Folio - Examples from 'Getting Started'
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| Type: | Portfolio optimization |
| Rating: | 3 (intermediate) |
| Description: | Different versions of a portfolio optimization problem. Basic modelling and solving tasks:
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| File(s): | Folio.cpp, FolioCB.cpp, FolioHeuristic.cpp, FolioIIS.cpp, FolioInit.cpp, FolioMip1.cpp, FolioMip2.cpp, FolioMipIIS.cpp, FolioQC.cpp, FolioQP.cpp |
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| Folio.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a MIP problem to perform portfolio optimization. <br>
* There are a number of shares available in which to invest. The problem is to
* split the available capital between these shares to maximize return on
* investment, while satisfying certain constraints on the portfolio:
* <ul>
* <li>A maximum of 7 distinct shares can be invest in.</li>
* <li>Each share has an associated industry sector and geographic region, and
* the capital may not be overly concentrated in any one sector or region.</li>
* <li>Some of the shares are considered to be high-risk, and the maximum
* investment in these shares is limited.</li>
* </ul>
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "folio10.cdat";
int const MAXNUM = 7; /* Max. number of different assets */
double const MAXRISK = 1.0 / 3; /* Max. investment into high-risk values */
double const MINREG = 0.2; /* Min. investment per geogr. region */
double const MAXREG = 0.5; /* Max. investment per geogr. region */
double const MAXSEC = 0.25; /* Max. investment per ind. sector */
double const MAXVAL = 0.2; /* Max. investment per share */
double const MINVAL = 0.1; /* Min. investment per share */
std::vector<double> RET; /* Estimated return in investment */
std::vector<int> RISK; /* High-risk values among shares */
std::vector<std::vector<bool>> LOC; /* Geogr. region of shares */
std::vector<std::vector<bool>> SEC; /* Industry sector of shares */
std::vector<std::string> SHARES;
std::vector<std::string> REGIONS;
std::vector<std::string> TYPES;
void readData();
int main() {
readData(); // Read data from file
XpressProblem prob;
// Create the decision variables
// Fraction of capital used per share
std::vector<Variable> frac = prob.addVariables(SHARES.size())
.withUB(MAXVAL)
.withName("frac %d")
.toArray();
// 1 if asset is in portfolio, 0 otherwise
std::vector<Variable> buy = prob.addVariables(SHARES.size())
.withType(ColumnType::Binary)
.withName("buy %d")
.toArray();
// Objective: total return
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
// Limit the percentage of high-risk values
prob.addConstraint(sum(RISK, [&](auto v) { return frac[v]; }) <= MAXRISK);
// Limits on geographical distribution
prob.addConstraints(REGIONS.size(), [&](auto r) {
return sum(SHARES.size(),
[&](auto s) { return (LOC[r][s] ? 1.0 : 0.0) * frac[s]; })
.in(MINREG, MAXREG);
});
// Diversification across industry sectors
prob.addConstraints(TYPES.size(), [&](auto t) {
return sum(SHARES.size(), [&](auto s) {
return (SEC[t][s] ? 1.0 : 0.0) * frac[s];
}) <= MAXSEC;
});
// Spend all the capital
prob.addConstraint(sum(frac) == 1);
// Limit the total number of assets
prob.addConstraint(sum(buy) <= MAXNUM);
// Linking the variables
for (unsigned s = 0; s < SHARES.size(); s++) {
prob.addConstraint(frac[s] <= MAXVAL * buy[s]);
prob.addConstraint(frac[s] >= MINVAL * buy[s]);
}
// Set a time limit of 10 seconds
prob.controls.setTimeLimit(10.0);
// Solve the problem
prob.optimize("");
std::cout << "Problem status: " << prob.attributes.getMipStatus()
<< std::endl;
if (prob.attributes.getMipStatus() != MIPStatus::Solution &&
prob.attributes.getMipStatus() != MIPStatus::Optimal)
throw std::runtime_error("optimization failed with status " +
to_string(prob.attributes.getMipStatus()));
// Solution printing
std::cout << "Total return: " << prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (unsigned s = 0; s < SHARES.size(); s++)
if (buy[s].getValue(sol) > 0.5)
std::cout << " " << s << ": " << frac[s].getValue(sol) * 100 << "% ("
<< buy[s].getValue(sol) << ")" << std::endl;
return 0;
}
// Minimalistic data parsing.
#include <fstream>
#include <iterator>
/**
* Read a list of strings. Iterates <code>it</code> until a semicolon is
* encountered or the iterator ends.
*
* @param it The token sequence to read.
* @param conv Function that converts a string to <code>T</code>.
* @return A vector of all tokens before the first semicolon.
*/
template <typename T>
std::vector<T> readStrings(std::istream_iterator<std::string> &it,
std::function<T(std::string const &)> conv) {
std::vector<T> result;
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token.size() > 0 && token[token.size() - 1] == ';') {
if (token.size() > 1) {
result.push_back(conv(token.substr(0, token.size() - 1)));
}
break;
} else {
result.push_back(conv(token));
}
}
return result;
}
/**
* Read a sparse table of booleans. Allocates a <code>nrow</code> by
* <code>ncol</code> boolean table and fills it by the sparse data from the
* token sequence. <code>it</code> is assumed to hold <code>nrow</code>
* sequences of indices, each of which is terminated by a semicolon. The indices
* in those vectors specify the <code>true</code> entries in the corresponding
* row of the table.
