Comparative analysis of neural network architectures for short-term FOREX forecasting
This work addresses the problem of optimizing short-term trading strategies for FOREX traders, but it is incremental as it compares existing neural network types without introducing a new paradigm.
The paper tackled short-term FOREX forecasting by comparing LSTM and custom ANN architectures, finding that the custom ANN achieved better prediction quality with higher sensitivity while using fewer resources and less time.
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.