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Folio - Advanced modelling and solving tasks

Advanced modelling and solving tasks for a portfolio optimization problem:
  • Automated solver tuning (foliolptune.mos)
  • Defining an integer solution callback (foliocb.mos)
  • Using the solution enumerator for multiple MIP solutions (folioenumsol.mos)
  • Handling infeasibility
    • handling infeasibility through deviation variables (folioinfeas.mos)
    • retrieving infeasible row/column from presolve (folioinfcause.mos)
    • retrieving IIS - LP or MIP infeasible (folioiis.mos, foliomiis.mos)
    • using the built-in infeasibility repair functionality (foliorep.mos)
    • same as foliorep, using an 'mpsol' object (foliorep_sol.mos)
  • Data transfer in memory
    • running foliomemio.mos with data transfer in memory (runfolio.mos)
    • same running foliomemio2.mos, grouping tables with identical index sets in "initializations" blocks (runfolio2.mos)
    • master model running several model instances in parallel (runfoliopar.mos)
  • Remote models on a distributed architecture
    • running foliomemio.mos on a remote instance of Mosel (runfoliodistr.mos)
    • master model running several model instances in parallel, each on a different (remote) instance of Mosel (runfoliopardistr.mos)
  • Remote execution via XPRD
    • See examples in the Mosel Whitepapers directory moselpar/XPRD
  • XML and JSON data formats
    • reading data from an XML file, solution output in XML format on screen and to a new file (folioxml.mos, folioxmlqp.mos)
    • generate HTML output file as an XML document (runfolioxml.mos)
    • using JSON-format data files, reading data from a JSON file, solution output in JSON format on screen and to a new file (foliojson.mos)
  • HTTP
    • starting an HTTP server managing requests from HTTP clients (foliohttpsrv.mos)
    • HTTP client exchanging XML data files with an HTTP server (foliohttpclient.mos)

Source Files

Data Files


   Mosel Example Problems

   file folioxmlqp.mos
   Modeling a small QP problem 
   to perform portfolio optimization.
   -- 1. QP: minimize variance
      2. MIQP: limited number of assets ---
   -- Reading/writing data in XML format --
  (c) 2010 Fair Isaac Corporation
      author: S.Heipcke, July 2010, rev. Sep. 2017

model "Portfolio optimization with QP/MIQP"
 uses "mmxprs", "mmnl"
 uses "mmxml"                       ! XML interface functions

  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

 forward procedure save_solution(num: integer)

  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

  AllData, Solution: xmldoc         ! XML document
  Share,Root,Sol: integer           ! XML nodes
  NodeList: list of integer

! Reading data from an XML file
 load(AllData, "folioqp.xml")
 getnodes(AllData, "portfolio/share", NodeList) 
 RISK:= union(l in NodeList | getattr(AllData,l,"risk")="high") 
 NA:= union(l in NodeList | getattr(AllData,l,"region")="NA") 
 forall(l in NodeList)
   RET(getintattr(AllData,l,"name")):= getintattr(AllData, l, "ret") 

! Read a second XML file
 reset(AllData)                     ! Empty the "xmldoc" object
 load(AllData, "folioqpvar.xml")
 getnodes(AllData, "variance/var", NodeList) 
 forall(l in NodeList)
   VAR(getintattr(AllData, l, "ind1"),getintattr(AllData, l, "ind2")):=
 reset(AllData)                     ! Free up memory

! Prepare XML structure for solution output
 Root:=addnode(Solution, 0, XML_ELT, "result")    ! Create root node "result"

  frac: array(SHARES) of mpvar      ! Fraction of capital used per share

! **** 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

! 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 ****

  buy: array(SHARES) of mpvar       ! 1 if asset is in portfolio, 0 otherwise

! 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)

! Solve the problem

 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, "%") 


! Write solution to a new XML file
 save(Solution, "qpresult.xml")


! Solution printing to a file
 procedure save_solution(num: integer)
   Sol:= addnode(Solution, Root, XML_LASTCHILD, XML_ELT, "solution")
                                       ! Append a "solution" node
   setattr(Solution, Sol, "num", num)  ! ... with attribute "num"
   forall(s in SHARES) do              ! Add node containing solution value
    Share:= addnode(Solution, Sol, "share", frac(s).sol)
    setattr(Solution, Share, "name", s)
   save(Solution, Sol, "")             ! Display XML solution on screen


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