About this dataset As cryptocurrency markets have gained prominence, individuals and organizations have shown an increased fascination with crafting automated trading strategies. The creation of algorithmic trading approaches, though, necessitates rigorous backtesting to ascertain their profitability. Consequently, the cornerstone of any triumphant algorithmic trading strategy lies in the availability of meticulously detailed historical trading data. This dataset will provide you a deeper understanding of working with this type of financial security, it provides you with open, high, low, close (OHLC) information, recorded at 1-hour intervals (not very high-velocity data), encompassing a multitude of cryptocurrency pairs. This data resource is invaluable for those seeking to devise and refine automated trading systems, data analysis, or predictions.

Content This dataset contains the historical trading data (OHLC) of 14 crypto securities at 1 1-hour resolution. The source of this data is Coindesk. The data in the CSV files is refined and cleaned for easier interpretation.

The data is free to use.

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

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

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.

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. I'm not perfect, and I'm also busy! 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 deals with Top 1000 cryptocurrency historical time series data and other crypto metrics.

Coingecko API is used to extract data, total are 1000 CSV files, 999 files deals with historical data for each project time series data from start date till today with interval of 4 days. Project need to be identify through file name as it is project id.

Last CSV file contains detail related to project rank, ATH, ATL, circulating supply, total supply, max supply etc.

Columns for Top1000_Cryptocurrency_File (1 CSV file):

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F7fab827c7391222d608c6749205838c0%2FFile_Columns.JPG?generation=1692273166787046&alt=media" alt="">

Columns for Project_Files (999 CSV files):

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2Fcc538b071266ac7c9005b930d184914c%2FFile_Columns1.JPG?generation=1692273220676144&alt=media" alt="">

Context

In the last few days, I have been hearing a lot of buzz around cryptocurrencies. 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 DS guy sleeping inside me (always lazy.!) woke up and started raising questions like

1. How many such cryptocurrencies are there and what are their prices and valuations?
2. Why is there a sudden surge in the interest in recent days? Is it due to the increase in the price in the last few days? etc.

For getting answers to all these questions (and if possible to predict the future prices ;)), I started getting the data from coinmarketcap about the cryptocurrencies.

Content

This dataset has the historical price information of some of the top cryptocurrencies by market capitalization. The currencies included are

• Bitcoin
• Ethereum
• Ripple
• Bitcoin cash
• Bitconnect
• Dash
• Ethereum Classic
• Iota
• Litecoin
• Monero
• Nem
• Neo
• Numeraire
• Stratis
• Waves

In case if you are interested in the prices of some other currencies, please post in comments section and I will try to add them in the next version. I am planning to revise it once in a week.

Dataset has one csv file for each currency. Price history is available on a daily basis from April 28, 2013 till Aug 07, 2017. The columns in the csv file are

• 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

General information

This repository includes all data needed to reproduce the experiments presented in [1].
The paper describes the BF skip index, a data structure based on Bloom filters [2] that can be used for answering inter-block queries on blockchains efficiently. The article also includes a historical analysis of logsBloom filters included in the Ethereum block headers, as well as an experimental analysis of the proposed data structure. The latter was conducted using the data set of events generated by the CryptoKitties Core contract, a popular decentralized application launched in 2017 (and also one of the first applications based on NFTs).

In this description, we use the following abbreviations (also adopted throughout the paper) to denote two different sets of Ethereum blocks.

1. D1: set of all Ethereum blocks between height 0 and 14999999.
2. D2: set of all Ethereum blocks between height 14000000 and 14999999.

Moreover, in accordance with the terminology adopted in the paper, we define the set of keys of a block as the set of all contract addresses and log topics of the transactions in the block. As defined in [3], log topics comprise event signature digests and the indexed parameters associated with the event occurrence.

Data set description

File structure

We now describe the structure of the files included in this repository.

