Abstract
Quantitative trading through automated systems has been vastly growing
in recent years. The advancement in machine learning algorithms has
pushed that growth even further, where their capability in extracting
high-level patterns within financial markets data is evident.
Nonetheless, trading with supervised machine learning can be challenging
since the system learns to predict the price to minimize the error
rather than optimize a financial performance measure. Reinforcement
Learning (RL), a machine learning paradigm that intersects with optimal
control theory, could bridge that divide since it is a goal-oriented
learning system that could perform the two main trading steps, market
analysis and making decisions to optimize a financial measure, without
explicitly predicting the future price movement. This survey reviews
quantitative trading under the different main RL methods. We first begin
by describing the trading process and how it suits the RL framework, and
we briefly discuss the historical aspect of RL inception. We then
abundantly discuss RL preliminaries, including the Markov Decision
Process elements and the main approaches of extracting optimal policies
under the RL framework. After that, we review the literature of QT under
both tabular and function approximation RL. Finally, we propose
directions for future research predominantly driven by the still open
challenges in implementing RL on QT applications.