The Bitcoin (BTC) price again reached an all-time high in 2025, as values exceeded over 111,842.71 USD on August 27, 2025. Price hikes in early 2025 were connected to the approval of Bitcoin ETFs in the United States, while previous hikes in 2021 were due to events involving Tesla and Coinbase, respectively. Tesla's announcement in March 2021 that it had acquired 1.5 billion U.S. dollars' worth of the digital coin, for example, as well as the IPO of the U.S.'s biggest crypto exchange, fueled mass interest. The market was noticeably different by the end of 2022, however, after another crypto exchange, FTX, filed for bankruptcy.Is the world running out of Bitcoin?Unlike fiat currency like the U.S. dollar - as the Federal Reserve can simply decide to print more banknotes - Bitcoin's supply is finite: BTC has a maximum supply embedded in its design, of which roughly 89 percent had been reached in April 2021. It is believed that Bitcoin will run out by 2040, despite more powerful mining equipment. This is because mining becomes exponentially more difficult and power-hungry every four years, a part of Bitcoin's original design. Because of this, a Bitcoin mining transaction could equal the energy consumption of a small country in 2021.Bitcoin's price outlook: a potential bubble?Cryptocurrencies have few metrics available that allow for forecasting, if only because it is rumored that only a few cryptocurrency holders own a large portion of the available supply. These large holders - referred to as 'whales'-are' said to make up two percent of anonymous ownership accounts, while owning roughly 92 percent of BTC. On top of this, most people who use cryptocurrency-related services worldwide are retail clients rather than institutional investors. This means outlooks on whether Bitcoin prices will fall or grow are difficult to measure, as movements from one large whale are already having a significant impact on this market.

By 2025, the Bitcoin market cap had grown to over ***** billion USD as the cryptocurrency kept growing. Market capitalization is calculated by multiplying the total number of Bitcoins in circulation by the Bitcoin price. The Bitcoin market capitalization increased from approximately *** billion U.S. dollars in 2013 to several times this amount since its surge in popularity. Dominance The Bitcoin market cap takes up a significant portion of the overall cryptocurrency market cap. This is referred to as "dominance". Within the crypto world, this so-called "dominance" ratio is one of the oldest and most investigated metrics available. It measures the coin's market cap relative to the overall crypto market — effectively showing how strong Bitcoin compared to all the other cryptocurrencies that are not BTC, called "altcoins". The Bitcoin dominance was above ** percent. Maximum supply and scarcity Bitcoin is unusual from other cryptocurrencies in that its maximum supply is getting closer. By 2025, well over ** million out of all 21 million possible Bitcoin had been created. Bitcoin's supply is expected to reach its maximum around the year 2140, likely making mining more energy-intensive.

Context

Blockchain technology, first implemented by Satoshi Nakamoto in 2009 as a core component of Bitcoin, is a distributed, public ledger recording transactions. Its usage allows secure peer-to-peer communication by linking blocks containing hash pointers to a previous block, a timestamp, and transaction data. Bitcoin is a decentralized digital currency (cryptocurrency) which leverages the Blockchain to store transactions in a distributed manner in order to mitigate against flaws in the financial industry.

Nearly ten years after its inception, Bitcoin and other cryptocurrencies experienced an explosion in popular awareness. The value of Bitcoin, on the other hand, has experienced more volatility. Meanwhile, as use cases of Bitcoin and Blockchain grow, mature, and expand, hype and controversy have swirled.

Content

In this dataset, you will have access to information about blockchain blocks and transactions. All historical data are in the bigquery-public-data:crypto_bitcoin dataset. It’s updated it every 10 minutes. The data can be joined with historical prices in kernels. See available similar datasets here: https://www.kaggle.com/datasets?search=bitcoin.

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_bitcoin.[TABLENAME]. Fork this kernel to get started.

Method & Acknowledgements

Allen Day (Twitter | Medium), Google Cloud Developer Advocate & Colin Bookman, Google Cloud Customer Engineer retrieve data from the Bitcoin network using a custom client available on GitHub that they built with the bitcoinj Java library. Historical data from the origin block to 2018-01-31 were loaded in bulk to two BigQuery tables, blocks_raw and transactions. These tables contain fresh data, as they are now appended when new blocks are broadcast to the Bitcoin network. For additional information visit the Google Cloud Big Data and Machine Learning Blog post "Bitcoin in BigQuery: Blockchain analytics on public data".

Photo by Andre Francois on Unsplash.

