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.

翻译：本文详细阐述了在外汇市场短期频率预测系统中，多种神经网络架构的分析、设计、实现及基准测试。我们的目标是利用一个能快速响应市场状况变化的系统，模拟人类专家（技术分析师）的判断，从而优化短期交易策略。我们设计并实现了一系列以汇率值为输入、生成短期市场趋势预测信号的LSTM神经网络架构，以及一种基于技术分析指标模拟器的自定义ANN架构。我们对结果进行了对比分析，并就每种架构的适用性及其在实现时所需的时间和计算资源成本得出了有益结论。自定义ANN架构相比LSTM架构，能以更少的资源和时间消耗，产生更高灵敏度的更优预测质量。该自定义ANN架构似乎非常适合用于低功耗计算系统以及需要以最小计算成本快速决策的应用场景。