In March 2024 Bitcoin BTC reached a new all-time high with prices exceeding 73000 USD marking a milestone for the cryptocurrency market This surge was due to the approval of Bitcoin exchange-traded funds ETFs in the United States allowing investors to access Bitcoin without directly holding it This development increased Bitcoin’s credibility and brought fresh demand from institutional investors echoing previous price surges in 2021 when Tesla announced its 15 billion investment in Bitcoin and Coinbase was listed on the Nasdaq By the end of 2022 Bitcoin prices dropped sharply to 15000 USD following the collapse of cryptocurrency exchange FTX and its bankruptcy which caused a loss of confidence in the market By August 2024 Bitcoin rebounded to approximately 64178 USD but remained volatile due to inflation and interest rate hikes Unlike fiat currency like the US dollar Bitcoin’s supply is finite with 21 million coins as its maximum supply By September 2024 over 92 percent of Bitcoin had been mined Bitcoin’s value is tied to its scarcity and its mining process is regulated through halving events which cut the reward for mining every four years making it harder and more energy-intensive to mine The next halving event in 2024 will reduce the reward to 3125 BTC from its current 625 BTC The final Bitcoin is expected to be mined around 2140 The energy required to mine Bitcoin has led to criticisms about its environmental impact with estimates in 2021 suggesting that one Bitcoin transaction used as much energy as Argentina Bitcoin’s future price is difficult to predict due to the influence of large holders known as whales who own about 92 percent of all Bitcoin These whales can cause dramatic market swings by making large trades and many retail investors still dominate the market While institutional interest has grown it remains a small fraction compared to retail Bitcoin is vulnerable to external factors like regulatory changes and economic crises leading some to believe it is in a speculative bubble However others argue that Bitcoin is still in its early stages of adoption and will grow further as more institutions and governments recognize its potential as a hedge against inflation and a store of value 2024 has also seen the rise of Bitcoin Layer 2 technologies like the Lightning Network which improve scalability by enabling faster and cheaper transactions These innovations are crucial for Bitcoin’s wider adoption especially for day-to-day use and cross-border remittances At the same time central bank digital currencies CBDCs are gaining traction as several governments including China and the European Union have accelerated the development of their own state-controlled digital currencies while Bitcoin remains decentralized offering financial sovereignty for those who prefer independence from government control The rise of CBDCs is expected to increase interest in Bitcoin as a hedge against these centralized currencies Bitcoin’s journey in 2024 highlights its growing institutional acceptance alongside its inherent market volatility While the approval of Bitcoin ETFs has significantly boosted interest the market remains sensitive to events like exchange collapses and regulatory decisions With the limited supply of Bitcoin and improvements in its transaction efficiency it is expected to remain a key player in the financial world for years to come Whether Bitcoin is currently in a speculative bubble or on a sustainable path to greater adoption will ultimately be revealed over time.

Bitcoin Historical Dataset 3M records from 2017-2023

Context:

Bitcoin, the pioneering cryptocurrency, has captured the world's attention as a decentralized digital asset with a fluctuating market value. This dataset offers a comprehensive record of Bitcoin's price evolution, spanning from August 2017 to July 2023. The data has been meticulously collected from the Binance API, with price data captured at one-minute intervals. Each record includes essential information such as the open, high, low, and close prices, alongside associated trading volume. This dataset provides an invaluable resource for those interested in studying Bitcoin's price trends and market dynamics.

Dataset Details:

Total Number of Entries: 3.126.000

Attributes: Timestamp, Open Price, High Price, Low Price, Close Price, Volume , Quote asset volume, Number of trades, Taker buy base asset volume, Taker buy quote asset volume.

Data Type: csv

Size: 133 MB

Date ranges: 2023/08/17 till 2023/07/31

Content:

This dataset provides granular insights into the price history of Bitcoin, allowing users to explore minute-by-minute changes in its market value. The dataset includes attributes such as the open price, high price, low price, close price, trading volume, and the timestamp of each recorded interval. The data is presented in CSV format, making it easily accessible for analysis and visualization.

