  In most real scenarios the construction of a risk-neutral portfolio must be
performed in discrete time and with transaction costs. Two human imposed
constraints are the risk-aversion and the profit maximization, which together
define a nonlinear optimization problem with a model-dependent solution. In
this context, an optimal fixed frequency hedging strategy can be determined a
posteriori by maximizing a sharpe ratio simil path dependent reward function.
Sampling from Heston processes, a convolutional neural network was trained to
infer which period is optimal using partial information, thus leading to a
dynamic hedging strategy in which the portfolio is hedged at various
frequencies, each weighted by the probability estimate of that frequency being
optimal.
