# -*- coding: utf-8 -*-
#******************************************************************************
# Name of the program: PyDDOP
# Title of the program: Python Module for Determining the Dimension of the
# Optimal Portfolio
# Version: 01
#
# This program is released under GNU General Public License, version 3.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# This software has been developed by Dr. Alan Mustafa under supervision of
# Prof. Abdulnasser Hatemi-J.
# Contacts:
# - Prof. Abdulnasser Hatemi-J: AHatemi@uaeu.ac.ae
# - Dr. Alan Mustafa: Alan.Mustafa@ieee.org
#
#
# In case this module is used it needs to be cited as the following:
# Mustafa A. and Hatemi-J A. (2024) PyDDOP: Python Module for Determining the Dimension of the Optimal Portfolio,
# Statistical Software Components,Boston College Department of Economics.
#
# Date: January 2024
#
# Financial support by the CBE Annual Research Program (CARP2022) at the UAE University is thankfully acknowledged.
#
# © 2024 Dr. Alan Mustafa and Prof. Abdulnasser Hatemi-J
#
#******************************************************************************
import numpy as np
import pandas as pd
import math
import os
from datetime import datetime
#######################################
import tkinter as tk
import itertools as it
from tkinter.filedialog import askopenfilename
from tabulate import tabulate
import matplotlib.pyplot as plt
###############################################################################
# Start of GUI
###############################################################################
#========================= functions =========================================
class create_window_menu_UI2(tk.Frame):
def __init__(self, master):
tk.Frame.__init__(self, master)
self.master = master
#============= Application Title --------------------------------------
self.lblTitle = tk.Label(self, text="Python Module for Determining the Dimension of the Optimal Portfolio", font=("Helvetica", 16))
self.lblTitle.grid(row=1, column=0, columnspan=5, sticky="EW")
# hr = tk.Frame(self,height=10,width=850,bg="green")
hr = tk.Frame(self,height=3,width=850,bg="green")
hr.grid(row=2, column=0, columnspan=5, sticky="NWNESWSE")
#============= Adding a Blank Row -------------------------------------
self.lblBlnkRow = tk.Label(self, text="", font=("Helvetica", 12))
self.lblBlnkRow.grid(row=16, column=0, sticky="EW")
#============= Load My Data Button ---------------------------------
self.btnSelectDataFile = tk.Button(self, text="Select the Dataset File", command=lambda: [var_DatasetFile.set(os.path.split(askopenfilename())[1]),activateBtnCalcPD(self)], font=("Arial", 12), state='normal')
self.btnSelectDataFile.grid(row=20, column=0, sticky="E")
var_DatasetFile = tk.StringVar()
self.tbx_DatasetFile = tk.Entry(self, textvariable=var_DatasetFile, font=("Helvetica", 10), state="disabled", justify="center")
self.tbx_DatasetFile.grid(row=20, column=1, sticky="EW")
#============= Adding a label for OR sign -----------------------------
self.btnLoadMydata = tk.Label(self, text="OR ", font=("Arial Narrow", 12))
self.btnLoadMydata.grid(row=20, column=2, sticky="EW")
#============= Load Sample Data Button --------------------------------
self.btnLoadSmplData = tk.Button(self, text="Load the Sample Data \nand Calculate the Portfolio", command=lambda:[loadSampleData(decNo,self),disableBtnSelectDataFile(self),showInfoOnSampleData()], font=("Arial", 12), state="normal")
self.btnLoadSmplData.grid(row=20, column=3, rowspan=2, sticky="WE")
#============= Format with the Number of Decimal points ---------------
self.btnLoadMydata = tk.Label(self, text="Present the output with number of decimal points:", font=("Arial", 12))