*
* @tparam R Type of row count.
* @tparam C Type of column count.
* @param it Token sequence.
* @param nrow Number of rows in the table.
* @param ncol Number of columns in the table.
* @return The boolean table.
*/
template<typename R,typename C>
std::vector<std::vector<bool>>
readBoolTable(std::istream_iterator<std::string> &it, R nrow, C ncol) {
std::vector<std::vector<bool>> tbl(nrow, std::vector<bool>(ncol));
for (R r = 0; r < nrow; r++) {
for (auto i : readStrings<int>(it, [](auto &s) { return stoi(s); }))
tbl[r][i] = true;
}
return tbl;
}
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
std::stringstream data(std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>())));
std::istream_iterator<std::string> it(data);
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token == "SHARES:")
SHARES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "REGIONS:")
REGIONS = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "TYPES:")
TYPES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "RISK:")
RISK = readStrings<int>(it, [](auto &s) { return stoi(s); });
else if (token == "RET:")
RET = readStrings<double>(it, [](auto &s) { return stod(s); });
else if (token == "LOC:")
LOC = readBoolTable(it, REGIONS.size(), SHARES.size());
else if (token == "SEC:")
SEC = readBoolTable(it, TYPES.size(), SHARES.size());
}
}
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| FolioCB.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a MIP problem to perform portfolio optimization. -- Defining an
* integer solution callback --
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "folio10.cdat";
int const MAXNUM = 15; /* Max. number of different assets */
double const MAXRISK = 1.0 / 3; /* Max. investment into high-risk values */
double const MINREG = 0.2; /* Min. investment per geogr. region */
double const MAXREG = 0.5; /* Max. investment per geogr. region */
double const MAXSEC = 0.25; /* Max. investment per ind. sector */
double const MAXVAL = 0.2; /* Max. investment per share */
double const MINVAL = 0.1; /* Min. investment per share */
std::vector<double> RET; /* Estimated return in investment */
std::vector<int> RISK; /* High-risk values among shares */
std::vector<std::vector<bool>> LOC; /* Geogr. region of shares */
std::vector<std::vector<bool>> SEC; /* Industry sector of shares */
std::vector<std::string> SHARES;
std::vector<std::string> REGIONS;
std::vector<std::string> TYPES;
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
void printProblemSolution(XpressProblem const &prob,
std::vector<Variable> const &buy,
std::vector<Variable> const &frac, bool isCallback) {
auto sol = isCallback ? prob.getCallbackSolution() : prob.getSolution();
std::cout << "Total return: "
<< (isCallback ? prob.attributes.getLpObjVal()
: prob.attributes.getObjVal())
<< std::endl;
for (unsigned i = 0; i < SHARES.size(); ++i) {
if (buy[i].getValue(sol) > 0.5)
std::cout << i << ": " << (100.0 * frac[i].getValue(sol)) << "%"
<< " (" << buy[i].getValue(sol) << ")" << std::endl;
}
}
void readData();
int main() {
readData();
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
std::vector<Variable> frac =
prob.addVariables(SHARES.size())
/* Fraction of capital used per share */
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(MAXVAL)
.toArray();
std::vector<Variable> buy = prob.addVariables(SHARES.size())
.withName("buy_%d")
.withType(ColumnType::Binary)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(RISK.size(),
[&](auto i) { return frac[RISK[i]]; }) <=
MAXRISK);
/* Limits on geographical distribution */
prob.addConstraints(REGIONS.size(), [&](auto r) {
return sum(SHARES.size(),
[&](auto s) { return (LOC[r][s] ? 1.0 : 0.0) * frac[s]; })
.in(MINREG, MAXREG);
});
/* Diversification across industry sectors */
prob.addConstraints(TYPES.size(), [&](auto t) {
return sum(SHARES.size(), [&](auto s) {
return (SEC[t][s] ? 1.0 : 0.0) * frac[s];
}) <= MAXSEC;
});
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0);
/* Limit the total number of assets */
prob.addConstraint(sum(buy) <= MAXNUM);
/* Linking the variables */
prob.addConstraints(SHARES.size(),
[&](auto i) { return frac[i] >= MINVAL * buy[i]; });
prob.addConstraints(SHARES.size(),
[&](auto i) { return frac[i] <= MAXVAL * buy[i]; });
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Callback for each new integer solution found */
prob.callbacks.addIntsolCallback(
[&](auto &p) { printProblemSolution(p, buy, frac, true); });
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
printProblemSolution(prob, buy, frac, false);
return 0;
}
// Minimalistic data parsing.
#include <fstream>
#include <iterator>
/**
* Read a list of strings. Iterates <code>it</code> until a semicolon is
* encountered or the iterator ends.
*
* @param it The token sequence to read.
* @param conv Function that converts a string to <code>T</code>.
* @return A vector of all tokens before the first semicolon.
