  Cyclic arbitrage chances exist abundantly among decentralized exchanges
(DEXs), like Uniswap V2. For an arbitrage cycle (loop), researchers or
practitioners usually choose a specific token, such as Ether as input, and
optimize their input amount to get the net maximal amount of the specific token
as arbitrage profit. By considering the tokens' prices from CEXs in this paper,
the new arbitrage profit, called monetized arbitrage profit, will be quantified
as the product of the net number of a specific token we got from the arbitrage
loop and its corresponding price in CEXs. Based on this concept, we put forward
three different strategies to maximize the monetized arbitrage profit for each
arbitrage loop. The first strategy is called the MaxPrice strategy. Under this
strategy, arbitrageurs start arbitrage only from the token with the highest CEX
price. The second strategy is called the MaxMax strategy. Under this strategy,
we calculate the monetized arbitrage profit for each token as input in turn in
the arbitrage loop. Then, we pick up the most maximal monetized arbitrage
profit among them as the monetized arbitrage profit of the MaxMax strategy. The
third one is called the Convex Optimization strategy. By mapping the MaxMax
strategy to a convex optimization problem, we proved that the Convex
Optimization strategy could get more profit in theory than the MaxMax strategy,
which is proved again in a given example. We also proved that if no arbitrage
profit exists according to the MaxMax strategy, then the Convex Optimization
strategy can not detect any arbitrage profit, either. However, the empirical
data analysis denotes that the profitability of the Convex Optimization
strategy is almost equal to that of the MaxMax strategy, and the MaxPrice
strategy is not reliable in getting the maximal monetized arbitrage profit
compared to the MaxMax strategy.
