A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning

作者：

Highlights：

• Combine recurrent reinforcement learning and particle swarm for portfolio trading.

• Enhance particle swarm portfolio optimization with Calmar ratio as fitness function.

• Prove effectiveness of the method through an efficient frontier and a cost analysis.

• Develop a dynamic adaptive long/short constrained portfolio trading system.

摘要

•Combine recurrent reinforcement learning and particle swarm for portfolio trading.•Enhance particle swarm portfolio optimization with Calmar ratio as fitness function.•Prove effectiveness of the method through an efficient frontier and a cost analysis.•Develop a dynamic adaptive long/short constrained portfolio trading system.

论文关键词：Recurrent reinforcement learning,Particle swarm optimization,Optimal portfolio rebalancing,Portfolio constraint

论文评审过程：Received 27 July 2018, Revised 5 April 2019, Accepted 6 April 2019, Available online 13 April 2019, Version of Record 19 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.013