量化投资是一种新兴的采用数学方法建立模型，并利用计算机编程实现的金融投资方式，因其决策均由计算机计算完成，故具有纪律性好、分散风险性强以及效率高等特点，目前在国际金融市场上已逐步取得主流投资方式的地位。近年来，中国股市的市场规模和投资者数量都开始了迅猛的增长，但由于国内的量化投资研究起步较晚，其理论依据和实践应用都还不够完善。为此，借鉴国外金融市场的经验，研究最新前沿的量化投资方法和开发新的量化金融产品对于中国股票市场的现代化、国际化都具有重大意义。

而随着计算机科学的飞速发展，人工智能被越来越多的行业和领域接受和应用，量化金融行业也在探索将机器学习算法融合进多因子选股策略的构建中。本文以沪深300指数的成分股为样本，选取了2022整年的高频因子数据，构建因子池，并从中筛选出6大类因子——品质因子、成长因子、价值类因子、常用技术类因子、动量类因子和情感类因子，再对每个因子进行显著性和相关性分析，剔除了其中选股能力较弱，以及因子相关性较大的若干因子。然后分别应用等权重线性回归算法、多元分类Logistic算法以及XGBoost算法构建选股模型，并计算其预测结果的各项评价指标，得到以下结论：

1.三种算法的预测结果均得出的夏普比率均大于1，说明其选取时间内的平均收益率大于无风险利率；

2.XGBoost算法的夏普比率是最高的，基于其构建的选股策略使股票的单位风险收益最大化；

3.三种算法的预测结果的α系数均大于0，说明此时市场存在超额收益，投资股票的收益大于银行储蓄等近乎无风险产品的收益；

4.等权重线性回归算法的最大回撤率最高，达到了22.3%，而XGBoost算法仅为6.14%，说明XGBoost算法显著地降低了投资期内可能发生的最大亏损的幅度。

Quantitative investment is a new way of financial investment which uses mathematical methods to build models and computer programming. Because its decisions are completed by computer calculation, it has the characteristics of good discipline, strong risk dispersion and high efficiency. At present, it has gradually gained the status of mainstream investment in the international financial market. In recent years, both the market size and the number of investors in China's stock market have begun to grow rapidly. However, due to the late start of quantitative investment research in China, its theoretical basis and practical application are not perfect. Therefore, it is of great significance to learn from the experience of foreign financial markets, study the latest frontier quantitative investment methods and develop new quantitative financial products for the modernization and internationalization of China's stock market.

With the rapid development of computer science, artificial intelligence has been accepted and applied by more and more industries and fields. The quantitative financial industry is also exploring the integration of machine learning algorithm into the construction of multi-factor stock selection strategy. This paper takes the CSI 300 stock as a sample, selects the high-frequency factor data of the whole year of 2022, constructs a factor pool, and selects six categories of factors -- quality factor, growth factor, value factor, common technology factor, momentum factor and emotion factor. Then carries out the significance and correlation analysis of each factor, and removes the weak stock selection ability among them. And a number of factors with high factor correlation. Then, the equal-weight linear regression algorithm, multiple Logistic algorithm and XGBoost algorithm were respectively applied to construct the stock selection model, and the evaluation indexes of the prediction results were calculated, and the following conclusions were obtained:

1. The Sharpe ratio obtained by the prediction results of the three algorithms is all greater than 1, indicating that the average rate of return within the selected time is greater than the risk-free rate of interest;

2. The Sharpe ratio of XGBoost algorithm is the highest, and the stock selection strategy constructed based on it maximizes the unit risk return of stocks;

3. The α coefficients of the prediction results of the three algorithms are all greater than 0, indicating that there is excess return in the market at this time, and the return of investment in stocks is greater than that of almost risk-free products such as bank savings.

4. The equal-weight linear regression algorithm has the highest maximum retracement rate, reaching 22.3%, while XGBoost algorithm is only 6.14%, indicating that XGBoost algorithm significantly reduces the maximum possible loss range during the investment period.