This study evaluates Reinforcement Learning (RL) techniques for financial trading during unpredictable market conditions, such as black swan events. Three experiments were conducted: one where the algorithms were trained and tested over the same period; another where they were trained and tested over different periods; and the final one where they were trained over a certain period and then tested during a period that included a black swan event (the market crash of March 2020). Results show that RL methods outperform traditional strategies in the in-sample period, but struggle to adapt during the black swan event. The results show the potential of RL techniques in financial trading with the right approach.