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
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3 min chart closer look.
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Goodmorning!
As you can see the newly rendered model H prediction cone is doing its job.. keeping the data within its prediction cone. :)
Understand a statistical outlier can knock us out of Model H at any time, at which point the outlier would have to complete itself upon re-entry into the modeled zone. A completed outlier keeps the Model True. I have shit load of research and homework to finish today, so I will try and keep this thread updated..
As always .. thanks for looking..
A word of warning.. Please do not trade based off of my charts.. You are responsible for your money. I have been making these charts for about a week. That is all the exp i have doing this. I love making these charts, but as stated multiple times these charts, constructs and ideas are completely 100% theoretical model that i designed from scratch based off of my neuropsychology research on neural network modeling of the brain.
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New Target 7200..
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MUST CROSS MODEL G INTERSECT LINE. MODEL G can still pull us down until we cross that threshhold.
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That line is indicating something.. I say 60% we blast off on that line.. and 40% we drop on that line.. Complete guess. but geometric indicators show an up but Model G was right on the money and we are nearing its peak..
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Model G says, 'get rekt bull trend.. Model G play no games..' 0_o
We must stay in the divergence zone.. or risk a FUD outlier outside Model H.
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I hate when i post a duplicate (angry face).
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Ya this zone indicated between red boundary lines, we must get out of.. The peak of Model G indicated a drop.. we are fighting through the peak zone now..
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well look at that shit.. all lower boundary paths have held and we are modeling a nice 100% accuracy with Model H. Still gotta get outta the danger zone though.. :)
no idea how i keep doing it.. but it is working whatever i am doing! ;)
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Playing around with some new tools..
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right on the boundary line.. will we see any action?... the world may never know..
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I think we are about to witness Statistical Outlier #22 which would consist of a FUD outlier.
It shows we are just outside the danger zone, but i have a feeling that danger zone should be expanded since, i am getting geometric indications of a drop..
Drop into Outlier #22 = 60% Rise back into new channel = 40%.. Just guesses..
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the train just keeps on rolling... right thru Model H prediction cone.. so far it stands at 100% accuracy. (the entire model sequence A-H, not necessarily MY calls).
It is hitting the divergent boundaries at all the correct points, so far we are still good.. as we get out further.. i feel things are going to get more dicey..
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The bigger picture now..
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not much to say... as we reach the outer limits of Model H, we can render a new prediction cone based on ANY other model's geometry in the modeling sequence (Model A thru H)..
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Still at 100% accuracy.. Kinda blown away.
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1 hr Chart.
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A new Model Connect line has been formed between Model A-F and Model H. It is depicted by yellow line, which so happens to be the best 'line of fit' to the data points at that specific zone in Model H. There are no coincidences in predictive modeling.. Past data + current data = future data (+/- statistical outliers).
Upon the connect line, a new geodivergence was present, this marked a new foundation line for geodivergence that i extended to ZONE C (goal area).
Can you please add your R_squared regression values for each model A-G, and then finally H. I understand H will be different. Many thanks.
Glitch420
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@lpninja, I am in the process of calculating my values.. It is tricky because each model covers a different vector of space. I need each model to have equal space in order to calculate some of these values. it has been a challenge and a half to calculate variance and my R_squared values. I keep getting wild numbers, that i know are not correct. I have been insanely busy with my classes, and my advanced statistics teacher who has been helping me with this little project has been insanely busy since we are at the end of the semester. In due time i will have them calculated..
I am really at the point of modeling coherence right now.. As you may know.. in order to calculate these values you need to make sure the Model's A thru G have coherence and set term-interactions.
Term interactions is where i am at now.. I am figuring out which models have overlapping variances with one another.. The models with the strongest variance overlaps will be calculated first, then we can compare strongly correlated Models thru variance shared, and best fit for the model sequence as a whole. I have never really done something like this before, so it def has a massive learning curve on how i want to present the data to those wanting calculated values for regression confirmation. I need more time. :) Hope i answered your question for now..
lpninja
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@Glitch420, Thanks, no this is great. Can you join the telegram group that I am on where there is a lot of regression modelling going on now right now. The basic precipitate of the argument right now is as follows; historical regression fits can be done, but future scenarios are hard. I am still coming up to speed with what you are actually doing with respect to coherence. Is it similar to monte-carlo walking paths? Maybe I am off here.
Anyway, historical can be done statistically. What is hard for me to understand is future scenarios? I understand that you are able to isolate statistical outliers because maybe they are at the P99, P95 or edges of the distributions if you were fitting to say an easy "normal bell curve distribution" with an expected P50 value, but what I am wondering is what kind of distribution you are fitting to? I think each time period is its own beast. And then future time periods are their own as well. Are you using Type B uncertainty models and /or Type A according to GUM methodology?
I really do like your ability to isolate FUD and FOMO events. This is helpful. Anyway keep up your great efforts! It is helping many people.
Best, lpninja.
Glitch420
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@lpninja, sure! PM the telegram group link. I will observe..
I can try and explain in theory my method.. It helps to understand my background is clinical neuropsychology, i am a student.. I am multi-faceted in my concentrations and my knowledge base digs across Jungian Psychoanalysis, Transpersonal psychology, clinical neuropsychology, behavioral data collection, quantum mechanics, and theoretical design. My research framework is two-fold but essentially using EEG to decode our brains microstate and global state neural algorithms, using individuals under hallucinogen-induced altered states of consciousness as the experimental groups. I will stop there. This research is very extensive.. Very ambitious.. and VERY detailed and well thought through, and the statistical modeling laid out before you is based off this knowledge i have gathered over the years.
Now to dig into your logic.. historical data done statistically only reveals one thing about the data in relation to the question be sought. The past. We observe or analyze a data set already formed (traditional TA indicators + traditional statistical analyses) to seek out patterns in the PAST data. This is great, but we want to know the FUTURE. So how do we bridge.. historical data to future data using 'now' data. Well.. in my neural modeling I know that brain regions are interconnected very intricately and I am looking for something called fractal dimensions, which is the brains dynamic criticality system. This is where we merge quantum theory + neuroscience. Wave/particle duality and the collapse of information in the brains neural networks, occurs thru instantaneous quanta time frames (Sergey B. Yurchenko) using memory to give us our ability to grasp time, which is superimposed thru our ability to be a quantum receiver. Think dark energy, and our brains ability to harness that connection to it. What is the point.. It is all connected. Always, you can never separate the neural model networks, ever.. They are ALWAYS interconnected.
What do i mean? There is a global pulse and a microstate pulse present in bitcoin.. Some people analyze one or the other to make estimates in predicting an outcome. I know that in neural modeling, you MUST find a global signal and decode (even partially) that signal in order to find a viable microstate signal that is COHERENT to the global model, it helps if you have some prerequisite parameter(s) you are looking for. In this case, i am looking for FUD and FOMO, as well as Market Manipulation, bots, and anomalies. A quick dig through peoples global trend TA's I saw an outlier of interest.. and used it as my primary foundation to make Model A.
I used historical data, to create parameters that acted as rules to a theoretical 'belief'. ( My modeling is designed from an alternative; to Type A or B uncertainty modeling.
"Dempster–Shafer theory" which is a generalization of the Bayesian theory of subjective probability. Belief functions base degrees of belief (or confidence, or trust) for one question on the probabilities for a related question. The degrees of belief itself may or may not have the mathematical properties of probabilities; how much they differ depends on how closely the two questions are related.[6] Put another way, it is a way of representing epistemic plausibilities but it can yield answers that contradict those arrived at using probability theory.
Dempster–Shafer theory is based on two ideas: obtaining degrees of belief for one question from subjective probabilities for a related question, and Dempster's rule[7] for combining such degrees of belief when they are based on independent items of evidence. In essence, the degree of belief in a proposition depends primarily upon the number of answers (to the related questions) containing the proposition, and the subjective probability of each answer. Also contributing are the rules of combination that reflect general assumptions about the data.
In a first step, subjective probabilities (masses) (FUD and FOMO) are assigned to all subsets of the frame; usually, only a restricted number of sets will have non-zero mass (focal elements).[2]:39f. Belief in a hypothesis is constituted by the sum of the masses of all sets enclosed by it. It is the amount of belief that directly supports a given hypothesis or a more specific one, forming a lower bound. Belief (usually denoted Bel) measures the strength of the evidence in favor of a proposition p. It ranges from 0 (indicating no evidence) to 1 (denoting certainty). Plausibility is 1 minus the sum of the masses of all sets whose intersection with the hypothesis is empty. Or, it can be obtained as the sum of the masses of all sets whose intersection with the hypothesis is not empty. It is an upper bound on the possibility that the hypothesis could be true, i.e. it “could possibly be the true state of the system” up to that value, because there is only so much evidence that contradicts that hypothesis. Plausibility (denoted by Pl) is defined to be Pl(p) = 1 − Bel(~p). It also ranges from 0 to 1 and measures the extent to which evidence in favor of ~p leaves room for belief in p.
For example, suppose we have a belief of 0.5 and a plausibility of 0.8 for a proposition, say “the cat in the box is dead.” This means that we have evidence that allows us to state strongly that the proposition is true with a confidence of 0.5. However, the evidence contrary to that hypothesis (i.e. “the cat is alive”) only has a confidence of 0.2. The remaining mass of 0.3 (the gap between the 0.5 supporting evidence on the one hand, and the 0.2 contrary evidence on the other) is “indeterminate,” meaning that the cat could either be dead or alive. This interval represents the level of uncertainty based on the evidence in your system". (good ol wiki)
There is PART of the modeling theory that designs my predictive modeling framework. The other parts; like visual geometric indicators (following special rules and laws of there own), and a two other things I will not mention at this time.
This was just a crazy idea, and it is turning out to be pretty serious.. and possibly VERY useful to a shitload of people.. my interest in this is quite peaked.. Hope this helps..