Before revision:  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 .
After revision:  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 and learns about these experiences to derive a multi-asset trading strategy that best reflects financial data .