Quantitative trading strategy research based on non-euclidean gaussian process neural network

With the diversification trend of stock market data, constructing a stock price prediction model that can integrate multi-source data, handle high-dimensional features, and fit the complex relationships between predictive and response variables is crucial for formulating effective quantitative trading strategies. Based on this, this paper proposes a stock price prediction model that combines multi-kernel learning with Gaussian Process Neural Networks. The main innovations of the model are reflected in the following aspects: First, by using kernel functions with different domains, it effectively integrates feature information from various data sources; Second, by combining Gaussian Process Regression with neural networks, the model fully considers the impact of features from different data sources on the prediction results while addressing the curse of dimensionality and maintaining good fitting ability for complex relationships. In addition, the model can quantify the uncertainty of stock price prediction results and perform statistical inference analysis, providing more detailed auxiliary information for investors or decision-makers. Simulation analysis results show that the proposed method outperforms some classic models in predictive performance, showing strong competitiveness. Finally, in quantitative trading back-testing, considering the introduction of non-Euclidean sentiment features, the model has achieved significant superior performance in indicators such as cumulative returns, Sharpe ratio, and maximum drawdown.