  This paper investigates the temporal patterns of activity in the
cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink
from January 2020 to December 2022. Market activity measures - logarithmic
returns, volume, and transaction number, sampled every 10 seconds, were divided
into intraday and intraweek periods and then further decomposed into recurring
and noise components via correlation matrix formalism. The key findings include
the distinctive market behavior from traditional stock markets due to the
nonexistence of trade opening and closing. This was manifest in three
enhanced-activity phases aligning with Asian, European, and U.S. trading
sessions. An intriguing pattern of activity surge in 15-minute intervals,
particularly at full hours, was also noticed, implying the potential role of
algorithmic trading. Most notably, recurring bursts of activity in bitcoin and
ether were identified to coincide with the release times of significant U.S.
macroeconomic reports such as Nonfarm payrolls, Consumer Price Index data, and
Federal Reserve statements. The most correlated daily patterns of activity
occurred in 2022, possibly reflecting the documented correlations with U.S.
stock indices in the same period. Factors that are external to the inner market
dynamics are found to be responsible for the repeatable components of the
market dynamics, while the internal factors appear to be substantially random,
which manifests itself in a good agreement between the empirical eigenvalue
distributions in their bulk and the random matrix theory predictions expressed
by the Marchenko-Pastur distribution. The findings reported support the growing
integration of cryptocurrencies into the global financial markets.