self.btnLoadMydata.grid(row=40, column=0, sticky="EW")
decNo = tk.IntVar(self, value=5, name=None) # variable to format the number of decimals of the outputing reports
self.radioDecNo5 = tk.Radiobutton(self, text="5", variable=decNo, value=5, font=("Arial", 12))
self.radioDecNo5.grid(row=40, column=1, sticky="W")
self.radioDecNo20 = tk.Radiobutton(self, text="20", variable=decNo, value=20, font=("Arial", 12))
self.radioDecNo20.grid(row=40, column=1, sticky="E")
vr = tk.Frame(self,height=10,width=1,bg="green")
vr.grid(row=40, column=2, rowspan=30, sticky="NS")
theDecNo = tk.IntVar() # theDecNo: The Decimal No value
self.radioDecNoOther = tk.Radiobutton(self, text="or prefered as:", variable=decNo, value=theDecNo.get(), command=lambda:activateTxtBox(self), font=("Arial", 12))
self.radioDecNoOther.grid(row=41, column=1, sticky="W")
self.tbx_DecNoValue = tk.Entry(self, textvariable=decNo, font=("Helvetica", 10), state="disable", justify="center", width=4)
self.tbx_DecNoValue.grid(row=41, column=1, sticky="E")
#============= Calculate Proftfolio Diversification Button ------------command=lambda:[funct1(),funct2()]
self.btnCalcPD = tk.Button(self, text="Calculate the Protfolio", command=lambda:[indicatorActive(self),LoadDataFile(var_DatasetFile,decNo,self),indicatorEnd(self)], font=("Arial", 12), state="disable")
self.btnCalcPD.grid(row=51, column=0, sticky="E")
#============= Data Processing Indicator ------------------------------
self.lblIndicator = tk.Label(self, text=chr(9608), font=("Arial Narrow", 12), fg='#eee')
self.lblIndicator.grid(row=51, column=1, sticky="WE")
#============= Dataset File Selection ---------------------------------
self.btnExit = tk.Button(self, text=" Close ", command=self.master.destroy, font=("Arial", 12))
self.btnExit.grid(row=51, column=3, sticky="WE")
#============= Blankrow -----------------------------------------------
self.lblBlankRow = tk.Label(self, text=" ", font=("Arial Narrow", 8))
self.lblBlankRow.grid(row=55, column=0, columnspan=3, sticky="WE")
#============= Output EndNote ---------------------------------
hr = tk.Frame(self,height=1,width=850,bg="green")
hr.grid(row=60, column=0, columnspan=4, sticky="NWNESWSE")
#============= Listbox Labels -----------------------------------------
self.lblTestlbl = tk.Label(self, text="Portfolio No | [No of Assets] | MV | MRAR | List of Assets", font=("Arial Narrow", 12))
self.lblTestlbl.grid(row=70, column=0, columnspan=3, sticky="W")
#============= Start Printing a Scrollbar of Portfolios ---------------
self.scrollbar = tk.Scrollbar(self, orient= 'vertical')
self.scrollbar.grid(row=110, column=2)
#============= Start of Listbox ---------------------------------------
list1 = ""
self.listbox = tk.Listbox(self, width= 50, yscrollcommand= self.scrollbar.set, listvariable=list1, selectmode = tk.MULTIPLE, bg='#ecf7dd', font=('Arial 10'))
self.listbox.config(width=50,height=15)
self.listbox.grid(row=110, column=0, columnspan=2, sticky="ew")
self.scrollbar.config(command= self.listbox.yview)
self.scrollbar.grid(row=110, column=2, sticky="nsw")
#============= Construct Selected Proftfolio(s) -----------------------
self.btnConstSP = tk.Button(self, text="Construct Selected Protfolio(s)", command=lambda: selected_item(fileNameP1,df_Portfolios,frmtDec,self), font=("Arial", 12), state="disable")
self.btnConstSP.grid(row=110, column=3, columnspan=2, sticky="NE")
#============= Key Description ----------------------------------------
txtKeyDesc = ' '
