How to normalise daily trading volume and trading value as features for RNN model of stock time series?
The immediate answer could be: taking the global min/max, mean/std for each stock across the whole time series window, normalise the daily volume/value of the corresponding stock, stock by stock. However, during each stock cycle, the trading volume/value range in the particular cycle could vary drastically compared to other cycles, in all these “local” window of a given stock, does it make sense to find dynamic local min/max, mean/std for local normalisation? Or, does it make sense to calculate derivatives of volume/value, 1st and 2nd orders to capture and velocity and acceleration of the market capital flow?