Adaptive Algorithm for Selecting the Optimal Trading Strategy Based on Reinforcement Learning for Managing a Hedge Fund

In hedge fund management, the ability to dynamically select optimal trading strategies is paramount for maximizing returns and mitigating risk. This paper presents a pioneering approach that integrates Reinforcement Learning (RL), specifically the Proximal Policy Optimization (PPO) algorithm, into the strategy selection process for hedge fund management. Our model considers a diverse array of strategies, including Mean Reversion and Momentum, and employs advanced mathematical frameworks to evaluate and select the strategies. By leveraging RL, our algorithm learns to adaptively adjusts strategy allocations to maximize cumulative returns while adhering to the risk constraints. We demonstrate the effectiveness of our approach through extensive backtesting and validation of historical market data, demonstrating superior performance compared to traditional methods. Nevertheless, it is important to understand that training trading agents requires a considerable amount of time, computing power, and other resources. Our research offers a novel perspective on leveraging RL to optimize strategy selection in hedge fund management and underscores the potential of AI-driven approaches in finance.