Online Learning

Online learning is a machine learning paradigm where a model is updated incrementally as new data arrives, rather than being trained on a static dataset. This is ideal for high-frequency trading and crypto markets where data is continuous and patterns evolve rapidly.

Instead of waiting for a batch of data to retrain the entire system, the model adapts to new information in real-time. This allows it to stay current with changing market conditions and mitigate the effects of predictive decay.

However, online learning also carries risks, as the model can be corrupted by outliers or malicious data injection if not properly guarded. It requires a robust framework for continuous evaluation and safety constraints to ensure the model remains stable while learning from the live market.