Inspiration

• How many bitcoins are sent each day?
• How many addresses receive bitcoin each day?
• Compare transaction volume to historical prices by joining with other available data sources

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.

distribution

The global user base of cryptocurrencies increased by nearly *** percent between 2018 and 2020, only to accelerate further in 2022. This is according to calculations from various sources, based on information from trading platforms and on-chain wallets. Increasing demographics might initially be attributed to a rise in the number of accounts and improvements in identification. In 2021, however, crypto adoption continued as companies like Tesla and Mastercard announced their interest in cryptocurrency. Consumers in Africa, Asia, and South America were most likely to be owners of cryptocurrencies, such as Bitcoin, in 2022. How many of these users have Bitcoin? User figures for individual cryptocurrencies are unavailable. Bitcoin, for instance, was created not to be tracked by banks and governments. What comes closest is the trading volume of Bitcoin against domestic fiat currencies. The source assumed, however, that UK residents were the most likely to make Bitcoin transactions with British pounds. This assumption might not be accurate for popular fiat currencies worldwide. Moreover, coins such as Tether or Binance Coin - referred to as "stablecoins"—are" often used to buy and sell Bitcoin. Those coins were not included in that particular statistic. Wallet usage declined Total crypto wallet downloads were significantly lower in 2022 than in 2021. The number of downloads of Coinbase, Blockchain.com, and MetaMask, among others, declined as the market hit a "crypto winter" over the year. The crypto market also suffered bad press when FTX, one of the largest crypto exchanges based on market share, collapsed in November 2022. Binance, on the other hand, regained some of the market share it had lost between September and October 2022, growing by *** percentage points in November. As of 2025, the highest forecast for the global user base of cryptocurrencies is projected to reach *** million.

During my Senior in the Shan Dong University, my tutor give me research direction of University thesis, which is bitcoin transaction data analysis, so I crawled all of bitcoin transaction data from January 2009 to February 2018.I make statistical analysis and quantitative analysis,I hope this data will give you some help, data mining is interesting and helping not only in the skill of data mining but also in our life.

I crawled these data from website https://www.blockchain.com/explorer, each file contains many blocks,the scope of blocks is reflected in the file name,e.g. this file 0-68732.csv is composed of zero block which is also called genesis block until 68732 block.if a block that didn't have input is not in this file. let's see the columns and rows, there has five columns, the Height column represent block height,the Input column represent the input address of this block,the Output column represent the output address of this block,the Sum column represent bitcoin transaction amount corresponding to the Output,the Time column represent the generation time of this block.A block contains many transactions.

The page is part three of all data, others can be found here https://www.kaggle.com/shiheyingzhe/datasets

Bitcoin forecast 2018-2021

This Bitcoin transaction network data as extracted with the bitcoind client. Due to the nature of Bitcoin, all transactions are published to a P2P network. These can be downloaded and extracted with any of the available client programs. This dataset includes the data used in our papers:

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. https://doi.org/10.1371/journal.pone.0086197
Kondor, D., Csabai, I., Szüle, J., Pósfai, M., & Vattay, G. (2014). Inferring the interplay between network structure and market effects in Bitcoin. New Journal of Physics, 16(12), 125003. https://doi.org/10.1088/1367-2630/16/12/125003

Bitcoin / outputs 2018-01 (Blockchair data). Documentaion and terms of use available at https://blockchair.com/dumps Speed limit filter https://gz.blockchair.com/README.html License: CC-BY 4.0

Dataset Card for Dataset Name

  Dataset Summary

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

[More Information Needed]

  Dataset Structure





  Data Instances

[More Information Needed]

  Data Fields

[More Information Needed]

  Data Splits

[More Information Needed]

  Dataset Creation… See the full description on the dataset page: https://huggingface.co/datasets/IceMasterT/BTC-Data-1Hour-2018-2023.

Cryptocurrency Market By Size, Share, Trends, Opportunity, and Forecast, 2018-2028, Segmented By Type, By End User, By Region, Competition Forecast and Opportunities

Bitcoin Cash / blocks 2018-09 (Blockchair data). Documentaion and terms of use available at https://blockchair.com/dumps Speed limit filter https://gz.blockchair.com/README.html License: CC-BY 4.0

Bitcoin / inputs 2018-11 (Blockchair data). Documentaion and terms of use available at https://blockchair.com/dumps Speed limit filter https://gz.blockchair.com/README.html License: CC-BY 4.0

Raw Data Files

**This data set contains Bitcoin data for years 2009-2011. For years 2011-2018 (~45GB), please see https://github.com/cakcora/CoinWorks/blob/master/data.MD

We provide input and output edges of transactions. This data is divided into yearly and monthly files. Each year's data is zipped together and contains 12 input edge files and 12 output edge files of transactions that were mined in the blocks of that year/month.