• filters_ones_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 3 columns. Note that it is not necessary to decompress this file, as the provided code is capable of processing it directly in its compressed form. The columns have the following meaning.
  1. blockId: the identifier of the block.
  2. timestamp: timestamp of the block.
  3. numOnes: number of bits set to 1 in the logsBloom filter of the block.
• receipt_stats_0-14999999.csv.xz is a compressed CSV file with 15 million rows (one for each block in D1) and 5 columns. As for the previous file, it is not necessary to decompress this file.
  1. blockId: the identifier of the block.
  2. txCount: number of transactions included in the block.
  3. numLogs: number of event logs included in the block.
  4. numKeys: number of keys included in the block.
  5. numUniqueKeys: number of distinct keys in the block (useful as the same key may appear multiple times).
• All CSV files related to the CryptoKitties Core events (i.e., Approval.csv, Birth.csv, Pregnant.csv, Transfer.csv) have the same structure. They consist of 1 million rows (one for each block in D2) and 2 columns, namely:
  1. blockId: identifier of the block.
  2. numOcc: number of event occurrences in the block.
• events.xz is a compressed binary file describing all unique event occurrences in the blocks of D2. The file contains 1 million data chunks (i.e., one for each Ethereum block). Each chunk includes the following information. Do note that this file only records unique event occurrences in each block, meaning that if an event from a contract is triggered more than once within the same block, there will be only one sequence within the corresponding chunk.
  1. blockId: identifier of the block (4 bytes).
  2. numEvents: number of event occurrences in the block (4 bytes).
  3. A list of numEvent sequences, each made up of 52 bytes. A sequence represents an event occurrence and is indeed the concatenation of two fields, namely:
     1. Address of the contract triggering the event (20 bytes).
     2. Event signature digest (32 bytes).
• keys.xz is a compressed binary file describing all unique keys in the blocks of D2. As for the previous file, duplicate keys only appear once. The file contains 1 million data chunks, each representing an Ethereum block and including the following information.
  1. blockId: identifier of the block (4 bytes)
  2. numAddr: number of unique contract addresses (4 bytes).
  3. numTopics: number of unique topics (4 bytes).
  4. A sequence of numAddr addresses, each represented using 20 bytes.
  5. A sequence of numTopics topics, each represented using 32 bytes.

Notes

For space reasons, some of the files in this repository have been compressed using the XZ compression utility. Unless otherwise specified, these files need to be decompressed before they can be read. Please make sure you have an application installed on your system that is capable of decompressing such files.

Cite this work

If the data included in this repository have been useful, please cite the following article in your work.

@article{loporchio2025skip,
 title={Skip index: Supporting efficient inter-block queries and query authentication on the blockchain},
 author={Loporchio, Matteo and Bernasconi, Anna and Di Francesco Maesa, Damiano and Ricci, Laura},
 journal={Future Generation Computer Systems},
 volume={164},
 pages={107556},
 year={2025},
 publisher={Elsevier}
}

References

1. Loporchio, Matteo et al. "Skip index: supporting efficient inter-block queries and query authentication on the blockchain". Future Generation Computer Systems 164 (2025): 107556. https://doi.org/10.1016/j.future.2024.107556
2. Bloom, Burton H. "Space/time trade-offs in hash coding with allowable errors." Communications of the ACM 13.7 (1970): 422-426.
3. Wood, Gavin. "Ethereum: A secure decentralised generalised transaction ledger." Ethereum project yellow paper 151.2014 (2014): 1-32.

About this dataset

With the rise of crypto currency markets the interest in creating automated trading strategies, or trading bots, has grown. Developing algorithmic trading strategies however requires intensive backtesting to ensure profitable performance. It follows that access to high resolution historical trading data is the foundation of every successful algorithmic trading strategy. This dataset therefore provides open, high, low, close (OHLC) data at 1 minute resolution of various crypto currency pairs for the development of automated trading systems.

Content

This dataset contains the historical trading data (OHLC) of more than 400 trading pairs at 1 minute resolution reaching back until the year 2013. It was collected from the Bitfinex exchange as described in this article. The data in the CSV files is the raw output of the Bitfinex API. This means, there are no timestamps for time periods in which the exchange was down. Also if there were time periods without any activity or trades there will be no timestamp as well.

Inspiration

This dataset is intended to facilitate the development of automatic trading strategies. Machine learning algorithms, as they are available through various open source libraries these days, typically require large amounts of training data to unveil their full power. Also the process of backtesting new strategies before deploying them rests on high quality data. Most crypto trading datasets that are currently available either have low temporal resolution, are not free of charge or focus only on a limited number of currency pairs. This dataset on the other hand provides high temporal resolution data of more than 400 currency pairs for the development of new trading algorithms.

# Data Set Information: Data on the historical price of bitcoin from 2013 until 2023 has been compiled by Coingecko.

# Attribute Information: snapped_at(Date & Time): 2013-04-28 00:00:00 UTC - 2023-04-07 00:00:00 UTC

price: 67.809 - 67617.01554

market_cap: 18408732920 - 1.2788E+12

total_volume: 0 - 1.78894E+11

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:

How many cryptocurrencies are there and what are their prices and valuations? 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 (1st jan 2014 to 1st jan 2022) 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-The Capital of this coin

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:

How many cryptocurrencies are there and what are their prices and valuations? 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

The dataset at hand encompasses 12 consecutive days, starting from January 9th, 2023, until January 20th, 2023, and comprises data from Binance Bitcoin perpetual data (BTCUSDT.P). The data points are sampled at a frequency of 250 milliseconds and contain a total of 3730870 rows and 42 columns. Within these columns, 20 of them represent bid data, while the other 20 columns contain ask data.