Robust Log-Optimal Strategy with Reinforcement Learning

TL;DR

This paper introduces a robust, computationally efficient log-optimal portfolio strategy enhanced with reinforcement learning, demonstrating improved profitability and stability in back tests over traditional methods.

Contribution

It proposes a new RLOS method that simplifies GLOS, and integrates it with RL to create RLOSRL, enhancing portfolio management with robustness and practical implementation.

Findings

RLOSRL outperforms traditional strategies in back tests.

RLOS avoids complex CDF estimation, increasing practicality.

The combined RLOSRL demonstrates stable profitability.

Abstract

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. The RLOSRL's performance is compared to some traditional strategies on several back tests, where we randomly choose a selection of constituent stocks of the CSI300 index as assets under management and the test results validate its profitability and stability.