| foliolp.mos |
(!******************************************************
Mosel Example Problems
======================
file foliolp.mos
````````````````
Modeling a small LP problem
to perform portfolio optimization.
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs"
declarations
SHARES = 1..10 ! Set of shares
RISK = {2,3,4,9,10} ! Set of high-risk values among shares
NA = {1,2,3,4} ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
RET:: [5,17,26,12,8,9,7,6,31,21]
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= 1/3
! Minimum amount of North-American values
sum(s in NA) frac(s) >= 0.5
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= 0.3
! Solve the problem
maximize(Return)
(!
declarations
status:array({XPRS_OPT,XPRS_UNF,XPRS_INF,XPRS_UNB,XPRS_OTH}) of string
end-declarations
status::([XPRS_OPT,XPRS_UNF,XPRS_INF,XPRS_UNB,XPRS_OTH])[
"Optimum found","Unfinished","Infeasible","Unbounded","Failed"]
writeln("Problem status: ", status(getprobstat))
!)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| foliolperr.mos |
(!******************************************************
Mosel Example Problems
======================
file foliolperr.mos
```````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Model with syntax errors --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003
*******************************************************!)
model "Portfolio optimization with LP"
declarations
SHARES = 1..10 ! Set of shares
RISK = {2,3,4,9,10} ! Set of high-risk values among shares
NA = {1,2,3,4} ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
RET:: [5,17,26,12,8,9,7,6,31,21
! Objective: total return
Return = sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= 1/3
! Minimum amount of North-American values
sum(s in NA) frac(s) >= 0.5
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= 0.3
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| foliolps.mos |
(!******************************************************
Mosel Example Problems
======================
file foliolps.mos
`````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Using string indices --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Mar. 2006
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs"
declarations
! Set of shares
SHARES = {"treasury", "hardware", "theater", "telecom", "brewery",
"highways", "cars", "bank", "software", "electronics"}
! Set of high-risk values among shares
RISK = {"hardware", "theater", "telecom", "software", "electronics"}
! Set of shares issued in N.-America
NA = {"treasury", "hardware", "theater", "telecom"}
RET: array(SHARES) of real ! Estimated return in investment
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
RET::(["treasury", "hardware", "theater", "telecom", "brewery", "highways",
"cars", "bank", "software", "electronics"])[5,17,26,12,8,9,7,6,31,21]
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= 1/3
! Minimum amount of North-American values
sum(s in NA) frac(s) >= 0.5
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= 0.3
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| foliolps_graph.mos |
(!******************************************************
Mosel Example Problems
======================
file foliolps_graph.mos
```````````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Using string indices --
-- Graphical representation of the solution --
(c) 2017 Fair Isaac Corporation
author: S.Heipcke, June 2017, rev. Sep. 2017
*******************************************************!)
model "Portfolio optimization with LP - graph"
uses "mmxprs" ! Use Xpress Optimizer
uses "mmsvg"
declarations
! Set of shares
SHARES = {"treasury", "hardware", "theater", "telecom", "brewery",
"highways", "cars", "bank", "software", "electronics"}
! Set of high-risk values among shares
RISK = {"hardware", "theater", "telecom", "software", "electronics"}
! Set of shares issued in N.-America
NA = {"treasury", "hardware", "theater", "telecom"}
RET: array(SHARES) of real ! Estimated return in investment
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
RET::(["treasury", "hardware", "theater", "telecom", "brewery", "highways",
"cars", "bank", "software", "electronics"])[5,17,26,12,8,9,7,6,31,21]
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= 1/3
! Minimum amount of North-American values
sum(s in NA) frac(s) >= 0.5
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= 0.3
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
! Solution drawing
forall(s in SHARES) svgaddgroup("gr"+s,s)
ttl:=0.0
forall(s in SHARES | frac(s).sol>0) do
svgaddpie("gr"+s, 200,200, 150, ttl, ttl+frac(s).sol)
svgsetstyle(svggetlastobj, SVG_STROKE, SVG_GRAY)
ttl+=frac(s).sol
end-do
! Optionally save graphic to file
svgsave("foliolps.svg")
! Set graph size
svgsetgraphviewbox(0,0,500,500)
! Display the graph and wait for window to be closed by the user
svgrefresh
svgwaitclose("Close browser window to terminate model execution.", 1)
end-model
|
|
| foliodata.mos |
(!******************************************************
Mosel Example Problems
======================
file foliodata.mos
``````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Parameters, data input from file,
result output to file --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. June 2018
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs"
parameters
DATAFILE= "folio.dat" ! File with problem data
OUTFILE= "result.dat" ! Output file
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
public declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
end-declarations
initializations from DATAFILE
RISK RET NA
end-initializations
public declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
Return: linctr ! Objective function
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing to a file
fopen(OUTFILE, F_OUTPUT)
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(strfmt(s,-12), ": \t", strfmt(getsol(frac(s))*100,5,2), "%")
fclose(F_OUTPUT)
end-model
|
|
| folioodbc.mos |
(!******************************************************
Mosel Example Problems
======================
file folioodbc.mos
``````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Parameters, data input through ODBC,
result output through ODBC --
IMPORTANT:
If this example is run more than once,
you need to delete the solution data from the
previous run in the database/spreasheet.
