  The present document delineates the analysis, design, implementation, and
benchmarking of various neural network architectures within a short-term
frequency prediction system for the foreign exchange market (FOREX). Our aim is
to simulate the judgment of the human expert (technical analyst) using a system
that responds promptly to changes in market conditions, thus enabling the
optimization of short-term trading strategies. We designed and implemented a
series of LSTM neural network architectures which are taken as input the
exchange rate values and generate the short-term market trend forecasting
signal and an ANN custom architecture based on technical analysis indicator
simulators We performed a comparative analysis of the results and came to
useful conclusions regarding the suitability of each architecture and the cost
in terms of time and computational power to implement them. The ANN custom
architecture produces better prediction quality with higher sensitivity using
fewer resources and spending less time than LSTM architectures. The ANN custom
architecture appears to be ideal for use in low-power computing systems and for
use cases that need fast decisions with the least possible computational cost.
