Bitcoin (BTC), the world’s most valuable digital asset, is currently struggling around the price level of $33,000 amid negative market sentiment. BTC has lost nearly 5% of its value since 7 July 2021.

According to the latest data published by Coinmarketcap, the overall market cap of digital currencies dropped by approximately $50 billion in the last 24 hours led by a correction in Bitcoin, Ethereum, Cardano, and Binance Coin. Ethereum remained the worst performer among the top 5 digital currencies with a drop of more than 5%.

Bitcoin’s total market cap dipped below $620 billion on 13 July 2021. The world’s largest cryptocurrency now has a market dominance of 45.6%. The price of BTC has remained below $40,000 since 16 June. The digital asset is currently trading near $32,500.

“Bitcoin’s average return for 6-month investors is sitting at a very low -27.81%. When traders are this underwater, FUD typically arises in the form of negative-driven posts,” 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 platform Santiment mentioned on Twitter.

Quiet Week for Bitcoin

Last week remained very quiet for Bitcoin as the  Volatility  Volatility In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. In finance, volatility refers to the amount of change in the rate of a financial instrument, such as commodities, currencies, stocks, over a given time period. Essentially, volatility describes the nature of an instrument’s fluctuation; a highly volatile security equates to large fluctuations in price, and a low volatile security equates to timid fluctuations in price. Volatility is an important statistical indicator used by financial traders to assist them in developing trading systems. Traders can be successful in both low and high volatile environments, but the strategies employed are often different depending upon volatility. Why Too Much Volatility is a ProblemIn the FX space, lower volatile currency pairs offer less surprises, and are suited to position traders.High volatile pairs are attractive for many day traders, due to quick and strong movements, offering the potential for higher profits, although the risk associated with such volatile pairs are many. Overall, a look at previous volatility tells us how likely price will fluctuate in the future, although it has nothing to do with direction.All a trader can gather from this is the understanding that the probability of a volatile pair to increase or decrease an X amount in a Y period of time, is more than the probability of a non-volatile pair. Another important factor is, volatility can and does change over time, and there can be periods when even highly volatile instruments show signs of flatness, with price not really making headway in either direction. Too little volatility is just as problematic for markets as too much, we uncertainty in excess can create panic and problems of liquidity. This was evident during Black Swan events or other crisis that have historically roiled currency and equity markets. Read this Term of BTC decreased significantly. BTC’s on-chain activity also dropped sharply in the last few days. “It has been an impressively quiet week in the Bitcoin market as volatility continues to seep out, and prices squeeze into a tight consolidation range. The week opened at a high of $35,128 and traded down to a low of $32,227. It is starting to feel like the calm before the storm as muted and quiet activity appears across both spot, derivative and on-chain metrics,” on-chain analysis firm Glassnode mentioned in its latest weekly report.

Last week, Meitu, one of the leading technology firms in Asia, revealed that the fair value of the company’s Bitcoin holdings decreased by more than $17 million due to the latest correction in the price of BTC. The company purchased nearly $100 million worth of Bitcoin and Ethereum (ETH) in March 2021.