First tick profit?

Trader's Mindset

Aug 06

A question I have wondered about, and I’m sure others have too, is if we have some bigger edge such as say a system that has multi-point profit target, could we simply capture that edge using a 1 tick profit? In other words, the question we want to think about is whether or not the edge will scale down.

In order to think about it, let us imagine we enter at time T and exit at T1 or at target TA1 or stop S1. If the edge is evident in every tick then the market price (P), must move in a linear fashion from P to to Ta1 or S1. The price movement must be equally distributed in time. One can imagine that this would look like a sloping line with a very gentle slope that would tick up or down at very regular predictable intervals. If it started moving toward Ta1 or S1 then it would continue in the same direction, never ticking the other way, until it reached it or time expired. If all that were true then yes the premise that you could translate a larger edge into a micro-edge would be true. Obviously, that is not true.

Now, let us imagine another scenario where the market randomly moves around within Ta1 and S1 some Z% of the time. If those micro-jumps were distributed such as to advantage Ta1 at the same or better probability then it would also scale down. On the other hand, if the micro-movements were pretty much random your results of capturing a 1 tick profit will be random even though there is an edge present on the “higher time frame”.

The question might be then where does that edge come from? If it cannot be discovered at the lower scales. The answer must be that it comes from random jumps that occur infrequently between time T and T1. I suspect there is more value in continuing this line of thinking at different scales.


About the Author

The author is passionate about markets. He has developed top ranked futures strategies. His core focus is (1) applying machine learning and developing systematic strategies, and (2) solving the toughest problems of discretionary trading by applying quantitative tools, machine learning, and performance discipline. You can contact the author at