Multi-agentní burzovní prostředí pro hledání robustních strategií pomocí zpětnovazebního učení

Mikuláš, Pavel

This thesis presents a comprehensive study of the application of reinforcement learning to algorithmic trading. The main focus of this thesis is on the generalization properties of various reinforcement learning algorithms, both from the data perspective and the applicability of the trained agents to real algorithmic trading. To that end, we develop a training environment taking into account various real-world factors influencing the performance of algorithmic trading strategies. We also experiment with the recurrent replay buffer extension of the DQN algorithm, known as R2D2, being, to the best of our knowledge, the first to employ this algorithm for the task of algorithmic trading. Each algorithm is evaluated against traditional algorithmic trading strategies, including the buy-and-hold strategy, to demonstrate the superior performance of the reinforcement learning strategies. On top of that we also provide a study on how the amount of training data and transaction costs influence the generalization of the algorithms to unseen market conditions. We show how transaction costs significantly increase the task complexity and that the R2D2 algorithm overperforms the commonly used baselines, as well as other state-of-the-art reinforcement learning algorithms in this task. 1

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