Abstract: Precious metals market, such as gold (XAU/USD), exhibit high volatility and significant microstructure noise in their financial time series, which degrade the reliability of algorithmic trading models. While deep reinforcement learning (DRL) has shown strong results in equities and cryptocurrencies, its application to precious metals remains limited by unstable signals and rapid market fluctuations. This study proposes a Kalman-enhanced DRL framework that integrates classical noise filtering with modern neural architectures to improve signal quality and trading performance in highly volatile environments. The methodology applies Kalman filtering to recursively denoise OHLCV price data, which then serves as an input alongside 22 technical indicators to train three state-of-the-art DRL agents: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Recurrent PPO (RPPO). Eight years of hourly XAU/USD data (January 2017 to January 2025, N = 47,304) were used for training and evaluation. Models were evaluated on cumulative return, CAGR, Sharpe ratio, maximum drawdown, and volatility. Results demonstrate substantial gains from noise attenuation: PPO with Kalman filtering achieved 80.21% cumulative return (27.1% CAGR, Sharpe 12.10, drawdown -0.48%) compared with raw PPO’s 8.70% (3.46% CAGR, Sharpe 0.45, drawdown - 12.52%). DQN and RPPO achieved comparable improvements, with 244 to 822% return increases, 88 to 96% drawdown reduction, and up to 29× Sharpe ratio enhancement. Statistical significance was confirmed (p < 0.001 for PPO/RPPO; p < 0.05 for DQN). These findings highlight Kalman-enhanced reinforcement learning as a scalable and robust framework for institutional algorithmic trading, bridging signal processing and artificial intelligence for next-generation adaptive trading systems.

Amine Kili, Brahim Raouyane, Mohamed Rachdi and Mostafa Bellafkih. “Kalman-Enhanced Deep Reinforcement Learning for Noise-Resilient Algorithmic Trading in Volatile Gold Markets”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161181

@article{Kili2025,
title = {Kalman-Enhanced Deep Reinforcement Learning for Noise-Resilient Algorithmic Trading in Volatile Gold Markets},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161181},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161181},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Amine Kili and Brahim Raouyane and Mohamed Rachdi and Mostafa Bellafkih}
}