Financial trading is one of the most popular problems for reinforcement learning in recent years. One of the important challenges is that investment is a multi-objective problem. That is, professional investors do not act solely on expected profi t but also carefully consider the potential risk of a given investment. To handle such a challenge, previous studies have explored various kinds of risk-sensitive rewards, for example, the Sharpe ratio as computed by a fi xed length of previous returns. This work proposes a new approach to deal with the profi t-to-risk tradeoff by applying distributional reinforcement learning to build a risk awareness policy instead of a simple risk-based reward function. Our new policy, termed C51-Sharpe, is to select the action based on the Sharpe ratio computed from the probability mass function of the return. This produces a signifi cantly higher Sharpe ratio and lower maximum drawdown without sacrifi cing profi t compared to the C51algorithm utilizing a purely profi t-based policy. Moreover, it can outperform other benchmarks, such as a Deep Q-Network (DQN) with a Sharpe ratio reward function. Besides the policy, we also studied the effect of using double networks and the choice of exploration strategies with our approach to identify the optimal training confi guration. We fi nd that the epsilon-greedy policy is the most suitable exploration for C51-Sharpe and that the use of double network has no signifi cant impact on performance. Our study provides statistical evidence of the effi ciency in risk-sensitive policy implemented by using distributional reinforcement algorithms along with an optimized training process.