I’ve started to look through the data a bit; Here’s one of the basic calculations I ran.

Simple counts:

I’m working with ~3500 days of data x 24 hours, giving me pretty good sample size.

As it turns out, over 50% of bars are inside move bars. However remember that these bars are only as such in respect to how I categorize them. An inside bar does not make a new high or new low, *taking into account the current days range*. What’s also interesting is how cell shaped this data is. There is very obvious clustering in the mid levels, and spread out the ends.

Originally, I thought I would have to flip the percentages in order to obtain a “true retracement” percentage. See, if price moved from 0 to 10 and then to 8, I would calculate the 8 as 80% [ 8/(10-0), or more specifically, (8-0)/(10-0) ]. Now, the move is at the 80%* level, *but price *retraced* 20%, or 100-80%. But with the data so uniform and obvious, I don’t need to perform such a statistic; both versions will show a bell curve.

What do these results really mean? One of the conclusions I can draw is that price isn’t floating. Price doesn’t make new highs or lows, and then sit in range forever. If I want good odds, I can’t favor, say the 40-60% range over the 30-40% + 60-70% range. (40-60% covers 28%, the latter is still covering 26%, only a 2% difference) I can only really favor the entire 20-80% over the areas outside of that (0-20 and 80-100). While it may not give great probability areas to trade, I would like to think it gives areas *not* to trade. If price is bullish and makes a 10 or 20% retracement, odds are favoring further retracement before a new move to make a high.

What’s next is to think about what could possibly be happening at these levels. For example, how often at retracements back to the 40-60% range leading to breakouts, and how often are 80-99% retracements leading to a breakout on the other side?

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