This research models the S&P 500’s underlying volatility states using a 2-State Hidden Markov Model (HMM). By assuming the market operates in hidden, non-stationary regimes, we can mathematically isolate periods of low-volatility “bull” markets from high-volatility “panic” phases without relying on lagging moving averages. Three strategies are evaluated out-of-sample using a walk-forward framework: Strategy A (Baseline): Hard binary switching with an expanding training window Strategy B (Rolling Window): Hard binary switching with a fixed 3-year rolling training window, preventing over-indexing on historical crises Strategy C (Soft Allocation): Probability-weighted exposure using the posterior normal-regime probability as a continuous position size, eliminating abrupt switches