Context

A cryptocurrency, crypto-currency, or crypto is a collection of binary data which is designed to work as a medium of exchange. Individual coin ownership records are stored in a ledger, which is a computerized database using strong cryptography to secure transaction records, to control the creation of additional coins, and to verify the transfer of coin ownership. Cryptocurrencies are generally fiat currencies, as they are not backed by or convertible into a commodity. Some crypto schemes use validators to maintain the cryptocurrency. In a proof-of-stake model, owners put up their tokens as collateral. In return, they get authority over the token in proportion to the amount they stake. Generally, these token stakes get additional ownership in the token overtime via network fees, newly minted tokens, or other such reward mechanisms.

Cryptocurrency does not exist in physical form (like paper money) and is typically not issued by a central authority. Cryptocurrencies typically use decentralized control as opposed to a central bank digital currency (CBDC). When a cryptocurrency is minted or created prior to issuance or issued by a single issuer, it is generally considered centralized. When implemented with decentralized control, each cryptocurrency works through distributed ledger technology, typically a blockchain, that serves as a public financial transaction database

A cryptocurrency is a tradable digital asset or digital form of money, built on blockchain technology that only exists online. Cryptocurrencies use encryption to authenticate and protect transactions, hence their name. There are currently over a thousand different cryptocurrencies in the world, and many see them as the key to a fairer future economy.

Bitcoin, first released as open-source software in 2009, is the first decentralized cryptocurrency. Since the release of bitcoin, many other cryptocurrencies have been created.

Content

This Dataset is a collection of records of 3000+ Different Cryptocurrencies. * Top 395+ from 2021 * Top 3000+ from 2023

Structure of the Dataset

https://i.imgur.com/qGVJaHl.png" alt="">

Acknowledgements

This Data is collected from: https://finance.yahoo.com/. If you want to learn more, you can visit the Website.

Cover Photo by Worldspectrum: https://www.pexels.com/photo/ripple-etehereum-and-bitcoin-and-micro-sdhc-card-844124/

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.

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.

This dataset contains a comprehensive collection of historical price records for the top 1000 cryptocurrencies. The data in this dataset is updated daily, providing a reliable and up-to-date source of information for cryptocurrency traders, researchers, and enthusiasts.

Each file in the dataset includes the following columns: date, open price, high price, low price, closing price, adjusted closing price, and trading volume. These columns provide a detailed picture of the daily price movements and trading activity of each cryptocurrency in the dataset.

The "date" column indicates the day on which the price data was recorded, while the "open" column provides the opening price of the cryptocurrency for that day. The "high" and "low" columns indicate the highest and lowest prices of the cryptocurrency on that day, respectively. The "close" column represents the closing price of the cryptocurrency on that day, while the "adjusted close" column takes into account any dividends or other corporate actions that may have affected the price. Finally, the "volume" column shows the trading volume of the cryptocurrency on that day.

With this dataset, users can analyze and visualize the performance of individual cryptocurrencies, compare them to one another, and track trends over time. The data is ideal for use in machine learning models, predictive analytics, and other data-driven applications.

Context

Bitcoin is the longest running and most well known cryptocurrency, first released as open source in 2009 by the anonymous Satoshi Nakamoto. Bitcoin serves as a decentralized medium of digital exchange, with transactions verified and recorded in a public distributed ledger (the blockchain) without the need for a trusted record keeping authority or central intermediary. Transaction blocks contain a SHA-256 cryptographic hash of previous transaction blocks, and are thus "chained" together, serving as an immutable record of all transactions that have ever occurred. As with any currency/commodity on the market, bitcoin trading and financial instruments soon followed public adoption of bitcoin and continue to grow. Included here is historical bitcoin market data for select bitcoin exchanges where trading takes place. Happy (data) mining!

