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 1m Data: Raw 1m historical data from multiple exchanges, covering the entire trading history of ETHUSD 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 ETHUSD 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/5ti89wM.png" alt="ETHUSD Dataset Summary">

https://i.imgur.com/DnpNF9R.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)

Analysis of ‘Ethereum Cryptocurrency Historical Dataset ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kaushiksuresh147/ethereum-cryptocurrency-historical-dataset on 30 September 2021.

--- Dataset description provided by original source is as follows ---

https://www.bernardmarr.com/img/What%20Is%20The%20Difference%20Between%20Bitcoin%20and%20Ethereum.png">

Context

Ethereum a decentralized, open-source blockchain featuring smart contract functionality was proposed in 2013 by programmer Vitalik Buterin. Development was crowdfunded in 2014, and the network went live on 30 July 2015, with 72 million coins premined.

Some interesting facts about Ethereum(ETH): - Ether (ETH) is the native cryptocurrency of the platform. It is the second-largest cryptocurrency by market capitalization, after Bitcoin. Ethereum is the most actively used blockchain. - Some of the world’s leading corporations joined the EEA(Ethereum Alliance, is a collaboration of many block start-ups) and supported “further development.” Some of the most famous companies are Samsung SDS, Toyota Research Institute, Banco Santander, Microsoft, J.P.Morgan, Merck GaA, Intel, Deloitte, DTCC, ING, Accenture, Consensys, Bank of Canada, and BNY Mellon.

Content

The dataset consists of ETH prices from March-2016 to the current date(1830days) and the dataset will be updated on a weekly basis.

Information regarding the data

The data totally consists of 1813 records(1813 days) with 7 columns. The description of the features is given below

| No |Columns | Descriptions | | -- | -- | -- | | 1 | Date | Date of the ETH prices | | 2 | Price | Prices of ETH(dollars) | | 3 | Open | Opening price of ETH on the respective date(Dollars) | | 4 | High | Highest price of ETH on the respective date(Dollars) | | 5 | Low | Lowest price of ETH on the respective date(Dollars) | | 6 | Vol. | Volume of ETH on the respective date(Dollars). | | 7 | Change % | Percentage of Change in ETH prices on the respective date | |

Acknowledgements

The dataset was extracted from investing.com

Inspiration

Experts say that ethereum has a huge potential in the future. Do you believe it? Well, let's find it by building our own creative models to predict if the statement is true.

--- Original source retains full ownership of the source dataset ---

Ethereum Classic is a cryptocurrency with shared history with the Ethereum cryptocurrency. On technical merits, the two cryptocurrencies are nearly identical, differing only in programming language features supported by the Ethereum Virtual machine which is used to write smart contracts. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. Interested in learning more about how Cloud Public Data is working to make data from blockchains and cryptocurrencies more accessible? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Ethereum Classic is a cryptocurrency with shared history with the Ethereum cryptocurrency. On technical merits, the two cryptocurrencies are nearly identical, differing only in programming language features supported by the Ethereum Virtual machine which is used to write smart contracts. This dataset contains the blockchain data in their entirety, pre-processed to be human-friendly and to support common use cases such as auditing, investigating, and researching the economic and financial properties of the system. Interested in learning more about how Cloud Public Data is working to make data from blockchains and cryptocurrencies more accessible? Check out our blog post on the Google Cloud Big Data Blog and try the sample query below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of ETH-USD pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!

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

Ethereum Classic is an open-source, public, blockchain-based distributed computing platform featuring smart contract (scripting) functionality. It provides a decentralized Turing-complete virtual machine, the Ethereum Virtual Machine (EVM), which can execute scripts using an international network of public nodes. Ethereum Classic and Ethereum have a value token called "ether", which can be transferred between participants, stored in a cryptocurrency wallet and is used to compensate participant nodes for computations performed in the Ethereum Platform.

Ethereum Classic came into existence when some members of the Ethereum community rejected the DAO hard fork on the grounds of "immutability", the principle that the blockchain cannot be changed, and decided to keep using the unforked version of Ethereum. Till this day, Etherum Classic runs the original Ethereum chain.

Content

In this dataset, you will have access to Ethereum Classic (ETC) historical block data along with transactions and traces. You can access the data from BigQuery in your notebook with bigquery-public-data.crypto_ethereum_classic dataset.

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

Acknowledgements

This dataset wouldn't be possible without the help of Allen Day, Evgeny Medvedev and Yaz Khoury. This dataset uses Blockchain ETL. Special thanks to ETC community member @donsyang for the banner image.

