One property of High-Frequency data is it's subject to bid-ask bounce.
Description : Unlike traditional data based on just closing prices, tick data carry additional supply-and-demand information in the form of bid and ask prices and offering sizes.
As a researcher, it can be an advantage because bid and ask quotes can carry valuable information about impending market moves.
However, bid and ask quotes are separated by a spread. Continuous movement from bid to ask and back introduces a jump process, difficult to deal with through many conventional models.
I am actually working on high-frequency trading project. At the beginning, I wanted to predict the price movement, but because the price is non-stationary and other reasons, it made the problem harder than I was expected. Now, instead of dealing with the price, I deal with the log-returns on the midprice (similar to the standard price) and the features.
Why am I talking of features?
For autoregressive problem, it is well known that RNN-LSTM perform pretty well on a time-series forecasting problem. As the midprices as well as the features are not stationary, then I applied the log-returns on all of them. I think it will be easier to approach the solution of my problem.
However, now, I think I face the problem of bid-ask bounce. How can I get over that setback? Does the log on the midprice is sufficient to face the bid-ask bounce problem?