CSV files for select bitcoin exchanges for the time period of September 2011 to June 2024, with updates of OHLC (Open, High, Low, Close), Volume in BTC and indicated currency, and weighted bitcoin price. Timestamps are in Unix time. Timestamps without any trades or activity have their data fields filled with NaNs. If a timestamp is missing, or if there are jumps, this may be because the exchange (or its API) was down, the exchange (or its API) did not exist, or some other unforeseen technical error in data reporting or gathering. All effort has been made to deduplicate entries and verify the contents are correct and complete to the best of my ability, but obviously trust at your own risk.

Acknowledgements and Inspiration

Bitcoin charts for the data. The various exchange APIs, for making it difficult or unintuitive enough to get OHLC and volume data that I set out on this data scraping project. Satoshi Nakamoto and the novel core concept of the blockchain, as well as its first execution via the bitcoin protocol. I'd also like to thank viewers like you! Can't wait to see what code or insights you all have to share.

How many cryptocurrencies are there? In short, there were over ***** as of June 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.

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).

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

Things like Block chain, Bitcoin, Bitcoin cash, Ethereum, Ripple etc are constantly coming in the news articles I read. So I wanted to understand more about it and this post helped me get started. Once the basics are done, the data scientist inside me started raising questions like:

1. How many cryptocurrencies are there and what are their prices and valuations?
2. Why is there a sudden surge in the interest in recent days?

So what next? Now that we have the price data, I wanted to dig a little more about the factors affecting the price of coins. I started of with Bitcoin and there are quite a few parameters which affect the price of Bitcoin. Thanks to Blockchain Info, I was able to get quite a few parameters on once in two day basis.

This will help understand the other factors related to Bitcoin price and also help one make future predictions in a better way than just using the historical price.

Content

The dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013. This dataset has the historical price information of some of the top crypto currencies by market capitalization.

• Date : date of observation
• Open : Opening price on the given day
• High : Highest price on the given day
• Low : Lowest price on the given day
• Close : Closing price on the given day
• Volume : Volume of transactions on the given day
• Market Cap : Market capitalization in USD

Acknowledgements

This data is taken from coinmarketcap and it is free to use the data.

Cover Image : Photo by Thomas Malama on Unsplash

Inspiration

Some of the questions which could be inferred from this dataset are:

1. How did the historical prices / market capitalizations of various currencies change over time?
2. Predicting the future price of the currencies
3. Which currencies are more volatile and which ones are more stable?
4. How does the price fluctuations of currencies correlate with each other?
5. Seasonal trend in the price fluctuations

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? 瞭解詳情

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.

Key Features Column Name Description place Numeric identifier for the country's ranking. pop2023 Estimated population count for the year 2023. growthRate Population growth rate for a specific country. area Land area in square kilometers for the country. country Name of the country. cca3 Three-letter country code (numeric) identifier. cca2 Two-letter country code (alpha-numeric) identifier. ccn3 Three-digit country code (numeric) identifier. region Geographic region where the country is situated. subregion Subregion where the country belongs. unMember Indicates if the country is a United Nations member. officialName Official name of the country. landAreaKm Land area in square kilometers for the country. density Population density (people per square kilometer). densityMi Population density in people per square mile. Rank Ranking based on the population in 2023. CryptoCurrencyStatus The legal status or restrictions of cryptocurrency usage in the country. CryptoCurrencyNotes Additional notes or explanations on cryptocurrency regulations. How to use this dataset 1. Population Trends: Examine 'pop2023' and 'growthRate' to understand population estimates and growth rates across countries.

1. Geographical Analysis: Utilize 'country' and 'region' for geographical comparisons and insights into cryptocurrency regulations.

2. Cryptocurrency Status: Assess 'CryptoCurrencyStatus' to differentiate countries based on their cryptocurrency legalities.

3. Rank Comparison: Use 'Rank' to compare countries by population size and explore their cryptocurrency stances.

4. Correlation Insights: Investigate potential links between population data, geography, and cryptocurrency regulations.

If you find this dataset useful, give it an upvote – it's a small gesture that goes a long way! Thanks for your support. 😄

License

CC0

Original Data Source: Worldwide Citizens & Crypto Guidelines

Introduction

According to Google, a cryptocurrency is "a digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank.

