Ethereum Crosses Market Cap of PayPal
- ETH registered an all-time high of approximately $2,750 on 29 April.

According to the latest data published by Coinmarketcap, Ethereum is up by more than 11% in the last 7 days. The cryptocurrency jumped approximately 3% in the last 24 hours. The latest surge in the price of ETH came after a massive jump in retail and institutional demand for the cryptocurrency.
The overall market cap of PayPal Holdings currently stands at around $318 billion, which means that Ethereum has crossed the total market cap of PayPal. Binance Coin (BNB) and ETH have surged significantly during the latest $200 billion crypto market recovery.
“Ethereum’s dormant tokens are moving rapidly to justify yet another All-Time High above $2,750 today. With many new ETH addresses being made and dormant tokens cycling rapidly, this is the youngest average investment we've seen since July 2018,” crypto Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term firm, Santiment mentioned on Twitter.
Last week, Ethereum 2.0 deposit contract reached $9 billion in staked ETH. According to the latest data published by Etherscan, the deposit contract of ETH’s network upgrade now has more than 4 million ETH with a total value of approximately $11 billion.
Ethereum’s Institutional Inflows
CoinShares, one of the leading digital asset managers, recently published its weekly digital asset fund inflows report and highlighted significant growth in ETH-related institutional inflows. Ethereum investment products attracted $34 million last week as the total inflows into ETH investment products reached $792 million since the start of this year. The world’s second-most valuable digital currency is getting popular among institutional investors as several organizations around the world have started adding Ethereum to their balance sheets.
As of writing, Ethereum is trading near $2,710 with a total market cap of $315 billion. The total market dominance of ETH has reached 15.1%, which is its highest level in more than three years.
According to the latest data published by Coinmarketcap, Ethereum is up by more than 11% in the last 7 days. The cryptocurrency jumped approximately 3% in the last 24 hours. The latest surge in the price of ETH came after a massive jump in retail and institutional demand for the cryptocurrency.
The overall market cap of PayPal Holdings currently stands at around $318 billion, which means that Ethereum has crossed the total market cap of PayPal. Binance Coin (BNB) and ETH have surged significantly during the latest $200 billion crypto market recovery.
“Ethereum’s dormant tokens are moving rapidly to justify yet another All-Time High above $2,750 today. With many new ETH addresses being made and dormant tokens cycling rapidly, this is the youngest average investment we've seen since July 2018,” crypto Analytics Analytics Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Analytics may be defined as the detection, analysis, and relay of consequential patterns in data. Analytics also seeks to explain or accurately reflect the relationship between data and effective decision making. In the trading space, analytics are applied in a predictive manner in an attempt to more accurately forecast the price. This predictive model of analytics generally involves the analysis of historical price patterns that are used in an attempt to determine certain price outcomes. Analytics may also be structured with a descriptive model, where readers attempt to draw a correlation and better understanding as to how and why traders react to a particular set of variables. Traders sometimes implement technical indicators such as moving averages, Bollinger Bands, and breakpoints which are built upon historical data and are used to predict future price movements. How Analytics Relates to Algo TradingAnalytics are relied upon in the concept of algorithmic trading where software is programmed to autonomously signal and/or execute buy and sell orders based upon a series of predetermined factors. In the institutional space, Algo-trading has become vastly competitive over the years as trading institutions seek to outperform competitors through automated systems and the virtual application of trading strategies.The digestion and computation of analytics are also seen in the emerging field of high-frequency trading, where supercomputers are used to analyze multiple markets simultaneously to make near-instantaneous automated trading decisions. Platforms that support HFT have the capability to significantly outperform human traders.This is due to the innate ability to be able to comprehensively analyze big data sets while taking under do consideration an innumerable sum of factors that humans are incapable of comprehending in such speed. Additionally, analytics are seen with backtesting. Backtesting is used by traders to test the consistency and effectiveness of trading strategies and software-based trading solutions against historical price data. Backtesting also serves as an ideal playground for the further development of high-frequency trading as well as evaluating the performance of manual or automated trades. Analytics will continue to have an increasingly significant role in trading as emerging technologies and the advancement of trading applications progress beyond human capability. Read this Term firm, Santiment mentioned on Twitter.
Last week, Ethereum 2.0 deposit contract reached $9 billion in staked ETH. According to the latest data published by Etherscan, the deposit contract of ETH’s network upgrade now has more than 4 million ETH with a total value of approximately $11 billion.
Ethereum’s Institutional Inflows
CoinShares, one of the leading digital asset managers, recently published its weekly digital asset fund inflows report and highlighted significant growth in ETH-related institutional inflows. Ethereum investment products attracted $34 million last week as the total inflows into ETH investment products reached $792 million since the start of this year. The world’s second-most valuable digital currency is getting popular among institutional investors as several organizations around the world have started adding Ethereum to their balance sheets.
As of writing, Ethereum is trading near $2,710 with a total market cap of $315 billion. The total market dominance of ETH has reached 15.1%, which is its highest level in more than three years.