## 1 Introduction  
The key to correctly identifying market trend is to correctly estimate both medium-term and long-term trends. Analyzing the financial time-series we can see that many trends follow well-established dynamics in which it is possible to identify recurrent graphic patterns. However, often trading systems are interested not only in identifying the current trend but rather in identifying those 'latent signals' generated by the market and that in fact constitute the first notices of a trend change. Usually, a change of trend in any financial market is preceded by a so-called transitional period of indefinite duration but in any case short to which a trend reversal will follow [1]. Obviously, if this is true for the stock or bond market, it is even more so for the foreign exchange market (Forex), which obviously involves much greater volumes of financial transactions than stocks [2; 3; 4; 5]. Many trading algorithms try to identify the current trend and therefore the signals of inversion through a correlation study between the analyzed financial instrument and others related to it because listed in the same market segment or for other reasons of a financial nature. One of the ways in which many tradersexploit these correlations is related to the so-called 'arbitrage'. In financial markets, arbitrage can occur when trying to get an advantage of differences in the price of single currency related to short-time misalignments between the traded currency with the related ones [2]. In the foreign exchange market, this approach would seem to be worthy of further investigation as it has been noted that many currency changes perform with very correlated dynamics also in terms of sharing an exchange rate (see for example EUR/USD cross currency with EUR/GBP or EUR/USD with GBP/USD). Figure 1 shows an example of related financial time-series (EUR/USD with GBP/USD).  
To the above we add the observation that the foreign exchange market presents strongly non-linear and non-stationary cross dynamics influenced by macroeconomic factors, national and international monetary policies, military conflicts, etc. These factors, more often than not unpredictable, generate a certain level of uncertainty and non-predictability which obviously will have a greater impact in the long term and are more contained in the short term if the trading system policy provides appropriate financial compensation or loss-cutting algorithms based on prudent use of dynamic stop-losses. For these reasons, the author propose the implementation of a high frequency trading (HFT) algorithm that allows fast and rapid financial transactions to be performed with the undoubted advantage of containing losses in the case of incorrect trades although this is counterbalanced by the consequent finding that even profit can only be contained. Obviously, this last aspect will also be compensated by the execution of a large number of transactions and by a careful trading strategy based on the robust  
Figure 1: (**a**) The EUR/USD and GBP/USD close price time-series at 1M timeframe (year: 2018). The strong correlation between the two financial series is evident from the figure reported in (**b**).  
identification of the medium-short term trend. The author has already investigated the development of HFT algorithms both in the stock market and in the currency market, through deep learning methods based mainly on the use of supervised learning architectures such as long short-term memory (LSTM) networks. The results obtained were satisfactory also due to both the high predictive nature of the proposed algorithm and the grid strategy applied in the Forex market [1; 2].  
However, in the literature several authors have investigated the use of deep learning and reinforcement learning (RL) methodologies in order to structure efficient trading systems. Below are some scientific contributions that show the advantages that can be obtained from the use of supervised deep learning and RL based algorithms.