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 may only reflect information available and know at the given time. ML algorithms trained on distorted historical data will almost certainly fail during live trading.

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