Ethereum's price history suggests that that crypto was worth more in 2025 than during late 2021, although nowhere near the highest price recorded. Much like Bitcoin (BTC), the price of ETH went up in 2021 but for different reasons altogether: Ethereum, for instance, hit the news when a digital art piece was sold as the world's most expensive NFT for over 38,000 ETH - or 69.3 million U.S. dollars. Unlike Bitcoin, of which the price growth was fueled by the IPO of the U.S.'s biggest crypto trader, Coinbase, the rally on Ethereum came from technological developments that caused much excitement among traders. First, the so-called 'Berlin update' rolled out on the Ethereum network in April 2021, an update that would eventually lead to the Ethereum Merge in 2022 and reduced ETH gas prices - or reduced transaction fees. The collapse of FTX in late 2022, however, changed much for the cryptocurrency. As of July 22, 2025, Ethereum was worth 3,765.45 U.S. dollars - significantly less than the 4,400 U.S. dollars by the end of 2021.Ethereum's future and the DeFi industryPrice developments on Ethereum are difficult to predict but cannot be seen without the world of DeFi, or decentralized finance. This industry used technology to remove intermediaries between parties in a financial transaction. One example includes crypto wallets such as Coinbase Wallet that grew in popularity recently, with other examples including smart contractor Uniswap, Maker (responsible for stablecoin DAI), moneylender Dharma and market protocol Compound. Ethereum's future developments are tied with this industry: Unlike Bitcoin and Ripple, Ethereum is technically not a currency but an open-source software platform for blockchain applications, with Ether being the cryptocurrency that is used inside the Ethereum network. Essentially, Ethereum facilitates DeFi, meaning that if DeFi does well, so does Ethereum.NFTs: the most well-known application of EthereumNFTs or non-fungible tokens, grew nearly tenfold between 2018 and 2020, as can be seen in the market cap of NFTs worldwide. These digital blockchain assets can essentially function as a unique code connected to a digital file, allowing to distinguish the original file from any potential copies. This application is especially prominent in crypto art, although there are other applications: gaming, sports, and collectibles are other segments where NFT sales occur.

Ethereum's price history suggests that that crypto was worth more in 2025 than during late 2021, although nowhere near the highest price recorded. Much like Bitcoin (BTC), the price of ETH went up in 2021 but for different reasons altogether: Ethereum, for instance, hit the news when a digital art piece was sold as the world's most expensive NFT for over 38,000 ETH - or 69.3 million U.S. dollars. Unlike Bitcoin, of which the price growth was fueled by the IPO of the U.S.'s biggest crypto trader, Coinbase, the rally on Ethereum came from technological developments that caused much excitement among traders. First, the so-called 'Berlin update' rolled out on the Ethereum network in April 2021, an update that would eventually lead to the Ethereum Merge in 2022 and reduced ETH gas prices - or reduced transaction fees. The collapse of FTX in late 2022, however, changed much for the cryptocurrency. As of July 30, 2025, Ethereum was worth 3,788.6 U.S. dollars - significantly less than the 4,400 U.S. dollars by the end of 2021.Ethereum's future and the DeFi industryPrice developments on Ethereum are difficult to predict but cannot be seen without the world of DeFi, or decentralized finance. This industry used technology to remove intermediaries between parties in a financial transaction. One example includes crypto wallets such as Coinbase Wallet that grew in popularity recently, with other examples including smart contractor Uniswap, Maker (responsible for stablecoin DAI), moneylender Dharma and market protocol Compound. Ethereum's future developments are tied with this industry: Unlike Bitcoin and Ripple, Ethereum is technically not a currency but an open-source software platform for blockchain applications, with Ether being the cryptocurrency that is used inside the Ethereum network. Essentially, Ethereum facilitates DeFi, meaning that if DeFi does well, so does Ethereum.NFTs: the most well-known application of EthereumNFTs or non-fungible tokens, grew nearly tenfold between 2018 and 2020, as can be seen in the market cap of NFTs worldwide. These digital blockchain assets can essentially function as a unique code connected to a digital file, allowing to distinguish the original file from any potential copies. This application is especially prominent in crypto art, although there are other applications: gaming, sports, and collectibles are other segments where NFT sales occur.

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)

Prices for ETHUSC Ether USD Coin including live quotes, historical charts and news. ETHUSC Ether USD Coin was last updated by Trading Economics this August 2 of 2025.

In April 2021, the Ethereum market cap reached new heights and grew to over *** billion U.S. dollars - the first time this cryptocurrency achieved that feat. The market capitalization in August 2020 was half this amount. Market capitalization is calculated by multiplying the total number of Ethereum in circulation by the Ethereum price. Compared to the Bitcoin market capitalization, however, Ethereum was not yet as popular.

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!

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.

Prices for ETHBTC Ethereum Bitcoin including live quotes, historical charts and news. ETHBTC Ethereum Bitcoin was last updated by Trading Economics this August 1 of 2025.

