Continuous control with Stacked Deep Dynamic Recurrent Reinforcement Learning for portfolio optimization.

•Incorporating portfolio constraints into recurrent reinforcement learning framework.•Reinforcement learning algorithm with continuous trading actions over multiple assets.•Simultaneous control of the portfolio constraints and policy network hyperparameters.•Hourglass shape network architectures emerge as a natural choice for asset management.