Technical indicator factors can quickly reflect the transformation of current market behavior. The application system in quantitative trading has become increasingly mature in recent years. Back testing is widely used in factor validity tests because of its validity. However, the current research on the effectiveness of technical indicator factors has ignored the adaptability to the model timing strategy, and the use of factors is not differentiated enough. How to carry out reasonable and effective factor validity research has become a difficult problem for many scholars to discuss. This paper first selected representative technical indicators as the research object and crawled the trading data of Chinese A-share listed companies through Python. It then calculated the sample data using computer databases such as Pandas and NumPy. Furthermore, this paper confirms the optimal interval of each factor with the method of back testing. On this basis, it tests the income distribution of each factor and the maximum pullback. It introduces the timing method of the simple moving average for comparison and discusses the feasibility of using a technical indicator strategy to conduct stock selection trading.