Market Regime Detection

Every trading strategy has conditions where it works and conditions where it falls apart. Many systems ignore this, running the same strategy and sizing whether the market is trending, mean-reverting, or in crisis.

Market Regime Detection teaches you to identify, model, and act on changing market states. You begin with why regimes matter and how to think about them. You then learn to model hidden market states with Hidden Markov Models in Python, so that you can detect states with evidence instead of guesswork. The final part connects those states to action, showing how to switch strategies and adjust allocation as conditions change, and how to evaluate that adaptive approach across historical periods.

You will come away with a regime-aware research workflow: define states, detect them, link them to suitable strategies, and test the results. It is written for readers with Python, probability, and backtesting experience, and it teaches method rather than promising returns.