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/

Cryptocurrency Market Analysis

Price Fluctuations and Market Cap Trends Over Time

By [source]

About this dataset

This dataset contains an in-depth look into the changing cryptocurrency market. It provides an insightful overview of the market capitalization, prices, circulating supplies, and symbols that make up various cryptocurrencies. This data can guide economists, financial advisors and investors to form strategies on investments they make while anticipating future cryptocurrency price movements. Researchers can utilize this data to identify correlations between different cryptocurrencies giving a clearer understanding of the market as a whole. By examining patterns and trends in the crypto market one can gain valuable information about its dynamics that could be beneficial for future investments

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How to use the dataset

This dataset provides a great opportunity to explore the cryptocurrency market and its movements. The data contained in this dataset presents key market information - such as prices, market capitalizations and symbols - that can be used in various ways to better understand the performance of different cryptocurrencies.

Here are some tips on how to use this dataset:

• Identify the cryptocurrency with highest or lowest market capitalization: Analyze the total number of coins issued by a particular cryptocurrency, then compare its associated market cap against those from other cryptocurrencies. This will give you a good indication of which cryptos are performing best or worst in terms of total value.

• Compare changes in price between different cryptos: Using our price data, users can identify how one crypto asset has performed over another over a given period of time. This will allow investors to make an informed decision on which asset might offer preferable returns for their portfolios going forward.

• Analyze circulating supply levels: Circulating supply is an important metric when analyzing any asset’s worth – simply put, if there is a higher amount of coins circulating among investors, then it’s likely that its worth will decrease slightly due to increased competition over available coins. Accordingly, observing supply levels can provide powerful insight into which assets may have more upside potential compared to others with similar volumes and prices (or visa-versa).

These are just some general guidelines – we encourage researchers and analysts alike to use this powerful tool however best suits their individual needs!

Research Ideas

• Use this dataset to develop models that predict changes in cryptocurrency prices over time, helping investors better manage their portfolios.
• Analyze trends in the cryptocurrency market to develop strategies for entering and exiting markets or improving the trading process.
• Study the relationships between different cryptocurrencies and explore correlations between them, which could help inform investment decisions or provide a deeper understanding of how cryptocurrencies are related to one another

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Cryptocurrency Historical Data Snapshot.csv | Column name | Description | |:---------------|:-----------------------------------------------------------------| | Name | The name of the cryptocurrency. (String) | | Market Cap | The total market capitalization of the cryptocurrency. (Integer) | | Price | The current price of the cryptocurrency. (Float) | | Symbol | The unique identifier of the cryptocurrency. (String) |

Acknowledgements

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

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.

Introduction: The "Cryptocurrency Price Analysis Dataset: BTC, ETH, XRP, LTC (2018-2023)" is a comprehensive dataset that captures the daily price movements of six popular cryptocurrencies. It covers a period from January 1, 2018, to May 31, 2023, providing a valuable resource for researchers, analysts, and enthusiasts interested in studying the historical price behavior of these digital assets.

Description: This dataset contains a wealth of information for six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC). The data spans a time frame of over five years, enabling users to explore long-term trends, analyze volatility patterns, and gain insights into market dynamics.

Columns:

1. Crypto: This column specifies the name of the cryptocurrency (e.g., BTC, ETH, XRP, LTC).
2. Date: The date on which the price data was recorded.
3. Open: The opening price of the cryptocurrency at the beginning of the day.
4. High: The highest price reached by the cryptocurrency during the day.
5. Low: The lowest price reached by the cryptocurrency during the day.
6. Close: The closing price of the cryptocurrency at the end of the day.

Use Cases: The dataset offers numerous possibilities for analysis and research within the field of cryptocurrencies. Here are a few potential use cases:

1. Price Analysis: Researchers can investigate the historical price movements of each cryptocurrency to identify trends, patterns, and potential correlations between different assets.
2. Volatility Study: The dataset enables the study of volatility in cryptocurrency markets, helping users understand the frequency and magnitude of price fluctuations.
3. Market Performance: Analysts can analyze the performance of individual cryptocurrencies over time, comparing returns and risk measures to assess their investment potential.
4. Trading Strategies: Traders can utilize the dataset to develop and backtest trading strategies based on technical indicators, price patterns, or machine learning algorithms.
5. Sentiment Analysis: Combine this dataset with external sentiment data to explore the relationship between market sentiment and cryptocurrency price movements. By sharing this dataset on Kaggle, you are providing a valuable resource to the data science community, encouraging collaborative research, and enabling the development of innovative models and solutions within the cryptocurrency domain.

