Hybrid ML for Market Regime Detection: HMM + K-Means on SPY, IWM, HYG, LQD, VIX

1. 1Import necessary libraries using pip install numpy pandas matplotlib scikit-learn
2. 2Load historical data for SPY, IWM, HYG, LQD, VIX using Yahoo Finance or Quandl
3. 3Apply PCA to reduce dimensionality and improve model performance
4. 4Implement Hidden Markov Model to identify regime transitions
5. 5Use K-Means clustering to group regimes and visualize results
6. 6Evaluate model performance using metrics such as accuracy and F1-score

Quantitative analysts and data scientists can benefit from this approach to identify market regimes and make informed investment decisions

💡 Hybrid ML approach can effectively detect market regimes by combining the strengths of HMM and K-Means clustering

Detect market regimes with Hybrid ML! Combine HMM, K-Means, and PCA in Python to identify equity, credit, and volatility regimes