Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer

By Gili Rosenberg, Poya Haghnegahdar, Phil Goddard, Peter Carr, Kesheng Wu, & Marcos López de Prado

We solve a multi-period portfolio optimization problem using D-Wave Systems’ quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.
Journal reference: IEEE Journal of Selected Topics in Signal Processing (JSTSP), Volume 10, Issue 6, 2016 and Proc. of the 8th Workshop on High Performance Computational Finance (WHPCF), p. 7, ACM, 2015 and The Journal of Investing, Fall 2016, Vol. 25, No. 3: pp. 81-87

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