Trading System

Trading system research focuses on developing algorithms and models to predict market movements and optimize trading strategies, aiming to maximize profitability and minimize risk. Current research employs diverse approaches, including machine learning techniques like recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and deep reinforcement learning (DRL), often combined with technical indicators and simulations of limit order books. These advancements offer improved accuracy in price prediction and strategy optimization, impacting both academic understanding of market dynamics and the practical development of automated trading systems.