This quantitative research short study is a simplistic approach to the application of the hidden markov model on time series pattern recognition and market regime inferences that is amenable to systematic strategies implementation. Hidden Markov Models shows the ability to determine market regime state defined by any selected features.

Hidden Markov Model (HMM) is a statistical model that uses observed data to infer the underlying hidden states and make predictions based on probabilistic transitions between states.

• Hidden Markov Modeling - ES - E-mini S&P 500
• Hidden-Markov-Models- ES - Systematic-Strategy

• Detecting bearish and bullish markets in financial time series using hierarchical hidden Markov models
• Markov Models for Commodity Futures: Theory and Practice
• Market Regime Identification Using Hidden Markov Models
• Predicting Daily Probability Distributions of S&P500 Returns
• Stock Market Trend Analysis Using Hidden Markov Model and Long Short Term Memory

• requirements.txt

• MIT License