| 1 | |
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| 2 | /*! |
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| 3 | \file |
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| 4 | \brief Robust |
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| 5 | \author Vasek Smidl |
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| 6 | |
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| 7 | */ |
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| 8 | |
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| 9 | #include "estim/arx.h" |
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| 10 | #include "robustlib.h" |
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| 11 | #include <vector> |
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| 12 | #include <iostream> |
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| 13 | #include <fstream> |
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| 14 | #include <itpp/itsignal.h> |
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| 15 | |
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| 16 | using namespace itpp; |
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| 17 | using namespace bdm; |
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| 18 | |
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| 19 | const int emlig_size = 2; |
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| 20 | |
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| 21 | |
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| 22 | int main ( int argc, char* argv[] ) { |
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| 23 | |
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| 24 | itpp::Laplace_RNG LapRNG = Laplace_RNG(); |
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| 25 | |
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| 26 | /* |
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| 27 | // EXPERIMENT: 100 AR model generated time series of length of 30 from y_t=0.95*y_(t-1)+0.05*y_(t-2)+0.2*e_t, |
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| 28 | // where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the |
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| 29 | // variance of location parameter estimators and compare it to the classical setup. |
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| 30 | vector<vector<vector<string>>> string_lists; |
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| 31 | string_lists.push_back(vector<vector<string>>()); |
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| 32 | string_lists.push_back(vector<vector<string>>()); |
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| 33 | string_lists.push_back(vector<vector<string>>()); |
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| 34 | |
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| 35 | char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"}; |
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| 36 | |
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| 37 | |
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| 38 | for(int i = 0;i<3;i++) |
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| 39 | { |
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| 40 | ifstream myfile(file_strings[i]); |
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| 41 | if (myfile.is_open()) |
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| 42 | { |
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| 43 | while ( myfile.good() ) |
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| 44 | { |
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| 45 | string line; |
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| 46 | getline(myfile,line); |
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| 47 | |
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| 48 | vector<string> parsed_line; |
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| 49 | while(line.find(',') != string::npos) |
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| 50 | { |
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| 51 | int loc = line.find(','); |
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| 52 | parsed_line.push_back(line.substr(0,loc)); |
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| 53 | line.erase(0,loc+1); |
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| 54 | } |
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| 55 | |
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| 56 | string_lists[i].push_back(parsed_line); |
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| 57 | } |
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| 58 | myfile.close(); |
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| 59 | } |
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| 60 | } |
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| 61 | |
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| 62 | for(int j = 0;j<string_lists.size();j++) |
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| 63 | { |
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| 64 | |
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| 65 | for(int i = 0;i<string_lists[j].size()-1;i++) |
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| 66 | { |
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| 67 | vector<vec> conditions; |
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| 68 | //emlig* emliga = new emlig(2); |
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| 69 | RARX* my_rarx = new RARX(2,30); |
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| 70 | |
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| 71 | for(int k = 1;k<string_lists[j][i].size();k++) |
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| 72 | { |
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| 73 | vec condition; |
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| 74 | //condition.ins(0,1); |
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| 75 | condition.ins(0,string_lists[j][i][k]); |
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| 76 | conditions.push_back(condition); |
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| 77 | |
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| 78 | //cout << "orig:" << condition << endl; |
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| 79 | |
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| 80 | if(conditions.size()>1) |
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| 81 | { |
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| 82 | conditions[k-2].ins(0,string_lists[j][i][k]); |
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| 83 | |
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| 84 | } |
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| 85 | |
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| 86 | if(conditions.size()>2) |
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| 87 | { |
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| 88 | conditions[k-3].ins(0,string_lists[j][i][k]); |
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| 89 | |
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| 90 | //cout << "modi:" << conditions[k-3] << endl; |
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| 91 | |
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| 92 | my_rarx->bayes(conditions[k-3]); |
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| 93 | |
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| 94 | |
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| 95 | //if(k>5) |
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| 96 | //{ |
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| 97 | // cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 98 | //} |
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| 99 | |
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| 100 | } |
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| 101 | |
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| 102 | } |
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| 103 | |
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| 104 | //emliga->step_me(0); |
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| 105 | /* |
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| 106 | ofstream myfile; |
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| 107 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 108 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
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| 109 | myfile.close(); |