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Mar. 2006, rev. Sep. 2014
*******************************************************!)
model "Portfolio optimization with LP (ODBC)"
uses "mmxprs", "mmodbc"
parameters
DATAFILE = "folio.mdb" ! Access database with problem data
! DATAFILE = "folio.sqlite" ! SQLite database with problem data
DBDATA = "folio" ! Database table with problem data
DBSOL = "foliosol" ! Table for solution data
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RET: array(SHARES) of real ! Estimated return in investment
RISK: array(SHARES) of boolean ! List of high-risk values among shares
NA: array(SHARES) of boolean ! List of shares issued in N.-America
end-declarations
! Data input from database
initializations from "mmodbc.odbc:" + DATAFILE
[RET,RISK,NA] as DBDATA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in SHARES | RISK(s)) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in SHARES | NA(s)) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(strfmt(s,-12), ": \t", strfmt(getsol(frac(s))*100,5,2), "%")
! Solution output to database
declarations
Solfrac: array(SHARES) of real ! Solution values
end-declarations
forall(s in SHARES) Solfrac(s):= getsol(frac(s))*100
! Optional: Clean up previous results (works only for databases, cannot
! be used with spreadsheets).
! Alternatively, delete data (by hand) directly in the database.
SQLconnect(DATAFILE)
SQLexecute("delete from " + DBSOL)
SQLdisconnect
! Data output to database
initializations to "mmodbc.odbc:" + DATAFILE
Solfrac as DBSOL
end-initializations
end-model
|
|
| folioexcel.mos |
(!******************************************************
Mosel Example Problems
======================
file folioexcel.mos
```````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Parameters, data input from Excel,
result output to Excel --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Feb. 2007
*******************************************************!)
model "Portfolio optimization with LP (Excel)"
uses "mmxprs", "mmsheet"
parameters
DATAFILE = "folio.xls" ! Spreadsheet with problem data
DBDATA = "foliodata" ! Spreadsheet range with problem data
DBSOL = "folioresult" ! Range for solution data
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RET: array(SHARES) of real ! Estimated return in investment
RISK: array(SHARES) of boolean ! List of high-risk values among shares
NA: array(SHARES) of boolean ! List of shares issued in N.-America
end-declarations
! Data input from spreadsheet
initializations from "mmsheet.excel:" + DATAFILE
[RET,RISK,NA] as DBDATA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in SHARES | RISK(s)) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in SHARES | NA(s)) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(strfmt(s,-12), ": \t", strfmt(getsol(frac(s))*100,5,2), "%")
! Solution output to spreadsheet
declarations
Solfrac: array(SHARES) of real ! Solution values
end-declarations
forall(s in SHARES) Solfrac(s):= getsol(frac(s))*100
initializations to "mmsheet.excel:" + DATAFILE
Solfrac as "grow;"+DBSOL
end-initializations
end-model
|
|
| foliosheet.mos |
(!******************************************************
Mosel Example Problems
======================
file foliosheet.mos
```````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Parameters, data input from Excel,
