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.

This dataset includes daily historical price data for Bitcoin (BTC-USD) from 2014 to 2025, obtained through web scraping from the Yahoo Finance page using Selenium. The primary data source can be accessed at Yahoo Finance - Bitcoin Historical Data . The dataset contains daily information such as opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and trading volume (Volume).

About Bitcoin: Bitcoin (BTC) is the world's first decentralized digital currency, introduced in 2009 by an anonymous creator known as Satoshi Nakamoto. It operates on a peer-to-peer network powered by blockchain technology, enabling secure, transparent, and trustless transactions without the need for intermediaries like banks. Bitcoin's limited supply of 21 million coins and its growing adoption have made it a popular asset for investment, trading, and as a hedge against inflation.

We are excited to share this dataset and look forward to seeing the insights it can provide. We hope it will inspire collaboration and innovation within the community. By leveraging this daily data, we can explore trends, develop predictive models, and design innovative trading strategies that deepen our understanding of Bitcoin's market behavior. Together, we can unlock new opportunities and contribute to the collective advancement of cryptocurrency research and analysis.

The following dataset contains the attributes: Date: Specific date to be observed for the corresponding price. Open: The opening price for the day High: The maximum price it has touched for the day Low: The minimum price it has touched for the day Close: The closing price for the day percent_change_24h: Percentage change for the last 24hours Volume: Volume of Bitcoin traded at the date Market Cap: Market Value of traded Bitcoin

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

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 contains historical price data for Bitcoin (BTC) against the U.S. Dollar (USD), spanning from June 2010 to November 2024. The data is organized on a daily basis and includes key market metrics such as the opening price, closing price, high, low, volume, and market capitalization for each day.

Columns: The dataset consists of the following columns:

Date: The date of the recorded data point (format: YYYY-MM-DD). Open: The opening price of Bitcoin on that day. High: The highest price Bitcoin reached on that day. Low: The lowest price Bitcoin reached on that day. Close: The closing price of Bitcoin on that day. Volume: The total trading volume of Bitcoin during that day. Market Cap: The total market capitalization of Bitcoin on that day (calculated by multiplying the closing price by the circulating supply of Bitcoin at the time). Source: The data is sourced from Yahoo Finance.

Time Period: The data spans from June 2010, when Bitcoin first began trading, to November 2024. This provides a comprehensive view of Bitcoin’s historical price movements, from its early days of trading at a fraction of a cent to its more recent valuation in the thousands of dollars.

Use Cases:

This dataset is valuable for a variety of purposes, including:

Time Series Analysis: Analyze Bitcoin price movements, identify trends, and develop predictive models for future prices. Financial Modeling: Use the dataset to assess Bitcoin as an asset class, model its volatility, or simulate investment strategies. Machine Learning: Train machine learning algorithms to forecast Bitcoin’s future price or predict market trends based on historical data. Economic Research: Study the impact of global events on Bitcoin’s price, such as regulatory changes, technological developments, or macroeconomic factors. Visualization: Generate visualizations of Bitcoin price trends, trading volume, and market capitalization over time.

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.

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 historical Bitcoin (BTC/USDT) price data from Binance exchange with the following specifications:

Timezone Information: - All timestamps are in UTC (Coordinated Universal Time) - Open time format: YYYY-MM-DD HH:MM:SS.ffffff UTC - Close time format: YYYY-MM-DD HH:MM:SS.ffffff UTC

Daily Timeframe Specific: - Open time: Always shows 00:00:00.000000 UTC (start of day) - Close time: Always shows 23:59:59.999000 UTC (end of day)

Timeframes Available: - 15-minute intervals (15m) - 1-hour intervals (1h) - 4-hour intervals (4h) - 1-day intervals (1d)

Data Columns: - Open time: Opening timestamp in UTC - Open: Opening price - High: Highest price during period - Low: Lowest price during period - Close: Closing price - Volume: Trading volume - Close time: Closing timestamp in UTC - Quote asset volume: Volume in quote asset (USDT) - Number of trades: Number of trades during period - Taker buy base asset volume: Volume of taker buy orders - Taker buy quote asset volume: Volume of taker buy orders in quote asset - Ignore: Unused field

Data is automatically updated and maintained through automated scripts.

Dataset: NUMENEX

This dataset contains questions and answers related to crypto market trends.

  Dataset Preview

created_at question answer score supporting_resources

2024-08-29T10:03:53.941933Z Is this a bull cycle in crypto? Yes 1 Link

2024-08-29T10:03:53.941933Z Is this a bear cycle in crypto? No 1 Link

2024-08-29T10:03:53.941933Z Are we nearby a new All-Time-High for BTC? No 1 Link

2024-08-29T10:03:53.941933Z Are we nearby a new All-Time-Low for BTC? No… See the full description on the dataset page: https://huggingface.co/datasets/Numen-ex/subnet-dataset.

