A goal of financial portfolio trading is maximizing the trader's utility by allocating capital to assets in a portfolio in the investment horizon. Our study suggests an approach for deriving an intelligent portfolio trading strategy using deep Q-learning. In this approach, we introduce a Markov decision process model to enable an agent to learn about the financial environment and develop a deep neural network structure to approximate a Q-function. In addition , we devise three techniques to derive a trading strategy that chooses reasonable actions and is applicable to the real world . First, the action space of the learning agent is modeled as an intuitive set of trading directions that can be carried out for individual assets in the portfolio . Second, we introduce a mapping function that can replace an infeasible agent action in each state with a similar and valuable action to derive a reasonable trading strategy . Last, we introduce a method by which an agent simulates all feasible actions  and learns about these experiences to utilize the training dataefficiently . To validate our approach, we conduct backtests for two representative portfolios , and we find that the intelligent strategy derived using our approach is superior to the benchmark strategies.
Portfolio traders strive to identify dynamic portfolio allocation schemes so that their total budgets are well 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 an algorithm called deep Q-learning. This study formulates a portfolio trading process as a Markov decision process in which the agent can learn about the financial market environment, and it identifies a deep neural network structure as an approximation of the Q-function. To ensure applicability to real-world trading , we devise three novel techniques that are both reasonable and implementable . First, the agent's action space is modeled as a combinatorial action space of trading directions with prespecified trading sizes for each asset . Second, we introduce a mapping function that can replace an initially-determined action that may be infeasible with a feasible action that is reasonably close to the original, ideal action . Last, we introduce a technique by which an agent simulates all feasible actions in each state and learns about these experiences to derive a multi-asset trading strategy that best reflects financial data . To validate our approach, we conduct backtests for two representative portfolios and demonstrate superior results over the benchmark strategies.
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