This work investigates how Deep Reinforcement Learning (DRL) can elevate algorithmic trading—especially in fast-paced, high-frequency markets. We propose a full-fledged framework to compare different setups, from solo agents to multi-agent systems, applying DRL methods like Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C), along with combinations of these. We trained on hourly stock data from 24 firms over two years (Jan 1, 2020–Jan 1, 2022) and tested performance over the next year (Jan 1, 2022–Jan 1, 2023). We evaluated key factors—returns, risk control, and how well these models adapt to changing markets. The single-agent PPO model stood out, achieving a remarkable profit factor of 28.07 on BIDU and keeping peak drawdowns frequently under 1%. This demonstrates both solid capital protection and high risk-adjusted performance. Ensemble models showed balanced performance in both single-agent and multi-agent setups, achieving a Sharpe ratio of 0.75 and Sortino ratios up to 7.7, outperforming existing benchmarks. Comparative analyses revealed that ensemble strategies enhance market responsiveness and improve both stability and profitability in volatile environments. Sensitivity analysis confirmed the robustness of model performance across various hyperparameter settings. Overall, the proposed DRL-based ensemble framework demonstrates strong potential to improve real-world HFT systems by delivering more adaptive, stable, and efficient algorithmic trading solutions.