When it comes to using mathematical models, knowledge of mathematics and statistics is essential. Because the domestic financial market is not complete, some derivatives transactions are restricted, so compared with foreign markets, less mathematical/statistical knowledge can be used. For investors with non-science background, it is necessary to supplement basic knowledge such as advanced mathematics, linear algebra, probability theory, statistics and optimization theory, which can be found in college textbooks. For some emerging trading strategies using machine learning, you need to know something about data mining. But since it is an introduction, this part is naturally unnecessary.

In addition, econometrics is widely used. When conducting strategy research, we often face a large number of time series and panel data Although we pay more attention to strategic results in practice, as long as we can make money, the strategy is a good strategy. With the support of rigorous econometric theory, the regression results are more accurate and can better describe the relationship behind the data, so it is often easier to get close to the expected results. Among them, the logic and assumptions of time series regression are quite different from those of cross-section and panel regression, and are widely used to describe and predict the returns and fluctuations of financial assets. Econometrics books recommend woodridge's Introduction to Econometrics: A Modern Perspective; Time series recommends Brooks's Introduction to Financial Econometrics.

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