# Basic counts on individual waves

Starting simple by doing a bit of house keeping concerning the different wave types:

Nothing too surprising considering what I was looking for in the data, but something does stick out to me in this comparison chart that I didn’t notice the first time. Does anyone else see it?. The “bigger” letters with more new waves in them (G, H) have a much higher LL and HH count by nature of their wave structure. It makes 8 or 9 HH/LL in a day, so they can’t have too many inside bars. Conversely, the A, B, and C structures have much more time in the middle. These pieces aren’t very useful now, but with other ides, they might provide some more info.

Next, redoing the time specific stat with all 8 wave types:

Excels auto coloring shows nothing special. All wave types look very similar in coloring, which it gradually becoming more block-y as there is less and less data to create a good number range. Looks cool though doesn’t it?

So what did I learn with these two statistics? Starting with the second statistic first, there doesn’t seem to be any noticeable difference in where significant actions occur time wise. That is, it doesn’t appear that HHs or LLs for example appear more frequently between the hours of 2:00 and 3:00 in B and C waves than they do in the D and E waves. The other thing, that I hinted towards earlier,

It could be nothing, and perhaps I will write this down as something to test more thoroughly with more data; as an aside, even though my data set is quite large, when I split the data between 8 wave types, the amount of data I have per wave type decreases dramatically. However, There seems to be a consistent “edge” in the percentages to the HHs in most of the wave types (with the exception of Type H which also interestingly is the smallest set). What this is saying the the purest sense is simply that HH appears the most in the days of these wave types. At this stage of the data, consecutive HHs and LLs have not been filtered out. What this could possibly suggest is support for an old old theory about “speed” and momentum in the market. Things fall faster than they rise. It requires more HHs to build up what a few LLs and bring down. There is of course, another explanation. That price from today versus price from the beginning of the dataset has gone up significantly. Always think of alternative reasoning to disprove a theory rather than to help prove it.