Hands-On Machine Learning for Algorithmic Trading

Sourcing and managing data

The dramatic evolution of data in terms of volume, variety, and velocity is both a necessary condition for and driving force of the application of ML to algorithmic trading. The proliferating supply of data requires active management to uncover potential value, including the following steps:

1. Identify and evaluate market, fundamental, and alternative data sources containing alpha signals that do not decay too quickly.
2. Deploy or access cloud-based scalable data infrastructure and analytical tools like Hadoop or Spark Sourcing to facilitate fast, flexible data access
3. Carefully manage and curate data to avoid look-ahead bias by adjusting it to the desired frequency on a point-in-time (PIT) basis. This means that data ...