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

Bitcoin(₿) is a cryptocurrency invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto. The currency began use in 2009 when its implementation was released as open-source software.

Bitcoin is a decentralized digital currency, without a central bank or single administrator, that can be sent from user to user on the peer-to-peer bitcoin network without the need for intermediaries. Transactions are verified by network nodes through cryptography and recorded in a public distributed ledger called a blockchain. Bitcoins are created as a reward for a process known as mining. They can be exchanged for other currencies, products, and services.

On 30 November 2020, bitcoin hit a new all-time high of $19,860 topping the previous high from December 2017. On 19 January 2021 Elon Musk placed #Bitcoin in his Twitter profile tweeting “In retrospect, it was inevitable”, which caused the price to briefly rise about $5000 in an hour to $37,299.

Content

The tweets have #Bitcoin and #btc hashtag.. Collection star started on 6/2/2021, with an initial 100,000 tweets, and will continue on a daily basis.

Information regarding the data

The data totally consists of 1 lakh+ records with 13 columns. The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #Bitcoin & #btc | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Inspiration

The tweets were extracted using tweepy, Refer to this notebook for the complete extraction process https://www.kaggle.com/kaushiksuresh147/twitter-data-extraction-for-ipl2020

You can use this data to dive into the subjects that use this hashtag, look to the geographical distribution, evaluate sentiments, looks at trends.

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.

Bitcoin's blockchain size was close to reaching 5450 gigabytes in 2024, 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 203 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.

Bitcoin's circulating supply has grown steadily since its inception in 2009, reaching over 19 million coins by early 2025. This gradual increase reflects the cryptocurrency's design, which put a limit of 21 million on the total number of bitcoins that can ever exist. This impacts the Bitcoin price somewhat, as its scarcity can lead to volatility on the market. Maximum supply and scarcity Bitcoin is unusual from other cryptocurrencies in that its maximum supply is getting closer. By 2025, more than 90 percent of all possible Bitcoin had been created. That said, Bitcoin's circulating supply is expected to reach its maximum around the year 2140. Meanwhile, mining becomes exponentially more difficult and energy-intensive. Institutional investors In 2025, countries like the United States openly started discussion the possibility of buying bitcoins to hold in reserve. By the time of writing, it was unclear whether this would happen. Nevertheless, institutional investors displayed more interest in the cryptocurrency than before. Certain companies owned several thousands of Bitcoin tokens in 2025, for example. This and the limited number of Bitcoin may further fuel price volatility.

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

Context

Bitcoin and other cryptocurrencies have captured the imagination of technologists, financiers, and economists. Digital currencies are only one application of the underlying blockchain technology. Like its predecessor, Bitcoin, the Ethereum blockchain can be described as an immutable distributed ledger. However, creator Vitalik Buterin also extended the set of capabilities by including a virtual machine that can execute arbitrary code stored on the blockchain as smart contracts.

Both Bitcoin and Ethereum are essentially OLTP databases, and provide little in the way of OLAP (analytics) functionality. However the Ethereum dataset is notably distinct from the Bitcoin dataset:

• The Ethereum blockchain has as its primary unit of value Ether, while the Bitcoin blockchain has Bitcoin. However, the majority of value transfer on the Ethereum blockchain is composed of so-called tokens. Tokens are created and managed by smart contracts.

• Ether value transfers are precise and direct, resembling accounting ledger debits and credits. This is in contrast to the Bitcoin value transfer mechanism, for which it can be difficult to determine the balance of a given wallet address.

• Addresses can be not only wallets that hold balances, but can also contain smart contract bytecode that allows the programmatic creation of agreements and automatic triggering of their execution. An aggregate of coordinated smart contracts could be used to build a decentralized autonomous organization.

Content

The Ethereum blockchain data are now available for exploration with BigQuery. All historical data are in the ethereum_blockchain dataset, which updates daily.

Our hope is that by making the data on public blockchain systems more readily available it promotes technological innovation and increases societal benefits.

Querying BigQuery tables

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

Acknowledgements

Cover photo by Thought Catalog on Unsplash

Inspiration

• What are the most popularly exchanged digital tokens, represented by ERC-721 and ERC-20 smart contracts?
• Compare transaction volume and transaction networks over time
• Compare transaction volume to historical prices by joining with other available data sources like Bitcoin Historical Data

LuckyBit bets

Datasets used in the creation of "How Bitcoin’s Ups and Downs Are Changing the Way You Bet" by Máté Csaba Sándor and Barna Bakó of Corvinus University of Budapest

These datasets can be used to reproduce the results presented in (1)

The necessary scripts are available at: https://github.com/sampaat/luckybit_gambling

Producing the gambling dataset

The gambling dataset was created using the transactional data extractable from the bitcoin ledger. To make things simpler we have been using a formated dataset from (2), available currently at (3). To replicate the dataset, use the script data_preparation_from_blockchain_excrept.R. Do not run the script as it is, since first you need to download the appropriate datasets from above which takes considerable time. Also there are some variables that need manual adjustment, to avoid overwhelming most desktop computers.

