Implementing Bayesian Optimization-Driven Machine Learning Algorithms for Algorithmic Decision-Making in Quantitative Stock Trading

This study develops a BO-driven dual-factor quantitative trading strategy integrating optimized XGBoost/LSTM models to address market nonlinearity and volatility. The BO-optimized XGBoost model shows superior accuracy and, when combined with a 9-day moving average, yields a 20.79% annualized return and a 1.14 Sharpe ratio. The strategy offers superior signal filtering, risk-adjusted returns, and robustness, laying a foundation for adaptive trading systems.