Quantitative Trading Model Building and Stock Investment Decision Making from a Data-Driven Perspective

This paper explores the problem of quantitative trading based on stock data from a data-driven perspective and develops a new model that relies deeply on buy, sell and hold behaviour to quantify and optimise investment returns. Considering the structural characteristics of the given model, traditional optimisation methods cannot meet the requirements. Therefore, we derive the optimal trading factors for the past time period based on historical stock price data and modify them so that they can be used to guide actual trading in the future. Although there are some differences between the modified trading factor and the ideal optimal trading factor, the results of the study show that the modified trading factor has utility in safeguarding investment returns, while the ideal optimal trading factor at future moments is not available. Meanwhile, we propose a data-driven investment strategy to further improve investment returns. Finally, the practical feasibility and generality of the proposed model and methodology are verified by detailed trading of actual stock price data.