I think you are familiar about the content of this dataset. Even then! I just want to say that If you read the news or keep up on market trends, you may have heard Bitcoin. Bitcoin is a cryptocurrency. Over the past year, cryptocurrencies have become extremely popular. Hope this dataset will enhance your trading strategy in digital currency with ML and deep learning algorithms.

Data

Date Pulled: 20/05/2023

Data Pulled From: https://coinmarketcap.com

Resources

A special thanks to The Concept Center for pulling the data and writing a simple research study

This comprehensive dataset offers a thorough and meticulous analysis of Bitcoin transactions, providing a detailed and all-encompassing view. It delves into crucial metrics such as transaction volume, fees, and the overall activity of the network, shedding light on the pulse of the cryptocurrency world. The daily updates not only reflect the dynamic nature of this digital landscape but also make this dataset an essential tool for a diverse range of individuals. Whether you're an astute financial expert conducting in-depth market analyses, a curious researcher unraveling the complexities of the blockchain, or simply a passionate cryptocurrency enthusiast eager to stay informed, this dataset caters to your needs.

If you require further insights or have any inquiries regarding this dataset, please don't hesitate to contact us at info@blockchair.com. Our team is dedicated to assisting you and ensuring you maximize the value of the information provided.

Crypto users rely on fast, trustworthy customer service. Whether you’re buying tokens, managing a DeFi wallet, or checking rewards, quick assistance is critical. Many users wonder if they can call Crypto.com using numbers like 1-888-416-9087. Here’s what you should know.

Common Reasons People Seek Crypto.com Support Login authentication issues

Delays with deposits or withdrawals

Locked or suspended accounts

Lost access to 2FA

Security-related concerns

Official Ways to Get Support Crypto.com does not currently operate a public phone support line. While numbers such as 1-888-416-9087 appear online, these are not confirmed by the company and could be misleading.

Use these trusted support channels:

In-app Chat Support: Found in the Help section of the app.

Help Center: Visit help.crypto.com to submit a ticket.

Security First Share only what’s necessary—never your seed phrase or full passwords.

Stick to official apps and websites to contact support.

Be wary of impersonators or phone numbers not listed on Crypto.com’s site.

Summary Phone numbers like 1-888-416-9087 may seem helpful, but they are not officially linked to Crypto.com. For safety and accuracy, always use the app’s built-in support or the website’s Help Center.

✅ Version 3: Crypto.com Support Guide – Avoid Phone Number Scams Like 1-888-416-9087 If you're having trouble with your Crypto.com account, reaching out for support is your next step. But before dialing a number like 1-888-416-9087, it’s important to understand how Crypto.com actually handles customer inquiries.

Why People Contact Support App won’t log in

Identity verification failed

Transaction stuck or missing

Withdrawal won’t process

Account access frozen

Is 1-888-416-9087 a Real Crypto.com Number? Crypto.com does not advertise a public phone number. Their official support happens only through:

In-app chat support

Help tickets via their website

Any number you see online (including 1-888-416-9087) might not be legitimate. Stay safe by sticking with their app and website.

Tips for Safe Communication Describe your issue clearly and concisely.

Never give out your private keys, password, or 2FA codes.

Use only official links and tools when seeking help.

Bottom Line Crypto.com doesn’t offer support through external phone lines. Use only in-app or online support to avoid scams.

Bitcoin's transaction volume was at its highest in December 2023, when the network processed over ******* coins on the same day. Bitcoin generally has a higher transaction activity than other cryptocurrencies, except Ethereum. This cryptocurrency is often processed more than *********** times per day. Note that the transaction volume here refers to transactions registered within the Bitcoin blockchain. It should not be confused with Bitcoin's 24-hour trade volume, a metric associated with crypto exchanges. The more Bitcoin transactions, the more it is used in B2C payments? A Bitcoin transaction recorded in the blockchain can be any transaction, including B2C but also P2P. While it is possible to see in the blockchain which address sent Bitcoin to whom, details on who this person is and where they are from are typically missing. Bitcoin was designed to go against monetary authorities and prides itself on being anonymous. An important argument against Bitcoin replacing cash or cards in payments is that the cryptocurrency was not allowed for such a task: Bitcoin ranks among the slowest cryptocurrencies in terms of transaction speed. Are cryptocurrencies taking over payments? Cryptocurrency payments are set to grow at a CAGR of nearly ** percent between 2022 and 2029, although the market is relatively small. The forecast is according to a market estimate made in early 2023, based on various conditions and sources available at that time. Research across ** countries during the same time suggested that the market share of cryptocurrency in e-commerce transactions was "less than *** percent" in all surveyed countries, with predictions being this would not change in the future.

