This is a continuation thread of the theoretical geometricc linear regression from 3.22.18. The modeling sequence starts at; Model A, and runs thru Model H. Model H is the newest Model. Each model is strictly built off of the preceding models geometricc regression points. The regression points from each model, creates a geometricc pattern of indicators, that can be read to PREDICT future trend movement, before actual traditional indicators occur.
I am going to try my best to explain, as we go... There will be lots of bubbles with text, explaining each move and why.. and how i make prediction cones, and patterns using geometricc boundary lines and regression modeling. This is A FULLY EXPERIMENTAL MODEL. Take it for what it is worth. I will continue to make these charts regardless of comments or jabs. They are made for a specific purpose and until my purpose is fulfilled, they will keep being made.
The idea here is to convince you, that what i am doing is not arbitrary but unique and useful. I know the immediate inclination is to doubt what I am doing. That is expected.. and understandable.. But human nature is unpredictable. And you never know when you can learn new things and be completely shocked at someones EXTREMELY insane ideas.. I like going against the norm.. being different is what makes you stand out.. So stand out from the rest.
So, watch what I do.. Ask questions, I will try my best to answer them.. if you are confused on how I got to Model A, B, C, D, E, F, G, H. Skim thru my old charts start from 3.22.18. It is about modeling sequencing, and appropriate modeling coherence. I have decided to explain each move I make regarding my theoretical modeling technique. This is part 13.
Red Bubbles = the past.
Blue Bubbles = Now + the predicted future.
Statistical Outliers = Emotions + and/or Market Manipulation. We are now at 22 Statistical Outliers from Model A thru Model G
Green Flags = Geometricc Convergence Indicators (There are almost 20 of them so far).
Converging Geometricc indicators = DROP
Diverging Geometricc indicators = RISE
I want to explain how I created Model H. First understand that Model A through Model G, was created based off of the preceding model. Model H is no different. Once i saw a geometric divergence in the background geometry, and it the widened. I knew it was possible we had reached bottom and were beginning our recovery.. Prediction Model C line boundary extended all the way to Model G. It was responsible for 13 convergence drops. It was a big deal to leave that line and stay above it. By staying above it the geometric indicators were fanning out (diverging) with no indication of converging geometry.. The geometry is based off of the lowest data point in the trend, and the best LINE OF FIT in the regression modeling. The 'line of fit' is simply the best line that fits between a set # of data points.. In this case, the data i am looking at are statistical outliers. So that is where i started my lower boundary Statistical Outlier #22 FOMO, I continue this method of placing lower boundary lines as more data appears.. (x over time). The boundary lines act as a background indicator, and by using the concepts of convergence and divergent vectors.. It seems by this field testing, that these geometric indicators should NOT be ignored.
Model H.. Simple is a divergent vector of paths, that do not converge at any one point along the path of the current data trend. If we get a convergent lower boundary intersect with a older divergent boundary.. it has been consistent that a drop may occur. Likewise, if we have a convergent lower boundary that is intersecting a divergent boundary and the trend moves in such a way that now that convergent boundary makes no sense.. a simple test to see if a divergent boundary will fit better is used.. You must use the BEST LINE OF FIT in order to have high accuracy. Incorrect line regression, will yield incorrect geo-indicators.
As always thanks for looking!
Glitch420