Encoding continuous values I've been reading about one-hot encoding for categorical values. Could similar mechanism make sense for continuous values? I'm looking at a problem, where I try to predict the volume of product X, with a variables being the price of product Y. 

The reason is to increase the strength of the signal from the price of Y, which otherwise could be understated because overall prices fluctuate in relatively low range. The idea would be to e.g. remove attribute of "Price Y" and replace it with "Promotion Y" based on a set threshold.

E.g.

Price X | Price Y | Vol X | Vol Y  | Promotion X  | Promotion Y
100     | 90      | 40    | 50     | 0            |  0
75      | 75      | 60    | 60     | 1            |  1
100     | 75      | 30    | 70     | 0            |  0


I could just test it out on a dataset, but this could be very specific to this very set and I just want some more theoretical suggestion if and where such approaches would make sense?
No. There is no similar mechanism for continuous variable.

If it worries you, that overall prices fluctuate in relatively low range,
you can

1) demean the price, that is subtract mean price from all price values. Then negative values will clearly show below-average and positive above-average prices.

2) after demeaning you can divide values by the standard deviation of the price. (1) and (2) together is called "standardization".

Alternatively you can 

3) rescale your price to the range of values you want. Usual choice is (0,1) range.

If you do this for one feature (price in your case), then it makes sense to do the same for other features. 

Whether or not this will help to get better prediction results depends on the model. Some models, a typical example would be SVM, do require such transformation.