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Abstract
In multi-period algorithmic trading, determining algorithms that are ideal for riskaverse strategies is a challenging task. This study explored the application of model-free reinforcement learning (RL) in algorithmic trading and analyzed the relationship between risk-averse strategies and implementation of RL algorithms including Q-learning, Greedy-GQ, and SARSA. The data for this quantitative research included one year of the E-mini-NASDAQ-100 futures (2023-2024). Over 7,500 simulation results substantiated a proof of concept that Q-learning can successfully generate risk-adjusted trading signals in the highly liquid technology-focused futures market. With an optimized configuration of hyperparameters including look-back period, basis and reward functions, Q-learning delivered nearly twice the returns of the competing RL algorithms. Beyond absolute returns, Q-learning exhibited lower volatility across key risk metrics and outperformed the NASDAQ-100 benchmark by approximately 75 percentage points. These findings suggest reinforcement learning as a promising artificial intelligence and machine learning framework for alpha generating strategies in systematic trading.
