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.

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.’ 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 June 30, 2025, Ethereum was worth 2,470.31 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.

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

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.

Liquid Staked ETH Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa Liquid Staked ETH sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng Liquid Staked ETH, tumaas ang halaga nito sa hindi pa naganap na peak sa 2024-03-11, na lumampas sa $4,281.72 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni Liquid Staked ETH, na karaniwang tinutukoy bilang "Liquid Staked ETH all-time low", ay naganap noong 2023-03-10. Kung ang isa ay bumili ng Liquid Staked ETH sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na 97%. Sa pamamagitan ng disenyo, ang 116,191.98 Liquid Staked ETH ay malilikha. Sa ngayon, ang circulating supply ng Liquid Staked ETH ay tinatayang 116,191.98. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang Liquid Staked ETH dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

EGG ETH Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa EGG ETH sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng EGG ETH, tumaas ang halaga nito sa hindi pa naganap na peak sa 2024-03-10, na lumampas sa $0.0002391 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni EGG ETH, na karaniwang tinutukoy bilang "EGG ETH all-time low", ay naganap noong 2024-10-09. Kung ang isa ay bumili ng EGG ETH sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na -100%. Sa pamamagitan ng disenyo, ang 6B EGG ETH ay malilikha. Sa ngayon, ang circulating supply ng EGG ETH ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang EGG ETH dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

f(x) Protocol Leveraged ETH Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa f(x) Protocol Leveraged ETH sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng f(x) Protocol Leveraged ETH, tumaas ang halaga nito sa hindi pa naganap na peak sa 2024-03-13, na lumampas sa $3.29 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni f(x) Protocol Leveraged ETH, na karaniwang tinutukoy bilang "f(x) Protocol Leveraged ETH all-time low", ay naganap noong 2023-12-18. Kung ang isa ay bumili ng f(x) Protocol Leveraged ETH sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na -100%. Sa pamamagitan ng disenyo, ang 3,864,706.5 f(x) Protocol Leveraged ETH ay malilikha. Sa ngayon, ang circulating supply ng f(x) Protocol Leveraged ETH ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang f(x) Protocol Leveraged ETH dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

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.

The CAPIVIX Index gives crypto traders something traditional markets have long relied on - a clear measure of expected market volatility. Think of it as the VIX for Bitcoin and Ethereum, showing what the market anticipates for price swings over the next 30 days.

This crypto volatility index tracks market sentiment for BTC/USD and ETH/USD pairs by analyzing options data from major derivatives exchanges. When CAPIVIX rises, it signals increased uncertainty and potential turbulence ahead. When it falls, markets are expecting calmer conditions.

What makes CAPIVIX valuable is its methodology - we've adapted the widely-trusted VIX calculation approach to work specifically with cryptocurrency options. This gives you a standardized way to gauge market anxiety or confidence across different market conditions.

The index updates continuously throughout trading hours, incorporating real-time options pricing to reflect the market's evolving risk perception. For traders and investors looking to understand market sentiment beyond price movements alone, CAPIVIX provides that crucial additional dimension of market intelligence.

➡️ Why choose us?

📊 Market Coverage & Data Types: ◦ Real-time and historical data since 2010 (for chosen assets) ◦ Full order book depth (L2/L3) ◦ Trade-by-trade data ◦ OHLCV across multiple timeframes ◦ Market indexes (VWAP, PRIMKT) ◦ Exchange rates with fiat pairs ◦ Spot, futures, options, and perpetual contracts ◦ Coverage of 90%+ global trading volume ◦ Bitcoin Price Data

🔧 Technical Excellence: ◦ 99% uptime guarantee ◦ Multiple delivery methods: REST, WebSocket, FIX, S3 ◦ Standardized data format across exchanges ◦ Ultra-low latency data streaming ◦ Detailed documentation ◦ Custom integration assistance

Whether you're hedging positions, timing entries and exits, or just wanting to better understand market psychology, our Bitcoin and Ethereum volatility data offers valuable insights into what the market collectively expects in the weeks ahead.

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!

ETH Shiba Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa ETH Shiba sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng ETH Shiba, tumaas ang halaga nito sa hindi pa naganap na peak sa 2025-04-26, na lumampas sa -- USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni ETH Shiba, na karaniwang tinutukoy bilang "ETH Shiba all-time low", ay naganap noong 2025-04-26. Kung ang isa ay bumili ng ETH Shiba sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na 0%. Sa pamamagitan ng disenyo, ang 1,000T ETH Shiba ay malilikha. Sa ngayon, ang circulating supply ng ETH Shiba ay tinatayang 0. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang ETH Shiba dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

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 Reshape the Cryptocurrency Landscape?