Please note that this dataset is for educational and research purposes only and should not be used for making financial decisions without thorough analysis and consultation with financial professionals.

Cryptocurrency data is a collection of information about crypto currency users. However, companies can filter this data by gender, age, and relationship status. This means they can find the right people easily. For example, companies can search for that group if they want to talk to young people. This filtering helps companies’ better reach specific groups of cryptocurrency users. Also, the data follows important rules called GDPR. These rules help make sure companies use it legally and safely. If any part of the data is not correct, the company can remove it. Cryptocurrency data is very useful for companies that want to connect with cryptocurrency users. By filtering the data, companies can reach the exact audience they want. They can focus on gender, age, or relationship status. Following GDPR rules helps protect both the company and the people in the database. This legal use of data builds trust between everyone. Regular updates keep the information fresh and relevant. Also, removing any wrong data keeps everything accurate. The WS Phone List helps you find contact information for businesses. This invaluable database can be found on List To Data. Cryptocurrency number database is a detailed collection of information about people who use cryptocurrencies like Bitcoin and Ethereum. It gathers data from reliable sources and includes links for easy access. Support is available 24/7 for any questions, so users can get the help they need. The database shares information only with consent, making it safe to use. Companies can take advantage of this database to connect with users and send them special offers and updates. The data is trustworthy and legal, and the database is regularly updated to provide the latest information. Overall, this database is essential for reaching the expanding community of cryptocurrency users. Get it from the List To Data website.

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).

This dataset consists of seven columns and 2740 rows collected from thirteen different sources for digital currencies. The dataset includes information on the opening price, closing price, highest price, lowest price, and volume, as well as the percentage change and the currencies collected in March 2024.

Here's a description of the contents based on the available columns in the data:

Last Price: The most recent recorded price of Bitcoin. Open Price: The opening price of Bitcoin at the start of the specified time period. Max: The maximum price of Bitcoin during the specified time period. Min: The minimum price of Bitcoin during the specified time period. Size: This may refer to the trading volume of Bitcoin during the specified time period, but requires further clarification to confirm its meaning. Change Persent: The percentage change in the price of Bitcoin compared to the previous time period, it seems there's a typographical error and it might mean "Change Percent". Class: The classification of the currency, in this context, all the data is classified under "Bitcoin". This data could be useful in financial market analytics, especially for those interested in cryptocurrencies and the dynamics of Bitcoin prices. It can be used to study price changes, market fluctuations, or even to develop models for predicting cryptocurrency prices.

Applications in Machine Learning and Beyond This dataset, focusing on Bitcoin prices and their fluctuations, has a wide range of applications, especially within the realm of machine learning and financial analysis:

Price Prediction: Utilizing historical data to train models that can predict future Bitcoin prices. Techniques like time series analysis, regression models, and more sophisticated neural networks (e.g., LSTM) could be applied. Volatility Modeling: Analyzing the variability in Bitcoin prices over time. Machine learning models can help understand patterns in price fluctuations, potentially leading to insights for investors about risk and volatility. Trend Analysis: Identifying long-term trends in Bitcoin's market performance. Machine learning algorithms can detect underlying patterns and trends, helping investors make informed decisions. Anomaly Detection: Spotting unusual patterns or outliers in Bitcoin prices that could indicate market manipulation, fraud, or significant market events. Machine learning models, especially unsupervised algorithms, are adept at detecting anomalies. Sentiment Analysis: By integrating this dataset with social media and news sentiment data, models can assess how public sentiment impacts Bitcoin prices. This involves natural language processing (NLP) techniques to gauge sentiment and correlate it with price movements. Portfolio Management: In the broader scope of financial management, machine learning models can use such datasets to optimize cryptocurrency portfolios, balancing risk and return based on historical performance. Risk Assessment: Analyzing the data to evaluate the financial risk associated with Bitcoin investments. Machine learning can provide probabilistic estimates of future price drops or gains, aiding in risk management strategies. Overall, the detailed data on Bitcoin's pricing and trading volume offers a rich foundation for various analytical and predictive modeling efforts in both academic research and practical financial applications. ​

Collected and Preprocessing: Wisam Abdullah , Dr. Modhar , and Dr. Ahmed Alsardly are lecturers in Tikrit University.

