Before revision:  We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations . Unlike many previous studies on discrete trading signals, we make the agent to short in a continuous action space for portfolio optimization. In addition, we design an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors.
After revision:  We improve on the existing Deep Reinforcement Learning Portfolio model and make many innovations , such as adding short mechanism, designing an arbitrage mechanism based on Arbitrage Pricing Theory, and redesign the activation function for acquiring action vectors.