Hidden Markov Model

Overview

This model uses a Hidden Markov Model to identify and predict market regimes in real-time. It is designed to probabilistically identify market regime changes and predict potential reversal point using a forward algorithm to calculate the probability of a state.

Unlike traditional technical indicators that rely on price patterns or moving averages, this HMM analyses the underlying statistical structure of market movements to detect when the market transitions between different behavioural states such as trending, ranging, or volatile periods

How it works

The HMM assumes that market behavior follows hidden states that aren't directly observable, but can be inferred from observable market data (emissions). The model uses a (somewhat simplified) Bayesian inference to estimate these probabilities.

State 0: (Normal Trading): Market continuation patterns, balanced buying/selling
State 1: (Top Formation): Exhaustion patterns at price highs
State 2: (Bottom Formation): Capitulation patterns at price lows

How to use

1) Identify the trend (you can also use it counter-trend)
2) For longing, look for a green arrow. The probability values should be red. For shorting, look for a red arrow. The probability values should be green
3) For added confluence, look for high probability values of above 25%.

Advantages and what makes it unique

Unlike moving averages or oscillators that react to price changes, the HMM proactively identifies the underlying market structure. This forward-looking approach can signal regime changes before they become apparent in price action, providing traders with an informational edge.