I would like to use xgboost to predict stock movement by using technical indicators as features. The first feature is 'rate of price change'. It does not rely on the magnitude of price, and therefore it is comparable for different stocks.
However, the second feature depends on the magnitude of price or trade volume, e.g., 'rate of price change' multiplied by 'trade volume'. Different stocks have different range of trade volume, thus this feature is not comparable for different stocks in the magnitude.
Because different stocks may have a bit common rules for their price movements, we use only one xgboost model to train and predict on all the stocks. But the second feature has different magnitudes for different stocks, which may make it hard to use one model to find common rules for different stocks.
Moreover, it is also meaningless to simply combine the second feature from all stocks to generate a new feature because of different magnitudes.