Problem statements and key decisions
The finance domain is large and complex, so you can easily spend several years learning something new every day. In our example, we will just scratch the surface a bit with our RL tools, and our problem will be formulated as simply as possible, using price as an observation. We will investigate whether it will be possible for our agent to learn when the best time is to buy one single share and then close the position to maximize the profit. The purpose of this example is to show how flexible the RL model can be and what the first steps are that you usually need to take to apply RL to a real-life use case.
As you already know, to formulate RL problems, three things are needed: observation of the environment, possible actions, and a reward system. In previous chapters, all three were already given to us, and the internal machinery of the environment was hidden. Now we're in a different situation, so we need to decide ourselves what our agent...
