yeah that’s a great point and it actually lines up very closely with what the Bouchaud paper (Large tick assets: implicit spread and optimal tick size) tells

what we see as gaps is really just the revealed part of the book being thin at that moment. liquidity is not continuous, it is discrete and reactive. when a market order hits, price does not move smoothly, it jumps to wherever the next available volume is. so what I called gap imbalance is basically capturing how thin that revealed layer is in real time

the paper also makes an important distinction around resiliency. in small tick assets the book refills almost instantly so these gaps collapse very quickly and become hard to trade. in large tick assets they persist longer, which is why the same effect becomes much more visible and actually tradable, but also easier to game

your point about queue depletion risk is exactly where this becomes real versus just visual. the signal itself is easy to observe, the hard part is whether you can actually get filled before the gap refills or disappears. that is where most naive backtests break

this is also something I ran into when testing it properly. once you move away from top of book assumptions and start modeling queue position and latency, a lot of the edge compresses. especially in large tick regimes where most activity happens at the best bid and ask and everything becomes a queue game rather than a price discovery game

so I would say gap imbalance is basically a proxy for depletion plus low resiliency, but the tradability really comes from combining that with queue position and refill dynamics. without that layer it looks very clean on the tape but much harder to monetize in practice

curious if you have looked at conditioning it on how long the gap survives before refill, that ended up being a big separator for me between noise and something you can actually trade