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains posts and comments from the subreddit ‘CryptoCurrency’, which is a widely-followed discussion board devoted to discussing cryptocurrencies, blockchain investments, and other related topics. The dataset contains a large number of posts from the subreddit and their associated scores, comment counts and creation timestamps. This dataset can be used in numerous ways for both research and practical business applications.
First, let's explore what columns are contained within this dataset: title, score, url, comms_num (number of comments), created (date and time post was created), body (actual content of the post), timestamp. With this information at hand you can begin answering key questions such as: What type of topics bring more attention? What topics are not popular? Are there any correlations between posts with higher scores(upvotes) or more comments?
To better understand these questions there are numerous tools that can be employed on this data including Natural Language Processing tools such as TF-IDF vectorizers or Latent Dirichlet Allocation to understand what type of themes dominate these conversations. Additionally machine learning algorithms such as clustering techniques like K Nearest Neighbors or Unsupervised Learning techniques like Principal Component Analysis could help uncover insights from this data set. For example if we wanted to find out which words in titles correlated with higher scores then KNN could give us a better understanding as it would build clusters based on similar titles/words and show how each vary in relation score wise giving us an overview on how related words influence scores before analyzing content or any other factors within the data set.
Furthermore Reddit users actively engage with posts so by looking at comment counts insight can also be taken into effect regarding popularity etc... For example one may observe that whenever new coin values arise they tend to have more comments than usual - an insight indicating high levels of user engagement at certain moments in time when compared to regular periods which could be useful when making comparisons between individual coins etc..
Overall this data can provide tremendous value depending on its usage case - whether it stands for research purposes only or applied analytics geared towards predicting prices/engagement/ user sentiment etc it all depends but nonetheless opportunities lie within unlocking financial opportunities through cryptocurrency discussion found on reddit thus making it highly valuable for multiple purposes utilized properly!

Research Ideas

• This dataset can be used to create a sentiment analysis of the comments and posts on CryptoCurrency topics and how these conversations have changed over time. This can help ascertain how different events within the crypto market have been received by investors, speculators, and other users on the subreddit.
• The dataset can also be utilized to identify trends in successful topics of conversation (in terms of post scores) and give insight into what types of topics are popular among Redditors in the CryptoCurrency space.
• Furthermore, this dataset could provide insight into user behavior on CryptoCurrency subreddits by enabling analysis around peak times for certain conversations or post popularity as well as which users tend to comment or post more frequently in response times vs others

Acknowledgements

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

...

About this dataset

With the rise of crypto currency markets the interest in creating automated trading strategies, or trading bots, has grown. Developing algorithmic trading strategies however requires intensive backtesting to ensure profitable performance. It follows that access to high resolution historical trading data is the foundation of every successful algorithmic trading strategy. This dataset therefore provides open, high, low, close (OHLC) data at 1 minute resolution of various crypto currency pairs for the development of automated trading systems.

Content

This dataset contains the historical trading data (OHLC) of more than 400 trading pairs at 1 minute resolution reaching back until the year 2013. It was collected from the Bitfinex exchange as described in this article. The data in the CSV files is the raw output of the Bitfinex API. This means, there are no timestamps for time periods in which the exchange was down. Also if there were time periods without any activity or trades there will be no timestamp as well.

Inspiration

This dataset is intended to facilitate the development of automatic trading strategies. Machine learning algorithms, as they are available through various open source libraries these days, typically require large amounts of training data to unveil their full power. Also the process of backtesting new strategies before deploying them rests on high quality data. Most crypto trading datasets that are currently available either have low temporal resolution, are not free of charge or focus only on a limited number of currency pairs. This dataset on the other hand provides high temporal resolution data of more than 400 currency pairs for the development of new trading algorithms.

Wikipedia Crypto Articles 🪙₿

This dataset is a collection of articles obtained from Wikipedia on January 5ᵗʰ, 2024. It contains two columns, title and article, containing the article's title as it is on the Wikipedia website and the article's content. The articles vary from specific cryptocurrencies—such as Bitcoin or Ethereum—to historical facts, companies, exchanges, entities, and relevant people in the history of cryptocurrencies. This dataset can be used to train machine… See the full description on the dataset page: https://huggingface.co/datasets/luisotorres/wikipedia-crypto-articles.