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| 110 | |
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| 111 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 112 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
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| 113 | myfile.close(); |
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| 114 | |
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| 115 | |
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| 116 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 117 | cout << "Step: " << i << endl; |
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| 118 | } |
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| 119 | |
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| 120 | cout << "One experiment finished." << endl; |
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| 121 | |
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| 122 | ofstream myfile; |
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| 123 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 124 | myfile << endl; |
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| 125 | myfile.close(); |
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| 126 | |
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| 127 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 128 | myfile << endl; |
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| 129 | myfile.close(); |
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| 130 | }*/ |
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| 131 | |
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| 132 | |
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| 133 | // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from |
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| 134 | // y_t=0.95*y_(t-1)+0.05*y_(t-2)+0.2*e_t, where e_t is normally, student(4) and cauchy distributed. It |
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| 135 | // can be compared to the classical setup. |
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| 136 | |
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| 137 | |
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| 138 | vector<vector<string>> strings; |
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| 139 | |
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| 140 | char* file_strings[3] = {"c:\\dataCDClosePercDiff", "c:\\ar_student_single","c:\\ar_cauchy_single"}; |
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| 141 | |
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| 142 | for(int i = 0;i<3;i++) |
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| 143 | { |
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| 144 | char dfstring[80]; |
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| 145 | strcpy(dfstring,file_strings[i]); |
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| 146 | strcat(dfstring,".txt"); |
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| 147 | |
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| 148 | ifstream myfile(dfstring); |
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| 149 | if (myfile.is_open()) |
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| 150 | { |
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| 151 | string line; |
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| 152 | getline(myfile,line); |
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| 153 | |
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| 154 | vector<string> parsed_line; |
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| 155 | while(line.find(',') != string::npos) |
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| 156 | { |
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| 157 | int loc = line.find(','); |
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| 158 | parsed_line.push_back(line.substr(0,loc)); |
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| 159 | line.erase(0,loc+1); |
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| 160 | } |
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| 161 | |
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| 162 | strings.push_back(parsed_line); |
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| 163 | |
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| 164 | myfile.close(); |
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| 165 | } |
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| 166 | } |
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| 167 | |
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| 168 | for(int j = 0;j<strings.size();j++) |
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| 169 | { |
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| 170 | vector<vec> conditions; |
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| 171 | //emlig* emliga = new emlig(2); |
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| 172 | RARX* my_rarx = new RARX(2,30,false); |
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| 173 | |
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| 174 | |
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| 175 | mat V0 = 0.0001 * eye ( 3 ); |
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| 176 | ARX* my_arx = new ARX(0.97); |
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| 177 | my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments) |
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| 178 | my_arx->set_constant ( false ); |
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| 179 | my_arx->validate(); |
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| 180 | |
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| 181 | |
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| 182 | for(int k = 1;k<strings[j].size();k++) |
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| 183 | { |
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| 184 | vec condition; |
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| 185 | //condition.ins(0,1); |
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| 186 | condition.ins(0,strings[j][k]); |
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| 187 | conditions.push_back(condition); |
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| 188 | |
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| 189 | //cout << "orig:" << condition << endl; |
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| 190 | |
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| 191 | if(conditions.size()>1) |
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| 192 | { |
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| 193 | conditions[k-2].ins(0,strings[j][k]); |
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| 194 | |
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| 195 | } |
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| 196 | |
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| 197 | if(conditions.size()>2) |
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| 198 | { |
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| 199 | conditions[k-3].ins(0,strings[j][k]); |
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| 200 | |
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| 201 | //cout << "Condition:" << conditions[k-3] << endl; |
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| 202 | |
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| 203 | my_rarx->bayes(conditions[k-3]); |
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| 204 | //my_rarx->posterior->step_me(1); |
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| 205 | |
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| 206 | vec cond_vec; |
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| 207 | cond_vec.ins(0,conditions[k-3][0]); |
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| 208 | |
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| 209 | my_arx->bayes(cond_vec,conditions[k-3].right(2)); |
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| 210 | |
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| 211 | |
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| 212 | if(k>8) |
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| 213 | { |
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| 214 | //my_rarx->posterior->step_me(0); |
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| 215 | |
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| 216 | mat samples = my_rarx->posterior->sample_mat(50); |
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| 217 | |
