  Stock portfolio optimization is the process of constant re-distribution of
money to a pool of various stocks. In this paper, we will formulate the problem
such that we can apply Reinforcement Learning for the task properly. To
maintain a realistic assumption about the market, we will incorporate
transaction cost and risk factor into the state as well. On top of that, we
will apply various state-of-the-art Deep Reinforcement Learning algorithms for
comparison. Since the action space is continuous, the realistic formulation
were tested under a family of state-of-the-art continuous policy gradients
algorithms: Deep Deterministic Policy Gradient (DDPG), Generalized
Deterministic Policy Gradient (GDPG) and Proximal Policy Optimization (PPO),
where the former two perform much better than the last one. Next, we will
present the end-to-end solution for the task with Minimum Variance Portfolio
Theory for stock subset selection, and Wavelet Transform for extracting
multi-frequency data pattern. Observations and hypothesis were discussed about
the results, as well as possible future research directions.1
