# How can I model open environment in reinforcement learning? [closed]

I'm studying reinforcement learning in order to implement a kind of time series pattern analyzer such as market.

The most examples I have seen are based on the maze environment.

But in real market environment, the signal changes endlessly as time passes and I can not guess how can I model environment and states.

Let's assume that the agent randomly buy at time $t$ and sell at time $t + \alpha$.
It's simple to calculate reward. The problem is how can I model $Q$ matrix and how can I model signals between buy and sell actions.