*/
template <typename T>
std::vector<T> readStrings(std::istream_iterator<std::string> &it,
std::function<T(std::string const &)> conv) {
std::vector<T> result;
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token.size() > 0 && token[token.size() - 1] == ';') {
if (token.size() > 1) {
result.push_back(conv(token.substr(0, token.size() - 1)));
}
break;
} else {
result.push_back(conv(token));
}
}
return result;
}
/**
* Read a sparse table of booleans. Allocates a <code>nrow</code> by
* <code>ncol</code> boolean table and fills it by the sparse data from the
* token sequence. <code>it</code> is assumed to hold <code>nrow</code>
* sequences of indices, each of which is terminated by a semicolon. The indices
* in those vectors specify the <code>true</code> entries in the corresponding
* row of the table.
*
* @tparam R Type of row count.
* @tparam C Type of column count.
* @param it Token sequence.
* @param nrow Number of rows in the table.
* @param ncol Number of columns in the table.
* @return The boolean table.
*/
template<typename R,typename C>
std::vector<std::vector<bool>>
readBoolTable(std::istream_iterator<std::string> &it, R nrow, C ncol) {
std::vector<std::vector<bool>> tbl(nrow, std::vector<bool>(ncol));
for (R r = 0; r < nrow; r++) {
for (auto i : readStrings<int>(it, [](auto &s) { return stoi(s); }))
tbl[r][i] = true;
}
return tbl;
}
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
std::stringstream data(std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>())));
std::istream_iterator<std::string> it(data);
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token == "SHARES:")
SHARES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "REGIONS:")
REGIONS = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "TYPES:")
TYPES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "RISK:")
RISK = readStrings<int>(it, [](auto &s) { return stoi(s); });
else if (token == "RET:")
RET = readStrings<double>(it, [](auto &s) { return stod(s); });
else if (token == "LOC:")
LOC = readBoolTable(it, REGIONS.size(), SHARES.size());
else if (token == "SEC:")
SEC = readBoolTable(it, TYPES.size(), SHARES.size());
}
}
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| FolioHeuristic.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a small LP problem to perform portfolio optimization. -- Heuristic
* solution
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/* Max. number of different assets */
int const MAXNUM = 4;
/* Number of shares */
int const NSHARES = 10;
/* Number of high-risk shares */
int const NRISK = 5;
/* Number of North-American shares */
int const NNA = 4;
/* Estimated return in investment */
std::vector<double> const RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/* High-risk values among shares */
std::vector<int> const RISK{1, 2, 3, 8, 9};
/* Shares issued in N.-America */
std::vector<int> const NA{0, 1, 2, 3};
class FolioHeuristic {
/** Optimizer problem. */
XpressProblem prob;
/** Fraction of capital used per share */
std::vector<Variable> frac;
/** 1 if asset is in portfolio, 0 otherwise */
std::vector<Variable> buy;
public:
FolioHeuristic()
: prob(), frac(prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(0.3)
.toArray()),
buy(prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("buy_%d")
.withType(ColumnType::Binary)
.toArray()) {}
/** Print current problem status. */
void printProblemStatus() const {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tLP status: " << prob.attributes.getLpStatus() << std::endl
<< "\tMIP status: " << prob.attributes.getMipStatus() << std::endl
<< "\tSol status: " << prob.attributes.getSolStatus()
<< std::endl;
}
/** Print current solution.
* @param solveFlag What kind of solution this is.
*/
void printProblemSolution(std::string solveFlag) {
std::cout << "Total return (" << solveFlag
<< "): " << prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (int i = 0; i < NSHARES; ++i) {
std::cout << frac[i].getName() << ": " << (100. * frac[i].getValue(sol))
<< "%"
<< " (" << buy[i].getValue(sol) << ")" << std::endl;
}
}
/** Load the model into the internal data structures. */
void model() {
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(NRISK, [&](auto i) { return frac[RISK[i]]; }) <=
1.0 / 3.0);
/* Minimum amount of North-American values */
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5);
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0);
/* Limit the total number of assets */
prob.addConstraint(sum(buy) <= MAXNUM);
/* Linking the variables */
prob.addConstraints(NSHARES, [&](auto i) { return frac[i] <= buy[i]; });
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
}
/** Solve resident model with a heuristic. */
void solveHeuristic() {
/* Disable automatic cuts */
prob.controls.setCutStrategy(XPRS_CUTSTRATEGY_NONE);
// Switch presolve off
prob.controls.setPresolve(XPRS_PRESOLVE_NONE);
prob.controls.setMipPresolve(0);
/* Get feasibility tolerance */
double tol = prob.controls.getFeasTol();
/* Solve the LP-problem */
prob.lpOptimize();
/* Get Solution */
auto sol = prob.getSolution();
/* Basis information */
std::vector<int> rowstat(prob.attributes.getRows());
std::vector<int> colstat(prob.attributes.getCols());
/* Save the current basis */
prob.getBasis(rowstat, colstat);
/*
* Fix all variables `buy' for which `frac' is at 0 or at a relatively large
* value
*/
std::vector<double> fsol(NSHARES);
for (int i = 0; i < NSHARES; ++i) {
/* Get the solution values of `frac' */
fsol[i] = frac[i].getValue(sol);
if (fsol[i] < tol)
buy[i].fix(0);
else if (fsol[i] > 0.2 - tol)
buy[i].fix(1);
}
/* Solve with the new bounds on 'buy' */
prob.mipOptimize();
printProblemStatus();
printProblemSolution("Heuristic solution");
/* Reset variables to their original bounds */
for (int i = 0; i < NSHARES; ++i) {
if ((fsol[i] < tol) || (fsol[i] > 0.2 - tol)) {
buy[i].setLB(0);
buy[i].setUB(1);
}
}
/* Load basis */
prob.loadBasis(rowstat, colstat);
}
/** Solve resident model with optimizer. */
void optimize() { prob.optimize(); }
};
int main() {
FolioHeuristic heur;
heur.model();
heur.solveHeuristic();
FolioHeuristic exact;
exact.model();
exact.optimize();
/* Solution printing */
exact.printProblemStatus();
exact.printProblemSolution("Exact Solve");
return 0;
}
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| FolioIIS.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a MIP problem to perform portfolio optimization.
