  Experience has shown that trading in stock and cryptocurrency markets has the
potential to be highly profitable. In this light, considerable effort has been
recently devoted to investigate how to apply machine learning and deep learning
to interpret and predict market behavior. This research introduces a new deep
neural network architecture and a novel idea of how to prepare financial data
before feeding them to the model. In the data preparation part, the first step
is to generate many features using technical indicators and then apply the
XGBoost model for feature engineering. Splitting data into three categories and
using separate autoencoders, we extract high-level mixed features at the second
step. This data preprocessing is introduced to predict price movements.
Regarding modeling, different convolutional layers, an long short-term memory
unit, and several fully-connected layers have been designed to perform binary
classification. This research also introduces a trading strategy to exploit the
trained model outputs. Three different datasets are used to evaluate this
method, where results indicate that this framework can provide us with
profitable and robust predictions.