Inspiration

One of the main questions we wanted to answer was the Gini coefficient of ETC data. We also wanted to analyze the DAO Smart Contract before and after the DAO Hack and the resulting Hardfork. We also wanted to analyze the network during the famous 51% attack and see what sort of patterns we can spot about the attacker.

Analysis of ‘Ethereum Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/varpit94/ethereum-data on 28 January 2022.

--- Dataset description provided by original source is as follows ---

What is Ethereum?

Ethereum is a decentralized, open-source blockchain with smart contract functionality. Ether (ETH or Ξ) is the native cryptocurrency of the platform. After Bitcoin, it is the largest cryptocurrency by market capitalization. Ethereum is the most actively used blockchain. Ethereum was proposed in 2013 by programmer Vitalik Buterin.

Data Description

This dataset provides the history of daily prices of Ethereum. The data starts from 07-Aug-2015. All the column descriptions are provided. Currency is USD.

--- Original source retains full ownership of the source dataset ---

Description: The "Ethereum Price USD (2018-2023)" dataset on Kaggle presents a comprehensive collection of historical Ethereum cryptocurrency price data in USD from the year 2018 to 2023. This dataset serves as a valuable resource for researchers, analysts, and enthusiasts interested in studying the trends and patterns of Ethereum's price movement over the years.

The dataset is carefully curated and includes daily price data for Ethereum, one of the leading cryptocurrencies in the market. Each entry in the dataset contains essential information, such as the date, closing price, and trading volume, enabling users to perform a wide range of analyses and gain insights into Ethereum's market behavior.

It includes the following columns:

Date: This column represents the date on which the price data was recorded. It typically follows the format of YYYY-MM-DD (year-month-day), indicating the specific day for which the price information is provided.

Price: The "Price" column contains the closing price of Ethereum on the corresponding date. It represents the value at which Ethereum was traded at the end of the trading day in USD.

Open: The "Open" column denotes the opening price of Ethereum on the given date. It signifies the price at which the first trade occurred on that day.

High: The "High" column shows the highest price of Ethereum recorded on the date. It represents the peak price level reached during the trading session.

Low: The "Low" column displays the lowest price of Ethereum recorded on the date. It indicates the minimum price level reached during the trading session.

Vol. (Volume): The "Volume" column represents the trading volume of Ethereum on the date. It reflects the total number of Ethereum coins traded during the entire trading session.

Change %: The "Change %" column provides the percentage change in Ethereum's price from the previous trading day's closing price to the current day's closing price. It shows how much the price has increased or decreased in percentage terms.

Key Features: 1. Timeframe: The dataset spans from the year 2016, capturing the early stages of Ethereum's existence, up to the current year 2023. This extensive temporal coverage allows users to observe how Ethereum's price has evolved over time, through both bullish and bearish market conditions.

1. Price Data: For each date in the dataset, the closing price of Ethereum in USD is provided. This data is crucial for analyzing price trends, volatility, and identifying significant price movements.

2. Trading Volume: In addition to price information, the dataset also includes daily trading volume, enabling users to assess the liquidity and trading activity surrounding Ethereum on any given day.

Potential Use Cases: The "Ethereum Price USD (2018-2023)" dataset opens up a wide range of possibilities for data analysis and research. Some potential use cases include:

1. Trend Analysis: Researchers can use this dataset to identify long-term price trends, recurring patterns, and cycles in Ethereum's price movement.

2. Volatility Assessment: Traders and analysts can study the volatility of Ethereum's price over different time periods and understand its impact on market sentiment.

3. Event Correlation: By combining this dataset with external event data, users can explore correlations between specific events (e.g., technological developments, regulatory changes) and Ethereum's price fluctuations.

4. Predictive Modeling: Data scientists and machine learning enthusiasts can use this dataset to build predictive models for forecasting Ethereum's future price movements.

5. Investment Strategy: Investors can analyze historical price data to make informed decisions about Ethereum's potential as an investment asset.

Data Source: The "Ethereum Price USD (2018-2023)" dataset is sourced from reliable and reputable cryptocurrency exchanges and market data providers. Users can be confident in the accuracy and quality of the data, ensuring the reliability of their analyses and insights.

Note: As with any financial dataset, users are advised to exercise caution and perform their due diligence when using this data for investment decisions or any other financial purposes. Historical price data may not guarantee future performance, and cryptocurrency markets can be highly volatile.

