  Portfolio traders strive to identify dynamic portfolio allocation schemes so
that their total budgets are efficiently allocated through the investment
horizon. This study proposes a novel portfolio trading strategy in which an
intelligent agent is trained to identify an optimal trading action by using
deep Q-learning. We formulate a Markov decision process model for the portfolio
trading process, and the model adopts a discrete combinatorial action space,
determining the trading direction at prespecified trading size for each asset,
to ensure practical applicability. Our novel portfolio trading strategy takes
advantage of three features to outperform in real-world trading. First, a
mapping function is devised to handle and transform an initially found but
infeasible action into a feasible action closest to the originally proposed
ideal action. Second, by overcoming the dimensionality problem, this study
establishes models of agent and Q-network for deriving a multi-asset trading
strategy in the predefined action space. Last, this study introduces a
technique that has the advantage of deriving a well-fitted multi-asset trading
strategy by designing an agent to simulate all feasible actions in each state.
To validate our approach, we conduct backtests for two representative
portfolios and demonstrate superior results over the benchmark strategies.