* Used infeasible model parameter values and illustrates retrieving IIS.
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "folio10.cdat";
int const MAXNUM = 5; /* Max. number of different assets */
double const MAXRISK = 1.0 / 3; /* Max. investment into high-risk values */
double const MINREG = 0.1; /* Min. investment per geogr. region */
double const MAXREG = 0.2; /* Max. investment per geogr. region */
double const MAXSEC = 0.1; /* Max. investment per ind. sector */
double const MAXVAL = 0.2; /* Max. investment per share */
double const MINVAL = 0.1; /* Min. investment per share */
std::vector<double> RET; /* Estimated return in investment */
std::vector<int> RISK; /* High-risk values among shares */
std::vector<std::vector<bool>> LOC; /* Geogr. region of shares */
std::vector<std::vector<bool>> SEC; /* Industry sector of shares */
std::vector<std::string> SHARES;
std::vector<std::string> REGIONS;
std::vector<std::string> TYPES;
void readData();
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tLP status: " << prob.attributes.getLpStatus() << std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
int main(void) {
readData();
XpressProblem prob;
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
/* Fraction of capital used per share */
std::vector<Variable> frac =
prob.addVariables(SHARES.size())
/* Fraction of capital used per share */
.withName([&](auto i) { return "frac" + SHARES[i]; })
/* Upper bounds on the investment per share */
.withUB(MAXVAL)
.toArray();
/* 1 if asset is in portfolio, 0 otherwise */
std::vector<Variable> buy =
prob.addVariables(SHARES.size())
.withName([&](auto i) { return "buy_" + SHARES[i]; })
.withType(ColumnType::Binary)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(
(sum(RISK.size(), [&](auto i) { return frac[RISK[i]]; }) <= MAXRISK)
.setName("Risk"));
/* Limits on geographical distribution */
for (unsigned r = 0; r < REGIONS.size(); ++r) {
Expression MinReg = sum(SHARES.size(), [&](auto s) {
return (LOC[r][s] ? 1.0 : 0.0) * frac[s];
});
Expression MaxReg = sum(SHARES.size(), [&](auto s) {
return (LOC[r][s] ? 1.0 : 0.0) * frac[s];
});
prob.addConstraint(
(MinReg <= MINREG).setName("MinReg(" + REGIONS[r] + ")"));
prob.addConstraint(
(MaxReg <= MAXREG).setName("MaxReg(" + REGIONS[r] + ")"));
}
/* Diversification across industry sectors */
for (unsigned t = 0; t < TYPES.size(); ++t) {
Expression LimSec = sum(SHARES.size(), [&](auto s) {
return (SEC[t][s] ? 1.0 : 0.0) * frac[s];
});
prob.addConstraint((LimSec <= MAXSEC).setName("LimSec(" + TYPES[t] + ")"));
}
/* Spend all the capital */
prob.addConstraint((sum(frac) == 1.0).setName("Cap"));
/* Limit the total number of assets */
prob.addConstraint((sum(buy) >= MAXNUM).setName("MaxAssets"));
/* Linking the variables */
prob.addConstraints(SHARES.size(), [&](auto i) {
return (frac[i] >= MINVAL * buy[i]).setName("link_lb_" + std::to_string(i));
});
prob.addConstraints(SHARES.size(), [&](auto i) {
return (frac[i] <= MAXVAL * buy[i])
.setName("link_ub_%d" + std::to_string(i));
});
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
if (prob.attributes.getSolStatus() == SolStatus::Infeasible) {
std::cout << "LP infeasible. Retrieving IIS." << std::endl;
// Check there is at least one IIS
int status = prob.firstIIS(1);
if (status != 0)
throw std::runtime_error("firstIIS() failed with status " +
std::to_string(status));
int iisIndex = 1; // First IIS has index 1
do {
XPRSProblem::IISStatusInfo info = prob.IISStatus();
std::cout << "IIS has " << info.rowsizes[iisIndex] << " constraints"
<< " and " << info.colsizes[iisIndex] << " columns"
<< ", " << info.numinfeas[iisIndex] << " infeasibilities"
<< " with an infeasibility of " << info.suminfeas[iisIndex]
<< std::endl;
IIS data = prob.getIIS(iisIndex);
std::cout << "Variables in IIS:" << std::endl;
for (IISVariable const &v : data.getVariables()) {
// Note that the IISVariable class has more fields than
// we print here. See the reference documentation for
// details.
std::cout << "\t" << v.getVariable().getName() << " " << v.getDomain()
<< std::endl;
}
std::cout << "Constraints in IIS:" << std::endl;
for (IISConstraint const &c : data.getConstraints()) {
// Note that the IISVariable class has more fields than
// we print here. See the reference documentation for
// details.
std::cout << "\t" << std::get<Inequality>(c.getConstraint()).getName()
<< std::endl;
}
++iisIndex; // Prepare for next IIS (if any)
} while (prob.nextIIS() == 0);
}
return 0;
}
// Minimalistic data parsing.