Cryptocurrencies have been constantly part of news articles lately. So we wanted to understand more about it. Some basic questions got us interested in looking for answers to the following questions: - How many cryptocurrencies are there and what are their prices and valuations? - Why is there a sudden surge in interest in recent days?

The Dataset has one CSV containing the Top 9 cryptocurrencies by market capitalization and their historical data from 8 Aug 2015 to 6 July 2021. Price history is available on a daily basis from 8 Aug 2015.

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

Acknowledgments This data is taken from Cryptocurrency Historical Prices (on Kaggle) Cover Image: Photo by Taxer on Unsplash

Problem Statement: 1. Recognizing the Top 5 most volatile currencies. 2. Predicting the next date closing price based on past cryptocurrency data for investments. 3. Finding patterns of cryptocurrencies using historical data. 4. Understanding the need for patterns in cryptocurrency historical data as a tool for building a crypto trading system as a future scope.

This comprehensive cryptocurrency dataset contains complete 1-minute OHLCV (Open, High, Low, Close, Volume) data for 196 different cryptocurrency trading pairs, spanning from December 31, 2023 to July 17, 2025.

The dataset provides high-frequency trading data sourced from major cryptocurrency exchanges.

🎯 Key Features

⏰ High-Frequency Data

Granularity: 1-minute intervals Coverage: 19+ months of continuous data Quality: Complete, gap-free dataset with proper handling of market closures

💰 Extensive Coin Coverage

196 cryptocurrency trading pairs including:

Major cryptocurrencies & Altcoins

📈 Complete Market Information Each record contains the following fields:

• timestamp: Unix timestamp for precise time indexing

• open: Opening price at interval start

• high: Highest price during the interval

• low: Lowest price during the interval

• close: Closing price at interval end

• volume: Total trading volume in base currency

• close_time: Closing timestamp of the interval

• quote_volume: Total trading volume in quote currency

• trades: Number of individual trades executed

• taker_buy_base: Volume of taker buy orders (base asset)

• taker_buy_quote: Volume of taker buy orders (quote asset)

• ignore: Reserved field (typically 0)

A league table of the 120 cryptocurrencies with the highest market cap reveals how diverse each crypto is and potentially how much risk is involved when investing in one. Bitcoin (BTC), for instance, had a so-called "high cap" - a market cap worth more than 10 billion U.S. dollars - indicating this crypto project has a certain track record or, at the very least, is considered a major player in the cryptocurrency space. This is different in Decentralize Finance (DeFi), where Bitcoin is only a relatively new player. A concentrated market The number of existing cryptocurrencies is several thousands, even if most have a limited significance. Indeed, Bitcoin and Ethereum account for nearly 75 percent of the entire crypto market capitalization. As crypto is relatively easy to create, the range of projects varies significantly - from improving payments to solving real-world issues, but also meme coins and more speculative investments. Crypto is not considered a payment method While often talked about as an investment vehicle, cryptocurrencies have not yet established a clear use case in day-to-day life. Central bankers found that usefulness of crypto in domestic payments or remittances to be negligible. A forecast for the world's main online payment methods took a similar stance: It predicts that cryptocurrency would only take up 0.2 percent of total transaction value by 2027.

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.

