Trading System: a Deep Reinforcement Learning Approach

The main objective of this work is to show the advantages of Reinforcement Learning-based approaches to develop a Trading System. The experimental results showed the great adaptability of the developed models, which obtained very satisfactory econometric performances in five datasets of Forex Market characterized by different volatilities. The TradingEnv baseline provided by OpenAi was used to simulate the financial market. The latter has been improved by implementing a rendering of the simulation and the commission plan applied by a real Electronic Communication Network. As regards the artificial agent, the main contributions are the use of the Gramian Angular Field transformation to encode the historical financial series in images and the experimental proof that the presence of Locally Connected Layers brings a benefit in terms of performances. Vanilla Saliency Map was used as an explainability method to tune the window size of the observations of the environment. From the explanation of the best performing model it is possible to observe how the most important information are the price changes observed with greater granularity in accordance with the theoretical results proven at the state of the art on the historical financial series.