Context:

This dataset is an extra updating dataset for the G-Research Crypto Forecasting competition.

Introduction

This is a daily updated dataset, automaticlly collecting market data for G-Research crypto forecasting competition. The data is of the 1-minute resolution, collected for all competition assets and both retrieval and uploading are fully automated. see discussion topic.

The Data

For every asset in the competition, the following fields from Binance's official API endpoint for historical candlestick data are collected, saved, and processed.

1. **timestamp** - A timestamp for the minute covered by the row.
2. **Asset_ID** - An ID code for the cryptoasset.
3. **Count** - The number of trades that took place this minute.
4. **Open** - The USD price at the beginning of the minute.
5. **High** - The highest USD price during the minute.
6. **Low** - The lowest USD price during the minute.
7. **Close** - The USD price at the end of the minute.
8. **Volume** - The number of cryptoasset u units traded during the minute.
9. **VWAP** - The volume-weighted average price for the minute.
10. **Target** - 15 minute residualized returns. See the 'Prediction and Evaluation section of this notebook for details of how the target is calculated.
11. **Weight** - Weight, defined by the competition hosts [here](https://www.kaggle.com/cstein06/tutorial-to-the-g-research-crypto-competition)
12. **Asset_Name** - Human readable Asset name.

Indexing

The dataframe is indexed by timestamp and sorted from oldest to newest. The first row starts at the first timestamp available on the exchange, which is July 2017 for the longest-running pairs.

Usage Example

The following is a collection of simple starter notebooks for Kaggle's Crypto Comp showing PurgedTimeSeries in use with the collected dataset. Purged TimesSeries is explained here. There are many configuration variables below to allow you to experiment. Use either GPU or TPU. You can control which years are loaded, which neural networks are used, and whether to use feature engineering. You can experiment with different data preprocessing, model architecture, loss, optimizers, and learning rate schedules. The extra datasets contain the full history of the assets in the same format as the competition, so you can input that into your model too.

Baseline Example Notebooks:

• Neural Network Starter
• LightGBM Starter
• Catboost Starter
• XGBoost Starter
• TabNet Starter
• Reinforcement Learning (PPO) Starter

These notebooks follow the ideas presented in my "Initial Thoughts" here. Some code sections have been reused from Chris' great (great) notebook series on SIIM ISIC melanoma detection competition here

Loose-ends:

This is a work in progress and will be updated constantly throughout the competition. At the moment, there are some known issues that still needed to be addressed:

• VWAP: - At the moment VWAP calculation formula is still unclear. Currently the dataset uses an approximation calculated from the Open, High, Low, Close, Volume candlesticks. [Waiting for competition hosts input]
• Target Labeling: There exist some mismatches to the original target provided by the hosts at some time intervals. On all the others - it is the same. The labeling code can be seen here. [Waiting for competition hosts] input]
• Filtering: No filtration of 0 volume data is taken place.

Example Visualisations

Opening price with an added indicator (MA50): https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fb8664e6f26dc84e9a40d5a3d915c9640%2Fdownload.png?generation=1582053879538546&alt=media" alt="">

Volume and number of trades: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2234678%2Fcd04ed586b08c1576a7b67d163ad9889%2Fdownload-1.png?generation=1582053899082078&alt=media" alt="">

License

This data is being collected automatically from the crypto exchange Binance.