A representative state of the game's website is observable as a web archive

The prepared gambling dataset

The excrept created, containing all transactions of the LuckyBit platform are featured in luckybit_bets_usered.csv

The columns featured in the dataset (names in the first row):

• txID transaction ID of the bet transaction [integer]
• addrID recieving address ID of the targeted game (see *luckybit_games_addresses.csv* for mapping them to games) [integer]
• addrID_in reciving/initiating address ID of the bet/answer transaction, the ID gathered from the dataset, not resolved to true bitcoin IDs [integer]
• value bet ammount (or wager) measured in satoshis (1 satoshi = 1e-8 BTC) [integer]
• block_timestamp blockchain block timestamp (UTC unixtime) used to time the bets [integer]
• userID assigned based on addrID_player using the methods and dataset of (**2**) [integer]

Supporting datasets

Luckybit addresses

Mapping table containing basic information about the possible LuckyBit games are featured in luckybit_games_address.csv

The columns featured in the dataset (names in the first row):

• Name color based identification of the game as shown in the web archive. [string]
• Address string format of the game's bitcoin address hash [string]
• addrID recieving address ID of the targeted game (see luckybit_games_addresses.csv for mapping them to games) [integer]
• ExpectedReturn expected payout of the game on unit bet [float]
• HousePercent expected loss of the game on unit bet [float]
• MaxWin maximum winning multiplier [integer]
• WinProb total probability of positive total payout (win) [float]
• LossProb total probability of negative total payout (loss) [float]
• FlatProb total probability of bet payback (no win or loss) [float]

Luckybit game table

Mapping table containing multipliers and probabilites of each game are featured luckybit_games_table.csv

The columns featured in the dataset (names in the first row):

• n label of result 1-17 [integer]
• blue multiplier of the n-th outcome for the game blue [float]
• green multiplier of the n-th outcome for the game green [float]
• yellow multiplier of the n-th outcome for the game yellow [float]
• red multiplier of the n-th outcome for the game red [float]
• p_win maximum winning multiplier [float]

Clustered users table

Mapping table containing the clusterings assigned to each user are featured clustered_users.csv

The columns featured in the dataset (names in the first row):

• userID assigned based on addrID_player using the methods and dataset of (**2**) [integer]
• cluster calculated in *player_clustering.Rmd* as 1 - All players, 2 - Casual, 3 - Regular, 4 - Extreme [integer]

Clustered users table

Bitcoin to USD exchange rate data featured in bitcoin_historical_data_coinmarketcap.csv

This dataset has been downloaded as of 2024-09-02 from Coinmarketcap

The columns featured in the dataset (names in the first row):

• timeOpen market open, datetime UTC [string]
• timeClose market close, datetime UTC [string]
• timeHigh timing of daily high, datetime UTC [string]
• timeLow timing of daily low, datetime UTC [string]
• name arbitrary ID from Coinmarketcap, uniform, dropable [integer]
• open daily opening exchange rate (USD/BTC) [float]
• high highest daily exchange rate (USD/BTC) [float]
• low lowest daily exchange rate (USD/BTC) [float]
• close daily closing exchange rate (USD/BTC) [float]
• volume daily total traded volume (BTC) [float]
• marketCap total Bitcoin market capitalization in USD [float]
• timeStamp timing of data recording, datetime UTC [string]

References

1. Bakó, B., Sándor, M.C. (2025). How Bitcoin’s Ups and Downs Are Changing the Way You Bet
2. Kondor, D., Pósfai, M., Csabai, I., & Vattay, G. (2014). Do the rich get richer? An empirical analysis of the BitCoin transaction network. PLoS ONE, 9(2), e86197.
3. https://doi.org/10.5061/dryad.qz612jmcf

This dataset contains historical 1-minute candlestick (OHLCV) data for the BTC/USDT trading pair from January 2020 to March 15, 2025. The data is sourced directly from the Binance API and covers both the spot market and futures market.

Structure The dataset is organized into annual folders (e.g., 2020/, 2021/, etc.). Each year contains monthly subfolders (e.g., 01/, 02/, ..., 12/). Each month includes two CSV files: - trading_data.csv → Spot market data - futures_data.csv → Futures market data

Data Frequency & Cleaning 1-minute interval candlestick data. From the 12 columns provided by the Binance API, only the most relevant and useful ones have been kept: - Open time - Open - High - Low - Close - Volume - Number of trades - Taker buy base asset volume

Updates This dataset will be updated periodically (every 5 to 15 days) to ensure it remains current.

Usage This dataset is ideal for: * Backtesting trading strategies * Analyzing market trends * Developing machine learning models for trading

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

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Financial data:

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

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

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

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

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

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

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

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

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

• Data cleaning and preprocessing are essential before model training

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