The Crypto Asset Management Market report segments the industry into By Type (Solutions, Services), By Deployment Mode (Cloud, On-Premise), By End-User Industry (BFSI, Retail & E-Commerce, Media & Entertainment, Other End-User Industries (Healthcare, Travel & Hospitality)), and Geography (North America, Europe, Asia-Pacific, Rest of the World). Five years of historical data and market forecasts are included.

«Datasets per la comparació de moviments i patrons entre els principals índexs borsatils espanyols i les crypto-monedes»

Context

En aquest cas el context és detectar o preveure els diferents moviments que es produeixen per una serie factors, tant de moviment interns (compra-venda), com externs (moviments polítics, econòmics, etc...), en els principals índexs borsatils espanyols i de les crypto-monedes.

Hem seleccionat diferents fonts de dades per generar fitxers «csv», guardar diferents valors en el mateix període de temps. És important destacar que ens interessa més les tendències alcistes o baixes, que podem calcular o recuperar en aquests períodes de temps.

Content

En aquest cas el contingut està format per diferents csv, especialment tenim els fitxers de moviments de cryptomoneda, els quals s’ha generat un fitxer per dia del període de temps estudiat.

Pel que fa als moviments del principals índexs borsatils s’ha generat una carpeta per dia del període, en cada directori un fitxer amb cadascun del noms dels índexs. Degut això s’han comprimit aquests últims abans de publicar-los en el directori de «open data» kaggle.com.

Pel que fa als camps, ens interessà detectar els moviments alcistes i baixistes, o almenys aquelles que tenen un patró similar en les cryptomonedes i els índexs. Els camps especialment destacats són:

• Nom: Nom empresa o cryptomoneda;
• Preu: Valor en euros d’una acció o una cryptomoneda;
• Volum: En euros/volum 24 hores,acumulat de les transaccions diàries en milions d’euros
• Simbol: Símbol o acrònim de la moneda
• Cap de mercat: Valor total de totes les monedes en el moment actual
• Oferta circulant: Valor en oportunitat de negoci
• % 1h, % 2h i %7d, tant per cent del valor la moneda en 1h, 2h o 7d sobre la resta de cyprtomonedes.

Acknowledgements

En aquest cas les fonts de dades que s’han utilitzat per a la realització dels datasets corresponent a:

• http://www.eleconomista.es
• https://coinmarketcap.com

Per aquest fet, les dades de borsa i crypto-moneda estan en última instància sota llicència de les webs respectivament. Pel que fa a la terminologia financera podem veure vocabulari en renta4banco.
[https://www.r4.com/que-necesitas/formacion/diccionario]

Inspiration

Hi ha un estudi anterior on poder tenir primícies de com han enfocat els algoritmes:

• https://arxiv.org/pdf/1410.1231v1.pdf

En aquest cas el «trading» en cryptomoneda és relativament nou, força popular per la seva formulació com a mitja digital d’intercanvi, utilitzant un protocol que garanteix la seguretat, integritat i equilibri del seu estat de compte per mitjà d’un entramat d’agents.

La comunitat podrà respondre, entre altres preguntes, a:

• Està afectant o hi ha patrons comuns en les cotitzacions de cryptomonedes i el mercat de valors principals del país d'Espanya?
• Els efectes o agents externs afecten per igual a les accions o cryptomonedes?
• Hi ha relacions cause efecte entre les acciones i cryptomonedes?

Project repository

https://github.com/acostasg/scraping

Datasets

Els fitxers csv generats que componen el dataset s’han publicat en el repositori kaggle.com:

• https://www.kaggle.com/acostasg/stock-index/
• https://www.kaggle.com/acostasg/crypto-currencies

Per una banda, els fitxers els «stock-index» estan comprimits per carpetes amb la data d’extracció i cada fitxer amb el nom dels índexs borsatil. De forma diferent, les cryptomonedes aquestes estan dividides per fitxer on són totes les monedes amb la data d’extracció.