This dataset contains the predicted prices of the asset Ethereum Volatility Index Token over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

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. D1: set of all Ethereum blocks between height 0 and 14999999. 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 Description filters_ones_0-14999999.csv.xz Compressed CSV file containing the number of ones for each logsBloom filter in D1. receipt_stats_0-14999999.csv.xz Compressed CSV file containing statistics about all transaction receipts in D1. Approval.csv CSV file containing the Approval event occurrences for the CryptoKitties Core contract in D2. Birth.csv CSV file containing the Birth event occurrences for the CryptoKitties Core contract in D2. Pregnant.csv CSV file containing the Pregnant event occurrences for the CryptoKitties Core contract in D2. Transfer.csv CSV file containing the Transfer event occurrences for the CryptoKitties Core contract in D2. events.xz Compressed binary file containing information about all contract events in D2. keys.xz Compressed binary file containing information about all keys in D2. 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. blockId: the identifier of the block. timestamp: timestamp of the block. 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. blockId: the identifier of the block. txCount: number of transactions included in the block. numLogs: number of event logs included in the block. numKeys: number of keys included in the block. 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: blockId: identifier of the block. 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. blockId: identifier of the block (4 bytes). numEvents: number of event occurrences in the block (4 bytes). 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: Address of the contract triggering the event (20 bytes). 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. blockId: identifier of the block (4 bytes) numAddr: number of unique contract addresses (4 bytes). numTopics: number of unique topics (4 bytes). A sequence of numAddr addresses, each represented using 20 bytes. 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. References Loporchio, Matteo et al. "Skip index: supporting efficient inter-block queries and query authentication on the blockchain". (2023). Bloom, Burton H. "Space/time trade-offs in hash coding with allowable errors." Communications of the ACM 13.7 (1970): 422-426. Wood, Gavin. "Ethereum: A secure decentralised generalised transaction ledger." Ethereum project yellow paper 151.2014 (2014): 1-32.

This dataset contains the predicted prices of the asset Inverse Ethereum Volatility Index Token over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

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 November 13, 2025, Ethereum was worth 3,409.61 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.

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

All-time high price data for Ethereum, including the peak value, date achieved, and current comparison metrics.

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

Ethereum price data for 2025-11-19 including currency, value, high, low, open, close, and percentage difference.

Prices for ETHUSD Ether / US Dollar including live quotes, historical charts and news. ETHUSD Ether / US Dollar was last updated by Trading Economics this December 2 of 2025.

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.

Blockchain data dashboard: Ethereum Main - Overall Stats

Index Time Series for VanEck Ethereum ETN A EUR. The frequency of the observation is daily. Moving average series are also typically included. NA

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 anticipates continued volatility, potential for future gains.

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

This dataset contains the predicted prices of the asset Index Coop CoinDesk ETH Trend Index over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

It was only a decade ago when Ethereum was dismissed as just another altcoin. It's clear that Ethereum has become the backbone of the decentralized internet. From NFT marketplaces to DeFi ecosystems, Ethereum powers the infrastructure of Web3 innovation. In this article, we unpack the key Ethereum statistics, digging into...

Blockchain data query: Index Adjusted OHM Price vs ETH vs BTC

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

📊 Overview

This dataset contains all deployed Ethereum smart contracts from block 0 up to block 22,298,844, collected directly from the blockchain.

Each entry includes:

• 🧠 contract_address – The deployed smart contract’s address
• 📦 block_number – The block in which it was deployed
• ⏱️ block_timestamp – The timestamp of the block

This dataset is fully unfiltered — it includes every smart contract, regardless of balance, verification, or type.

🎯 Purpose

This release is meant to support:

• 📚 Researchers and developers
• 🤖 AI & ML training datasets
• 📦 Blockchain indexing and analytics
• 🧪 Exploratory data analysis

🛠️ What’s Next?

I’m currently working on enriching this dataset with:

• ✅ Verification status
• 💰 Live ETH balance
• 🧬 Bytecode & ABI
• 🔍 Contract type (ERC20, ERC721, Proxy…)
• 🔑 Function selectors
• 🧾 Verified source code for each contract

Stay tuned for the next phase — a fully indexed smart contract database with powerful insights for devs, researchers, and analysts.

🪪 License

Released under Creative Commons CC BY-SA 4.0
Free to use, share, and build upon — with attribution.

📩 Questions? Ideas? Feel free to reach out via Kaggle or X.

View daily updates and historical trends for Ethereum Average Gas Price. Source: Etherscan. Track economic data with YCharts analytics.

The graph shows the changes in the h-index of ^ and its corresponding percentile for the sake of comparison with the entire literature. H-index is a common scientometric index, which is equal to h if the journal has published at least h papers having at least h citations.

This is not the historical change in the h-index. Instead, it calculates the h-index by articles before a given year, not the citations before that year.