These state-of-the-art Reinforcement Learning techniques have already demonstrated to be profitable, being efficient in different market conditions and beating the benchmarks. Despite the results, the classic rewarding methodology brings to model instability, overfitting and ineffectiveness in taking into account medium and long-term trends, and difficulties in diversifying the portfolio exposure to the market, while not being interpretable enough to be inspected before any deployment. Solely providing the agent with historical price data information is often not enough to enable it to learn comprehensive and reliable strategies. Especially when dealing with multistock trading problems the complexity of the process increases, due to the fluctuating nature of the market and the demand to manage a structured portfolio, as it is necessary for the agent to take into account several factors such as distinguishing how much to allocate on each asset, which positions to open in what direction, and what is the best condition to close them in order to accumulate the maximum reward in an unknown environment.