A stock's beta and volatility are not constants, and nobody tells you today's value. This skill treats them as a hidden state that drifts, and explains how to track it in plain steps with short checks (numbers you compute, steps you order, concepts you match) and finance examples: why a filter beats reading one noisy observation, what the Kalman filter does when everything is a bell curve, how a cloud of weighted particles does the same when it is not, why weights collapse and how resampling repairs them, how to follow volatility on real SPY returns, how to learn the model's own numbers, how to spot a change point, and how rolling, exponential and online estimators compare. The capstone is a code lab where you build the particle filter, a Kalman filter and a rolling regression and track the beta of Apple against SPY.
Hidden Markov models are expected (filtering, Bayes' rule, a hidden state and its observations), together with basic probability and normal distributions.