#include <fstream>
#include <iterator>
/**
* Read a list of strings. Iterates <code>it</code> until a semicolon is
* encountered or the iterator ends.
*
* @param it The token sequence to read.
* @param conv Function that converts a string to <code>T</code>.
* @return A vector of all tokens before the first semicolon.
*/
template <typename T>
std::vector<T> readStrings(std::istream_iterator<std::string> &it,
std::function<T(std::string const &)> conv) {
std::vector<T> result;
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token.size() > 0 && token[token.size() - 1] == ';') {
if (token.size() > 1) {
result.push_back(conv(token.substr(0, token.size() - 1)));
}
break;
} else {
result.push_back(conv(token));
}
}
return result;
}
/**
* Read a sparse table of booleans. Allocates a <code>nrow</code> by
* <code>ncol</code> boolean table and fills it by the sparse data from the
* token sequence. <code>it</code> is assumed to hold <code>nrow</code>
* sequences of indices, each of which is terminated by a semicolon. The indices
* in those vectors specify the <code>true</code> entries in the corresponding
* row of the table.
*
* @tparam R Type of row count.
* @tparam C Type of column count.
* @param it Token sequence.
* @param nrow Number of rows in the table.
* @param ncol Number of columns in the table.
* @return The boolean table.
*/
template<typename R,typename C>
std::vector<std::vector<bool>>
readBoolTable(std::istream_iterator<std::string> &it, R nrow, C ncol) {
std::vector<std::vector<bool>> tbl(nrow, std::vector<bool>(ncol));
for (R r = 0; r < nrow; r++) {
for (auto i : readStrings<int>(it, [](auto &s) { return stoi(s); }))
tbl[r][i] = true;
}
return tbl;
}
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
std::stringstream data(std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>())));
std::istream_iterator<std::string> it(data);
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token == "SHARES:")
SHARES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "REGIONS:")
REGIONS = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "TYPES:")
TYPES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "RISK:")
RISK = readStrings<int>(it, [](auto &s) { return stoi(s); });
else if (token == "RET:")
RET = readStrings<double>(it, [](auto &s) { return stod(s); });
else if (token == "LOC:")
LOC = readBoolTable(it, REGIONS.size(), SHARES.size());
else if (token == "SEC:")
SEC = readBoolTable(it, TYPES.size(), SHARES.size());
}
}
|
| FolioInit.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/** Modeling a small LP problem to perform portfolio optimization. */
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/* Number of shares */
int const NSHARES = 10;
/* Number of high-risk shares */
int const NRISK = 5;
/* Number of North-American shares */
int const NNA = 4;
/* Estimated return in investment */
std::vector<double> const RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/* High-risk values among shares */
std::vector<int> RISK{1, 2, 3, 8, 9};
/* Shares issued in N.-America */
std::vector<int> NA{0, 1, 2, 3};
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
int main(void) {
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
std::vector<Variable> frac =
prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(0.3)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(NRISK, [&](auto i) { return frac[RISK[i]]; }) <=
1.0 / 3.0)
.setName("Risk");
/* Minimum amount of North-American values */
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5)
.setName("NA");
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0).setName("Cap");
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
std::cout << "Total return: " << prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (int i = 0; i < NSHARES; ++i) {
std::cout << frac[i].getName() << ": " << (100.0 * frac[i].getValue(sol))
<< "%" << std::endl;
}
return 0;
}
|
| FolioMip1.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a small LP problem to perform portfolio optimization. -- Limiting
* the total number of assets
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/* Max. number of different assets */
int const MAXNUM = 4;
/* Number of shares */
int const NSHARES = 10;
/* Number of high-risk shares */
int const NRISK = 5;
/* Number of North-American shares */
int const NNA = 4;
/* Estimated return in investment */
std::vector<double> const RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/* High-risk values among shares */
std::vector<int> RISK{1, 2, 3, 8, 9};
/* Shares issued in N.-America */
std::vector<int> NA{0, 1, 2, 3};
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
int main() {
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
/* Fraction of capital used per share */
std::vector<Variable> frac =
prob.addVariables(NSHARES)
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(0.3)
.toArray();
/* Fraction of capital used per share */
std::vector<Variable> buy = prob.addVariables(NSHARES)
.withName("buy_%d")
.withType(ColumnType::Binary)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(NRISK, [&](auto i) { return frac[RISK[i]]; }) <=
1.0 / 3.0)
.setName("Risk");
/* Minimum amount of North-American values */
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5)
.setName("NA");
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0).setName("Cap");
/* Limit the total number of assets */
prob.addConstraint(sum(buy) <= MAXNUM).setName("MaxAssets");
/* Linking the variables */
/* frac .<= buy */
prob.addConstraints(NSHARES, [&](auto i) {
return (frac[i] <= buy[i]).setName("link_" + std::to_string(i));
});
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
std::cout << "Total return: " << prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (int i = 0; i < NSHARES; ++i)
std::cout << frac[i].getName() << ": " << (100.0 * frac[i].getValue(sol))
<< "%"
<< " (" << buy[i].getValue(sol) << ")" << std::endl;
return 0;
}
|
| FolioMip2.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a small LP problem to perform portfolio optimization. -- Imposing a
* minimum investment per share.