The dataset includes news articles that have been web scraped from various internet sources. A notable trend observed in the articles is the increasing number of long-term Bitcoin holders, with a significant portion of Bitcoin remaining unmoved for at least a year. This trend contributes to the scarcity of the asset, as fewer Bitcoins are available for trading or sale on the market. The articles cover diverse topics, including updates on Bitcoin mining operations and the launch of Bitcoin NFT marketplaces.

• Columns

  The dataset is structured with the following columns:

• article_id: A unique identifier for each news article.

• title: The headline or title of the news article.

• author: The individual or entity credited as the author of the article.

• published_date: The date and time when the article was published.

• link: The direct URL to the original news article.

• clean_url: A simplified or cleaned version of the article's URL.

• excerpt: A brief, introductory summary of the article's content.

• summary: The full text of the article, typically limited to a maximum of 250 words.

• rights: Specifies the owner or rights holder of the article content.

• article_rank: A numerical rank indicating the article's engagement or relevance.

• topic: The subject or category of the article (e.g., finance).

• country: The country associated with the article or its publisher (e.g., US).

• language: The language in which the article is written (e.g., en for English).

• authors: A list of authors for the article, potentially including multiple names.

• media: A link to associated media, such as an image.

• twitter_account: The Twitter handle related to the article's author or source.

• article_score: A numerical score indicating the article's quality or relevance.

• Distribution

  This dataset contains news article corpora provided in CSV files. It features 2,402 unique article IDs. The articles span a time range from 10 March 2022 to 11 September 2022, with varying article counts across different date intervals. For instance, there were 471 articles between 3 March 2022 and 28 March 2022, and 503 between 28 March 2022 and 15 April 2022. The dataset includes 2,324 unique values for authors, with PRNewswire accounting for 11% and 9% being null. The articles originate from various cleaned URLs, with forextv.com contributing 7%, bitrss.com 4%, and stl.news 5%. Article ranks range from 31.00 to 993,405.00, with the majority (1,777 articles) falling into the 31.00 - 99,368.40 range.

• Usage

  This dataset is ideally suited for:

• Text mining and text analytics: Extracting patterns, insights, and structured information from news content.

• Sentiment analysis: Determining the sentiment (positive, negative, neutral) expressed in Bitcoin-related news.

• Topic modelling: Identifying and tracking key themes and topics discussed in Bitcoin news over time.

• Word embeddings: Generating numerical representations of words for natural language processing tasks.

• Market trend analysis: Understanding the impact of news on Bitcoin's perceived scarcity due to long-term holding patterns.

• Research on cryptocurrency adoption and behaviour: Analysing how news coverage reflects or influences investor behaviour.

• Tracking industry-specific news: Monitoring developments related to Bitcoin mining operations and NFT marketplaces.

• Coverage

  The dataset's time coverage extends from at least March 2022 to September 2022. Geographically, the articles include content relevant to the US, and the dataset's reach is global. All articles within the dataset are in English.

• License

  CC0

• Who Can Use It

  This dataset is beneficial for a wide range of users, including:

• Data analysts and scientists: For conducting in-depth text analysis, pattern recognition, and predictive modelling based on news sentiment.

• Financial researchers: To study the dynamics between news cycles and cryptocurrency market behaviour, particularly regarding Bitcoin's value and investor holding trends.

• Developers and AI/ML engineers: For training and validating natural language processing (NLP) models, such as those for sentiment analysis, topic extraction, or news summarisation in the financial domain.

• Business intelligence professionals: To gain insights into public perception, emerging trends, and key events influencing the Bitcoin ecosystem.

• Academics and students: For educational purposes, research projects, and case studies on digital currencies and blockchain technology.

