We will proceed to train the data, based on our agent and helper methods. This will provide us with one of three actions, based on the states of the stock prices at the end of the day. These states can be to buy, sell, or hold. During training, the prescribed action for each day is predicted, and the price (profit, loss, or unchanged) of the action is calculated. The cumulative sum will be calculated at the end of the training period, and we will see whether there has been a profit or a loss. The aim is to maximize the total profit.
Let's start with the imports, as follows:
from agent import Agentfrom helper import getStockData, getStateimport sys
- Next, define the number of market days to consider as the window size, and ...
