## 3 Discussion and Conclusions  
We have accurately validated our pipeline using a complete and comprehensive dataset. To this end, as described in Section 2.1, ad-hoc financial dataset has been properly organized. The cross-currency dataset was downloaded from the Tickstory website [33] and--as specified--contains 1M timeframe \begin{table}
\begin{tabular}{c c c} \hline \hline
**Financial Year** & \
\begin{tabular}{c} **Deep Learning** \\ **Prediction Block** \\ \end{tabular} &
\begin{tabular}{c} **Deep Learning Prediction Block +** \\ **RL Trend Correction** \\ \end{tabular} \\ \hline
2015 & 71.11 \% & 85.24 \% \\
2016 & 69.25 \% & 83.29 \% \\
2017 & 73.98 \% & 87.16 \% \\
2018 & 70.23 \% & 84.92 \% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Trend forecast performance accuracy for \(\backslash\)EUR/USD currency quotes (99.9% accuracy) of the EUR/USD, GBP/USD, EUR/GBP cross currencies for the years 2004-2018. As mentioned in Section 2.1, we performed training simulations and annual validation, dividing the training set as follows: 70% of the data has been used as training set while the remaining 30% were used to validate and test the proposed pipeline. The simulations were performed on an AMD Ryzen 16 Cores server with Nvidia RTX 2080 Ti GPU supporting CUDA operations and in MATLAB 2018b full toolboxes. We supposed a broker account with 30,000.00 US dollars as the initial balance and 2 pips of bid/ask spreads (the spread is average gain cashed by the broker). We supposed an account leverage of 1:400 and a LOT size for each single FX trade order. The following indexes are defined in order to provide a benchmark comparison with similar Forex trading systems [2]  
\[\textbf{ROI}=\ \frac{\textbf{100}*(\textbf{{Gain}}-\textbf{{Investment}})}{ \textbf{{Investment}}} \tag{18}\]  
\[\textbf{{MD}}=\ \frac{\left|\textbf{{BalanceValley}}-\textbf{{BalancePeak}} \right|}{\textbf{{BalancePeak}}} \tag{19}\]  
The proposed approach has been validated over all available years (from 2004 to 2018) performing several tests selecting different daily time-window which means a different stock exchange [2]. Table 2 shows the benchmark comparison of the proposed approach with respect to similar ones proposed in literature (for each comparison the author has used the same forex currency dataset as per cited method):  
Regarding the performance in terms of the HFT system, the proposed method performs many more operations than the previous approach developed by the authors [2]. The increase in the number of trading transactions related to the fine trend prediction performance (see Table 1), means that the proposed HFT system has a good profitability and a moderate drawdown due to the fact that the loss operations are in any case reduced due to the usage of a timeframe of one minute (1M) so that the currency price fluctuations are still limited. The proposed method is able to perform, under favorable trend conditions, from 50 to 100 daily transactions on the EUR/USD cross with a timeframe of one minute, thus confirming that it falls within the class of HFT systems. A careful analysis of Table 2, showing the performance of the proposed method in comparison with others proposed in the literature, allows us to identify the undoubted advantages in terms of ROI both with respect to the previous version of the pipeline (implemented by the author in [2]) and having regard to the other methods proposed in the literature and which implement HFT methods in the FOREX market. The ROI indicator has greater significance if it is combined with the maximum drawdown (MD) data that is found on average in the validation period of the proposed pipeline. In fact, although the drawdown has slightly increased compared to the previous version of the pipeline (previous MD: 11.25% against a current pipeline MD equal to 15.97%) there is also a corresponding increase in ROI which therefore appears to justify this modest disadvantage of the algorithm proposed in this article.