result output to Excel using
generic worksheet access --
(c) 2012 Fair Isaac Corporation
author: S.Heipcke, Nov. 2012
*******************************************************!)
model "Portfolio optimization with LP (Excel)"
uses "mmxprs", "mmsheet"
parameters
DATAFILE = "folio.xls" ! Spreadsheet with problem data
DBDATA = "foliodata" ! Spreadsheet range with problem data
DBSOL = "folioresult" ! Range for solution data
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RET: array(SHARES) of real ! Estimated return in investment
RISK: array(SHARES) of boolean ! List of high-risk values among shares
NA: array(SHARES) of boolean ! List of shares issued in N.-America
end-declarations
! Data input from spreadsheet
initializations from "mmsheet.xls:" + DATAFILE
[RET,RISK,NA] as DBDATA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in SHARES | RISK(s)) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in SHARES | NA(s)) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(strfmt(s,-12), ": \t", strfmt(getsol(frac(s))*100,5,2), "%")
! Solution output to spreadsheet
declarations
Solfrac: array(SHARES) of real ! Solution values
end-declarations
forall(s in SHARES) Solfrac(s):= getsol(frac(s))*100
initializations to "mmsheet.xls:" + DATAFILE
Solfrac as "grow;"+DBSOL
end-initializations
end-model
|
|
| foliocsv.mos |
(!******************************************************
Mosel Example Problems
======================
file foliocsv.mos
`````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Parameters, data input from CSV,
result output to CSV format --
(c) 2012 Fair Isaac Corporation
author: S.Heipcke, Nov. 2012
*******************************************************!)
model "Portfolio optimization with LP (CSV)"
uses "mmxprs", "mmsheet"
parameters
DATAFILE = "folio.csv" ! CSV spreadsheet with problem data
DBDATA = "[C6:F15]" ! Spreadsheet range with problem data
DBSOL = "[H6:I6]" ! Range for solution data
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RET: array(SHARES) of real ! Estimated return in investment
RISK: array(SHARES) of boolean ! List of high-risk values among shares
NA: array(SHARES) of boolean ! List of shares issued in N.-America
end-declarations
! Data input from spreadsheet
initializations from "mmsheet.csv:" + DATAFILE
[RET,RISK,NA] as DBDATA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in SHARES | RISK(s)) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in SHARES | NA(s)) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(strfmt(s,-12), ": \t", strfmt(getsol(frac(s))*100,5,2), "%")
! Solution output to spreadsheet
declarations
Solfrac: array(SHARES) of real ! Solution values
end-declarations
forall(s in SHARES) Solfrac(s):= getsol(frac(s))*100
initializations to "mmsheet.csv:" + DATAFILE
Solfrac as "grow;"+DBSOL
end-initializations
end-model
|
|
| folioloop.mos |
(!******************************************************
Mosel Example Problems
======================
file folioloop.mos
``````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Re-solving with varied parameter settings --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs"
parameters
DATAFILE= "folio.dat" ! File with problem data
DEVDATA= "foliodev.dat" ! File with deviation data
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
DEV: array(SHARES) of real ! Standard deviation
SOLRET: array(range) of real ! Solution values (total return)
SOLDEV: array(range) of real ! Solution values (average deviation)
end-declarations
initializations from DATAFILE
RISK RET NA
end-initializations
initializations from DEVDATA
DEV
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
Return, Risk: linctr
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem for different limits on high-risk shares
ct:=0
forall(r in 0..20) do
! Limit the percentage of high-risk values
Risk:= sum(s in RISK) frac(s) <= r/20
maximize(Return) ! Solve the problem
if (getprobstat = XPRS_OPT) then ! Save the optimal solution value
ct+=1
SOLRET(ct):= getobjval
SOLDEV(ct):= getsol(sum(s in SHARES) DEV(s)*frac(s))
writeln(r/20, "%: ret ", SOLRET(ct), " dev ", SOLDEV(ct))
else
writeln("No solution for high-risk values <= ", 100*r/20, "%")
end-if
end-do
! Print the data
writeln("Low risk shares:")
forall (s in SHARES - RISK) writeln(s, ": ret ", RET(s), " dev ", DEV(s))
writeln("High risk shares:")
forall (s in RISK) writeln(s, ": ret ", RET(s), " dev ", DEV(s))
end-model
|
|
| folioloop_graph.mos |
(!******************************************************
Mosel Example Problems
======================
file folioloop_graph.mos
````````````````````````
Modeling a small LP problem
to perform portfolio optimization.
