Bitcoin to C. Model A-G. Predictive Modeling Pt. 12. FUD & FOMO
Updated
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 G. Model G 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 and G. 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 12.
Red Bubbles = the past.
Blue Bubbles = Now + the predicted future.
Statistical Outliers = Emotions + and/or Market Manipulation. We are now at 20 Statistical Outliers.
Green Flags = Geometricc Convergence Indicators (There are almost 20 of them so far).
Converging Geometricc indicators = DROP
Diverging Geometricc indicators = RISE
I want you all to pay attention to statistical outliers.. These are patterns in the data that skew the line of fit for the geometric linear regression modeling that I use. Outliers are best understood as human emotions and/or market manipulation.. This is very important because; Fear, Uncertainty and Doubt (FUD).. and Fear of Missing Out (FOMO) are very well documented phenomena in cryptocurrency. The psychology of this phenomena is not well understood but a quick jab at the constructs, allow one to understand parts of this phenomena. In behavioral data collection in psychology, we document the observations made based on emotional responding to commands. (parameters given). If behavior or behavioral trends consistently occur outside the parameters given, this is noted.. and noted particularly as important because those parameters followed certain rules, rules that were maybe ignored.. or misunderstood or purposefully planned. As i am witnessing in the data collected from the Modeling sequences.. We follow micro and global patterns within the data.. Anytime we project outside the modeling cone, we are witnessing one of two phenomena occur. SO #1, is essentially emotional responding based on some news, event or arising situation that will negatively or positively effect a tokens price. Next we have, SO #2 which is purposeful market manipulation for a specific goal.. (usually to create FUD or FOMO).. These outliers.. can be behaviorally analyzed thru statistical means.. That is what i am attempting to do with my modeling design. I don't care about anything other then SO #1 and SO #2. If you measure the emotions and the manipulation.. You will accurately predict a trend.. This is what i am doing here.. Prediction of a trending token, based on Statistical Outliers using emotional and market manipulation as the foundation for my TA's.
As always thanks for looking!
Glitch420
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 and G. 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 12.
Red Bubbles = the past.
Blue Bubbles = Now + the predicted future.
Statistical Outliers = Emotions + and/or Market Manipulation. We are now at 20 Statistical Outliers.
Green Flags = Geometricc Convergence Indicators (There are almost 20 of them so far).
Converging Geometricc indicators = DROP
Diverging Geometricc indicators = RISE
I want you all to pay attention to statistical outliers.. These are patterns in the data that skew the line of fit for the geometric linear regression modeling that I use. Outliers are best understood as human emotions and/or market manipulation.. This is very important because; Fear, Uncertainty and Doubt (FUD).. and Fear of Missing Out (FOMO) are very well documented phenomena in cryptocurrency. The psychology of this phenomena is not well understood but a quick jab at the constructs, allow one to understand parts of this phenomena. In behavioral data collection in psychology, we document the observations made based on emotional responding to commands. (parameters given). If behavior or behavioral trends consistently occur outside the parameters given, this is noted.. and noted particularly as important because those parameters followed certain rules, rules that were maybe ignored.. or misunderstood or purposefully planned. As i am witnessing in the data collected from the Modeling sequences.. We follow micro and global patterns within the data.. Anytime we project outside the modeling cone, we are witnessing one of two phenomena occur. SO #1, is essentially emotional responding based on some news, event or arising situation that will negatively or positively effect a tokens price. Next we have, SO #2 which is purposeful market manipulation for a specific goal.. (usually to create FUD or FOMO).. These outliers.. can be behaviorally analyzed thru statistical means.. That is what i am attempting to do with my modeling design. I don't care about anything other then SO #1 and SO #2. If you measure the emotions and the manipulation.. You will accurately predict a trend.. This is what i am doing here.. Prediction of a trending token, based on Statistical Outliers using emotional and market manipulation as the foundation for my TA's.
As always thanks for looking!
Glitch420
Note
Closer view.Note
We need an outlier to blast out of Model G or we will follow it for a 3rd bottoming out.. Note
Converging vector found. The past 20 drops have had converging vectors..
Just something to note..
But seriously what do i know..
Note
close up 3 min..Note
geometry shows a drop here at some point, and it just so happends to end at the intersect of Model D... so let us see how this plays out.. Def not enough data to create a new prediction cone yet.. Model D is still in play here..
Note
We found support on the lower boundary ... This puts us in a path that diverges geometrically.. If we stay inside the projection cone upward.. we may leave the danger zone.. but that will take some serious power. If we re-enter Model D. It will be a complete outlier. And a great representation of what FUD and FOMO in statistical modeling is all about..
I think if you have a sharp eye you can see what i am headed with this.. Below the model was FUD and above Model D was FOMO. POSSIBLE. not a for sure.. but a guess.. it fits the modeling consistently from A thru G.
Note
5 min chart. Note
3 min chart..Note
That lower boundary test was insane.. It has to hold or we may re-enter Model D prediction cone.. IF we re-enter. It completes it as a statistical outlier to Model D. If we continue on this path outward away from Model D, a new prediction cone will be formed. We must wait now for the next move.
Note
We must stay above that lower boundary line in order to continue on the diverging pathway. We also bounced off the resistance line, which COMPLETES the FUD and FOMO outliers!!Ok. So this is my understanding.. Statistical outlier #21 was FUD. This is marked by the intense downward trend we saw until we hit out new bottom. Then we blast off back into the Model D prediction cone and work our way to the Model C prediction line that is responsible for 13 or so converging drops.. We blasted thru this resistance zone and continued upward, only to be followed by a correction, and onto a new lower boundary line. We fell from this boundary line and currently fight to stay on this path..
We may very well go back into the Model D zone, but also from here on out.. Statistical Outlier #22 is considered complete.. these next 4 hours dictate if we create Model H or keep to Model G..
Note
1 min chart. Note
Converging vector emerged.. Drop was indicated.. Note
well look at that we stayed in the convergence zone.. that's a first..Note
Massive update...Our convergence vector has now turned into a divergent vector..
This occurred due to a steep price recovery which created a new angle.. this angle now diverges from a center point outward indicating to me.. we may break free from this hold.. we may be seriously transitioning into a new model soon..
Note
still in the divergence vector and aimed well to escape that pit of hell..Note
Boom!Note
look at the size of that divergence vector..Note
Model H rendering zone..Data captured in this zone, will be used to Model H.
Model H coming soon.. This is so exciting! This maybe the start of something amazing!
Note
Testing lower boundary.. blast off initiated?Note
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