Live Tool

Markov Trend

Markov Trend estimates the next move by asking how similar short state sequences behaved historically, then turning that pattern memory into probabilities.

Transition framework

\[ P(X_{t+1}=s_j \mid X_t=s_i) \]

The next state depends on the current pattern state, not the full distant past.

\[ \hat{P}(s_j \mid \pi)=\frac{N(\pi \rightarrow s_j)}{\sum_k N(\pi \rightarrow s_k)} \]

For a sequence \(\pi\), the model estimates the next-state probability from historical transitions.

Example use case

If the latest pattern is N X P N and history shows the next day was positive 12 times out of 20, the model assigns a 60% positive probability for that setup. That is not certainty; it is quantified context.

Best use case

Use Markov Trend when you want short-horizon pattern context expressed as probabilities instead of pure intuition.

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