-- Re-solving with varied parameter settings,
graphical solution display --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. Sep. 2017
*******************************************************!)
model "Portfolio optimization with LP"
uses "mmxprs", "mmsvg" ! Use Xpress Optimizer with SVG graphing
parameters
DATAFILE= "folio.dat" ! File with problem data
DEVDATA= "foliodev.dat" ! File with deviation data
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
DEV: array(SHARES) of real ! Standard deviation
SOLRET: array(range) of real ! Solution values (total return)
SOLDEV: array(range) of real ! Solution values (average deviation)
end-declarations
initializations from DATAFILE
RISK RET NA
end-initializations
initializations from DEVDATA
DEV
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
Return, Risk: linctr
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem for different limits on high-risk shares
ct:=0
forall(r in 0..20) do
! Limit the percentage of high-risk values
Risk:= sum(s in RISK) frac(s) <= r/20
maximize(Return) ! Solve the problem
if (getprobstat = XPRS_OPT) then ! Save the optimal solution value
ct+=1
SOLRET(ct):= getobjval
SOLDEV(ct):= getsol(sum(s in SHARES) DEV(s)*frac(s))
else
writeln("No solution for high-risk values <= ", 100*r/20, "%")
end-if
end-do
! Drawing a graph to represent results (`GrS') and data (`GrL' & `GrH')
svgaddgroup("GrS", "Solution values", SVG_GREY)
svgaddgroup("GrL", "Low risk", SVG_GREEN)
svgaddgroup("GrH", "High risk", SVG_RED)
forall(r in 1..ct) svgaddpoint("GrS", SOLRET(r), SOLDEV(r))
svgaddline("GrS", sum(r in 1..ct) [SOLRET(r), SOLDEV(r)])
forall(s in SHARES - RISK) do
svgaddpoint("GrL", RET(s), DEV(s))
svgaddtext("GrL", RET(s)+1, 1.3*(DEV(s)-1), s)
end-do
forall(s in RISK) do
svgaddpoint("GrH", RET(s), DEV(s))
svgaddtext("GrH", RET(s)-2.5, DEV(s)-1, s)
end-do
! Scale the size of the displayed graph
svgsetgraphscale(10)
svgsetgraphpointsize(2)
svgsetgraphlabels("Expected return", "Standard deviation")
! Optionally save graphic to file
svgsave("foliograph.svg")
! Display the graph and wait for window to be closed by the user
svgrefresh
svgwaitclose("Close browser window to terminate model execution.", 1)
end-model
|
|
| foliomip1.mos |
(!******************************************************
Mosel Example Problems
======================
file foliomip1.mos
``````````````````
Modeling a small MIP problem
to perform portfolio optimization.
-- Limiting the total number of assets --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. Oct. 2011
*******************************************************!)
model "Portfolio optimization with MIP"
uses "mmxprs"
parameters
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
MAXNUM = 4 ! Max. number of different assets
end-parameters
declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
end-declarations
initializations from "folio.dat"
RISK RET NA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
buy: array(SHARES) of mpvar ! 1 if asset is in portfolio, 0 otherwise
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Limit the total number of assets
sum(s in SHARES) buy(s) <= MAXNUM
forall(s in SHARES) do
buy(s) is_binary ! Turn variables into binaries
frac(s) <= buy(s) ! Linking the variables
end-do
! Uncomment this line to see the Optimizer log
setparam("XPRS_VERBOSE",true) ! Enable logging output
! Uncomment the following lines to obtain a branch-and-bound tree
(!
setparam("XPRS_CUTSTRATEGY",0) ! Disable automatic cut generation
setparam("XPRS_HEURSTRATEGY",0) ! Disable MIP heuristics
setparam("XPRS_PRESOLVE",0) ! Disable presolve
!)