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

Уникальный идентификатор https://doi.org/10.5281/zenodo.4718440 Набор данных обновлен Dec 19, 2022 Набор данных предоставлен Zenodo Авторы Can Özturan; Can Özturan; Alper Şen; Alper Şen; Baran Kılıç; Baran Kılıç Лицензия Attribution 4.0 (CC BY 4.0) Информация о лицензии была получена автоматически Описание This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain. Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset. Details of the datasets are given below: FILENAME FORMAT: The filenames have the following format: eth-tx- where For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from block 1000000 to block 1099999 inclusive. The files are compressed with bzip2. They can be uncompressed using command bunzip2. TRANSACTION FORMAT: Each line in a file corresponds to a transaction. The transaction has the following format: units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 contract) are indicated by token symbol. For example GUSD is Gemini USD stable coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens. Failed transactions are prefixed with "F-". BLOCK TIME FORMAT: The block time file has the following format: erc20tokens.json FILE: This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files. ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC IMPORTANT NOTE: Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything. NOTE: If you use this dataset, please do not forget to add the DOI number to the citation. If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0 @article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

This dataset contains ether as well as popular ERC20 token transfer transactions extracted from the Ethereum Mainnet blockchain.

Only send ether, contract function call, contract deployment transactions are present in the dataset. Miner reward (static block reward) and "uncle block inclusion reward" are added as transactions to the dataset. Transaction fee reward and "uncles reward" are not currently included in the dataset.

Details of the datasets are given below:

FILENAME FORMAT:

The filenames have the following format:

eth-tx--.txt.bz2

where is the starting block number and final block number.

For example file eth-tx-1000000-1099999.txt.bz2 contains transactions from

block 1000000 to block 1099999 inclusive.

The files are compressed with bzip2. They can be uncompressed using command bunzip2.

TRANSACTION FORMAT:

Each line in a file corresponds to a transaction. The transaction has the following format:

is the abreviation for the asset. For example ETH means ether transfer in Wei 


          units. ERC20 tokens transfers (transfer and transferFrom function calls in ERC20 


          contract) are indicated by token symbol. For example GUSD is Gemini USD stable


          coin. The JSON file erc20tokens.json given below contains the details of ERC20 tokens.


          Failed transactions are prefixed with "F-".


  Number of the block which contains the transaction. 


    Position of the transaction in the block (i.e. transaction number in the block). Static block reward and uncle block inclusion reward has 0 as tx no. The position of transactions in the block is shifted by 1. The first transaction has 1 as tx no.

Source ethereum address of the transfer. For static block rewards, the from addr is ETHMAINBLOCK. For uncle block inclusion rewards, the from addr is ETHMAINUNCLE.

  Destination ethereum address of the transfer. The to addr is 0x0 for contract deployment transactions instead of null.


   Amount of transfer. The amount is given as hexadecimal a number. 

BLOCK TIME FORMAT:

The block time file has the following format:

  Number of the block. 


 Unix timestamp at which the block is mined as a hexadecimal number.

erc20tokens.json FILE:

This file contains the list of popular ERC20 token contracts whose transfer/transferFrom transactions appear in the data files.

ERC20 token list: USDT TRYb XAUt BNB LEO LINK HT HEDG MKR CRO VEN INO PAX INB SNX REP MOF ZRX SXP OKB XIN OMG SAI HOT DAI EURS HPT BUSD USDC SUSD HDG QCAD PLUS BTCB WBTC cWBTC renBTC sBTC imBTC pBTC

IMPORTANT NOTE:

Public Ethereum Mainnet blockchain data is open and can be obtained by connecting as a node on the blockchain or by using the block explorer web sites such as http://etherscan.io . The downloaders and users of this dataset accept the full responsibility of using the data in GDPR compliant manner or any other regulations. We provide the data as is and we cannot be held responsible for anything.

NOTE:

If you use this dataset, please do not forget to add the DOI number to the citation.

If you use our dataset in your research, please also cite our paper: https://link.springer.com/article/10.1007/s10586-021-03511-0

@article{kilic2022parallel, title={Parallel Analysis of Ethereum Blockchain Transaction Data using Cluster Computing}, journal={Cluster Computing}, author={K{\i}l{\i}{\c{c}}, Baran and {"O}zturan, Can and Sen, Alper}, year={2022}, month={Jan} }

Ethereum trading volume more than doubled on May 19, 2021, compared to the previous day, following news that China banned crypto services inside the country. Following this news, the market cap for the entire crypto market fell by billions of U.S. dollars. The Ethereum price that day also witnessed one of its biggest single-day declines on record. The price decline potentially may have attracted new investors to the cryptocurrency, leading to the increase in trading volume.

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

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

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.

S&P Ethereum Index: The Future of Crypto?

