![]() import tejapiįrom sklearn.preprocessing import StandardScaler This article uses Windows OS and employs Jupyter as the editor. Methods for backtesting portfolio performance, applicable to various investment strategies.Įditing Environment and Module Requirements.Understanding the eigenvalues and eigenvectors of PCA and using them to design an investment portfolio. ![]() Readers of this article will see the following key points: The main purpose of this study is to utilize daily stock return data, apply PCA to obtain principal components, and construct an investment portfolio. Its core idea is to break down the original data into representative principal components, achieving dimensionality reduction and providing a new description of the data. Principal Component Analysis (PCA), a crucial technique in unsupervised learning, is widely used in the fields of machine learning and statistics to analyze data and reduce data dimensionality. The essence of mathematics is not to complicate simple things but to simplify complex things.” – Stan Gudder Searching for the Optimal PCA Portfolio.Creating an Investment Portfolio using PCA.Editing Environment and Module Requirements.
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