## 3 Discussion and Conclusions
Furthermore, although the method proposed in [32] has a lower drawdown, the ROI is significantly smaller compared to that achieved by the proposed method. Even with the same ROI (see the method reported in Table 2 with a ROI of 97.687 %), it should be noted that the methods in literature require a greater use of capital to be invested in a condition of counter-trend [31; 32] therefore a greater drawdown, as evident from Table 2 which reports that for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FX Currency Cross** & **ROI (\%)** & **MD (\%)** \\ \hline Grid Trading System [2] & EUR/USD & 94.11 & **11.25** \\ Trading System [31] & EUR/USD & 60.77 & 21 \\ Threshold (0.03\%)—Strategy 2 [32] & EUR/USD & 97.687 & 50.47 \\ Threshold (0.07\%)—Strategy 2 [32] & EUR/USD & 62.707 & 9.93 \\ Proposed & EUR/USD & **98.23** & 15.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trading performance analysis of the proposed pipeline. ROIs comparable to the performance of the proposed method, they show a 50.47% MD far superior to the 15.97% obtained by the authors, on the same currency cross (EUR/USD). Table 3 also reports some information about the dynamic of the ROI and the MD, i.e., how these values vary over the time frame in which the proposed pipeline has been tested. As evident from Table 3, while the ROI tends to remain almost constant during the validation sessions, the use of capital to compensate for the temporary losses due to incorrectly predicted trends (i.e., the MD indicator), varies significantly presenting the maximum values that arrive in certain years at a 19.28% against MD of 12.66 % in others. On average, as mentioned, the value of MD is 15.97%, however a greater stability over time of this important indicator of financial sustainability is certainly to be studied. Figure 10 shows an instance of the GUI of the proposed whole system (trend forecast plus grid trading system):  
The data reported in Table 2 openly confirm the robustness of the proposed method both in the forecast of the medium-term trend and in the execution of the operations using HFT grid algorithms. Compared to the methods produced in the literature and having regard to the previous version of the pipeline proposed by the author, the results reported in Table 2 show excellent performance (higher than the previous method) although there was a slight increase in the drawdown compared to the previous version and this due to the fact that instead of proceeding with a punctual forecast of the cross currency, we proceed with a forecast of the medium-term trend which, although it shows an increase in the average drawdown, allows instead for having a robust estimate of a financial period wider. This advantage will be used by the author in a future work (currently being validated) that has already given very promising preliminary results and will propose a trading strategy on a financial portfolio composed of several instruments, correlated with each other. By this advantage, it is therefore possible, with an approach similar to the one presented in this paper, to predict the medium-term trend  
Figure 10: GUI interface of the proposed trading system pipeline. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year(s)** & **FX Currency Cross** & **ROI Min (\%)** & **MD Min (\%)** & **ROI Max (\%)** & **MD Max (\%)** \\ \hline
2004–2018 & EUR/USD & 97.65 & 12.66 & 98.81 & 19.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Trading performance analysis of the proposed pipeline: dynamic ROI/MD. so as to plan trading strategies that exploit these correlations, which are able to maximize gains and minimize losses. For the proposed algorithm, porting is underway at STM32 (with embedded several Cortex Ax) architectures equipped with an environment suitable for the implementation of both LSTM architectures and the RL framework [34].