# txtKeyDesc = '_______________________\n'
# txtKeyDesc = txtKeyDesc + 'Reports in *.txt format are produced in the same folder as this module resides in.\n'
# txtKeyDesc = txtKeyDesc + 'PyDDOP_YMD_HMS_P[PFNo].txt\n' # File name: [PyDDOP (title of the code)] _ [Date_Time] _ [P[PFNo] (Portfolio + Number)]
# txtKeyDesc = txtKeyDesc + 'PyDDOP_YMD_HMS_All.txt\n'
# txtKeyDesc = txtKeyDesc + '_______________________\n'
# txtKeyDesc = txtKeyDesc + 'Denotations:\nMV: Minimum Variance approach\nMRAR: Maximum Risk Adjusted Return'
self.lblListDesc_Key = tk.Label(self, text=txtKeyDesc, font=("Arial Narrow", 10), justify="left", wraplength=200)
self.lblListDesc_Key.grid(row=110, column=3, columnspan=2, sticky="SWE")
#============= Output EndNote ---------------------------------
hr = tk.Frame(self,height=1,width=850,bg="green")
hr.grid(row=200, column=0, columnspan=4, sticky="NWNESWSE")
self.msgEndNote = tk.Message(self, text="", font=("Helvetica", 8, "italic"), anchor="w", justify="left", bg="#d4d4d4")
self.msgEndNote.bind("<Configure>", lambda e: self.msgEndNote.configure(width=e.width-10))
self.msgEndNote.grid(row=210, column=0, columnspan=4, sticky="ew")
###############################################################################
# End of GUI #
###############################################################################
###############################################################################
################## START OF SAMPLE DATA ###############################
###############################################################################
def loadSampleData(frmtDec_,self):
frmtDec = frmtDec_.get()
headers=["Date_of_Assets","USD-JPY","Brent Oil","DAX","Dow Jones"]
dates = ['02/01/2019', '03/01/2019', '04/01/2019', '07/01/2019', '08/01/2019', '09/01/2019', '10/01/2019', '11/01/2019', '14/01/2019', '15/01/2019', '16/01/2019',
'17/01/2019', '18/01/2019', '21/01/2019', '22/01/2019', '23/01/2019', '24/01/2019', '25/01/2019', '28/01/2019', '29/01/2019', '30/01/2019', '31/01/2019',
'01/02/2019', '04/02/2019', '05/02/2019', '06/02/2019', '07/02/2019', '08/02/2019', '11/02/2019', '12/02/2019', '13/02/2019', '14/02/2019', '15/02/2019',
'18/02/2019', '19/02/2019', '20/02/2019', '21/02/2019', '22/02/2019', '25/02/2019', '26/02/2019', '27/02/2019', '28/02/2019', '01/03/2019', '04/03/2019',
'05/03/2019', '06/03/2019', '07/03/2019', '08/03/2019', '11/03/2019', '12/03/2019', '13/03/2019', '14/03/2019', '15/03/2019', '18/03/2019', '19/03/2019',
'20/03/2019', '21/03/2019', '22/03/2019', '25/03/2019', '26/03/2019', '27/03/2019', '28/03/2019', '29/03/2019']
asset1 = [108.88, 107.67, 108.53, 108.72, 108.75, 108.17, 108.42, 108.55, 108.17, 108.67, 109.09, 109.24, 109.78, 109.67, 109.38, 109.6, 109.64, 109.55, 109.36,
109.39, 109.03, 108.88, 109.5, 109.89, 109.97, 109.97, 109.81, 109.73, 110.38, 110.48, 111, 110.48, 110.5, 110.62, 110.62, 110.86, 110.7, 110.69, 111.06, 110.58,
111, 111.39, 111.92, 111.75, 111.89, 111.77, 111.59, 111.17, 111.2, 111.36, 111.17, 111.72, 111.47, 111.42, 111.39, 110.69, 110.81, 109.92, 109.97, 110.64, 110.52,
110.64, 110.86]
asset2 = [54.91, 55.95, 57.06, 57.33, 58.72, 61.44, 61.68, 60.48, 58.99, 60.64, 61.32, 61.18, 62.7, 62.74, 61.5, 61.14, 61.09, 61.64, 59.93, 61.32, 61.65, 61.89,
62.75, 62.51, 61.98, 62.69, 61.63, 62.1, 61.51, 62.42, 63.61, 64.57, 66.25, 66.5, 66.45, 67.08, 67.07, 67.12, 64.76, 65.21, 66.39, 66.03, 65.07, 65.67, 65.86, 65.99,
66.3, 65.74, 66.58, 66.67, 67.55, 67.23, 67.16, 67.54, 67.61, 68.5, 67.86, 67.03, 67.21, 67.97, 67.83, 67.82, 68.39]
asset3 = [10580.19, 10416.66, 10767.69, 10747.81, 10803.98, 10893.32, 10921.59, 10887.46, 10855.91, 10891.79, 10931.24, 10918.62, 11205.54, 11136.2, 11090.11,
11071.54, 11130.18, 11281.79, 11210.31, 11218.83, 11181.66, 11173.1, 11180.66, 11176.58, 11367.98, 11324.72, 11022.02, 10906.78, 11014.59, 11126.08, 11167.22, 11089.79,
