MF-DFA 에서 Matalb 을 줄리아 구현

나는 작업 MF-DFA 방법에 Matlab 지만 필요해요 그것을 구현하기 위해서 줄리아입니다. 의 목표는 상점을 얻을 허스트의 지수,S&P500. Matlab 코드는 다음과 같습니다:

sp500 = readtable('sp500_Nasdaq.csv','PreserveVariableNames', true) ;
spClose = table2array(sp500(:,2))  ;

SP1=cumsum(spClose - mean(spClose)) ;
SP1_ordinary=sqrt(mean(SP1.^2));
X=cumsum(spClose-mean(spClose));
X=transpose(X);
scale=[16,32,64,128,256,512,1024];
q=[-5,-3,-1,0,1,3,5];
m=1;
for ns=1:length(scale),
segments(ns)=floor(length(X)/scale(ns));
for v=1:segments(ns)
  Index=( ( ( (v-1)*scale(ns) )+1):(v*scale(ns)));
  C = polyfit(Index,X(Index),m) ;
  fit=polyval(C,Index);
  RMS{ns}(v)=sqrt(mean((X(Index)-fit).^2));
end
for nq=1:length(q),
  qRMS{nq,ns}=RMS{ns}.^q(nq);
  Fq(nq,ns)=mean(qRMS{nq,ns}).^(1/q(nq));
end
  Fq(q==0,ns)=exp(0.5*mean(log(RMS{ns}.^2)));
end

줄리아는 코드는 다음과 같습니다:

using DelimitedFiles, TimeSeries, Plots, DelimitedFiles, Plots, StatsBase
using Polynomials, LinearAlgebra, CSV, DataFrames

sp500 = CSV.read("sp500_Nasdaq.csv", DataFrame) 
sp500_V = values(sp500[:,2]) 
SP1 = cumsum(sp500_V .- mean(sp500_V) ) ; 
SP1_Ord = sqrt(mean(SP1.^2)) ;
X = SP1 ;
X = X';
function polyfit(xVals,yVals)
   n = length(xVals)
   xBar, yBar = @fastmath mean(xVals), mean(yVals)
   sXX, sXY = @fastmath ones(n)'*(xVals.-xBar).^2 , dot(xVals.-xBar,yVals.-yBar)
   b1A = @fastmath sXY/sXX
   b0A = @fastmath yBar - b1A*xBar
return b0A, b1A
end

scales = [16,32,64,128,256,512,1024];
q = [-5,-3,-1,0,1,3,5] ;
segments = zeros(Int64, (1,length(scales)))
global qRMS = zeros( length(q) ,length(scales)  ) ;
global Fq = zeros( length(q) , length(scales) ) ;
@inbounds for ns = 1:length(scales)
global segments[ns] = Int(floor( length(X)/scales[ns] ) ) ;  
global Index = Array{UnitRange{Int128}}(undef, (segments[ns], length(scales))  ) ;
global ft = zeros(Float64, (segments[ns], length(scales) ) ) ;
global RMS = zeros(Float64, (length(scales) ,segments[ns] ) ) ;

@inbounds  for v=1:segments[ns]
    global RMSk = Array{Float64}[] ;
    Index =  ( ( (v-1)*scales[ns] ) + 1 ):( v*scales[ns] ) ;
    global C = polyfit( Index, X[Index]) ;
    global p = Polynomial(C)
    ft =p.(Index);
    RMS[ns,v] = sqrt(mean((X[Index] .- ft).^2));
    push!(RMSk,RMS )
end
@inbounds for nq = 1:length(q)
    qRMS[nq,ns] = RMS[ns].^q[nq];
    Fq[nq,ns] = mean( qRMS[nq,ns] ).^(1/q[nq] );
end
Fq[findall(x->x==0, q)[1], ns] = exp( 0.5*mean(log.(RMS[ns].^2) ) ) ;
end

이 것은 편에서 RMS 를 Matlab 의 코드의 배열이:

RMS =

1×7 세포 배열

{1×159 double}    {1×79 double}    {1×39 double}    {1×19 double}    {1×9 double}    {1×4 double}    {1×2 double}

하지만 줄리아 반환 마지막 배열

RMS
7×2 Matrix{Float64}:
 0.0      0.0
 0.0      0.0
 0.0      0.0
 0.0      0.0
 0.0      0.0
 0.0      0.0
62178.0  18238.2

는 어떻게 얻을 수 있습니 같은 출력으로서는 Matlab? 할 수 있는 방법을 배열을 저장으로 배열에 줄리아?