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

By March 2022, over *** million Ethereum tokens were issued and in active circulation - but it is expected new coins will not arrive at a fast pace. Although the cryptocurrency has an unlimited supply - unlike Bitcoin, of which there can only be ** million tokens and not a single more - the Ethereum blockchain received an update in August 2021, EIP-1559, that both increased the block size needed to create new coins and destroyed (“burned”) any transactions fees, rather than send them to the original miners. This led to a decline in issuance, as mining Ethereum essentially was made less profitable. Issuance is expected to decline further when Ethereum *** arrives.

Ethereum: a counter to inflation?

In a time when inflation rates became a big talking point, Ethereum received much social media attention in late 2021 for possibly being deflationary. This argument stems from August 2021, or “London Hard Fork”, upgrade in August 2021: Each transaction on the Ethereum network would entirely remove a portion of Ethereum from the total supply in circulation. On days of high transaction activity of Ethereum, for example, after a change in the price of Ethereum, this can effectively mean more coins are being destroyed than there are being created.

Ethereum supply to change after the upgrade to ***?

Experts state burning on a scale that the supply of Ethereum declines only happens on occasion, stating it acts more as a temporary slowdown of growth rather than an active attempt to continuously shrink supply. This could change, however, when Ethereum *** arrives – or when Ethereum switches from Proof-of-Work (PoW) to Proof-of-Stake (PoS). The general assumption for this is that staking rewards are generally lower than rewards for Proof-of-Work (mining), lowering the incentive for the creation of new coins. If usage – which some measure via the Ethereum gas price, or transaction fee per transaction – remains unchanged otherwise, this would lower the threshold for Ethereum to become deflationary.

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-EUR 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!

The GRID3 ETH - Settlement Extents v3.0 consists of a geographic representation of settlements in Ethiopia, in two forms: (1) settlement polygons, and (2) spatial points depicting the centroids of settled grid cells at 3-arc seconds (or ~100 meters) contained within settlement polygons.

Both layers include attributes as described in the codebooks.

The current version supersedes GRID3 Ethiopia Settlement Extents, Version 02; the following changes were made: (1) Development of a new methodology to derive building counts and settlement polygons with open data. (2) Estimation of a probability value for settlement polygons (3) Addition of a building-area measure (4) Replacement of degree of urbanization with classification based on built-up areas, small settlement areas, and hamlets (5) Elimination of building-count ranges (6) Elimination of variable comparing to previous versions

Recommended Citation: Center for International Earth Science Information Network (CIESIN), Columbia University. 2024. GRID3 ETH - Settlement Extents v3.0. New York: GRID3. [URL]. Accessed [DAY MONTH YEAR].

3MEth Dataset OverviewSection 1: Token TransactionsThis section provides 303 million transaction records from 3,880 tokens and 35 million users on the Ethereum blockchain. The data is stored in 3,880 CSV files, each representing a specific token. Each transaction includes the following information:Sender and receiver wallet addresses: Enables network analysis and user behavior studies.Token address: Links transactions to specific tokens for token-specific analysis.Transaction value: Reflects the number of tokens transferred, essential for liquidity studies.Blockchain timestamp: Captures transaction timing for temporal analysis.Apart from the large dataset, we also provide a smaller CSV file containing 267,242 transaction records from 29,164 wallet addresses. This smaller dataset involves a total of 1,194 tokens, covering the time period September 2016 to November 2023. This detailed transaction data is critical for studying user behavior, liquidity patterns, and tasks such as link prediction and fraud detection.Section 2: Token InformationThis section offers metadata for 3,880 tokens, stored in corresponding CSV files. Each file contains:Timestamp: Marks the time of data update.Token price: Useful for price prediction and volatility studies.Market capitalization: Reflects the token's market size and dominance.24-hour trading volume: Indicates liquidity and trading activity.Section 3: Global Market IndicesThis section provides macro-level data to contextualize token transactions, stored in separate CSV files. Key indicators include:Bitcoin dominance: Tracks Bitcoin's share of the cryptocurrency market.Total market capitalization: Measures the overall market's value, with breakdowns by token type.Stablecoin market capitalization: Highlights stablecoin liquidity and stability.24-hour trading volume: A key measure of market activity.These indices are essential for integrating global market trends into predictive models for volatility and risk-adjusted returns.Section 4: Textual IndicesThis section contains sentiment data from Reddit's Ethereum community, covering 7,800 top posts from 2014 to 2024. Each post includes:Post score (net upvotes): Reflects engagement and sentiment strength.Timestamp: Aligns sentiment with price movements.Number of comments: Gauges sentiment intensity.Sentiment indices: Sentiment scores computed using methods detailed in the data preprocessing section.The full Reddit textual dataset is available upon request; please contact us for access. Alternatively our open-source repository includes a tool to guide users in collecting Reddit data. Researchers are encouraged to apply for a Reddit API Key and adhere to Reddit's policies. This data is valuable for understanding social dynamics in the market and enhancing sentiment analysis models that can explain market movements and improve behavioral predictions.

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.

Will the S&P Ethereum Index Lead the Crypto Market?

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