Inspiration:

The Bitcoin Price Dataset opens up numerous avenues for exploration and analysis, driven by the availability of high-frequency data. Potential research directions include:

Intraday Price Patterns: How do Bitcoin prices vary within a single day? Are there recurring patterns or trends during specific hours? Volatility Analysis: What are the periods of heightened volatility in Bitcoin's price history, and how do they correlate with external events or market developments? Correlation with Events: Can you identify instances where significant price movements coincide with notable events in the cryptocurrency space or broader financial markets? Long-Term Trends: How has the average price of Bitcoin evolved over different years? Are there multi-year trends that stand out? Trading Volume Impact: Is there a relationship between trading volume and price movement? How does trading activity affect short-term price fluctuations?

Acknowledgements:

The dataset has been sourced directly from the Binance API, a prominent cryptocurrency exchange platform. The collaboration with Binance ensures the dataset's accuracy and reliability, offering users a trustworthy foundation for conducting analyses and research related to Bitcoin's price movements.

Licensing:

Users are welcome to utilize this dataset for personal, educational, and research purposes, with attribution to the Binance API as the source of the data.

Hope you enjoy this dataset as much as I enjoyed putting it together. Can't wait to see what you can come up with :)

I am a new developer and I would greatly appreciate your support. If you find this dataset helpful, please consider giving it an upvote!

Key Features:

Complete 1d Data: Raw 1d historical data from multiple exchanges, covering the entire trading history of BTCUSD available through their API endpoints. This dataset is updated daily to ensure up-to-date coverage.

Combined Index Dataset: A unique feature of this dataset is the combined index, which is derived by averaging all other datasets into one, please see attached notebook. This creates the longest continuous, unbroken BTCUSD dataset available on Kaggle, with no gaps and no erroneous values. It gives a much more comprehensive view of the market i.e. total volume across multiple exchanges.

Superior Performance: The combined index dataset has demonstrated superior 'mean average error' (MAE) metric performance when training machine learning models, compared to single-source datasets by a whole order of MAE magnitude.

Unbroken History: The combined dataset's continuous history is a valuable asset for researchers and traders who require accurate and uninterrupted time series data for modeling or back-testing.

https://i.imgur.com/8pw9H5E.png" alt="BTCUSD Dataset Summary">

https://i.imgur.com/AuFSkzb.png" alt="Combined Dataset Close Plot"> This plot illustrates the continuity of the dataset over time, with no gaps in data, making it ideal for time series analysis.

Included Resources:

Two Notebooks:

Dataset Usage and Diagnostics: This notebook demonstrates how to use the dataset and includes a powerful data diagnostics function, which is useful for all time series analyses.

Aggregating Multiple Data Sources: This notebook walks you through the process of combining multiple exchange datasets into a single, clean dataset. (Currently unavailable, will be added shortly)

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

This dataset offers a detailed examination of Bitcoin's price behavior over the last eight years, featuring a range of technical indicators for analyzing its trends. It captures the daily opening, highest, lowest, and closing prices, along with trading volume. The dataset includes momentum indicators such as the 7-day and 14-day Relative Strength Index (RSI) to determine if the asset is overbought or oversold. It also contains the 7-day and 14-day Commodity Channel Index (CCI), which compares the current price to the historical average to spot short- and medium-term trends. Additionally, it encompasses moving averages like the 50-day and 100-day Simple Moving Average (SMA) and Exponential Moving Average (EMA), which shed light on the asset's trend direction. Other essential indicators in the dataset are the Moving Average Convergence Divergence (MACD), Bollinger Bands for assessing price volatility, the True Range, and the 7-day and 14-day Average True Range (ATR) that provide insights into market volatility. This dataset's primary goal is to forecast the closing price for the next day, making it a crucial tool for predicting future market movements.

