MetaTrader: An Reinforcement Learning Approach Integrating Diverse Policies for Portfolio Optimization

TL;DR

MetaTrader is a novel two-stage reinforcement learning framework that integrates diverse expert trading policies and learns to adaptively select the best policy based on market conditions, improving portfolio management performance.

Contribution

It introduces a two-stage RL approach combining imitation learning of diverse policies with a meta-policy for market adaptation, advancing portfolio optimization techniques.

Findings

Outperforms state-of-the-art baselines in profit-risk balance

Effectively learns to adapt policies to market conditions

Thorough ablation studies confirm component effectiveness

Abstract

Portfolio management is a fundamental problem in finance. It involves periodic reallocations of assets to maximize the expected returns within an appropriate level of risk exposure. Deep reinforcement learning (RL) has been considered a promising approach to solving this problem owing to its strong capability in sequential decision making. However, due to the non-stationary nature of financial markets, applying RL techniques to portfolio optimization remains a challenging problem. Extracting trading knowledge from various expert strategies could be helpful for agents to accommodate the changing markets. In this paper, we propose MetaTrader, a novel two-stage RL-based approach for portfolio management, which learns to integrate diverse trading policies to adapt to various market conditions. In the first stage, MetaTrader incorporates an imitation learning objective into the reinforcement learning framework. Through imitating different expert demonstrations, MetaTrader acquires a set of trading policies with great diversity. In the second stage, MetaTrader learns a meta-policy to recognize the market conditions and decide on the most proper learned policy to follow. We evaluate the proposed approach on three real-world index datasets and compare it to state-of-the-art baselines. The empirical results demonstrate that MetaTrader significantly outperforms those baselines in balancing profits and risks. Furthermore, thorough ablation studies validate the effectiveness of the components in the proposed approach.