  Deep learning (DL) has been applied extensively in a wide range of fields.
However, it has been shown that DL models are susceptible to a certain kinds of
perturbations called \emph{adversarial attacks}. To fully unlock the power of
DL in critical fields such as financial trading, it is necessary to address
such issues. In this paper, we present a method of constructing perturbed
examples and use these examples to boost the robustness of the model. Our
algorithm increases the stability of DL models for candlestick classification
with respect to perturbations in the input data.
