Optimization of technical analysis-driven algorithmic trading using deep reinforcement learning

Abstract

Technical analysis indicators are critical for trend prediction in algorithmic trading. However, relying on a single technical indicator strategy cannot obtain consistent profits in dynamic markets. To address this limitation, we develop a dual-agent adaptive trading framework, and combine the double deep Q-network (DDQN) with three classic technical indicators (relative strength index (RSI), Williams percent range (WR), and commodity channel index (CCI)) respectively to construct three distinct base models (RSI-DDQN, WR-DDQN, CCI-DDQN). The framework classifies market data into different market strengths by comparing indicator values with a preset neutral threshold. For each base model, two specialized agents are developed for strong and weak market data. These agents optimize their trading strategies by interacting with environments constructed from the corresponding classified data. The framework dynamically selects the most suitable agent’s decisions as trading signals, based on market strength assessments. To further improve decision reliability, an integrated model is introduced by combining signals from the three base models through a hard voting mechanism. Empirical results from historical data of Devon Energy (DVN), Tesla (TSLA), NVIDIA (NVDA), and Apple (AAPL) show the framework’s effectiveness, yielding average Sharpe ratios of 1.06, 0.46, 0.48, and 0.81 for the RSI-DDQN, WR-DDQN, CCI-DDQN, and integrated models, respectively.