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| 218 | vec sample_prediction; |
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| 219 | for(int t = 0;t<50;t++) |
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| 220 | { |
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| 221 | vec lap_sample = conditions[k-3].left(2); |
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| 222 | //lap_sample.ins(lap_sample.size(),1.0); |
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| 223 | |
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| 224 | lap_sample.ins(0,LapRNG()); |
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| 225 | |
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| 226 | sample_prediction.ins(0,lap_sample*samples.get_col(t)); |
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| 227 | } |
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| 228 | |
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| 229 | |
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| 230 | vec sample_pow = sample_prediction; |
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| 231 | vec poly_coefs; |
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| 232 | bool stop_iteration = false; |
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| 233 | int en = 0; |
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| 234 | do |
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| 235 | { |
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| 236 | double poly_coef = ones(sample_pow.size())*sample_pow/sample_pow.size(); |
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| 237 | |
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| 238 | if(abs(poly_coef)>numeric_limits<double>::epsilon()) |
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| 239 | { |
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| 240 | sample_pow = elem_mult(sample_pow,sample_prediction); |
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| 241 | poly_coefs.ins(poly_coefs.size(),((-1)^en)*poly_coef); |
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| 242 | } |
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| 243 | else |
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| 244 | { |
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| 245 | stop_iteration = true; |
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| 246 | } |
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| 247 | |
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| 248 | en++; |
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| 249 | } |
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| 250 | while(!stop_iteration); |
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| 251 | |
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| 252 | // cout << "Coefficients: " << poly_coefs << endl; |
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| 253 | |
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| 254 | cvec actions = roots(poly_coefs); |
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| 255 | bool is_max = false; |
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| 256 | for(int t = 0;t<actions.size();t++) |
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| 257 | { |
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| 258 | if(actions[t].imag() == 0 && actions[t].real()>-1 && actions[t].real()<1) |
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| 259 | { |
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| 260 | cout << "Action:" << actions[t].real() << endl; |
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| 261 | is_max = true; |
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| 262 | } |
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| 263 | } |
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| 264 | |
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| 265 | if(!is_max) |
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| 266 | { |
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| 267 | cout << "No maximum." << endl; |
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| 268 | } |
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| 269 | |
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| 270 | // cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl; |
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| 271 | |
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| 272 | double prediction = 0; |
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| 273 | for(int s = 1;s<samples.rows();s++) |
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| 274 | { |
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| 275 | |
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| 276 | double avg_parameter = samples.get_row(s)*ones(samples.cols())/samples.cols(); |
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| 277 | |
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| 278 | prediction += avg_parameter*conditions[k-3][s-1]; |
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| 279 | |
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| 280 | |
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| 281 | |
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| 282 | /* |
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| 283 | ofstream myfile; |
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| 284 | char fstring[80]; |
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| 285 | strcpy(fstring,file_strings[j]); |
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| 286 | |
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| 287 | char es[5]; |
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| 288 | strcat(fstring,itoa(s,es,10)); |
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| 289 | |
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| 290 | strcat(fstring,"_res.txt"); |
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| 291 | |
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| 292 | |
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| 293 | myfile.open(fstring,ios::app); |
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| 294 | |
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| 295 | //myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
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| 296 | myfile << avg_parameter; |
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| 297 | |
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| 298 | if(k!=strings[j].size()-1) |
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| 299 | { |
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| 300 | myfile << ","; |
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| 301 | } |
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| 302 | else |
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| 303 | { |
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| 304 | myfile << endl; |
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| 305 | } |
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| 306 | myfile.close(); |
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| 307 | */ |
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| 308 | } |
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| 309 | |
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| 310 | cout << "Prediction: "<< prediction << endl; |
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| 311 | |
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| 312 | enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2)); |
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| 313 | double prediction2 = pred_mat->mean()[0]; |
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| 314 | |
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| 315 | |
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| 316 | ofstream myfile; |
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| 317 | char fstring[80]; |
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| 318 | char f2string[80]; |
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| 319 | strcpy(fstring,file_strings[j]); |
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| 320 | strcpy(f2string,fstring); |
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| 321 | |
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| 322 | strcat(fstring,"pred.txt"); |
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| 323 | strcat(f2string,"2pred.txt"); |
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| 324 | |
