Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.

The market is projected to reach USD 1,074 Million in 2025 and is expected to grow to USD 7,975.7 Million by 2035, registering a CAGR of 22.2% over the forecast period. The expansion of Web3 infrastructure, advancements in multi-chain API solutions, and increasing demand for secure and scalable blockchain integrations are fueling market expansion. Additionally, rising adoption of tokenization, cross-chain interoperability, and API-driven NFT marketplaces is shaping the industry's future.

Metric	Value
Market Size (2025E)	USD 1,074 Million
Market Value (2035F)	USD 7,975.7 Million
CAGR (2025 to 2035)	22.2%

Country-wise Insights

Country	CAGR (2025 to 2035)
USA	22.5%

Country	CAGR (2025 to 2035)
UK	21.8%

Region	CAGR (2025 to 2035)
European Union (EU)	22.2%

Country	CAGR (2025 to 2035)
Japan	22.4%

Country	CAGR (2025 to 2035)
South Korea	22.7%

Competitive Outlook

Company Name	Estimated Market Share (%)
Coinbase Cloud	18-22%
Binance API	12-16%
Chainalysis	10-14%
Alchemy	8-12%
CryptoAPIs	6-10%
Other Companies (combined)	30-40%

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 at 1-min intervals for select bitcoin exchanges where trading takes place. Happy (data) mining!

Content

(See https://github.com/mczielinski/kaggle-bitcoin/ for automation/scraping script)

btcusd_1-min_data.csv

CSV files for select bitcoin exchanges for the time period of Jan 2012 to Present (Measured by UTC day), with minute to minute updates of OHLC (Open, High, Low, Close) and Volume in BTC.

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, originally. Now thank you to the Bitstamp API directly. The various exchange APIs, for making it difficult or unintuitive enough to get OHLC and volume data at 1-min intervals 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 is described in Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020), a study that aims to map and assess the extent of cryptocurrency manipulations within and across the online ecosystems of Twitter, Telegram, and Discord. Starting from tweets mentioning cryptocurrencies, we leveraged and followed invite URLs from platform to platform, building the invite-link network, in order to study the invite link diffusion process.

Please, refer to the paper below for more details.

Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M. & Ferrara, E. (2020). Charting the Landscape of Online Cryptocurrency Manipulation. IEEE Access (2020).

This dataset is composed of:

~16M tweet ids shared between March and May 2019, mentioning at least one of the 3,822 cryptocurrencies (cashtags) provided by the CryptoCompare public API; ~13k nodes of the invite-link network, i.e., the information about the Telegram/Discord channels and Twitter users involved in the cryptocurrency discussion (e.g., id, name, audience, invite URL); ~62k edges of the invite-link network, i.e., the information about the flow of invites (e.g., source id, target id, weight).

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).

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E-Brokerage Market Size 2025-2029

The e-brokerage market size is forecast to increase by USD 7.39 billion at a CAGR of 7.9% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the widespread availability of internet access. This digital transformation has led to an increasing number of investors turning to online platforms for brokerage services. Additionally, the demand for customized and personalized solutions is on the rise, with e-brokerage firms responding by offering tailored investment strategies and tools to meet individual client needs. However, this market growth is not without challenges. Cybersecurity risks have emerged as a major concern, with the increasing use of digital platforms presenting new vulnerabilities.
As e-brokerage firms continue to expand their online presence, they must prioritize robust security measures to protect sensitive customer information and maintain trust. Companies seeking to capitalize on market opportunities and navigate these challenges effectively should focus on implementing advanced security technologies, providing personalized services, and building strong customer relationships.

What will be the Size of the E-Brokerage Market during the forecast period?

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The market continues to evolve, driven by the intersection of technology and finance. High-frequency trading firms and fintech startups are revolutionizing the industry with advanced algorithms, trading tools, and real-time data. Active traders seek execution speed and personalized investment strategies, while retail investors demand user-friendly platforms and commission-free trading. Financial technology is transforming account opening, fund transfers, and order execution, enabling digital investment and mobile trading apps. Institutional investors leverage machine learning and artificial intelligence for risk management and data analysis. Pricing transparency and big data analytics are key differentiators, as are security protocols and customer satisfaction. Disruptive technologies, such as blockchain and API integrations, are reshaping the landscape, offering new opportunities for innovation.
Options trading, futures trading, and cryptocurrency trading are gaining popularity, requiring sophisticated trading algorithms and robust risk management systems. Trading platforms must adapt to meet the evolving needs of their customers, offering advanced charting tools, automated trading, and order routing. User experience, account management, and customer support are critical components of success in this dynamic market. The market is characterized by continuous change and innovation, driven by the convergence of technology and finance. From account opening to retirement planning, the industry is undergoing a digital transformation, with new players and technologies disrupting traditional business models.

How is this E-Brokerage Industry segmented?

The e-brokerage industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service Type

  Full time broker
  Discounted broker


Application

  Individual investor
  Institutional investor


Ownership

  Privately held
  Publicly held


Platform

  Web-based
  Mobile apps
  Desktop


Assest Type

  Equities
  Bonds
  Derivatives
  Cryptocurrencies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    Australia
    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Service Type Insights

The full time broker segment is estimated to witness significant growth during the forecast period.

In the dynamic world of financial services, brokerage firms have embraced technological advancements to cater to diverse investor needs. Blockchain technology underpins secure fund transfers, while digital investment platforms offer commission-free trading and fractional shares. Account opening is streamlined with user-friendly mobile apps, and algorithmic trading, powered by machine learning and artificial intelligence, enables personalized investment strategies. Options trading and margin trading are accessible to retail investors, with real-time data ensuring execution speed. Institutional investors leverage advanced trading tools, charting tools, and order routing for high-frequency trading. Financial advisors provide risk management and data security, ensuring customer satisfaction. Disruptive technologies like fintech startups and automated trading have transformed the landscape.

Beginner investors benefit from accessible investment management and wealth management solutions. Cryptocurrency trading, with its integration o