Reinforcement Learning for Finance

Preface

Tell me and I forget. Teach me and I remember. Involve me and I learn.

Benjamin Franklin

Reinforcement learning (RL) has enabled a number of breakthroughs in AI. One of the key algorithms in RL is deep Q-learning (DQL) that can be applied to a large number of dynamic decision problems. Popular examples are arcade games and board games, such as Go, in which RL and DQL algorithms have achieved superhuman performance in many instances. This has often happened despite the belief of experts that such feats would be impossible for decades to come.

Finance is a discipline with a strong connection between theory and practice. Theoretical advancements often find their way quickly into the applied domain. Many problems in finance are dynamic decision problems, such as the optimal allocation of assets over time. Therefore it is, on the one hand, theoretically interesting to apply DQL to financial problems. On the other hand, it is also in general quite easy and straightforward to apply such algorithms—usually after some thorough testing—in the financial markets.

In recent years, financial research has seen a strong growth in publications related to RL, DQL, and related methods applied to finance. However, there is hardly any resource in book form—beyond the purely theoretical ones—for those who are looking for an applied introduction to this exciting field. This book closes the gap in that it provides the required background in a concise fashion and otherwise focuses on the implementation ...