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| 325 | |
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| 326 | myfile.open(fstring,ios::app); |
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| 327 | |
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| 328 | // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
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| 329 | myfile << prediction; |
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| 330 | |
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| 331 | if(k!=strings[j].size()-1) |
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| 332 | { |
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| 333 | myfile << ","; |
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| 334 | } |
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| 335 | else |
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| 336 | { |
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| 337 | myfile << endl; |
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| 338 | } |
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| 339 | myfile.close(); |
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| 340 | |
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| 341 | |
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| 342 | myfile.open(f2string,ios::app); |
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| 343 | myfile << prediction2; |
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| 344 | |
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| 345 | if(k!=strings[j].size()-1) |
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| 346 | { |
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| 347 | myfile << ","; |
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| 348 | } |
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| 349 | else |
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| 350 | { |
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| 351 | myfile << endl; |
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| 352 | } |
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| 353 | myfile.close(); |
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| 354 | |
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| 355 | |
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| 356 | } |
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| 357 | } |
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| 358 | |
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| 359 | //emliga->step_me(0); |
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| 360 | /* |
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| 361 | ofstream myfile; |
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| 362 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 363 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
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| 364 | myfile.close(); |
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| 365 | |
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| 366 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 367 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
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| 368 | myfile.close(); |
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| 369 | |
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| 370 | |
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| 371 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 372 | cout << "Step: " << i << endl;*/ |
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| 373 | } |
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| 374 | |
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| 375 | |
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| 376 | } |
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| 377 | |
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| 378 | |
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| 379 | // EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading |
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| 380 | // with maximization of logarithm of one-step ahead wealth. |
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| 381 | |
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| 382 | |
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| 383 | |
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| 384 | /* |
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| 385 | cout << "One experiment finished." << endl; |
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| 386 | |
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| 387 | ofstream myfile; |
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| 388 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 389 | myfile << endl; |
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| 390 | myfile.close(); |
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| 391 | |
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| 392 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 393 | myfile << endl; |
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| 394 | myfile.close();*/ |
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| 395 | |
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| 396 | |
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| 397 | //emlig* emlig1 = new emlig(emlig_size); |
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| 398 | |
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| 399 | //emlig1->step_me(0); |
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| 400 | //emlig* emlig2 = new emlig(emlig_size); |
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| 401 | |
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| 402 | /* |
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| 403 | emlig1->set_correction_factors(4); |
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| 404 | |
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| 405 | for(int j = 0;j<emlig1->correction_factors.size();j++) |
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| 406 | { |
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| 407 | for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++) |
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| 408 | { |
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| 409 | cout << j << " "; |
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| 410 | |
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| 411 | for(int i=0;i<(*vec_ref).size();i++) |
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| 412 | { |
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| 413 | cout << (*vec_ref)[i]; |
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| 414 | } |
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| 415 | |
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| 416 | cout << endl; |
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| 417 | } |
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| 418 | }*/ |
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| 419 | |
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| 420 | /* |
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| 421 | vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5"; |
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| 422 | |
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| 423 | emlig1->add_condition(condition5); |
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| 424 | //emlig1->step_me(0); |
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| 425 | |
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| 426 | |
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| 427 | vec condition1a = "-1.0 1.02 0.5"; |
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| 428 | //vec condition1b = "1.0 1.0 1.01"; |
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| 429 | emlig1->add_condition(condition1a); |
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| 430 | //emlig2->add_condition(condition1b); |
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| 431 | |
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| 432 | vec condition2a = "-0.3 1.7 1.5"; |
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| 433 | //vec condition2b = "-1.0 1.0 1.0"; |
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| 434 | emlig1->add_condition(condition2a); |
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| 435 | //emlig2->add_condition(condition2b); |
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| 436 | |
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| 437 | vec condition3a = "0.5 -1.01 1.0"; |
