Robust Log-Optimal Strategy with Reinforcement Learning

Understand

We proposed a new Portfolio Management method termed as Robust Log-Optimal Strategy (RLOS), which ameliorates the General Log-Optimal Strategy (GLOS) by approximating the traditional objective function with quadratic Taylor expansion.

• It avoids GLOS's complex CDF estimation process,hence resists the "Butterfly Effect" caused by estimation error.
• Besides,RLOS retains GLOS's profitability and the optimization problem involved in RLOS is computationally far more practical compared to GLOS.
• Further, we combine RLOS with Reinforcement Learning (RL) and propose the so-called Robust Log-Optimal Strategy with Reinforcement Learning (RLOSRL), where the RL agent receives the analyzed results from RLOS and observes the trading environment to make comprehensive investment decisions.