• Dataset Name Suggestions

• Bitcoin News Articles Corpus 2022

• Cryptocurrency News Text Data

• Bitcoin Market & Mining News

• Digital Currency News Dataset

• Bitcoin Global News Archive

• Attributes

Original Data Source: [Bitco

Dataset Construction This dataset captures the temporal network of Bitcoin (BTC) flow exchanged between entities at the finest time resolution in UNIX timestamp. Its construction is based on the blockchain covering the period from January, 3rd of 2009 to January the 25th of 2021. The blockchain extraction has been made using bitcoin-etl (https://github.com/blockchain-etl/bitcoin-etl) Python package. The entity-entity network is built by aggregating Bitcoin addresses using the common-input heuristic [1] as well as popular Bitcoin users' addresses provided by https://www.walletexplorer.com/ [1] M. Harrigan and C. Fretter, "The Unreasonable Effectiveness of Address Clustering," 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 2016, pp. 368-373, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0071.keywords: {Online banking;Merging;Protocols;Upper bound;Bipartite graph;Electronic mail;Size measurement;bitcoin;cryptocurrency;blockchain}, Dataset Description Bitcoin Activity Temporal Coverage: From 03 January 2009 to 25 January 2021 Overview: This dataset provides a comprehensive representation of Bitcoin exchanges between entities over a significant temporal span, spanning from the inception of Bitcoin to recent years. It encompasses various temporal resolutions and representations to facilitate Bitcoin transaction network analysis in the context of temporal graphs. Every dates have been retrieved from bloc UNIX timestamp and GMT timezone. Contents: The dataset is distributed across three compressed archives: All data are stored in the Apache Parquet file format, a columnar storage format optimized for analytical queries. It can be used with pyspark Python package. orbitaal-stream_graph.tar.gz: The root directory is STREAM_GRAPH/ Contains a stream graph representation of Bitcoin exchanges at the finest temporal scale, corresponding to the validation time of each block (averaging approximately 10 minutes). The stream graph is divided into 13 files, one for each year Files format is parquet Name format is orbitaal-stream_graph-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory STREAM_GRAPH/EDGES/ orbitaal-snapshot-all.tar.gz: The root directory is SNAPSHOT/ Contains the snapshot network representing all transactions aggregated over the whole dataset period (from Jan. 2009 to Jan. 2021). Files format is parquet Name format is orbitaal-snapshot-all.snappy.parquet. These files are in the subdirectory SNAPSHOT/EDGES/ALL/ orbitaal-snapshot-year.tar.gz: The root directory is SNAPSHOT/ Contains the yearly resolution of snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-file-id-[ID].snappy.parquet, where [YYYY] stands for the corresponding year and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year ordering These files are in the subdirectory SNAPSHOT/EDGES/year/ orbitaal-snapshot-month.tar.gz: The root directory is SNAPSHOT/ Contains the monthly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-file-id-[ID].snappy.parquet, where [YYYY] and [MM] stands for the corresponding year and month, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year and month ordering These files are in the subdirectory SNAPSHOT/EDGES/month/ orbitaal-snapshot-day.tar.gz: The root directory is SNAPSHOT/ Contains the daily resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-file-id-[ID].snappy.parquet, where [YYYY], [MM], and [DD] stand for the corresponding year, month, and day, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, and day ordering These files are in the subdirectory SNAPSHOT/EDGES/day/ orbitaal-snapshot-hour.tar.gz: The root directory is SNAPSHOT/ Contains the hourly resoluted snapshot networks Files format is parquet Name format is orbitaal-snapshot-date-[YYYY]-[MM]-[DD]-[hh]-file-id-[ID].snappy.parquet, where [YYYY], [MM], [DD], and [hh] stand for the corresponding year, month, day, and hour, and [ID] is an integer from 1 to N (number of files here) such as sorting in increasing [ID] ordering is similar to sort by increasing year, month, day and hour ordering These files are in the subdirectory SNAPSHOT/EDGES/hour/ orbitaal-nodetable.tar.gz: The root directory is NODE_TABLE/ Contains two files in parquet format, the first one gives information related to nodes present in stream graphs and snapshots such as period of activity and associated global Bitcoin balance, and the other one contains the list of all associated Bitcoin addresses. Small samples in CSV format orbitaal-stream_graph-2016_07_08.csv and orbitaal-stream_graph-2016_07_09.csv These two CSV files are related to stream graph representations of an halvening happening in 2016.

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