no amount of historical data, whether from years ago and days ago determines the current price of bitcoin
You are going against your hypothesis. Also, tell me what would be the basis of prediction model if not the previous data?
the superposition principle does not determine the price of bitcoin, don’t know where you got that impression.
Are you kidding me?
You have mentioned it yourself in the paper, in fact, that is the core of your paper.
(i) Quantum Superposition
Thus, to effectively model the first challenge of the dualistic overlapping uncertainty is to utilize the
quantum principle of superposition. The first postulate of quantum mechanics is “the state of an isolated
physical system is represented, at a fixed time t, by a state vector |ψ⟩ belonging to a Hilbert space ℋ called
the state space [81].” Thus, when something is in a superposed state, all the possible states can be expressed by
a state |ψ⟩, which can be represented as a linear combination of the states of the observable as (5). Essentially
a superposed state is where all the possible states are simultaneously existent until it is observed [82], just like
how Schrodinger’s Cat can be dead and alive before the box is opened [83] or a qubit can be in a state of 0 and
1 until it is actually observed [84].
|ψ⟩ = c1
|ψ1
⟩+ c2
|ψ2
⟩ + ⋯ + cn|ψn
⟩ (5)
There are corresponding observed values of o1
, o2
, …, on, and once the measurement happens [85] only
one of these values on can be observed with a probability of |cn
|
2
, as in (6).
|ψ⟩ → |ψn
⟩ (6)
By superposing all the possible states of the market (either going up or down) and all the possible actions
that the traders can take (either buy or sell) together according to the principle of quantum superposition we
are able to postulate an effective model of both the potential states of the market and the collective possible
actions taken by all the traders as in (7) and (8).
|Q⟩ = c1
|q1
⟩+ c2
|q2
⟩ (7)
where |q1
⟩ denoting the market trending upwards; |q2
⟩ denoting the market trending downwards. ω1 =
|c1
|
2
is the objective frequency of the increase; ω2 = |c2
|
2
is the objective frequency of the decrease.
|A⟩ = μ1
|a1
⟩+ μ2
|a2
⟩ (8)
where |a1
⟩ denotes the buy action; |a2
⟩ denotes the sell action. p1 = |μ1
|
2 are the degree of beliefs to buy;
p2 = |μ2
|
2 are the degree of beliefs to sell.
. In this paper, we
put forth a quantum-like evolutionary algorithm for time series forecasting – highlighting the dual uncertainty
challenge and the corresponding methodology used.
And lastly our methodology can be utilized to time series forecasting generically, stocks and bitcoin are just one case study,
But you jumped straight to price prediction, I wonder why?
Our algorithm has been tested on many real-world datasets,
Proof? Let the community examine.
I highly suggest that you read a textbook on quantum mechanics and actually understand what quantum superposition really is. Good luck, hope your Einstein brain can figure it out, end of discussion. By the way welcome all in the community to examine our research work and give more constructive feedback about it.
