Recent advancement in reinforcement learning has enabled robust data-driven direct optimization on the investor’s objectives without estimating the stock movements as in the traditional two-step approach [8]. Given diverse investment styles, a single trading strategy cannot serve different investor objectives. We propose an objective function formulation to augment the direct optimization approach in AlphaPortfolio (Cong et al. [6]). In addition to simple baseline Sharpe ratio used in AlphaPortfolio, we add three investor’s objectives for (i) achieving excess alpha by maximizing the information ratio; (ii) mitigating downside risks through optimizing maximum drawdown-adjusted return; and (iii) reducing transaction costs via restricting the turnover rate. We also introduce four new features: momentum, short-term reversal, drawdown, and maximum drawdown to the framework. Our objective function formulation allows for controlling the trade-off between both maximum drawdown and turnover with respect to realized return, creating flexible trading strategies for various risk appetites. The maximum drawdown efficient frontier curve, derived using a range of values of hyper-parameter α, reflects the similar concave relationship as observed in the theoretical study by Chekhlov et al. [5]. To improve the interpretability of the deep neural network and drive insights into traditional factor investment, we further explore the drivers that contribute to the top and bottom performing firms by running regression analysis using Random Forest, which achieves R2 of approximately 0.8 in producing the same winner scores as our model. Finally, to uncover the balance between profits and diversification, we investigate the impact of the trading size on strategy behaviors.