Large Bitcoin accounts have expanded their BTC holdings in the last few weeks amid market  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. According to the latest data posted by the 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, Bitcoin whale addresses holding between 100 and 10,000 coins now own approximately 9.13 million coins, which is nearly 49% of the total supply.

Despite the latest accumulation from the leading Bitcoin accounts, the overall activity across the BTC network has remained slow since May 2021. According to Glassnode, the total number of active BTC addresses dropped sharply in June 2021.

In addition, the Bitcoin exchange supply ratio has dipped substantially in the last month as whale BTC accounts started moving their coins from digital exchanges to crypto wallets.

“Our heavily touted price-leading Bitcoin whale holder tier have continued their accumulation in July. BTC addresses holding between 100 to 10,000 BTC now own 9.13M coins once again, their highest mark since April 14th. This is 48.7% of the total supply,” Santiment mentioned.

In the last week of June 2021, a leading Bitcoin wallet moved 6,713 BTC from a digital wallet to Xapo. Moreover, BTC outflow from the crypto exchange, Coinbase increased during the first week of July 2021 when two crypto wallets moved a total of 7,062 BTC from Coinbase to digital wallets.

Bitcoin’s Token Circulation

In addition to the latest drop in the total number of active Bitcoin addresses, BTC’s token circulation in the past 30 days has reached its lowest level since October 2015. “As Bitcoin's movement has continued to become more dormant while traders wait for the next major move in either direction, our 30-day tracking of unique token circulation has dropped to levels not seen since October 2015,” Santiment added.

The world’s largest cryptocurrency saw immense selling pressure in the last 24 hours as BTC dropped below $33,000. As of writing, Bitcoin is trading near $33,200 with a market cap of over $620 billion. BTC’s market dominance currently stands at around 44.3%, which is down by approximately 2% within the last 7 days.