So when a trend ends in DV or T FTT bar, gaussians dont recognize that price extreme as new pt1? Maybe P on different tf, but then you need to be searching for it and already know it should be there.
A new extreme wouldnt always be pt3 or xo rtl. What Im saying to understand drill 4, maybe consider the sequence: t < iv < p > dv/t, in real market data? Though this model seems more rdbms than gaussian, if gaussians follow the pattern so closely.
Rdbms sequence: p1(ass.) t1 p2 t2p
t2f = end nearing
and: p1p1 t1 t1 p2 p2 t2p t2p
Here p1p1 become P. Assigned P1 could be IV or T.
What supported me in differentiation was understanding the history and progression of the various systems Jack shared. So fortunately for me I started at the beginning with PVT. I figured out how to program it and made good money with it and then put it on hold when Yahoo broke their api. It was good timing in that it served as ‘proof of concept’ to go ‘all in’ on Jack’s teachings.
Gaussians can be perplexing and on one level follow a rigid ruleset and on another level don’t. I interpret that is why so many in the past had trouble implementing them.
Rdbms is more defined in this way in that it accounts for routine and/or complete trends as well as failsafe and/or incomplete ones.
It defines high volume breakouts (peaks) as well as low volume breakouts (troughs) as ftt’s.
It also defines trend segments that can last a single bar which gaussians have a difficult time conveying.
The one thing that rdbms doesn’t share completely with gaussians is the rigidness of the geometric container. This can be a perplexing crux given training one’s eye and applying SCT in real market conditions.
It’s like one has to put an acquired skill to the side in order to gain a new one.
How I see Jack’s work was moving a practitioner’s mind from coarse to finer and finer detail. He was constantly innovating.
To use a metaphor, when one looks through a microscope, what was once perceived as smooth can appear very textured.
Thus how initial ftt’s can lead to fbo’s and fanning as ‘What wasn’t That’ leads one to the true ftt.
I interpret RDBMS as a descriptive and anticipatory language not necessarily a predictive one. It’s much more fluid.
It does anticipate turning points pretty well on the 5m frame but I have yet to integrate it on larger timeframes (D,W,M) which include price action and the corresponding low volume of non-RTH trading segments.
a huge THANK YOU to you three.