Training Set Refresh

Training set refresh is the process of updating the historical data used to train a machine learning model. By including the most recent market data, the model can adapt to new trends and patterns.

This is essential for preventing prediction decay and ensuring the model remains accurate in a changing environment. The frequency of the refresh depends on the speed of the market and the volatility of the asset.

In high-frequency trading, this might happen continuously, while in longer-term strategies, it might be monthly. A well-managed refresh cycle is critical for the long-term success of any quantitative model.

It ensures that the model's knowledge base is current and relevant.