07. Practical Application: K-means

AI For Trading C1 L2 A07 Practical Application K-Means V4

Applying K-Means Clustering to S&P 500 Stocks

Objective: Understand how to use k-means clustering to categorize the S&P 500 stocks using Python.

  • K-Means Clustering: A technique to group similar data points into clusters based on their features.
  • Tools Used:
    • Pandas Library: Manage and organize stock data.
    • Scikit-Learn Library: Perform clustering computations.

Process Overview:

  • Challenge: Analyzing individual stock returns can mask broader patterns.
  • Solution: K-means clusters stocks with similar characteristics, revealing insights at a glance.

Stock Categories:

  • Basket of Stock Examples: From diverse sectors like tech, healthcare, and finance.
  • Characteristic Analysis: Assess factors such as earnings per share (EPS) and price-to-earnings (P/E) ratios.
    • Group 1: Stocks with strong fundamentals and low valuations (potential buys).
    • Group 2: Stocks with opposite traits (potential sells).

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