Not really -- in many forecasting applications in fast-changing markets, it is fairly common to dynamically retrain your recursive model to a moving window of historical data in order to adapt to your current environment (with some regularization). The length of the window depends on how fast the market changes.

For these types of recursive model applications, you cannot just fit the model once and forget about it.

as long as it works well on out of sample data at deployment time, it is okay.

Until some major data drift happens, but you would notoce it anyways

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