About this Dataset

This dataset contains the prices of Bitcoin every minute over a period from 2017-11-06 03:00 to 2023-03-10 2:59 (YYYY-MM-DD). The data includes the time, close time, open, high, low, close prices, the volume exchanged per minute and the number of trades per minute. It represent Bitcoin prices over 2.8 millions values. This dataset is ideal for anyone who want to track, study and analyze BTC/USDT values over more than 5 years.

Time range: From 2017-11-06 04:00 to 2023-03-40 14:00

File format: Datas are in .csv format

Columns values: - time: Date in milliseconds where observation begins - open: Opening ETH price in the minute - high: Highest ETH price in the minute - low: Lowest ETH price in the minute - close: Closing ETH price in the minute - volume: Volume exchanges between time and close_time - close_time: Date in milliseconds were observation ends

Category

Economic

Keywords

Bitcoin,BTC,#btc,Cryptocurrency,Crypto

Row Count

2808000

Price

$149.00

Find my notebook : Advanced EDA & Data Wrangling - Crypto Market Data where I cover the full EDA and advanced data wrangling to get beautiful dataset ready for analysis.

Find my Deep Reinforcement Learning v1 notebook: "https://www.kaggle.com/code/franoisgeorgesjulien/deep-reinforcement-learning-for-trading">Deep Reinforcement Learning for Trading

Find my Quant Analysis notebook:"https://www.kaggle.com/code/franoisgeorgesjulien/quant-analysis-visualization-btc-v1">💎 Quant Analysis & Visualization | BTC V1

Dataset Presentation:

This dataset provides a comprehensive collection of hourly price data for 34 major cryptocurrencies, covering a time span from January 2017 to the present day. The dataset includes Open, High, Low, Close, Volume (OHLCV), and the number of trades for each cryptocurrency for each hour (row).

Making it a valuable resource for cryptocurrency market analysis, research, and trading strategies. Whether you are interested in historical trends or real-time market dynamics, this dataset offers insights into the price movements of a diverse range of cryptocurrencies.

This is a pure gold mine, for all kind of analysis and predictive models. The granularity of the dataset offers a wide range of possibilities. Have Fun!

Ready to Use - Cleaned and arranged dataset less than 0.015% of missing data hour: crypto_data.csv

First Draft - Before External Sources Merge (to cover missing data points): crypto_force.csv

Original dataset merged from all individual token datasets: cryptotoken_full.csv

crypto_data.csv & cryptotoken_full.csv highly challenging wrangling situations: - fix 'Date' formats and inconsistencies - find missing hours and isolate them for each token - import external data source containing targeted missing hours and merge dataframes to fill missing rows

see notebook 'Advanced EDA & Data Wrangling - Crypto Market Data' to follow along and have a look at the EDA, wrangling and cleaning process.

Date Range: From 2017-08-17 04:00:00 to 2023-10-19 23:00:00

Date Format: YYYY-MM-DD HH-MM-SS (raw data to be converted to datetime)

Data Source: Binance API (some missing rows filled using Kraken & Poloniex market data)

Crypto Token in the dataset (also available as independent dataset): - 1INCH - AAVE - ADA (Cardano) - ALGO (Algorand) - ATOM (Cosmos) - AVAX (Avalanche) - BAL (Balancer) - BCH (Bitcoin Cash) - BNB (Binance Coin) - BTC (Bitcoin) - COMP (Compound) - CRV (Curve DAO Token) - DENT - DOGE (Dogecoin) - DOT (Polkadot) - DYDX - ETC (Ethereum Classic) - ETH (Ethereum) - FIL (Filecoin) - HBAR (Hedera Hashgraph) - ICP (Internet Computer) - LINK (Chainlink) - LTC (Litecoin) - MATIC (Polygon) - MKR (Maker) - RVN (Ravencoin) - SHIB (Shiba Inu) - SOL (Solana) - SUSHI (SushiSwap) - TRX (Tron) - UNI (Uniswap) - VET (VeChain) - XLM (Stellar) - XMR (Monero)

