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Cryptocurrencies such as Bitcoin and Ethereum have recently gained a lot ofpopularity, not only as a digital form of currency but also as an investmentvehicle. Online marketplaces and exchanges allow users across the world toconvert between dozens of different cryptocurrencies and regular currenciessuch as euros or dollars. Due to the novelty of this concept, the volatility ofthese markets and the differences in maturity and usage of particularmarketplaces, currency pairs may appear at multiple marketplaces but atdifferent trading prices. This paper proposes a novel algorithmic approach totake advantage of these mispricings and capitalize upon the pricing differencesthat exist between exchanges and currency pairs. To do so, we model eachcombination of a currency and a market as one node in a graph. A directed linkbetween two nodes indicates that a conversion between these two currency/marketpairs is possible. The weight of the link relates to the exchange rate ofexecuting this particular currency exchange. To leverage the mispricings, weseek for cycles in the graph such that upon multiplying the weights of thelinks in the cycle, a value greater than 1 is found and thus a profit can bemade. Our goal is to do this efficiently, without exhaustively enumerating allpossible cycles in the graph. Therefore, we convert our data and address theproblem in terms of finding minimum weight triangles in graphs with integerweights, for which efficient algorithms can be utilized. We experiment withparameter settings (heuristics) related to the conversion of exchange rate datainto integer weight values. We show that our approach improves upon areasonable baseline algorithm in terms of computation time. Furthermore, usinga real-world dataset, we demonstrate how the obtained minimal weight cyclesindeed unveil a number of currency exchange cycles that result in a net profit.

Computing Minimum Weight Cycles to Leverage Mispricings in Cryptocurrency Market Networks