Incorporating Realistic Margin Constraints: A Data-Driven Deep Reinforcement Learning Framework for Advanced Portfolio Management

Jingyi Gu, Wenlu Du, Xinyun Zhao, A. Rahman, Guiling Wang

Abstract

In portfolio management via data-driven reinforcement learning techniques, existing approaches mainly focus on cash-only trading, overlooking potential financial data-related benefits and risks associated with margin trading. Integrating margin accounts and their constraints, particularly in short sale scenarios, presents a significant yet ignored opportunity for advanced financial data engineering solutions. To bridge this gap, we introduce Margin Trader, an innovative reinforcement learning framework for margin trading. Margin Trader incorporates margin accounts and constraints into a realistic trading environment via sophisticated data engineering to handle complex financial data, accommodating long and short positions. It aims to optimize profit maximization and risk management, through Margin Adjustment Module and Maintenance Detection Module to process trading data under real market. Margin Trader supports various Deep Reinforcement Learning (DRL) algorithms and allows traders to customize key settings, such as equity allocation, margin ratios, and maintenance requirements, utilizing knowledge management in adapting strategies to market conditions, individual preferences, and risk tolerance. Experiments, utilizing distinct RL algorithms and rolling-based testing periods, demonstrate that Margin Trader effectively learns profitable trading strategies and manages risks in both bullish and bearish markets, achieving superior performance with highest Sharpe ratio, underscoring critical role of data engineering in enhancing trading strategies and performance.

Source: semanticscholar

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