Transformer-based Reinforcement Learning Model for Optimized Quantitative Trading

Stock market prediction has long been a focal point of financial research due to its immense potential for investors and policymakers. It is a challenging task, as it involves forecasting the future prices of assets based on historical data. Traditional quantitative trading strategies often face challenges in adapting to dynamic market conditions and capturing intricate patterns in financial data. In response, this work explores a novel approach to stock prediction that combines the power of Transformer architecture with the decision-making capabilities of reinforcement learning. We propose a quantitative trading framework that integrates a transformer-based encoder-decoder network to predict future stock prices and a reinforcement learning agent to optimize investment strategies based on these predictions. The Transformer network leverages its self-attention mechanism to capture complex relationships within historical price data, while the reinforcement learning agent learns optimal actions through trial and error in a simulated environment. The experimental results demonstrate the effectiveness of our approach in improving stock prediction accuracy compared to existing methods. This work highlights the potential of combining Transformer and reinforcement learning for efficient and profitable stock market navigation.