My team and I are busy coding up a python implementation of the information driven bars (imbalance and run bars) mentioned in Chapter 2 of the text book Advances in Financial Machine Learning.
There really isn't a lot of information published on this technique. I have read the papers in the bib, mainly:
- The volume clock: Insights into the high frequency paradigm
- Flow toxicity and liquidity in a high frequency world.
The two papers really highlight the importance of volume sampling and how market micro-structure features like VPIN can be used as an important features but neither provide deeper insight into the imbalance or run bars.
I then turned to chapter 19 which has a very nice explanation on the tick rule and the various micro-structure models and their generations.
However I still don't have a firm grasp on the implementation or details for the information driven bars.
The following blog post has helped us with the implementation Maks Ivanov.
Main question: Is there a piece of literature that I have missed? Where can we learn more about this technique. Is it mentioned somewhere in another journal or a slide show?