  In this paper we apply a specific type ANNs - convolutional neural networks
(CNNs) - to the problem of finding start and endpoints of trends, which are the
optimal points for entering and leaving the market. We aim to explore long-term
trends, which last several months, not days. The key distinction of our model
is that its labels are fully based on expert opinion data. Despite the various
models based solely on stock price data, some market experts still argue that
traders are able to see hidden opportunities. The labelling was done via the
GUI interface, which means that the experts worked directly with images, not
numerical data. This fact makes CNN the natural choice of algorithm. The
proposed framework requires the sequential interaction of three CNN submodels,
which identify the presence of a changepoint in a window, locate it and finally
recognize the type of new tendency - upward, downward or flat. These submodels
have certain pitfalls, therefore the calibration of their hyperparameters is
the main direction of further research. The research addresses such issues as
imbalanced datasets and contradicting labels, as well as the need for specific
quality metrics to keep up with practical applicability. This paper is the full
text of the research, presented at the 20th International Conference on
Artificial Intelligence and Soft Computing Web System (ICAISC 2021)
