ML Models Fail in HFT Due to Shifting Market Rules

The world of High-Frequency Trading (HFT) and Market Microstructure is a dynamic and ever-changing landscape, where the rules of the game change rapidly. Machine Learning (ML) models, which rely on historical data, are often seen as a magic box that can be used to generate signals. However, this approach has a fatal flaw: it assumes that the market is a static map, when in fact it is a shifting ocean.

Standard Machine Learning models are built on the assumption that the statistical rules of the game stay the same. But in HFT, those rules change every millisecond. If you try to 'fit' a model to a fixed dataset, you aren't building a strategy that can adapt to the changing market conditions.