Many households struggled both physically and financially during the COVID-19 crisis. In a time of such uncertainty, one might expect households to respond differently to financial instruments considered risker than others. Given the nature and general feelings around cryptocurrency, we expected there might be differences in how households that owned cryptocurrencies fared during the COVID-19 crisis as compared to those that did not own cryptocurrency. Our research found that cryptocurrency-owning households reported fewer financial challenges during the pandemic than households that did not own cryptocurrency. Specifically, they were less likely to experience food insecurity or miss payments on a variety of bills, including medical expenses and utilities. Crypto households experienced less unemployment, as both the head of the household and the partner more readily adapted to working from home. Crypto households were also less likely to experience death from COVID-19 than their counterparts were. Data from the Federal Reserve’s 2022 Survey of Consumer Finances (SCF) reveal that cryptocurrency-owning households in fact fared better than those who did not. The linear probability model results hold after correction for data imputation and controlling for financial literacy, willingness to take risks in the short- and long-term, income, wealth, gender, age, education level, work status, and race. These findings suggest a counternarrative to the mainstream opinion of cryptocurrency owners as risk-loving, irrational, retail day traders. This research contributes to the overall literature by showing households working with cryptocurrency make financially savvy decisions and are better off generally than their counterparts. As cryptocurrency continues to gain traction and assuming it grows at current rates, society will be greatly affected. First, more households may consider expanding their portfolios with cryptocurrency. Assuming this occurs, more individuals and companies will need to become more familiar with this risky asset and other mechanisms through which one can invest in crypto assets, such as exchange-traded funds. Second, cryptocurrency usage is not only increasing among households but businesses too, including public companies. Investors may want to review their other investments, particularly stocks, to see how they may be indirectly invested in cryptocurrency. This consideration may affect other investment motivation considerations in the impact investing space. Last, we demonstrated households had different experiences with the COVID-19 shock event. Individuals may consider cryptocurrency as another asset to diversify in moving forward depending on other potential shock events besides pandemics, such as global or regional recessions or country currency changes in international markets due to political risk.

Crypto currency user email list is a database containing the email addresses of individuals who have an interest in or use crypto currency. These lists help crypto businesses. They let you contact potential customers directly. The main purpose is to promote your products and services. You can reach people who are already interested in crypto. This can include new tokens, crypto exchanges, wallet services, or block chain-related events. You can use it to build a community of followers. Businesses can send out newsletters, educational content, and market updates to keep their audience engaged and informed.

Crypto currency user email list is used to find new potential clients and investors for a crypto project. By sending targeted emails, companies can guide potential investors through the process of learning about and using their service. These lists are powerful, but be careful. The crypto world has many scams. Watch out for phishing and other bad activities. Always be cautious with email lists. Get this list from our website, List to Data. Crypto Currency user email database is a powerful tool. It helps you reach your target audience and grow your business in the exciting world of crypto currency. While this list is very useful, you must be careful. The crypto world has many scams. Always be honest with your customers. Make sure your emails are helpful and safe. Do not use this list for bad things. Purchase this database at a budget-friendly price from our website, List to Data.

Crypto Currency user email database is a great way to find new clients and investors. You can share information about your project and get people to join. Also, you can send emails about your new crypto product. You can invite people to a crypto event. This list helps your messages get to the right people.

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.

Historically, gold and silver have played distinct roles in traditional monetary systems. While gold has primarily been revered as a superior store of value, prompting individuals to hoard it, silver has commonly been used as a medium of exchange. As the financial world evolves, the emergence of cryptocurrencies has introduced a new paradigm of value and exchange. However, the store-of-value characteristic of these digital assets remains largely uncharted. Charlie Lee, the founder of Litecoin, once likened Bitcoin to gold and Litecoin to silver. To validate this analogy, our study employs several metrics, including UTXO, STXO, WAL, CoinDaysDestroyed (CDD), and public on-chain transaction data. Furthermore, we've devised trading strategies centered around the Price-to-Utility (PU) ratio, offering a fresh perspective on crypto-asset valuation beyond traditional utilities. Our back-testing results not only display trading indicators for both Bitcoin and Litecoin but also substantiate Lee's metaphor, underscoring Bitcoin's superior store-of-value proposition relative to Litecoin. We anticipate that our findings will drive further exploration into the valuation of crypto assets. For enhanced transparency and to promote future research, we've made our datasets available on Harvard Dataverse and shared our Python code on GitHub as open source.

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Context

• The metaverse, a living and breathing space that blends physical and digital, is quickly evolving from a science fiction dream into a reality with endless possibilities. A world where people can interact virtually, create and exchange digital assets for real-world value, own digital land, engage with digitized real-world products and services, and much more.

