  Candlesticks are graphical representations of price movements for a given
period. The traders can discovery the trend of the asset by looking at the
candlestick patterns. Although deep convolutional neural networks have achieved
great success for recognizing the candlestick patterns, their reasoning hides
inside a black box. The traders cannot make sure what the model has learned. In
this contribution, we provide a framework which is to explain the reasoning of
the learned model determining the specific candlestick patterns of time series.
Based on the local search adversarial attacks, we show that the learned model
perceives the pattern of the candlesticks in a way similar to the human trader.