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/* Number of shares */
int const NSHARES = 10;
/* Number of high-risk shares */
int const NRISK = 5;
/* Number of North-American shares */
int const NNA = 4;
/* Estimated return in investment */
std::vector<double> RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/* High-risk values among shares */
std::vector<int> RISK{1, 2, 3, 8, 9};
/* Shares issued in N.-America */
std::vector<int> NA{0, 1, 2, 3};
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
int main(void) {
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
std::vector<Variable> frac =
prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("frac_%d")
.withType(ColumnType::SemiContinuous)
/* Upper bounds on the investment per share */
.withUB(0.3)
/* Investment limit */
.withLimit(0.1)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(NRISK, [&](auto i) { return frac[RISK[i]]; }) <=
1.0 / 3.0);
/* Minimum amount of North-American values */
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5);
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0);
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
std::cout << "Total return: " << prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (int i = 0; i < NSHARES; ++i)
std::cout << frac[i].getName() << " : " << 100.0 * frac[i].getValue(sol)
<< "%" << std::endl;
return 0;
}
|
| FolioMipIIS.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a MIP problem to perform portfolio optimization.
* Same model as in FolioMip3.java.
* Uses infeasible model parameter values and illustrates retrieving MIIS.
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "folio10.cdat";
int const MAXNUM = 5; /* Max. number of different assets */
double const MAXRISK = 1.0 / 4; /* Max. investment into high-risk values */
double const MINREG = 0.1; /* Min. investment per geogr. region */
double const MAXREG = 0.25; /* Max. investment per geogr. region */
double const MAXSEC = 0.15; /* Max. investment per ind. sector */
double const MAXVAL = 0.225; /* Max. investment per share */
double const MINVAL = 0.1; /* Min. investment per share */
std::vector<double> RET; /* Estimated return in investment */
std::vector<int> RISK; /* High-risk values among shares */
std::vector<std::vector<bool>> LOC; /* Geogr. region of shares */
std::vector<std::vector<bool>> SEC; /* Industry sector of shares */
std::vector<std::string> SHARES;
std::vector<std::string> REGIONS;
std::vector<std::string> TYPES;
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tLP status: " << prob.attributes.getLpStatus() << std::endl
<< "\tMIP status: " << prob.attributes.getMipStatus() << std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
void readData();
int main() {
readData();
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
/* Fraction of capital used per share */
std::vector<Variable> frac =
prob.addVariables(SHARES.size())
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withLB(0.0)
.withUB(MAXVAL)
.toArray();
std::vector<Variable> buy = prob.addVariables(SHARES.size())
.withName("buy_%d")
.withType(ColumnType::Binary)
.toArray();
/**** CONSTRAINTS ****/
/* Limit the percentage of high-risk values */
prob.addConstraint(sum(RISK.size(),
[&](auto i) { return frac[RISK[i]]; }) <=
MAXRISK)
.setName("Risk");
/* Limits on geographical distribution */
prob.addConstraints(REGIONS.size(), [&](auto r) {
return sum(SHARES.size(),
[&](auto s) { return (LOC[r][s] ? 1.0 : 0.0) * frac[s]; })
.in(MINREG, MAXREG)
.setName("MinMaxReg_" + REGIONS[r]);
});
/* Diversification across industry sectors */
prob.addConstraints(TYPES.size(), [&](auto t) {
return sum(SHARES.size(),
[&](auto s) { return (SEC[t][s] ? 1.0 : 0.0) * frac[s]; })
.in(0.0, MAXSEC)
.setName("LimSec(" + TYPES[t] + ")");
});
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0).setName("Cap");
/* Limit the total number of assets */
prob.addConstraint(sum(buy) <= MAXNUM).setName("MaxAssets");
/* Linking the variables */
prob.addConstraints(SHARES.size(), [&](auto i) {
return (frac[i] >= MINVAL * buy[i]).setName("link_lb_" + std::to_string(i));
});
prob.addConstraints(SHARES.size(), [&](auto i) {
return (frac[i] <= MAXVAL * buy[i]).setName("link_ub_" + std::to_string(i));
});
/* Objective: maximize total return */
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
if (prob.attributes.getSolStatus() == SolStatus::Infeasible) {
std::cout << "MIP infeasible. Retrieving IIS." << std::endl;
// Check there is at least one IIS
int status = prob.firstIIS(1);
if (status != 0)
throw std::runtime_error("firstIIS() failed with status " +
std::to_string(status));
XPRSProblem::IISStatusInfo info = prob.IISStatus();
std::cout << "IIS has " << info.rowsizes[1] << " constraints and "
<< info.colsizes[1] << " columns, " << info.numinfeas[1]
<< " infeasibilities with an infeasibility of "
<< info.suminfeas[1] << std::endl;
IIS data = prob.getIIS(1);
std::cout << "Variables in IIS:" << std::endl;
for (auto &v : data.getVariables()) {
// Note that the IISVariable class has more fields than
// we print here. See the reference documentation for
// details.
std::cout << "\t" << v.getVariable().getName() << " " << v.getDomain()
<< std::endl;
}
std::cout << "Constraints in IIS:" << std::endl;
for (auto &c : data.getConstraints()) {
// Note that the IISVariable class has more fields than
// we print here. See the reference documentation for
// details.
std::cout << "\t" << std::get<Inequality>(c.getConstraint()).getName()
<< std::endl;
}
}
return 0;
}
// Minimalistic data parsing.
