Online learning is a crucial machine learning method for real-time applications, particularly where data streams are rapidly evolving. It allows models to adaptively learn from incoming data, essential for sectors such as trading, where timely and precise predictions are necessary. Implementing online learning in platforms like MetaTrader 5 requires a distinct approach, integrating Python with MQL5 to handle model training and deployment efficiently.

By automating processes using libraries like CatBoost and GRU, models can be updated continuously in response to new data. This setup ensures that models remain relevant, leveraging an infrastructure tailored for dynamic environments. However, moderation is key, as frequently updating models can introduce sensitivity to data sequences, necessitating periodic human review.

Models built incrementally can be...
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