Finding Optimal Arbitrage Opportunities Using a Quantum Annealer
We present two formulations for finding optimal arbitrage opportunities as a quadratic unconstrained binary optimization problem, which can be solved using a quantum annealer. The formulations are based on finding the most profitable cycle in a graph in which the nodes are the assets and the edge weights are the conversion rates. The edge-based formulation is simpler, whereas the node-based formulation allows for the identification of specific optimal arbitrage strategies, while possibly requiring fewer variables. In addition, an alternative form is presented which allows the arbitrage opportunities that best balance profit and risk to be found, based on the trader’s risk aversion. We discuss considerations for usage in practice. In particular, we suggest an application to illiquid assets and present an illustrative example.
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The CME Market Sentiment Meter (MSM) provides a daily risk–return estimate for eight products traded on CME Group exchanges: corn (C), crude oil (CL), euro/USD FX (EC), S&P 500 index e-minis (ES), gold (GC), natural gas (NG), soybeans (S), and 10-year treasury notes (TYF). The Market Sentiment Meter is computed by 1QBit using end-of-day settlement data published by CME Group. It is available as a subscription product through CME DataMine.
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