Probability, how do you turn it into a pattern ...?
A pattern is a function (model) of the change in probability of the same event.
Construct a graph of the change in probability. Then see for yourself.
P.S. There is also a reverse way: you yourself determine the regularity. Then look for a suitable algorithm to calculate the probability of the event.
Construct a graph of the change in probability. Then see for yourself.
P.S. There is also a reverse way: you yourself determine the regularity. Then look for a suitable algorithm to calculate the probability of the event.
В дальнейшем буду демонстрировать достаточно интересные результаты...
getch >>:
Закономерность - это функция (модель) изменения вероятности одного и того же события.
Постройте график изменения вероятности. Далее смотрите сами.
Закономерность - это функция (модель) изменения вероятности одного и того же события.
Постройте график изменения вероятности. Далее смотрите сами.
The problem is defined (set) incorrectly.
getch >>:
Закономерность - это функция (модель) изменения вероятности одного и того же события.
Постройте график изменения вероятности. Далее смотрите сами.
P.S. Есть и обратный путь: вы сами определяете закономерность. После чего ищите подходящий алгоритм расчета вероятности события.
Закономерность - это функция (модель) изменения вероятности одного и того же события.
Постройте график изменения вероятности. Далее смотрите сами.
P.S. Есть и обратный путь: вы сами определяете закономерность. После чего ищите подходящий алгоритм расчета вероятности события.
Do you work in a kindergarten? Teach kids how to draw? =)
That's great!
That's great!
Pity about the children
Let's speculatively draw a horizontal line through any price chart and think - what is the probability that the price will cross it from below or above, what is the probability that after the crossing the price will come back or not? ))) And we will easily obtain a 50% probability, because meteorology is the most precise science. )))
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I am starting this thread in order to demonstrate a sustainable model of behaviour in the market that is based on an understanding of the consequences of chaotic processes, and exploits the most persistent tendency, - the chaotic tendency to destabilise.
My official position.
Definition:
Probability (probability measure) is a measure of the certainty of a random event. An estimate of the probability of an event is the frequency with which it occurs in a long series of independent repetitions of a random experiment. According to P. Laplace's definition, a probability measure is a fraction, the numerator of which is the number of all favourable events, and the denominator of which is the number of all possible events.
Conditional probability is the probability of one event if another event has already occurred.
Causes, consequences and preconditions do not matter to me, if the price on the market changes in one direction or another. Only the actual price change in real time. There is a single main indicator, which my TS is based on and reacts to.
"The probability of any event occurring can be predicted with relative accuracy, but that it will happen can be taken as an absolute regularity "
This quote defines exactly the basic principles laid down in the TS
I have bet on the inevitability of an expected event, and built the logic of TC with the regularity that sooner or later the event is bound to happen.
I take the environment in which I operate, as a three-dimensional system in a state of dynamic chaos.
Definition:
Dynamic chaos is a phenomenon in dynamical systems theory in which the behavior of a nonlinear system appears random, although it is determined by deterministic laws.
I use as TC tools
An algorithm for calculating the probability of occurrence of an event
The controllable time factor of the expected event
The initial point of direction coordinates, which is a derivative of the result of the first cycle of the system.
Independent (but integral) elements of the TS include the logic of the "Thin Thread" that physically approaches and activates the coming events, as well as the rules and order of execution of "Virtual Orders".
As a result, I got a model of system actions that is resistant to external influences and non-linearly balanced.
It is based on the principle of stabilising the balance of given (initial) parameters.