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Using K-Means Clustering for Market Segmentation and Performance Analysis. Grouping companies based on volatility and return patterns to identify optimal trading opportunities.
Implementing unsupervised machine learning to group 30+ major S&P 500 companies based on their historical price movement patterns.
Using Normalizer to scale features independently, ensuring that volume and price
variations are comparable across different market caps.
Engineering features from Daily Returns and Intraday Volatility to provide a multifaceted view of market dynamics.