Developing a ML solution for an algorithmic trading strategy requires a systematic approach to maximize the chances of success while economizing on resources. It is also very important to make the process transparent and replicable in order to facilitate collaboration, maintenance, and later refinements.

The following chart outlines the key steps from problem definition to the deployment of a predictive solution:

The process is iterative throughout the sequence, and the effort required at different stages will vary according to the project, but this process should generally include the following steps:

1. Frame the problem, identify a target metric, and define success
2. Source, clean, and validate the data
3. Understand your data and generate informative features
4. Pick one or more machine learning algorithms suitable for your data
5. Train, test, and tune your models
6. Use your model to solve the original problem

We will walk through these steps in the following sections using a simple example to illustrate some of the key points.