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In most real scenarios the construction of a risk-neutral portfolio must beperformed in discrete time and with transaction costs. Two human imposedconstraints are the risk-aversion and the profit maximization, which togetherdefine a nonlinear optimization problem with a model-dependent solution. Inthis context, an optimal fixed frequency hedging strategy can be determined aposteriori by maximizing a sharpe ratio simil path dependent reward function.Sampling from Heston processes, a convolutional neural network was trained toinfer which period is optimal using partial information, thus leading to adynamic hedging strategy in which the portfolio is hedged at variousfrequencies, each weighted by the probability estimate of that frequency beingoptimal.

Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets