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🧵 | It’s interesting, the relationship that Standard Deviations share with Chaos Theory & Nonlinear Dynamics.
ICT Standard Deviations:
2.5x & 4x
Feigenbaum Constants:
Alpha = -2.5029…
Delta = 4.669…
Malthusian Parameter:
R = 3.5699
You’ll love this.
Consider the following:
ICT Standard Deviations:
2.5x & 4x
Feigenbaum Constants:
Alpha = -2.5029…
Delta = 4.669…
Malthusian Parameter:
R = 3.5699
You’ll love this.
Consider the following:
This thread was made for those with little Mathematical background. It’s designed to be easy to digest.
Please read through all, and do not forget the video towards the end!
It’s important to understand why something works, not just how it works or what makes something work.
Please read through all, and do not forget the video towards the end!
It’s important to understand why something works, not just how it works or what makes something work.
Delta (δ): 4.669…
Imagine a ball rolling down a bumpy hill. Each time it hits a bump, it could bounce left or right.
As the ball continues, the number of potential paths it could take doubles with each bump.
‘Delta’ tells us how fast these potential paths are diverging from… twitter.com/i/web/status/1…
Imagine a ball rolling down a bumpy hill. Each time it hits a bump, it could bounce left or right.
As the ball continues, the number of potential paths it could take doubles with each bump.
‘Delta’ tells us how fast these potential paths are diverging from… twitter.com/i/web/status/1…
Alpha (α): -2.5029…
Now, imagine you’re zooming in on the path the ball took.
You might notice that the pattern of bounces start to look similar at different levels of zoom, like a fractal.
‘Alpha’ tells us how the pattern changes as we zoom in or out (Rate of -2.5029).
Now, imagine you’re zooming in on the path the ball took.
You might notice that the pattern of bounces start to look similar at different levels of zoom, like a fractal.
‘Alpha’ tells us how the pattern changes as we zoom in or out (Rate of -2.5029).
Malthusian Parameter: 3.5699…
Next, imagine tracking the Temperature of a Lake…
Depending on the season, the temperature follows a certain pattern. This predictable, regular change in temperature is a periodic cycle.
Then, imagine a El-Niño hits (Anomaly). The Snowy Lake… twitter.com/i/web/status/1…
Next, imagine tracking the Temperature of a Lake…
Depending on the season, the temperature follows a certain pattern. This predictable, regular change in temperature is a periodic cycle.
Then, imagine a El-Niño hits (Anomaly). The Snowy Lake… twitter.com/i/web/status/1…
R < 1: All Anomalies eventually die out
R < 3: Rate of Anomalies becomes Stable
R < 3.449: Rate of Anomalies oscillates between two States
R < 3.544: Rate of Anomalies oscillates, starting with 4 and doubling into 8, 16, etc...
R >= 3.5699: Rate of Anomalies become fully… twitter.com/i/web/status/1…
R < 3: Rate of Anomalies becomes Stable
R < 3.449: Rate of Anomalies oscillates between two States
R < 3.544: Rate of Anomalies oscillates, starting with 4 and doubling into 8, 16, etc...
R >= 3.5699: Rate of Anomalies become fully… twitter.com/i/web/status/1…
Now, what is “Manipulation”?
It’s that of an Anomaly.
It is (by definition) “Fraudulent Price-Discovery”. In Mathematics, we call this a perturbation.
If a ball is rolling down a hill, it is stuck to its path. It requires interference to make it change course, and to do that,… twitter.com/i/web/status/1…
It’s that of an Anomaly.
It is (by definition) “Fraudulent Price-Discovery”. In Mathematics, we call this a perturbation.
If a ball is rolling down a hill, it is stuck to its path. It requires interference to make it change course, and to do that,… twitter.com/i/web/status/1…
Manipulation is that exact perturbation, and the measurement of a perturbation + the Initial Conditions will define the next trajectory.
You’ll find that these trajectories oscillate between Periodic & Chaotic states.
These are where the numbers come from.
You’ll find that these trajectories oscillate between Periodic & Chaotic states.
These are where the numbers come from.
Finally, the Attractor:
Using our prior example of a ball rolling down a hill, there is a force that is pushing that ball down said hill.
Once all Energy within the ball is depleted, the ball will come to a rest. This fixed point of rest is known as the Stable Attraction Point… twitter.com/i/web/status/1…
Using our prior example of a ball rolling down a hill, there is a force that is pushing that ball down said hill.
Once all Energy within the ball is depleted, the ball will come to a rest. This fixed point of rest is known as the Stable Attraction Point… twitter.com/i/web/status/1…
However, there are two Attractors on this hill…
The first is the “Stable Attractor” mentioned above, but the second is called an “Unstable Attractor”, or a “Repeller”.
This would be at the peak of the hill, where the ball is sitting unstable atop. The smallest perturbations in… twitter.com/i/web/status/1…
The first is the “Stable Attractor” mentioned above, but the second is called an “Unstable Attractor”, or a “Repeller”.
This would be at the peak of the hill, where the ball is sitting unstable atop. The smallest perturbations in… twitter.com/i/web/status/1…
Lastly, there’s an Attractor beyond the Stable & Unstable point, and it is the structure of the entire system from beginning to end.
The entire path the ball can take ranges from the top of the hill to the bottom of the hill. It is stuck within that local space, but there is a… twitter.com/i/web/status/1…
The entire path the ball can take ranges from the top of the hill to the bottom of the hill. It is stuck within that local space, but there is a… twitter.com/i/web/status/1…
Each individual path is an orbit or a trajectory from one unstable point to the next. It is Chaotic and will seem random.
The entire Topology from the top of the hill to the bottom is the “Strange Attractor”, and this is like the hidden structure behind what makes the ball move.… twitter.com/i/web/status/1…
The entire Topology from the top of the hill to the bottom is the “Strange Attractor”, and this is like the hidden structure behind what makes the ball move.… twitter.com/i/web/status/1…
If you put legitimate thought into what I’m suggesting, you will find that there is no other explanation that makes more sense for the formation of price - Logically, Algorithmically, and Scientifically.
Combine it with what you know and make sense of how it gives rise to… twitter.com/i/web/status/1…
Combine it with what you know and make sense of how it gives rise to… twitter.com/i/web/status/1…
This Theory not only explains most Phenomena that take place, but it also explains how most indicators *do not* work - being purely Stochastic in nature.
The market is very rarely in a purely Stochastic state, as shown through my research thus far.
Retail has been bamboozled.
The market is very rarely in a purely Stochastic state, as shown through my research thus far.
Retail has been bamboozled.
As described in the “Exploratory Trading” Report by A. D. Clark-Joseph, Liquidity Providers engage in Momentum Ignition to “ignite” Momentum in a false direction.
Perhaps, we can consider IPDA is that of an Algorithm of Nonlinear Control.
These are those Perturbations…
Perhaps, we can consider IPDA is that of an Algorithm of Nonlinear Control.
These are those Perturbations…
https://twitter.com/trstnglrd/status/1604899415218565120
This is not a coincidence, and these are Universal concepts for a reason.
Market Dynamics come from Complex Systems of Universality, the same Complex Systems that make up the movements of both Particle & Planetary systems.
This is a true “Theory of Everything”.
Market Dynamics come from Complex Systems of Universality, the same Complex Systems that make up the movements of both Particle & Planetary systems.
This is a true “Theory of Everything”.
Chaos Theory is also used in the modeling of Population Densities.
This video below explains the exact same concepts as above, only through the perspective of Population over Time.
It is the same exact concept, just with a different application.
This video below explains the exact same concepts as above, only through the perspective of Population over Time.
It is the same exact concept, just with a different application.
Thus, I leave you with an ending thought to consider deeply:
As the markets have fallen in the population of Long-Term Traders over the last two Decades…
As HFT Algorithms have grown to make up 70%+ Market Volumes…
Is Nonlinear Control necessary for Market Efficiency?
As the markets have fallen in the population of Long-Term Traders over the last two Decades…
As HFT Algorithms have grown to make up 70%+ Market Volumes…
Is Nonlinear Control necessary for Market Efficiency?
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