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES)
writeln(s, ": ", getsol(frac(s))*100, "% (", getsol(buy(s)), ")")
end-model
|
|
| foliomip2.mos |
(!******************************************************
Mosel Example Problems
======================
file foliomip2.mos
``````````````````
Modeling a small MIP problem
to perform portfolio optimization.
-- Imposing a minimum investment per share --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003
*******************************************************!)
model "Portfolio optimization with MIP"
uses "mmxprs"
parameters
MAXRISK = 1/3 ! Max. investment into high-risk values
MINAM = 0.5 ! Min. investment into N.-American values
MAXVAL = 0.3 ! Max. investment per share
MINVAL = 0.1 ! Min. investment per share
end-parameters
declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
end-declarations
initializations from "folio.dat"
RISK RET NA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper and lower bounds on the investment per share
forall(s in SHARES) do
frac(s) <= MAXVAL
frac(s) is_semcont MINVAL
end-do
! Solve the problem
maximize(Return)
! Solution printing
writeln("Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| folioqp.mos |
(!******************************************************
Mosel Example Problems
======================
file folioqp.mos
````````````````
Modeling a small QP problem
to perform portfolio optimization.
-- 1. QP: minimize variance
2. MIQP: limited number of assets ---
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. Sep. 2017
*******************************************************!)
model "Portfolio optimization with QP/MIQP"
uses "mmxprs", "mmnl"
parameters
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
MAXNUM = 4 ! Max. number of different assets
TARGET = 9.0 ! Minimum target yield
end-parameters
declarations
SHARES = 1..10 ! Set of shares
RISK: set of integer ! Set of high-risk values among shares
NA: set of integer ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
VAR: array(SHARES,SHARES) of real ! Variance/covariance matrix of
! estimated returns
end-declarations
initializations from "folioqp.dat"
RISK RET NA VAR
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! **** First problem: unlimited number of assets ****
! Objective: mean variance
Variance:= sum(s,t in SHARES) VAR(s,t)*frac(s)*frac(t)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Target yield
sum(s in SHARES) RET(s)*frac(s) >= TARGET
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
minimize(Variance)
! Solution printing
writeln("With a target of ", TARGET, " minimum variance is ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
! **** Second problem: limit total number of assets ****
declarations
buy: array(SHARES) of mpvar ! 1 if asset is in portfolio, 0 otherwise
end-declarations
! Limit the total number of assets
sum(s in SHARES) buy(s) <= MAXNUM
forall(s in SHARES) do
buy(s) is_binary
frac(s) <= buy(s)
end-do
! Solve the problem
minimize(Variance)
writeln("With a target of ", TARGET," and at most ", MAXNUM,
" assets,\n minimum variance is ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| folioqp_graph.mos |
(!******************************************************
Mosel Example Problems
======================
file folioqp_graph.mos
``````````````````````
Modeling a small QP problem
to perform portfolio optimization.
Minimize variance subject to different target return.
Graphical output.