Each line in the input edge file is tab separated with the format:

Unix time of transaction\thash of transaction\thash of first input transaction\tindex of output from first input transaction\thash of second input transaction\tindex of output from second input transaction\t(additional inputs, if exist)\r

Each line in the output edge file is tab separated with the format:

Unix time of transaction\thash of transaction\thash of first output address\tamount of first output bitcoins\thash of second output address\tamount of second output bitcoins\t(additional outputs, if exist)\r

https://user-images.githubusercontent.com/6596905/38154759-80cbf57a-3439-11e8-8d84-9706e5825d5c.png" alt="Bitcoin Graph">

Consider the Bitcoin graph in the figure above, where transactions and addresses are shown with rectangles and circles, respectively. This graph would be given in two files: inputsYear_Month.txt and outputsYear_Month.txt. Files would include these lines:

-- inputsYear_Month.txt

UnixTimeOft_1  HashOft_1  HashOft_x1 0  HashOft_x2 8
UnixTimeOft_2  HashOft_2  HashOft_x3 1  HashOft_x4 3  HashOft_x5 0
UnixTimeOft_3  HashOft_3   HashOft_1  1
UnixTimeOft_4  HashOft_4   HashOft_3  2  HashOft_2  0

-- outputsYear_Month.txt

UnixTimeOft_1  HashOft_1  HashOfa_6  10^8  HashOfa_7  0.8^0.8
UnixTimeOft_2  HashOft_2  HashOfa_8  3.8*10^8
UnixTimeOft_3  HashOft_3 HashOfa_9 0.2*10^8  HashOfa_10 0.2*10^8  HashOfa_11 0.3*10^8
UnixTimeOft_4  HashOft_4 HashOfa_12  3.7*10^8  HashOfa_13 0.3*10^8

• 2009 data (0.1MB)
• 2010 data (15MB)
• 2011 data (300MB)
• 2012 data (1.2GB)
• 2013 data (3.2GB)
• 2014 data (5.2GB)
• 2015 data (9.6GB)
• 2016 inputs (8.1GB)
• 2016 outputs (8.5GB)
• 2017 data until August (13.2GB)

Please visit the full dataset page for your data related questions.

In 2018, Bitcoin was the preferred crypto-currency of over ************** of potential investors in Poland. Among experienced investors, ** percent of respondents preferred this currency.

Dataset

This dataset was created by Akash Dogra

Contents

We develop a strong diagnostic for bubbles and crashes in Bitcoin, by analyzing the coincidence (and its absence) of fundamental and technical indicators. Using a generalized Metcalfe's law based on network properties, a fundamental value is quantified and shown to be heavily exceeded, on at least four occasions, by bubbles that grow and burst. In these bubbles, we detect a universal super-exponential unsustainable growth. We model this universal pattern with the Log-Periodic Power Law Singularity (LPPLS) model, which parsimoniously captures diverse positive feedback phenomena, such as herding and imitation. The LPPLS model is shown to provide an ex-ante warning of market instabilities, quantifying a high crash hazard and probabilistic bracket of the crash time consistent with the actual corrections; although, as always, the precise time and trigger (which straw breaks the camel's back) being exogenous and unpredictable. Looking forward, our analysis identifies a substantial but not unprecedented overvaluation in the price of Bitcoin, suggesting many months of volatile sideways Bitcoin prices ahead (from the time of writing, March 2018).

The dataset of this paper is collected based on Google, Blockchain, and the Bitcoin market. Generally, there is a total of 26 features, however, a feature whose correlation rate is lower than 0.3 between the variations of price and the variations of feature has been eliminated. Hence, a total of 21 practical features including Market capitalization, Trade-volume, Transaction-fees USD, Average confirmation time, Difficulty, High price, Low price, Total hash rate, Block-size, Miners-revenue, N-transactions-total, Google searches, Open price, N-payments-per Block, Total circulating Bitcoin, Cost-per-transaction percent, Fees-USD-per transaction, N-unique-addresses, N-transactions-per block, and Output-volume have been selected. In addition to the values of these features, for each feature, a new one is created that includes the difference between the previous day and the day before the previous day as a supportive feature. From the point of view of the number and history of the dataset used, a total of 1275 training data were used in the proposed model to extract patterns of Bitcoin price and they were collected from 12 Nov 2018 to 4 Jun 2021.

Bitcoin Price Prediction Instruction-Tuning Dataset

  Dataset Description

This is a comprehensive, instruction-based dataset formatted specifically for fine-tuning Large Language Models (LLMs) to perform financial forecasting. The dataset is structured as a collection of prompt-response pairs, where each record challenges the model to predict the next 10 days of Bitcoin's price based on a rich, multimodal snapshot of daily data. The dataset covers the period from early 2018… See the full description on the dataset page: https://huggingface.co/datasets/tahamajs/bitcoin-llm-finetuning-dataset.