Reinforcement Learning: Prediction, Control and Value Function Approximation

TL;DR

This paper explores reinforcement learning algorithms for developing automatic financial trading systems, detailing their core components and potential applications in creating model-free trading algorithms.

Contribution

It provides a comprehensive overview of RL algorithms, including definitions and applications relevant to financial trading systems, highlighting their potential for model-free decision making.

Findings

RL algorithms can effectively model financial trading decisions

Detailed definitions of reward, action, and policy functions in RL

Potential for RL to improve automated trading performance

Abstract

With the increasing power of computers and the rapid development of self-learning methodologies such as machine learning and artificial intelligence, the problem of constructing an automatic Financial Trading Systems (FTFs) becomes an increasingly attractive research topic. An intuitive way of developing such a trading algorithm is to use Reinforcement Learning (RL) algorithms, which does not require model-building. In this paper, we dive into the RL algorithms and illustrate the definitions of the reward function, actions and policy functions in details, as well as introducing algorithms that could be applied to FTFs.