#include <fstream>
#include <iterator>
/**
* Read a list of strings. Iterates <code>it</code> until a semicolon is
* encountered or the iterator ends.
*
* @param it The token sequence to read.
* @param conv Function that converts a string to <code>T</code>.
* @return A vector of all tokens before the first semicolon.
*/
template <typename T>
std::vector<T> readStrings(std::istream_iterator<std::string> &it,
std::function<T(std::string const &)> conv) {
std::vector<T> result;
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token.size() > 0 && token[token.size() - 1] == ';') {
if (token.size() > 1) {
result.push_back(conv(token.substr(0, token.size() - 1)));
}
break;
} else {
result.push_back(conv(token));
}
}
return result;
}
/**
* Read a sparse table of booleans. Allocates a <code>nrow</code> by
* <code>ncol</code> boolean table and fills it by the sparse data from the
* token sequence. <code>it</code> is assumed to hold <code>nrow</code>
* sequences of indices, each of which is terminated by a semicolon. The indices
* in those vectors specify the <code>true</code> entries in the corresponding
* row of the table.
*
* @tparam R Type of row count.
* @tparam C Type of column count.
* @param it Token sequence.
* @param nrow Number of rows in the table.
* @param ncol Number of columns in the table.
* @return The boolean table.
*/
template<typename R,typename C>
std::vector<std::vector<bool>>
readBoolTable(std::istream_iterator<std::string> &it, R nrow, C ncol) {
std::vector<std::vector<bool>> tbl(nrow, std::vector<bool>(ncol));
for (R r = 0; r < nrow; r++) {
for (auto i : readStrings<int>(it, [](auto &s) { return stoi(s); }))
tbl[r][i] = true;
}
return tbl;
}
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
std::stringstream data(std::string((std::istreambuf_iterator<char>(ifs)),
(std::istreambuf_iterator<char>())));
std::istream_iterator<std::string> it(data);
while (it != std::istream_iterator<std::string>()) {
std::string token = *it++;
if (token == "SHARES:")
SHARES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "REGIONS:")
REGIONS = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "TYPES:")
TYPES = readStrings<std::string>(it, [](auto &s) { return s; });
else if (token == "RISK:")
RISK = readStrings<int>(it, [](auto &s) { return stoi(s); });
else if (token == "RET:")
RET = readStrings<double>(it, [](auto &s) { return stod(s); });
else if (token == "LOC:")
LOC = readBoolTable(it, REGIONS.size(), SHARES.size());
else if (token == "SEC:")
SEC = readBoolTable(it, TYPES.size(), SHARES.size());
}
}
|
| FolioQC.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a small QCQP problem to perform portfolio optimization. -- Maximize
* return with limit on variance ---
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "/foliocppqp.dat";
/** Max. allowed variance */
double const MAXVAR = 0.55;
/** Number of shares */
int const NSHARES = 10;
/** Number of North-American shares */
int const NNA = 4;
/** Estimated return in investment */
std::vector<double> const RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/** Shares issued in N.-America */
std::vector<int> const NA{0, 1, 2, 3};
/** Variance/covariance matrix of estimated returns */
std::vector<std::vector<double>> VAR;
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tLP status: " << prob.attributes.getLpStatus() << std::endl
<< "\tMIP status: " << prob.attributes.getMipStatus() << std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
void readData();
int main() {
readData();
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/**** VARIABLES ****/
// Fraction of capital used per share
std::vector<Variable> frac =
prob.addVariables(NSHARES)
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(0.3)
.toArray();
/**** CONSTRAINTS ****/
// Limit variance
Expression variance = sum(NSHARES, [&](auto s) {
return sum(NSHARES, [&](auto t) {
// v * fs * ft
return VAR[s][t] * frac[s] * frac[t];
});
});
prob.addConstraint(variance <= MAXVAR).setName("Variance");
// Minimum amount of North-American values
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5)
.setName("NA");
// Spend all the capital
prob.addConstraint(sum(frac) == 1.0).setName("Cap");
// Objective: maximize total return
prob.setObjective(scalarProduct(frac, RET), ObjSense::Maximize);
// Solve
prob.optimize();
// Solution printing
printProblemStatus(prob);
std::cout << "With of max variance of " << MAXVAR << " total return is "
<< prob.attributes.getObjVal() << std::endl;
auto sol = prob.getSolution();
for (int i = 0; i < NSHARES; ++i)
std::cout << frac[i].getName() << " : " << (100.0 * frac[i].getValue(sol))
<< "%" << std::endl;
return 0;
}
#include <fstream>
#include <sstream>
#include <cctype>
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
VAR = std::vector<std::vector<double>>(NSHARES, std::vector<double>(NSHARES));
std::string line;
int row = 0;
while (std::getline(ifs, line)) {
// Strip comments (comments start with a '!')