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. Sep. 2017
*******************************************************!)
model "Portfolio optimization with QP, graphical output"
uses "mmxprs", "mmnl", "mmsvg" ! Use Xpress Optimizer with QP solver
parameters
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
end-parameters
declarations
SHARES = 1..10 ! Set of shares
RISK: set of integer ! Set of high-risk values among shares
NA: set of integer ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
VAR: array(SHARES,SHARES) of real ! Variance/covariance matrix of
! estimated returns
SOLRET: array(range) of real ! Solution values (total return)
SOLDEV: array(range) of real ! Solution values (average deviation)
end-declarations
initializations from "folioqp.dat"
RISK RET NA VAR
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: mean variance
Variance:= sum(s,t in SHARES) VAR(s,t)*frac(s)*frac(t)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem for a range of returns: this is the efficient frontier
target:= min(s in SHARES) RET(s)
RMAX:= max(s in SHARES) RET(s)
while(target < RMAX) do
Return:= sum(s in SHARES) RET(s)*frac(s) >= target ! Target yield
minimize(Variance) ! Solve the problem
if (getprobstat = XPRS_OPT) then ! Save the optimal solution value
ct+=1
SOLDEV(ct):= getobjval
SOLRET(ct):= target
else
writeln("No solution for target return >= ", ct, "%")
break
end-if
target += 1
end-do
! Drawing a graph to represent results (`GrS') and data (`GrL' & `GrH')
declarations
DEV: array(SHARES) of real ! Standard deviation
NAMES: array(SHARES) of string ! Names of shares
end-declarations
initializations from "folioqpgraph.dat"
DEV NAMES
end-initializations
svgaddgroup("GrS", "Solution values", SVG_GREY)
svgaddgroup("GrL", "Low risk", SVG_GREEN)
svgaddgroup("GrH", "High risk", SVG_RED)
forall(r in 1..ct) svgaddpoint("GrS", SOLRET(r), SOLDEV(r));
svgaddline("GrS", sum(r in 1..ct) [SOLRET(r), SOLDEV(r)])
forall (s in SHARES-RISK) do
svgaddpoint("GrL", RET(s), DEV(s))
svgaddtext("GrL", RET(s)+1, 1.3*(DEV(s)-1), NAMES(s))
end-do
forall (s in RISK) do
svgaddpoint("GrH", RET(s), DEV(s))
svgaddtext("GrH", RET(s)-2.5, DEV(s)-1, NAMES(s))
end-do
! Scale the size of the displayed graph
svgsetgraphscale(10)
svgsetgraphpointsize(2)
svgsetgraphlabels("Expected return", "Standard deviation")
! Optionally save graphic to file
svgsave("folioqpgraph.svg")
! Display the graph and wait for window to be closed by the user
svgrefresh
svgwaitclose("Close browser window to terminate model execution.", 1)
end-model
|
|
| folioqc.mos |
(!******************************************************
Mosel Example Problems
======================
file folioqc.mos
````````````````
Modeling a small QCQP problem
to perform portfolio optimization.
-- Maximize return with limit on variance ---
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, July 2008
*******************************************************!)
model "Portfolio optimization with QCQP"
uses "mmxprs", "mmnl"
parameters
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
MAXVAR = 0.55 ! Max. allowed variance
end-parameters
declarations
SHARES = 1..10 ! Set of shares
NA: set of integer ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
VAR: array(SHARES,SHARES) of real ! Variance/covariance matrix of
! estimated returns
end-declarations
initializations from "folioqp.dat"
RET NA VAR
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Limit variance
sum(s,t in SHARES) VAR(s,t)*frac(s)*frac(t) <= MAXVAR
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Solve the problem
maximize(Return)
! Solution printing
writeln("With a max. variance of ", MAXVAR, " total return is ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| foliomiqc.mos |
(!******************************************************
Mosel Example Problems
======================
file foliomiqc.mos
``````````````````
Modeling a small MIQCQP problem
to perform portfolio optimization.
-- Maximize return with limit on variance
and limited number of assets ---
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, July 2008, rev. Mar. 2013
*******************************************************!)
model "Portfolio optimization with MIQCQP"
uses "mmxnlp"
parameters
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
MAXNUM = 4 ! Max. number of different assets
MAXVAR = 1.25 ! Max. allowed variance
end-parameters
declarations
SHARES = 1..10 ! Set of shares
NA: set of integer ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
VAR: array(SHARES,SHARES) of real ! Variance/covariance matrix of
! estimated returns
end-declarations
initializations from "folioqp.dat"
RET NA VAR
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
buy: array(SHARES) of mpvar ! 1 if asset is in portfolio, 0 otherwise
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Limit variance
sum(s,t in SHARES) VAR(s,t)*frac(s)*frac(t) <= MAXVAR
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Limit the total number of assets
sum(s in SHARES) buy(s) <= MAXNUM
forall(s in SHARES) do
buy(s) is_binary
frac(s) <= buy(s)
end-do
setparam("XPRS_verbose", true)
! Solve the problem
maximize(Return)
! Solution printing
writeln("With a max. variance of ", MAXVAR, " and at most ", MAXNUM,
" assets,\n total return is ", getsol(Return))
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-model
|
|
| folioheur.mos |
(!******************************************************
Mosel Example Problems
======================
file folioheur.mos
``````````````````
Modeling a small MIP problem
to perform portfolio optimization.