Date column presents some inconsistencies that need to be cleaned before formatting to datetime: - For column 'Symbol' and 'ETCUSDT' = '23-07-27': it is missing all hours (no data, no hourly rows for this day). I fixed it by using the only one row available for that day and duplicated the values for each hour. Can be fixed using this code:

start_timestamp = pd.Timestamp('2023-07-27 00:00:00')
end_timestamp = pd.Timestamp('2023-07-27 23:00:00')

hourly_timestamps = pd.date_range(start=start_timestamp, end=end_timestamp, freq='H')

hourly_data = {
  'Date': hourly_timestamps,
  'Symbol': 'ETCUSDT',
  'Open': 18.29,
  'High': 18.3,
  'Low': 18.17,
  'Close': 18.22,
  'Volume USDT': 127468,
  'tradecount': 623,
  'Token': 'ETC'
}

hourly_df = pd.DataFrame(hourly_data)
df = pd.concat([df, hourly_df], ignore_index=True)

df = df.drop(550341)

• Some rows for 'Date' have extra digits '.000' '.874' etc.. instead of the right format YYYY-MM-DD HH-MM-SS. To clean it you can use the following code:

# Count the occurrences of the pattern '.xxx' in the 'Date' column
count_occurrences_before = df['Date'].str.count(r'\.\d{3}')
print("Occurrences before cleaning:", count_occurrences_before.sum()) 

# Remove '.xxx' pattern from the 'Date' column
df['Date'] = df['Date'].str.replace(r'\.\d{3}', '', regex=True)

# Count the occurrences of the pattern '.xxx' in the 'Date' column after cleaning
count_occurrences_after = df['Date'].str.count(r'\.\d{3}')
print("Occurrences after cleaning:", count_occurrences_after.sum()) 

**Disclaimer: Any individual or entity choosing to engage in market analysis, develop predictive models, or utilize data for trading purposes must do so at their own discretion and risk. It is important to understand that trading involves potential financial loss, and decisions made in the financial mar...

tahamajs/bitcoin-individual-news-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Bitcoin's blockchain size was close to reaching 673.58 gigabytes in September 2025, as the database saw exponential growth by nearly one gigabyte every few days. The Bitcoin blockchain contains a continuously growing and tamper-evident list of all Bitcoin transactions and records since its initial release in January 2009. Bitcoin has a set limit of 21 million coins, the last of which will be mined around 2140, according to a forecast made in 2017. Bitcoin mining: A somewhat uncharted world Despite interest in the topic, there are few accurate figures on how big Bitcoin mining is on a country-by-country basis. Bitcoin's design philosophy is at the heart of this. Created out of protest against governments and central banks, Bitcoin's blockchain effectively hides both the country of origin and the destination country within a (mining) transaction. Research involving IP addresses placed the United States as the world's most Bitcoin mining country in 2022, but the source admits IP addresses can easily be manipulated using VPN. Note that mining figures are different from figures on Bitcoin trading: Africa and Latin America were more interested in buying and selling BTC than some of the world's developed economies. Bitcoin developments Bitcoin's trade volume slowed in the second quarter of 2023 after hitting a noticeable growth at the beginning of the year. The coin outperformed most of the market. Some attribute this to the announcement in June 2023 that BlackRock filed for a Bitcoin ETF. This iShares Bitcoin Trust was to use Coinbase Custody as its custodian. Regulators in the United States had not yet approved any applications for spot ETFs on Bitcoin.

OHLCV is an abbreviation for the five critical data points: Open, High, Low, Close, and Volume. It refers to the key points in analyzing an asset such as Bitcoin (BTC) in the market over a specified time. The dataset is important for not only traders and analysts but also for data scientists who work on BTC market prediction using artificial intelligence. The 'Open' and 'Close' prices represent the starting and ending price levels, while the 'High' and 'Low' are the highest and lowest prices during that period (a daily time frame (24h)). The 'Volume' is a measure of the total number of trades. This dataset provides five OHLCV data columns for BTC along with a column called "Next day close price" for regression problems and machine learning applications. The dataset includes daily information from 1/1/2012 to 8/6/2022.