std::string::size_type comment = line.find('!');
if (comment != std::string::npos)
line = line.substr(0, comment);
// Remove leading whitespace
line.erase(line.begin(),
std::find_if(line.begin(), line.end(), [](unsigned char ch) {
return !std::isspace(ch);
}));
// skip empty lines
if (line.size() == 0)
continue;
// Now read NSHARES values into the current row in VAR
std::stringstream s(line);
for (int i = 0; i < NSHARES; ++i)
s >> VAR[row][i];
++row;
}
}
|
| FolioQP.cpp |
// (c) 2024-2024 Fair Isaac Corporation
/**
* Modeling a small QP problem to perform portfolio optimization. -- 1. QP:
* minimize variance 2. MIQP: limited number of assets ---
*/
#include <iostream>
#include <xpress.hpp>
using namespace xpress;
using namespace xpress::objects;
using xpress::objects::utils::scalarProduct;
using xpress::objects::utils::sum;
/** The file from which data for this example is read. */
char const *const DATAFILE = "foliocppqp.dat";
/* Target yield */
int const TARGET = 9;
/* Max. number of different assets */
int const MAXNUM = 4;
/* Number of shares */
int const NSHARES = 10;
/* Number of North-American shares */
int const NNA = 4;
/* Estimated return in investment */
std::vector<double> RET{5, 17, 26, 12, 8, 9, 7, 6, 31, 21};
/* Shares issued in N.-America */
std::vector<int> NA{0, 1, 2, 3};
/* Variance/covariance matrix of estimated returns */
std::vector<std::vector<double>> VAR;
void readData();
void printProblemStatus(XpressProblem const &prob) {
std::cout << "Problem status:" << std::endl
<< "\tSolve status: " << prob.attributes.getSolveStatus()
<< std::endl
<< "\tSol status: " << prob.attributes.getSolStatus() << std::endl;
}
int main() {
readData();
XpressProblem prob;
// Output all messages.
prob.callbacks.addMessageCallback(XpressProblem::console);
/***** First problem: unlimited number of assets *****/
/**** VARIABLES ****/
std::vector<Variable> frac =
prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("frac_%d")
/* Upper bounds on the investment per share */
.withUB(0.3)
.toArray();
/**** CONSTRAINTS ****/
/* Minimum amount of North-American values */
prob.addConstraint(sum(NNA, [&](auto i) { return frac[NA[i]]; }) >= 0.5);
/* Spend all the capital */
prob.addConstraint(sum(frac) == 1.0);
/* Target yield */
prob.addConstraint(scalarProduct(frac, RET) >= TARGET);
/* Objective: minimize mean variance */
Expression variance = sum(NSHARES, [&](auto s) {
return sum(NSHARES, [&](auto t) { return VAR[s][t] * frac[s] * frac[t]; });
});
prob.setObjective(variance, ObjSense::Minimize);
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
std::cout << "With a target of " << TARGET << " minimum variance is "
<< prob.attributes.getObjVal() << std::endl;
auto sollp = prob.getSolution();
for (int i = 0; i < NSHARES; ++i)
std::cout << frac[i].getName() << " : " << 100.0 * frac[i].getValue(sollp)
<< "%" << std::endl;
/***** Second problem: limit total number of assets *****/
std::vector<Variable> buy = prob.addVariables(NSHARES)
/* Fraction of capital used per share */
.withName("buy_%d")
.withType(ColumnType::Binary)
.toArray();
/* Limit the total number of assets */
prob.addConstraint(sum(buy) <= MAXNUM);
/* Linking the variables */
/* frac .<= buy */
prob.addConstraints(NSHARES, [&](auto i) { return frac[i] <= buy[i]; });
/* Solve */
prob.optimize();
/* Solution printing */
printProblemStatus(prob);
std::cout << "With a target of " << TARGET << " and at most " << MAXNUM
<< " assets"
<< ", minimum variance is " << prob.attributes.getObjVal()
<< std::endl;
auto solmip = prob.getSolution();
for (int i = 0; i < NSHARES; ++i)
std::cout << frac[i].getName() << " : " << 100.0 * frac[i].getValue(solmip)
<< "%" << std::endl;
return 0;
}
#include <fstream>
#include <sstream>
#include <cctype>
void readData() {
std::string dataDir("../../data");
#ifdef _WIN32
size_t len;
char buffer[1024];
if ( !getenv_s(&len, buffer, sizeof(buffer), "EXAMPLE_DATA_DIR") &&
len && len < sizeof(buffer) )
dataDir = buffer;
#else
char const *envDir = std::getenv("EXAMPLE_DATA_DIR");
if (envDir)
dataDir = envDir;
#endif
std::string dataFile = dataDir + "/" + DATAFILE;
std::ifstream ifs(dataFile);
if (!ifs)
throw std::runtime_error("Could not open " + dataFile);
VAR = std::vector<std::vector<double>>(NSHARES, std::vector<double>(NSHARES));
std::string line;
int row = 0;
while (std::getline(ifs, line)) {
// Strip comments (comments start with a '!')
std::string::size_type comment = line.find('!');
if (comment != std::string::npos)
line = line.substr(0, comment);
// Remove leading whitespace
line.erase(line.begin(),
std::find_if(line.begin(), line.end(), [](unsigned char ch) {
return !std::isspace(ch);
}));
// skip empty lines
if (line.size() == 0)
continue;
// Now read NSHARES values into the current row in VAR
std::stringstream s(line);
for (int i = 0; i < NSHARES; ++i)
s >> VAR[row][i];
++row;
}
}
|
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