The machine learning workflow

Developing an 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:

Figure 6.1: Key steps of the machine learning workflow

The process is iterative throughout, and the effort required at different stages will vary according to the project. Generally, however, this process should 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...