Disclaimer: The dataset provided on Kaggle is intended for informational and educational purposes only. The uploader and Kaggle are not responsible for any financial or investment decisions made based on the data. Users are encouraged to seek professional financial advice before making any investment decisions.

Ethereum is a crypto currency which leverages blockchain technology to store transactions in a distributed ledger. A blockchain is an ever-growing "tree" of blocks, where each block contains a number of transactions. To learn more, read the "Ethereum in BigQuery: a Public Dataset for smart contract analytics" blog post by Google Developer Advocate Allen Day. This dataset is part of a larger effort to make cryptocurrency data available in BigQuery through the Google Cloud Public Datasets program . The program is hosting several cryptocurrency datasets, with plans to both expand offerings to include additional cryptocurrencies and reduce the latency of updates. You can find these datasets by searching "cryptocurrency" in GCP Marketplace. For analytics interoperability, we designed a unified schema that allows all Bitcoin-like datasets to share queries. Interested in learning more about how the data from these blockchains were brought into BigQuery? Looking for more ways to analyze the data? Check out the Google Cloud Big Data blog post and try the sample queries below to get started. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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

The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

Datasets

Become a Data Provider

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

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

Step into the world of Ethereum, a revolutionary blockchain-based platform that offers more than just a cryptocurrency. With its native digital currency, ether, you can now send and receive value seamlessly without any intervention from third-party entities. But hold on, that's not all that Ethereum has to offer!

Invented by the visionary Russian-Canadian programmer, Vitalik Buterin, back in 2013, Ethereum has opened up a new horizon for cryptocurrencies by enabling developers to create their very own unique applications. These Ethereum-based applications, or "decentralized applications" (dapps), are unlike anything we've seen before. They are self-executing programs that run independently and autonomously, thanks to the utilization of smart contracts.

Smart Contracts are digital codes that are securely stored on the Ethereum blockchain and are programmed to carry out specific actions when predetermined conditions are met. They have the power to automate various functions, such as sending a transaction when a certain event occurs or providing loans once collateral is deposited into a designated wallet. All dapps built on Ethereum, as well as those created across other blockchain platforms, rely on smart contracts as their foundation. Ethereum truly is a game-changer!

What is Ethereum?

Ethereum is a decentralized, open-source blockchain with smart contract functionality. Ether (ETH or Ξ) is the native cryptocurrency of the platform. After Bitcoin, it is the largest cryptocurrency by market capitalization. Ethereum is the most actively used blockchain. Ethereum was proposed in 2013 by programmer Vitalik Buterin.

Data Description

This dataset provides the history of daily prices of Ethereum. The data starts from 07-Aug-2015. All the column descriptions are provided. Currency is USD.

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of OMG-ETH pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!

Context

This is a primitive dataset and my first collection. The transactions in this dataset occurred over a 14hrs period prior to my scrape on 02/03/18 at approx 7:30PM GMT. Was interested in doing some exploratory analysis to see how much Ethereum was being sent to each address over this 17 hour period. So scraped 10000 page table results.

Content

There are 8 Columns:

• Record (Containing Record Number)
• TxHash (The Transaction ID)
• Block (Block number that this transaction was on)
• Age (Age from Approx 7:30PM GMT on 02/03/18)
• From (Senders Wallet Address)
• To (Recipients Wallet Address)
• Value (Transaction Value. Please Note: If 0 will have been a smart contract)
• TxFee (The gas price and cost of the transaction)

Inspiration

Whats next? Block Dataset which is much larger.

What is FMA-rank?

FMA is a music dataset from the Free Music Archive, containing over 8000 hours of Creative Commons-licensed music from 107k tracks across 16k artists and 15k albums. It was created in 2017 by Defferrard et al. in collaboration with Free Music Archive. FMA contains a lot of good music, and a lot of bad music, so the question is: can we rank the samples in FMA? FMA-rank is a CLAP-based statistical ranking of each sample in FMA. We calculate the log-likelihood of each… See the full description on the dataset page: https://huggingface.co/datasets/disco-eth/FMA-rank.

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of DOT-ETH pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!

Context

Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

Content

Data provided in this dataset are historical data from the beginning of ETH-CHF pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

Trading history

Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

OHLCVT

In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

Don't hesitate to tell me if you need other period interval 😉 ...

Update

This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

Inspiration

Can you beat the market? Let see what you can do with these data!