Part 2

1 second timeframe dataset from 2017-08-17 and 2024-08-28. Dataset contain 221343465 rows. Due to kaggle doesn't allow upload 20GB+, I decided to slice into half. This part contain only 110671734 rows.

Python code to slice: ```python midpoint = 110671733 # Includes the header in the first half !head -n {midpoint} combined_BTCUSDT_1s_2017-08-17_to_2024-08-27.csv > half1_BTCUSDT_1s.csv !tail -n +{midpoint + 1} combined_BTCUSDT_1s_2017-08-17_to_2024-08-27.csv > half2_BTCUSDT_1s.csv

**Here is [Part 1](https://www.kaggle.com/datasets/tzelal/binance-bitcoin-dataset-1s-timeframe-p1) !**

**Note: This is public data of binance everyone can access it. I only merge large files that you don't need to do it.**

### **Beside of OHLCV there is information about other columns:**


**1. Quote Asset Volume:** The total volume of the quote currency traded during the interval. Format: A floating-point number. The quote asset is the second currency in the trading pair (e.g., USDT in BTC/USDT).

**2. Number of Trades:** The total number of trades that took place during the interval. Format: An integer representing the number of trades.

**3. Taker Buy Base Asset Volume:** The amount of the base asset bought by takers during the interval. Format: A floating-point number. The base asset is the first currency in the trading pair (e.g., BTC in BTC/USDT).

**4. Taker Buy Quote Asset Volume:**The amount of the quote asset spent by takers to buy the base asset during the interval. Format: A floating-point number.

**5. Ignore:** This column is often present but usually contains no relevant data. It may be a placeholder for future data or included for backward compatibility. Format: This column typically contains zeros or null values.

###### ==================================================================================
This dataset collected from https://data.binance.vision/ and merged. 

##### My Social Media:
- https://www.youtube.com/@dataworm_official
- https://github.com/tzelalouzeir
- https://www.linkedin.com/in/tzelalouzeir/
- https://tafou.io/
- https://algo.tafou.io/

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.

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.

Context

This minute by minute historical dataset of bitcoin prices offers a wealth of information for data scientists and analysts. In addition to the OHLC prices for each minute, this dataset also includes the volume of bitcoin traded during that time period. This granular data, going back to 2015, allows for in-depth analysis of the market fluctuations and trends of the world's most popular cryptocurrency.

With this dataset, researchers can study the underlying mechanisms of the bitcoin network, traders can gain a better understanding of market movements, and investors can make more informed decisions about their investments. The open, high, low, and close prices, as well as the volume data, provide a wealth of information for analyzing the market and identifying potential opportunities.

Whether you're looking to gain a competitive edge as a trader, conduct research on the bitcoin market, or simply want to learn more about the world of cryptocurrency, this dataset is a valuable resource. With its rich and detailed data, you'll be able to dive deep into the world of bitcoin and uncover insights that can help you make better decisions.

Content

• Timestamp: This is the UNIX timestamp or the "Epoch Time", number of seconds elapsed since 00:00:00 UTC on 1 January 1970.
• Date: Date and time of price recording.
• Open - This is the opening price of the time period (in US Dollars).
• High - This is the highest price of the time period (in US Dollars).
• Low - This is the lowest price of the time period (in US Dollars).
• Close - This is the closing price of the time period (in US Dollars).
• Volume BTC - This is the volume of ₿ transacted in the time interval.
• Volume USD - This is the volume of $ transacted in the time interval.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.

Description

This dataset contains the Pagerank values and rankings of Bitcoin addresses and transaction IDs (TXID). It contains a total of 1.608.748.675 addresses or TXIDs.

