股票预测源码程序 - matlab算法设计

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标题：股票预测源码程序
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所属分类: 算法设计资源类型：文件大小: 33.73 KB 上传时间: 2016-01-25 20:24:45 下载次数: 6 资源积分：1分提供者: xiaopeng2 股票预测源码程序

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股票预测源码程序，程序员在编程的过程中可以参考学习使用，希望对IT程序员有用，此源码程序简单易懂、方便阅读，有很好的学习价值！

function log_L = Kalman_Estimation(y, psi, matur, dt, a0, P0, N, nobs, locked_parameters)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Extracting initial parameter values from initial psi

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

k = psi(1,1);

sigmax = psi(2,1);

lambdax = psi(3,1);

mu = psi(4,1);

sigmae = psi(5,1);

rnmu = psi(6,1);

pxe = psi(7,1);

if sum(locked_parameters) == 0

k = psi(1,1);

sigmax = psi(2,1);

lambdax = psi(3,1);

mu = psi(4,1);

sigmae = psi(5,1);

rnmu = psi(6,1);

pxe = psi(7,1);

s = zeros(1, size(psi,1)-7);

for i = 1:size(s,2)

s(1, i) = psi(i+7,1);

end

if sum(locked_parameters) ~= 0

s = zeros(1, size(psi,1)-7+size(locked_parameters,1));

j = 1;

for i = 1:size(s,2)

if all(abs(i-(locked_parameters))) == 1

s(1, i) = psi(7+j,1);

j = j+1;

end

% m = Number of state variables (number of rows in a0)

m = size(a0,1);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% THE TRANSITION EQUATION

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% S&S NOTATION: x(t)=c+G*x(t-1)+w(t) w~N(0,W) Equation (14)

% NEW NOTATION: a(t)=c+T*a(t-1)+R(t)*n(t) n~N(0,Q)

% c is a {m x 1} Vector

% T is a {m x m} Matrix

c=[0;mu*dt];

T=[exp(-k*dt),0;0,1];

% Defining Q = var[n(t)] and R

xx=(1-exp(-2*k*dt))*(sigmax)^2/(2*k);

xy=(1-exp(-k*dt))*pxe*sigmax*sigmae/k;

yx=(1-exp(-k*dt))*pxe*sigmax*sigmae/k;

yy=(sigmae)^2*dt;

Q=[xx,xy;yx,yy];

R=eye(size(Q,1));

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% THE MEASUREMENT EQUATION

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% S&S NOTATION: y(t)=d(t)+F(t)'x(t)+v(t) v~N(0,V) Equation (15)

% NEW NOTATION: y(t)=d(t)+Z(t)a(t)+e(t) e~N(0,H)

% d is a {N x 1} Vector

% Z is a {N x m} Matrix

for i=1:N

p1=(1-exp(-2*k*matur(i)))*(sigmax)^2/(2*k);

p2=(sigmae)^2*matur(i);

p3=2*(1-exp(-k*matur(i)))*pxe*sigmax*sigmae/k;

d(i,1)=rnmu*matur(i)-(1-exp(-k*matur(i)))*lambdax/k+.5*(p1+p2+p3);

Z(i,1)=exp(-k*matur(i));

Z(i,2)=1;

end

% Measurment errors Var-Cov Matrix: Cov[e(t)]=H

H=diag(s);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% RUNNING THE KALMAN FILTER

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% Creating placeholder vectors/matrices for variables to be stored in

global save_vt save_att save_dFtt_1 save_vFv save_vtt save_Ptt_1 save_Ftt_1 save_Ptt

save_ytt_1 = zeros(nobs,N);

save_vtt = zeros(nobs,N);

save_vt = zeros(nobs,N);

save_att_1 = zeros(nobs,m);

save_att = zeros(nobs,m);

save_Ptt_1 = zeros(nobs,m*m);

save_Ptt = zeros(nobs,m*m);

save_Ftt_1 = zeros(nobs,N*N);

save_dFtt_1 = zeros(nobs,1);

save_vFv = zeros(nobs,1);

%save_log_Lt = zeros(nobs,1);

Ptt = P0;

att = a0;

% Running the kalman filter for t = 1,...,nobs

for t = 1:nobs

Ptt_1 = T*Ptt*T'+R*Q*R';

Ftt_1 = Z*Ptt_1*Z'+H;

dFtt_1 = det(Ftt_1);

%Ptt_1_test = [Ptt_1(1,1) 0; 0 Ptt_1(2,2)];

%Ftt_1_test = Z*Ptt_1_test*Z'+H;

%dFtt_1_test = det(Ftt_1_test);

att_1 = T*att + c;

yt = y(t,:)';

ytt_1 = Z*att_1+d;

vt = yt-ytt_1;

att = att_1 + Ptt_1*Z'*inv(Ftt_1)*(vt);

Ptt = Ptt_1 - Ptt_1*Z'*inv(Ftt_1)*Z*Ptt_1;

ytt = Z*att+d;

vtt = yt-ytt;

% save_ytt_1(t,:) = ytt_1';

save_vtt(t,:) = vtt';

save_vt(t,:) = (vt)';

% save_att_1(t,:) = att_1';

save_att(t,:) = att';

save_Ptt_1(t,:) = [Ptt_1(1,1), Ptt_1(1,2), Ptt_1(2,1), Ptt_1(2,2)];

save_Ptt(t,:) = [Ptt(1,1), Ptt(1,2), Ptt(2,1), Ptt(2,2)];

% save_Ftt_1(t,:) = [Ftt_1(1,1), Ftt_1(1,2), Ftt_1(1,3), Ftt_1(1,4), Ftt_1(1,5), Ftt_1(2,1), Ftt_1(2,2), Ftt_1(2,3), Ftt_1(2,4), Ftt_1(2,5), Ftt_1(3,1), Ftt_1(3,2), Ftt_1(3,3), Ftt_1(3,4), Ftt_1(3,5), Ftt_1(4,1), Ftt_1(4,2), Ftt_1(4,3), Ftt_1(4,4), Ftt_1(5,5), Ftt_1(5,1), Ftt_1(5,2), Ftt_1(5,3), Ftt_1(5,4), Ftt_1(5,5)];

%save_dFtt_1(t,:)= dFtt_1_test;

%save_vFv(t,:) = vt'*inv(Ftt_1_test)*vt;

save_dFtt_1(t,:)= dFtt_1;

save_vFv(t,:) = vt'*inv(Ftt_1)*vt;

end

logL = -(N*nobs/2)*log(2*pi)-0.5*sum(log(save_dFtt_1))-0.5*sum(save_vFv);

% logL = -(N*nobs/2)*log(2*pi)-0.5*sum(save_vFv);

% logL = sum(diag(save_vt'*save_vt));

log_L = -logL;

文件列表（点击上边下载按钮，如果是垃圾文件请在下面评价差评或者投诉）:

股票预测源码程序/
股票预测源码程序/code/
股票预测源码程序/code/Kalman_Estimation.m
股票预测源码程序/code/MainSS.m
股票预测源码程序/code/ScreenShot.png
股票预测源码程序/code/data.mat

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