main_02_pjm

%%%%%%%%%%    상단주석
%%
%%  1. Ticker, Price에 대한 Date 계열 데이터 csv를 읽음
%%  2. 평균,모분산,모표준편차,RMS 분석
%%
%%  2025. 03. 26. wed
%%  박준민 선임연구원
clc; clear;
pkg load signal


%%  CSV파일 불러오기
filename = 'google_data_2024.csv';
gg_2024 = csv2struct(filename,4,[2 1]);   % structure : Output.("Ticker").("Price") = (N,1) array


%%  통계지표 계산 1
stats_custom = statistics_custom(gg_2024.GOOGL.Close);   # 1x4 [ avg, var, std, rms ]
%%  통계지표 검증
stats_octave(1) = mean(gg_2024.GOOGL.Close);
stats_octave(2) =  var(gg_2024.GOOGL.Close,1);
stats_octave(3) =  std(gg_2024.GOOGL.Close,1);
stats_octave(4) =  rms(gg_2024.GOOGL.Close);

fprintf('\n Compare Results\n     AVG      VAR      STD      RMS\n')
fprintf('% 8.2f % 8.2f % 8.2f % 8.2f  --- %s\n',stats_custom,'Custom Fn')
fprintf('% 8.2f % 8.2f % 8.2f % 8.2f  --- %s\n',stats_octave,'Octave Fn')


%%  통계지표 계산 2
N_date = length(gg_2024.date);
N_wind = 30;
idx_date_ini = 30;

for idx_date = idx_date_ini:N_date
  range_date = idx_date - [0:N_wind-1];    # 입력 순서와 무관한 통계치 도출

  GgClose_stats_30d(idx_date,:) = statistics_custom( gg_2024.GOOGL.Close(range_date) );
end


%%  플로팅
LegendNames = {'mean','variance','std.dev.','rms'};
LegendProps = {'location','northwestoutside'};

xLim = [1 N_date-idx_date_ini];
xt_labels = gg_2024.date(idx_date_ini:44:N_date);

figure(1,'position',[300 300 640 480],'defaultaxesfontsize',14)
plot(GgClose_stats_30d(idx_date_ini:end,:),'linewidth',1)
title('Staticstics Properties for 30 market-days')
legend(LegendNames{:},LegendProps{:})
xlabel('Date')
xlim(xLim)
xticks(xLim(1):44:xLim(2))
xticklabels(xt_labels)
ylabel('Statistics Indicators')
grid on