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| 438 | //vec condition3b = "0.5 -1.01 1.0"; |
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| 439 | |
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| 440 | emlig1->add_condition(condition3a); |
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| 441 | //emlig2->add_condition(condition3b); |
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| 442 | |
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| 443 | vec condition4a = "-0.5 -1.0 1.0"; |
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| 444 | //vec condition4b = "-0.5 -1.0 1.0"; |
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| 445 | |
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| 446 | emlig1->add_condition(condition4a); |
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| 447 | //cout << "************************************************" << endl; |
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| 448 | //emlig2->add_condition(condition4b); |
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| 449 | //cout << "************************************************" << endl; |
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| 450 | |
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| 451 | //cout << emlig1->minimal_vertex->get_coordinates(); |
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| 452 | |
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| 453 | //emlig1->remove_condition(condition3a); |
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| 454 | //emlig1->step_me(0); |
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| 455 | //emlig1->remove_condition(condition2a); |
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| 456 | //emlig1->remove_condition(condition1a); |
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| 457 | //emlig1->remove_condition(condition5); |
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| 458 | |
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| 459 | |
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| 460 | //emlig1->step_me(0); |
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| 461 | //emlig2->step_me(0); |
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| 462 | |
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| 463 | |
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| 464 | // DA SE POUZIT PRO VYPIS DO SOUBORU |
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| 465 | // emlig1->step_me(0); |
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| 466 | |
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| 467 | //emlig1->remove_condition(condition1); |
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| 468 | |
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| 469 | |
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| 470 | |
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| 471 | |
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| 472 | |
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| 473 | /* |
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| 474 | for(int i = 0;i<100;i++) |
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| 475 | { |
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| 476 | cout << endl << "Step:" << i << endl; |
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| 477 | |
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| 478 | double condition[emlig_size+1]; |
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| 479 | |
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| 480 | for(int k = 0;k<=emlig_size;k++) |
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| 481 | { |
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| 482 | condition[k] = (rand()-RAND_MAX/2)/1000.0; |
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| 483 | } |
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| 484 | |
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| 485 | |
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| 486 | vec* condition_vec = new vec(condition,emlig_size+1); |
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| 487 | emlig1->add_condition(*condition_vec); |
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| 488 | |
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| 489 | /* |
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| 490 | for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly) |
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| 491 | { |
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| 492 | cout << ((toprow*)toprow_ref)->probability << endl; |
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| 493 | } |
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| 494 | */ |
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| 495 | /* |
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| 496 | cout << emlig1->statistic_rowsize(emlig_size) << endl << endl; |
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| 497 | |
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| 498 | /* |
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| 499 | if(i-emlig1->number_of_parameters >= 0) |
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| 500 | { |
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| 501 | pause(30); |
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| 502 | } |
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| 503 | */ |
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| 504 | |
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| 505 | // emlig1->step_me(i); |
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| 506 | |
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| 507 | /* |
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| 508 | vector<int> sizevector; |
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| 509 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 510 | { |
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| 511 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 512 | } |
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| 513 | */ |
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| 514 | //} |
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| 515 | |
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| 516 | |
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| 517 | |
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| 518 | |
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| 519 | /* |
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| 520 | emlig1->step_me(1); |
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| 521 | |
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| 522 | vec condition = "2.0 0.0 1.0"; |
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| 523 | |
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| 524 | emlig1->add_condition(condition); |
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| 525 | |
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| 526 | vector<int> sizevector; |
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| 527 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 528 | { |
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| 529 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 530 | } |
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| 531 | |
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| 532 | emlig1->step_me(2); |
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| 533 | |
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| 534 | condition = "2.0 1.0 0.0"; |
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| 535 | |
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| 536 | emlig1->add_condition(condition); |
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| 537 | |
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| 538 | sizevector.clear(); |
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| 539 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 540 | { |
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| 541 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 542 | } |
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| 543 | */ |
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| 544 | |
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| 545 | return 0; |
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| 546 | } |
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| 547 | |
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| 548 | |
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