Trading Strategy

Trading strategy research aims to optimize investment decisions by leveraging advanced computational methods to analyze market data and predict asset price movements. Current research heavily employs machine learning, particularly deep learning architectures like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and reinforcement learning (RL) algorithms such as Deep Q-Networks (DQNs) and Deep Deterministic Policy Gradients (DDPGs), often combined with classical techniques like Kalman filtering or technical indicators. These approaches are applied across various asset classes and trading frequencies, from high-frequency algorithmic trading to long-term portfolio management, with the goal of improving risk-adjusted returns and overall profitability. The findings contribute to both theoretical advancements in financial modeling and the development of more sophisticated and effective trading systems.