Ethereum's price history suggests that that crypto was worth significantly less in 2022 than during late 2021, although nowhere near the lowest 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.’ 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 which 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 May 4, 2025, Ethereum was worth 1,808.59 U.S. dollars - significantly less than the 4,400 U.S. dollars by the end of 2021. Ethereum’s future and the DeFi industry Price 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 Ethereum NFTs or non-fungible tokens grew nearly ten-fold 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.

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.

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.

This dataset is about stocks per day. It has 151 rows and is filtered where the stock is ETH and the date is after the 25th of September 2024. It features 3 columns: stock, and highest price.

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 ---

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!

This dataset contains historical price data for the top global cryptocurrencies, sourced from Yahoo Finance. The data spans the following time frames for each cryptocurrency:

BTC-USD (Bitcoin): From 2014 to December 2024 ETH-USD (Ethereum): From 2017 to December 2024 XRP-USD (Ripple): From 2017 to December 2024 USDT-USD (Tether): From 2017 to December 2024 SOL-USD (Solana): From 2020 to December 2024 BNB-USD (Binance Coin): From 2017 to December 2024 DOGE-USD (Dogecoin): From 2017 to December 2024 USDC-USD (USD Coin): From 2018 to December 2024 ADA-USD (Cardano): From 2017 to December 2024 STETH-USD (Staked Ethereum): From 2020 to December 2024

Key Features:

Date: The date of the record. Open: The opening price of the cryptocurrency on that day. High: The highest price during the day. Low: The lowest price during the day. Close: The closing price of the cryptocurrency on that day. Adj Close: The adjusted closing price, factoring in stock splits or dividends (for stablecoins like USDT and USDC, this value should be the same as the closing price). Volume: The trading volume for that day.

Data Source:

The dataset is sourced from Yahoo Finance and spans daily data from 2014 to December 2024, offering a rich set of data points for cryptocurrency analysis.

Use Cases:

Market Analysis: Analyze price trends and historical market behavior of leading cryptocurrencies. Price Prediction: Use the data to build predictive models, such as time-series forecasting for future price movements. Backtesting: Test trading strategies and financial models on historical data. Volatility Analysis: Assess the volatility of top cryptocurrencies to gauge market risk. Overview of the Cryptocurrencies in the Dataset: Bitcoin (BTC): The pioneer cryptocurrency, often referred to as digital gold and used as a store of value. Ethereum (ETH): A decentralized platform for building smart contracts and decentralized applications (DApps). Ripple (XRP): A payment protocol focused on enabling fast and low-cost international transfers. Tether (USDT): A popular stablecoin pegged to the US Dollar, providing price stability for trading and transactions. Solana (SOL): A high-speed blockchain known for low transaction fees and scalability, often seen as a competitor to Ethereum. Binance Coin (BNB): The native token of Binance, the world's largest cryptocurrency exchange, used for various purposes within the Binance ecosystem. Dogecoin (DOGE): Initially a meme-inspired coin, Dogecoin has gained a strong community and mainstream popularity. USD Coin (USDC): A fully-backed stablecoin pegged to the US Dollar, commonly used in decentralized finance (DeFi) applications. Cardano (ADA): A proof-of-stake blockchain focused on scalability, sustainability, and security. Staked Ethereum (STETH): A token representing Ethereum staked in the Ethereum 2.0 network, earning staking rewards.

This dataset provides a comprehensive overview of key cryptocurrencies that have shaped and continue to influence the digital asset market. Whether you're conducting research, building prediction models, or analyzing trends, this dataset is an essential resource for understanding the evolution of cryptocurrencies from 2014 to December 2024.

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

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.

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

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)

The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect, or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.

Bitcoin ranked as one of the most expensive cryptocurrencies existing by April 2024 - although values changed noticeably. Bitcoin had the most expensive cryptocurrency for a while, but Ethereum was significantly cheaper, with a price that was roughly 30 times less than that of the most well-known digital currency. However, Bitcoin is in a unique position. Ethereum is one of several cryptocurrencies, for instance, that come from blockchains that focus on making financial applications possible. Bitcoin, or a digital equivalent of gold When one categorizes the different types of cryptocurrencies, Bitcoin stands out as it is one of the few that are essentially meant to store digital value. Some describe Bitcoin as a digital version of gold, purely designed to hold or possibly purchasing power over time. It has no other applications built around it, and is considered too slow to perform financial transactions. Stablecoins, the less volatile cryptocurrency Many coins in this ranking stand out as their price seemingly has not changed as much as others. This is because these are stablecoins - cryptocurrencies pegged to the price development of an external asset. This group of digital assets comprise an increasing share within the overall crypto market. Some see these coins as the future of retail payments, whereas others view these coins as a "safe" addition to their crypto investments.

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 dataset used in this research is a historical record of Bitcoin, Ethereum, and Litecoin’s daily trading activity, containing essential financial metrics for each date. This sample includes the following columns: Date: The specific day of each recorded entry, showing a continuous timeline. Open: The price of currencies at the start of the trading day. High: The highest price of currencies reached during the day. Low: The lowest price of currencies traded throughout the day. Close: The closing price of the currencies at the end of the trading day. Volume: The total trading volume, indicating the number of currencies traded that day in units. Market Cap: The total market capitalization of currencies, calculated as the total supply multiplied by the closing price.

This dataset provides daily historical data for 10 major cryptocurrencies. Each row represents a single trading day, covering the maximum range that was available at the time of extraction.

Key Features

Closing Price and Volume: For each cryptocurrency, two columns are provided:

xxx_closing_price – The daily closing price in USD

xxx_volume – The daily trading volume

Date Format: Each date is listed in “dd/mm/yy” format for easy reading.

Top 10 Cryptocurrencies: Includes well-known coins such as Bitcoin, Ethereum, and others with high market capitalization.

• Potential Uses

1.Exploratory data analysis or visualizations of crypto market trends

2.Time-series modeling, forecasting, or anomaly detection

3.Comparative studies between multiple cryptocurrencies

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!

Content

These are historical datasets of the current top 10 most popular cryptocurrencies. As of now: 1. Bitcoin 2. Ethereum 3. Binance Coin 4. Tether 5. Solana 6. Cardano 7. USD Coin 8. XRP 9. Polkadot 10. Terra

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

Acknowledgements

Found all the historical data from website: https://coinmarketcap.com/

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

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