  Algorithmic trading is well studied in traditional financial markets.
However, it has received less attention in centralized cryptocurrency
exchanges. The Commodity Futures Trading Commission (CFTC) attributed the
$2010$ flash crash, one of the most turbulent periods in the history of
financial markets that saw the Dow Jones Industrial Average lose $9\%$ of its
value within minutes, to automated order "spoofing" algorithms. In this paper,
we build a set of methodologies to characterize and empirically measure
different algorithmic trading strategies in Binance, a large centralized
cryptocurrency exchange, using a complete data set of historical trades. We
find that a sub-strategy of triangular arbitrage is widespread, where bots
convert between two coins through an intermediary coin, and obtain a favorable
exchange rate compared to the direct one. We measure the profitability of this
strategy, characterize its risks, and outline two strategies that algorithmic
trading bots use to mitigate their losses. We find that this strategy yields an
exchange ratio that is $0.144\%$, or $14.4$ basis points (bps) better than the
direct exchange ratio. $2.71\%$ of all trades on Binance are attributable to
this strategy.