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.

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

Frax Staked Ether Ang pagsubaybay sa kasaysayan ng presyo ay nagbibigay-daan sa mga crypto investor na madaling masubaybayan ang performance ng kanilang pamumuhunan. Maginhawa mong masusubaybayan ang opening value, high, at close sa Frax Staked Ether sa paglipas ng panahon, pati na rin ang trade volume. Bukod pa rito, maaari mong agad na tingnan ang pang-araw-araw na pagbabago bilang isang porsyento, na ginagawang effortless na tukuyin ang mga araw na may significant fluctuations. Ayon sa aming data ng history ng presyo ng Frax Staked Ether, tumaas ang halaga nito sa hindi pa naganap na peak sa 2023-04-19, na lumampas sa $4,362.39 USD. Sa kabilang banda, ang pinakamababang punto sa trajectory ng presyo ni Frax Staked Ether, na karaniwang tinutukoy bilang "Frax Staked Ether all-time low", ay naganap noong 2023-01-09. Kung ang isa ay bumili ng Frax Staked Ether sa panahong iyon, kasalukuyan silang masisiyahan sa isang kahanga-hangang kita na 91%. Sa pamamagitan ng disenyo, ang 90,264.39 Frax Staked Ether ay malilikha. Sa ngayon, ang circulating supply ng Frax Staked Ether ay tinatayang 90,264.39. Ang lahat ng mga presyong nakalista sa pahinang ito ay nakuha mula sa Bitget, galing sa isang reliable source. Napakahalagang umasa sa iisang pinagmulan upang suriin ang iyong mga investment, dahil maaaring mag-iba ang mga halaga sa iba't ibang nagbebenta. Kasama sa aming makasaysayang Frax Staked Ether dataset ng presyo ang data sa pagitan ng 1 minuto, 1 araw, 1 linggo, at 1 buwan (bukas/mataas/mababa/close/volume). Ang mga dataset na ito ay sumailalim sa mahigpit na pagsubok upang matiyak ang consistency, pagkakumpleto, at accurancy. Ang mga ito ay partikular na idinisenyo para sa trade simulation at mga layunin ng backtesting, madaling magagamit para sa libreng pag-download, at na-update sa real-time.

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 NANO-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!

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: A Glimpse into 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.

S&P Ethereum Index: Harbinger of Crypto Adoption?

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

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 AAVE-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!

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

Ripple - or XRP - prices surged in 2021, but went down significantly as 2022 progressed. As of June 30, 2025, one XRP token was worth 2.19 U.S. dollars. Ethereum's price, for example, kept on reaching new all-time highs, a feat not performed by XRP. Indeed, XRP's more price spikes followed relatively late - only occurring in early 2021, against late 2020 for most other cryptos - after the US SEC filed a legal complaint against Ripple in November 2020. This legal action caused the XRP price to plummet from around 0.70 U.S. dollars to 0.20 U.S. dollars. Ripple versus XRP: two become one Technically speaking, Ripple is not a cryptocurrency. Renamed from a protocol called OpenCoin in 2013, Ripple facilitates open-source payments. XRP, on the other hand, is the cryptocurrency that runs on this network. In that sense, Ripple and XRP have a similar symbiosis to each other like the Ethereum network and its cryptocurrency Ether. Unlike Ethereum - whose price changes are connected to the world of Decentralized Finance or DeFI - Ripple/XRP mostly looks at developments in cross-border payments for companies. In 2020, companies worldwide began to favor fintech solutions for future B2B solutions and, in a way, Ripple is an extension of that. What affects the price of Ripple? Ripple is mostly active in Southeast Asia - a region with a splintered payment landscape and that heavily investigates its own types of state-issued cryptocurrency to make cross-border payments a lot easier. Price spikes tend to follow news on this topic in this specific region. In 2019, for example, the XRP price grew after Japan and South Korea began testing to reduce time and costs for transferring international funds between the two countries. In March 2021, Ripple announced that it had agreed to acquire 40 percent of Malaysian cross-border payments firm Tranglo to meet growing demand in Southeast Asia.