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The autonomous trading agent is one of the most actively studied areas ofartificial intelligence to solve the capital market portfolio managementproblem. The two primary goals of the portfolio management problem aremaximizing profit and restrainting risk. However, most approaches to thisproblem solely take account of maximizing returns. Therefore, this paperproposes a deep reinforcement learning based trading agent that can manage theportfolio considering not only profit maximization but also risk restraint. Wealso propose a new target policy to allow the trading agent to learn to preferlow-risk actions. The new target policy can be reflected in the update byadjusting the greediness for the optimal action through the hyper parameter.The proposed trading agent verifies the performance through the data of thecryptocurrency market. The Cryptocurrency market is the best test-ground fortesting our trading agents because of the huge amount of data accumulated everyminute and the market volatility is extremely large. As a experimental result,during the test period, our agents achieved a return of 1800% and provided theleast risky investment strategy among the existing methods. And, anotherexperiment shows that the agent can maintain robust generalized performanceeven if market volatility is large or training period is short.

Automatic Financial Trading Agent for Low-risk Portfolio Management using Deep Reinforcement Learning