Markov chain · daily direction model
Enter one or more symbols. We pull six months of daily closes, classify each day as up, down, or flat, build a transition matrix, and simulate the next ten trading days.
A Markov chain is a simple model of state-to-state transitions: the probability of the next state depends only on the current state, not on the full past. Here, each trading day is one of three states based on its percent change versus the prior close:
From the historical sequence of states we count how often each state follows each other state, then normalize the rows to get a transition matrix. Starting from the latest day, we sample forward ten steps using a seeded pseudo-random draw, so results are reproducible.
Educational only. This is a statistical exercise, not investment advice.
Enter a ticker above to build its Markov chain.
Market snapshot
A quick, educational read of each analyzed symbol: 52-week range, P/E ratio, a heuristic options-activity check, volume versus its average, upcoming earnings, and a monthly seasonality breakdown. Fields show N/A when public data is unavailable or throttled.
Markov chain learning center
These short educational posts explain the same ideas behind the ticker tool: states, transition probabilities, transition matrices, and why simple models need careful interpretation.
Post 1
A Markov chain starts by converting a messy sequence into a small set of states. In this site, every trading day becomes up, flat, or down based on the day’s price change.
That simplification makes the model readable. Instead of asking where price will go exactly, we ask how often the market moved from one state to another in the recent past.
Post 2
After each day is labeled, the model counts transitions: up-to-up, up-to-flat, up-to-down, and so on. Each row is then normalized so the probabilities add to one.
Reading across a row answers a simple question: “Given the current state, what state historically came next most often?”
Post 3
The ten-step sequence is not a forecast. It is one possible path sampled from the transition probabilities. Run the model with different history windows or thresholds and the path can change.
The useful part is not any single simulated path. The useful part is seeing whether recent state transitions were balanced, persistent, or clustered around one condition.
Post 4
A basic Markov chain assumes the next state only depends on the current state. Real markets are influenced by news, liquidity, interest rates, positioning, earnings, and many other variables.
That is why this site presents the output as an educational probability model, not a buy or sell signal. The model is best used for learning how transition-based thinking works.