• Major tech giants are beginning to recognize the viability and potential of metaverses, following Facebook’s groundbreaking Meta rebrand announcement. In addition to tech companies, entertainment brands like Disney have also announced plans to take the leap into virtual reality.

• While the media hype is deafening, your average netizen isn’t fully aware of what a metaverse is, how it operates and, most importantly—what benefits and opportunities it can offer them as a user.

https://cdn.images.express.co.uk/img/dynamic/22/590x/Metaverse-tokens-cryptocurrency-explained-ethereum-killers-new-coins-digital-currency-meta-news-1518777.jpg?r=1638256864800" alt="">

What Is The Metaverse?

• In its digital iteration, a metaverse is a virtual world based on blockchain technology. This all-encompassing space allows users to work and play in a virtual reflection of real-life and fantasy scenarios, an online reality, ranging from sci-fi and dragons to more practical and familiar settings like shopping centers, offices, and even homes.

• Users can access metaverses via computer, handheld device, or complete immersion with a VR headset. Those entering the metaverse get to experience living in a digital realm, where they will be able to work, play, shop, exercise, and socialize. Users will be able to create their own avatars based on face recognition, set up their own businesses of any kind, buy real estate, create in-world content and asset,s and attend concerts from real-world superstars—all in one virtual environment,

• With that said, a metaverse is a virtual world with a virtual economy. In most cases, it is an online reality powered by decentralized finance (DeFi), where users exchange value and assets via cryptocurrencies and Non-Fungible Tokens.

What Are Metaverse Tokens?

• Metaverse tokens are a unit of virtual currency used to make digital transactions within the metaverse. Since metaverses are built on the blockchain, transactions on underlying networks are near-instant. Blockchains are designed to ensure trust and security, making the metaverse the perfect environment for an economy free of corruption and financial fraud.

• Holders of metaverse tokens can access multiple services and applications inside the virtual space. Some tokens give special in-game abilities. Other tokens represent unique items, like clothing for virtual avatars or membership for a community. If you’ve played MMO games like World of Warcraft, the concept of in-game items and currencies are very familiar. However, unlike your traditional virtual world games, metaverse tokens have value inside and outside the virtual worlds. Metaverse tokens in the form of cryptocurrency can be exchanged for fiat currencies. Or if they’re an NFT, they can be used to authenticate ownership to tethered real-world assets like collectibles, works or art, or even cups of coffee.

• Some examples of metaverse tokens include SAND of the immensely popular Sandbox metaverse. In The Sandbox, users can create a virtual world driven by NFTs. Another token is MANA of the Decentraland project, where users can use MANA to purchase plots of digital real estate called “LAND”. It is even possible to monetize the plots of LAND purchased by renting them to other users for fixed fees. The ENJ token of the Enjin metaverse is the native asset of an ecosystem with the world’s largest game/app NFT networks.

Dataset Information

• The dataset brings 198 metaverse cryptos. Pls refer to the file Metaverse coins.csv to find the list of metaverse crypto coins.

• The dataset will be updated on a weekly basis with more and more additional metaverse tokens, Stay tuned ⏳

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.

orbitaal-snapshot-2016_07_08.csv and orbitaal-snapshot-2016_07_09.csv

These two CSV files are related to daily snapshot representations of an halvening happening in 2016.

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? Saiba mais

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.

Context

Cryptocurrencies like bitcoin, ethereum are very extremely volatile, many have gained a staggering amount of money through it, again many have lost doing so. So I started collecting the data of some cryptocurrencies in a usable form (.csv) and made it public so that anyone can use it to further analyze their risk of investment through code.

Content

The dataset has one csv file for each currency.

• 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
• Circulating Supply: The amount of coins that are circulating in the market and are in public hands.

The Circulating Supply is not directly taken from coinmarketcap, but it is calculated using the formula, Circulating Supply = Market Cap / Closing price of the respective day

Acknowledgements

This data is taken from coinmarketcap and it is free to use the data. The cover photo is taken from pixabay, which is free for commercial use and no attribution required

Inspiration

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

1. Predicting the future price of the currencies
2. How does the price fluctuations of currencies correlate with each other?
3. Seasonal trend in the price fluctuations