Why can we not split train test data with 0.01 as parameter or 99% training data
The idea of splitting 80%-20% is to use as much data as possible for training while keeping enough (labeled) data in the test set in order to reliably evaluate the performance. It's fine to use a smaller test set if it's big enough, but if it's too small the evaluation might not reflect the real performance of the model. 

Your idea of using future test data is fine, it simply means that the reliable evaluation is delayed until you obtain enough test data. In the meantime you cannot be sure that your model performs well, so I would avoid using it for real trading on the stock exchange ;)