Pay attention to some cases.
You may have already noticed, I prioritize indicators because they are mathematical. And in the same way, I try to follow mathematical principles applied in economics.
A little known, autoregressive-moving-average, which is, in short, stationary stochastic process in terms of two polynomials, one for the autoregression and the other for the moving average.
Currencies that have not undergone correction tend to regress the moving average.
How to find the correlation?
Calculating matrices and determinants between assets.
Ex: matrices between the fluctuations of the network asset (a) X asset derived from the same network (b).
Determinant: total market 1 2 3
It is a more advanced and complex subject, which I will try to explain more clearly later, with practical examples.
The formula with 3 matrices (3x3) looks more or less like this:
A = [[a, b, c],
[d, e, f],
[g, h, i]]
det(A) = a * (e * i - f * h) - b * (d * i - f * g) + c * (d * h - e * g)
determinant looks like this:
Cij = (-1)^(i+j) * det(Aij)
det(A) = Σ (aij * Cij)
Of course, you shouldn't have to calculate it manually every time, but it's important to understand the logic behind it. When I write the article, I will give concrete and applied examples.
You may have already noticed, I prioritize indicators because they are mathematical. And in the same way, I try to follow mathematical principles applied in economics.
A little known, autoregressive-moving-average, which is, in short, stationary stochastic process in terms of two polynomials, one for the autoregression and the other for the moving average.
Currencies that have not undergone correction tend to regress the moving average.
How to find the correlation?
Calculating matrices and determinants between assets.
Ex: matrices between the fluctuations of the network asset (a) X asset derived from the same network (b).
Determinant: total market 1 2 3
It is a more advanced and complex subject, which I will try to explain more clearly later, with practical examples.
The formula with 3 matrices (3x3) looks more or less like this:
A = [[a, b, c],
[d, e, f],
[g, h, i]]
det(A) = a * (e * i - f * h) - b * (d * i - f * g) + c * (d * h - e * g)
determinant looks like this:
Cij = (-1)^(i+j) * det(Aij)
det(A) = Σ (aij * Cij)
Of course, you shouldn't have to calculate it manually every time, but it's important to understand the logic behind it. When I write the article, I will give concrete and applied examples.