Python Implementation and Analysis of Quadratic Optimization Algorithm in Quantitative Investment Strategy

Now, the quantitative investment strategy is getting increasingly popular, and the quadratic optimization algorithm is getting more and more attention from investors because it can help us solve the portfolio optimization problem better. This article is to study how quadratic optimization algorithms are used in quantitative investment, and how to implement them with Python. Firstly, this paper gives the basic concept of quadratic optimization algorithms and how important they are in financial engineering. Then, through several commonly used quadratic optimization algorithms, such as linear programming, quadratic programming, and semidefinite programming, it is studied that they are used in quantitative investment. In this article, we will use specific Python code examples, such as how to implement these algorithms with SciPy and CVXPY libraries. Finally, through the analysis, show the performance and role of these algorithms in the real quantitative investment strategy, and what should be paid attention to. This article can not only provide some practical help to quantitative investors, but also bring some new inspiration to researchers in the field of financial engineering.