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.
Another question is about buy-sell modeling.
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.
Can you share some source code or guidance for similar situation?