Part 2 is available at https://zenodo.org/deposit/6077428

File format

The dataset is compressed with bzip2. It can be uncompressed using the command bunzip2. The dataset is divided into multiple files since it was large. The files are space-delimited plain text files and have the following five fields:

Label: A alphanumeric Bitcoin address (e.g. 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV) or a 64 character hexadecimal transaction ID (e.g. 000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51) Type: String

Label type: It's value is 0 if the label is transaction ID and 1 if the label is a Bitcoin address. Type: Integer

Rank: Unique Pagerank rank where the ties (addresses having the same Pagerank value) are resolved by sorting the addresses. Type: Integer

Rank with ties: Pagerank rank where the ties (addresses having the same Pagerank value) have the same rank. Type: Integer

Pagerank value: Pagerank of the address and transaction IDs calculated using Pagerank algorithm. Type: Floating-point number

Sample lines:

000000000fdf0c619cd8e0d512c7e2c0da5a5808e60f12f1e0d01522d2986a51 0 427225664 266976712 0.979246 1DzTCMmWABEDM1rYFL1RgdLyE59jXMzEHV 1 1114666798 508037940 0.877961

"head.txt" contains the first 10 lines of each file. "tail.txt" contains the last 10 lines of each file.

Dataset Generation

The Bitcoin transactions between blocks 0 (mined on 03.01.2009) and 713.999 (mined on 13.12.2021) are extracted. A transaction graph is constructed, where Bitcoin addresses and transaction IDs are nodes of the graph and the transaction inputs and outputs are edges of the graph. Pagerank is applied on this transaction graph. This computation is performed using the system presented in the paper 'Parallel analysis of Ethereum blockchain transaction data using cluster computing'.

Note

If you use our dataset in your research, please cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0

@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

Other Datasets

If you are interested, please also check out our Pagerank Dataset for Ethereum Blockchain.

Graph and download economic data for Coinbase Bitcoin (CBBTCUSD) from 2014-12-01 to 2025-12-01 about cryptocurrency and USA.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.

Bitcoin Price Chronicles: 10 Years of USD-BTC Market Data (2014-2024)

Overview

This dataset contains daily historical market data for Bitcoin (BTC) priced in USD, spanning 10 years from Origin till 2024-05-01. It includes key financial metrics such as Open, High, Low, Close, Adjusted Close, and Volume. This dataset is perfect for economic analysis, time series modelling, and cryptocurrency research.

Details

• File Size: [291.37 kB]
• Number of Rows: 3,511 (daily data points)
• Number of Columns: 7
• Data Source: Likely sourced from a cryptocurrency exchange or financial data provider.
• Geospatial Coverage: Global, as Bitcoin is a decentralized cryptocurrency.

Usage

This dataset is ideal for: 1. Financial Analysis: Analyzing Bitcoin price trends, volatility, and market behaviour over a decade. 2. Time Series Analysis: Using historical data to build predictive models for Bitcoin prices. 3. Algorithmic Trading: Developing trading strategies and backtesting them. 4. Cryptocurrency Research: Studying the adoption and market dynamics of Bitcoin. 5. Data Visualization: Creating charts and graphs to visualize Bitcoin’s price history.

With the rapid expansion of non-customized data assets, developing reliable and objective methods for their valuation has become essential. However, current evaluation techniques often face challenges such as incomplete indicator systems and an over-reliance on subjective judgment. To address these issues, this study presents a structured framework comprising 17 key indicators for assessing data asset value. A neural network is employed to calculate indicator weights, which reduces subjectivity and enhances the accuracy of the assessment. Additionally, knowledge graph techniques are used to organize and visualize relationships among the indicators, providing a comprehensive evaluation view. The proposed model combines information entropy and the TOPSIS method to refine asset valuation by integrating indicator weights and performance metrics. To validate the model, it is applied to two datasets: Bitcoin market data from the past seven years and BYD stock data. The Bitcoin dataset demonstrates the model’s capability to capture market trends and assess purchasing potential, while the BYD stock dataset highlights its adaptability across diverse financial assets. The successful application of these cases confirms the model’s effectiveness in supporting data-driven asset management and pricing. This framework provides a systematic methodology for data asset valuation, offering significant theoretical and practical implications for asset pricing and management.