The goal of this project is to create an options trading web application that creates a QLearning model from the IBM stock data. Then the app will extract the output from the model as a JSON object and show the result to the user. Figure 10, shows the overall workflow:

The compute API prepares the input for the Q-learning algorithm, and the algorithm starts by extracting the data from the files to build the option model. Then it performs operations on the data such as normalization and discretization. It passes all of this to the Q-learning algorithm to train the model. After that, the compute API gets the model from the algorithm, extracts the best policy data, and puts it onto JSON to be returned to the web browser. Well, the implementation of the options trading strategy using Q-learning consists of the following steps:

• Describing the property of an option
• Defining the function approximation
• Specifying the constraints on the state transition