*The theme of this post is confirming hunches. Most of this isn’t really new or groundbreaking, but rather providing statistical information on things that already make sense.*

Finished v2 of the fractal model, which I think will end up in v3 or v4, but I haven’t quite figured out how I want to approach the other iterations. There isn’t too much of a difference between v1 and v2, but it cleans up what I call “false extensions”.

Here, the normal swing progression is SFFTE, however, the trend leg isn’t what I consider a true trend leg because it doesn’t break the lowest low before it. The model removes the 2 swings before it, and re-classifies the leg as a Flat, and changing the leg after it to a Trend.

The baseline stats are relatively unchanged (v2 on the bottom):

Noticing that the Trend and Expansion legs have significantly higher averages than means compared to their swing and flat counter-parts due to expected trend anomalies. Swings and Flats are confined to the 100% range space that defines then, while Trend and Expansion legs are not.

**Swing Leg Findings:**

When it comes to swing legs, the question I’m trying to answer is: “where is the safest place to be able to place a trade in preparation of a continuation of the trend?

All the same photo just enhanced. There’s a small gap area shown in the triangle that suggests that when the previous wave is small, the retracement tends to be quite large in proportion, or at least there’s a small probability that it is in the “normal” 30-60% range. Smaller waves naturally have a harder time successfully printing a retracement while keeping the range small.

Past previous swing lengths of around 60 pips, the distribution looks a bit more random and even.

This next picture is the swing retracement when compared to the swing leg itself. The correlation is more obvious which is great but there isn’t much predictive use of this if at all. Nevertheless, just for comprehensiveness, I think it’s nice to have.

**Trend Leg Findings:**

Full View on top, the condensed version below.

Kind of shows the limitations on how big ratios can be. Legs that are smaller (less than 150 pips or so) have the potential to have following legs that are up to 5x more, although even in the complete set of data so far it’s not very common. On the other hand, very large legs (200+ pips) have a “cap” of less than 3x.

The large bulk of swing ratios being under 1 gives a slightly different way to track the bounds of where a leg can end. Noting from above that the median/average T leg extension is around 1.5/2.6 respectively, initially thought that I could just subtract 1 and that would leave the ratio of 0.5-1.6, but that’s not quite correct.

Overall I’m still kind of mixed if this is any “better”. There are simply tradeoffs. The net direction of T->T direction is ~57% in this model compared to 63% here. Although it’s pretty close to 60% either way, if we’re going to be technical about it, the sample size is statistically significant and the trend “conversion” rate of the previous model is *better* than the new model (not great). However, the new model is much cleaner and allows me to be more confident that we’ve established leg ‘x’ ending/starting much much earlier which is a huge win.

Wave patterns from T leg to next T leg:

H4:

H1 and H4 Fixed patterns when first leg starts as a T Leg:

# Next steps:

looking at trend waves in 2 different ways is new to me and it got me thinking a lot about what “normal” waves look like.

It’s hard to say whether or not one trend wave is more “correct” than another or if some should be excluded. Some of them certainly look more textbook than others. Since normal sounds so normal, it’s probably a rabbit hole of information. Till next time.