-- Heuristic solution --
(c) 2008 Fair Isaac Corporation
author: S.Heipcke, Aug. 2003, rev. June 2010
*******************************************************!)
model "Portfolio optimization solved heuristically"
uses "mmxprs"
parameters
MAXRISK = 1/3 ! Max. investment into high-risk values
MAXVAL = 0.3 ! Max. investment per share
MINAM = 0.5 ! Min. investment into N.-American values
MAXNUM = 4 ! Max. number of assets
end-parameters
forward procedure solve_heur ! Binary variable fixing heuristic
declarations
SHARES: set of string ! Set of shares
RISK: set of string ! Set of high-risk values among shares
NA: set of string ! Set of shares issued in N.-America
RET: array(SHARES) of real ! Estimated return in investment
end-declarations
initializations from "folio.dat"
RISK RET NA
end-initializations
declarations
frac: array(SHARES) of mpvar ! Fraction of capital used per share
buy: array(SHARES) of mpvar ! 1 if asset is in portfolio, 0 otherwise
end-declarations
! Objective: total return
Return:= sum(s in SHARES) RET(s)*frac(s)
! Limit the percentage of high-risk values
sum(s in RISK) frac(s) <= MAXRISK
! Minimum amount of North-American values
sum(s in NA) frac(s) >= MINAM
! Spend all the capital
sum(s in SHARES) frac(s) = 1
! Upper bounds on the investment per share
forall(s in SHARES) frac(s) <= MAXVAL
! Limit the total number of assets
sum(s in SHARES) buy(s) <= MAXNUM
forall(s in SHARES) do
buy(s) is_binary
frac(s) <= buy(s)
end-do
! Solve problem heuristically
solve_heur
! Solve the problem
maximize(Return)
! Solution printing
if getprobstat=XPRS_OPT then
writeln("Exact solution: Total return: ", getobjval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
else
writeln("Heuristic solution is optimal.")
end-if
!-----------------------------------------------------------------
procedure solve_heur
declarations
TOL: real ! Solution feasibility tolerance
fsol: array(SHARES) of real ! Solution values for `frac' variables
bas: basis ! LP basis
end-declarations
setparam("XPRS_VERBOSE",true) ! Enable message printing in mmxprs
setparam("XPRS_CUTSTRATEGY",0) ! Disable automatic cuts (optional)
setparam("XPRS_HEURSTRATEGY",0) ! Disable MIP heuristics (optional)
setparam("XPRS_PRESOLVE",0) ! Switch off presolve (required)
TOL:=getparam("XPRS_FEASTOL") ! Get feasibility tolerance
setparam("ZEROTOL",TOL) ! Set comparison tolerance
maximize(XPRS_LPSTOP,Return) ! Solve the LP problem
savebasis(bas) ! Save the current basis
! Fix all variables `buy' for which `frac' is at 0 or at a relatively
! large value
forall(s in SHARES) do
fsol(s):= getsol(frac(s))
if (fsol(s) = 0) then
setub(buy(s), 0)
elif (fsol(s) >= 0.2) then
setlb(buy(s), 1)
end-if
end-do
maximize(XPRS_CONT,Return) ! Solve the MIP problem
ifgsol:=false
if getprobstat=XPRS_OPT then ! If an integer feas. solution was found
ifgsol:=true
solval:=getobjval ! Get the value of the best solution
writeln("Heuristic solution: Total return: ", solval)
forall(s in SHARES) writeln(s, ": ", getsol(frac(s))*100, "%")
end-if
! Reset variables to their original bounds
forall(s in SHARES)
if ((fsol(s) = 0) or (fsol(s) >= 0.2)) then
setlb(buy(s), 0)
setub(buy(s), 1)
end-if
loadbasis(bas) ! Load the saved basis: bound changes are
! immediately passed on to the optimizer
! if the problem has not been modified
! in any other way, so that there is no
! need to reload the matrix
if ifgsol then ! Set cutoff to the best known solution
setparam("XPRS_MIPABSCUTOFF", solval+TOL)
end-if
end-procedure
end-model
|
|