11299.8, 11299.2, 11309.21, 11401.97, 11423.28, 11457.7, 11505.39, 11540.79, 11487.33, 11515.64, 11601.68, 11592.66, 11620.74, 11587.63, 11517.8, 11457.84, 11543.48,
11524.17, 11572.41, 11587.47, 11685.69, 11657.06, 11788.41, 11603.89, 11549.96, 11364.17, 11346.65, 11419.48, 11419.04, 11428.16, 11526.04]
asset4 = [23346.24, 22686.22, 23433.16, 23531.35, 23787.45, 23879.12, 24001.92, 23995.95, 23909.84, 24065.59, 24207.16, 24370.1, 24706.35, 24706.35, 24404.48,
24575.62, 24553.24, 24737.2, 24528.22, 24579.96, 25014.86, 24999.67, 25063.89, 25239.37, 25411.52, 25390.3, 25169.53, 25106.33, 25053.11, 25425.76, 25543.27, 25439.39,
25883.25, 25883.25, 25891.32, 25954.44, 25850.63, 26031.81, 26091.95, 26057.98, 25985.16, 25916, 26026.32, 25819.65, 25806.63, 25673.46, 25473.23, 25450.24, 25650.88,
25554.66, 25702.89, 25709.94, 25848.87, 25914.1, 25887.38, 25745.67, 25962.51, 25502.32, 25516.83, 25657.73, 25625.59, 25717.46, 25928.68]
csvFileName = 'PyDDOP_SampleData.csv' # PD: Portfolio Diversification
df = pd.DataFrame(dates)
df['Asset1'] = asset1
df['Asset2'] = asset2
df['Asset3'] = asset3
df['Asset4'] = asset4
df.columns = headers
df.to_csv(csvFileName, header=True, index=False)
calc_pd_vrar(df,frmtDec,self)
###############################################################################
################## END SAMPLE DATA ##################################
###############################################################################
###############################################################################
################## START OF LOADING DATA FILE ######################
###############################################################################
def LoadDataFile(fileName,frmtDec_,self): # df: DataFrame
frmtDec = frmtDec_.get()
theFile = pd.read_csv(fileName.get(), sep = ',', decimal = ',', header=0, index_col=False)
df2 = pd.DataFrame(theFile)
headers_list = list(df2.columns.values)
for column in headers_list[1:]:
df2[column] = df2[column].astype(np.float64)
calc_pd_vrar(df2,frmtDec,self)
###############################################################################
################## END OF LOADING DATA FILE ####################
###############################################################################
###############################################################################
# Start of Importing Selected Portfolios for Construction #
###############################################################################
def selected_item(fileNameP1,df_Portfolios,frmtDec,self):
selected_PFs = []
for i in self.listbox.curselection():
idxOfPortfolios1 = self.listbox.get(i)
idxOfPortfolios2 = idxOfPortfolios1.split('[')
idxOfPortfolios = str(idxOfPortfolios2[0]).replace('Portfolio ', '')
selected_PFs.append(idxOfPortfolios)
for s in range(len(selected_PFs)):
print_outcome_Selected(fileNameP1,df_Portfolios,int(selected_PFs[s]),frmtDec,self)
###############################################################################
# End of Importing Selected Portfolios for Constructdion #
###############################################################################
###############################################################################
# Start of Calculations: Construction of Portfolios #
###############################################################################
def calc_pd_vrar(df_,frmtDec,self): # df: DataFrame
print("\014")
noOfCols = len(df_.iloc[0, :].values);
#==========================================================================
# Start of loop
#==========================================================================
M = 0
n = noOfCols - 1
for i in range(0,n-1):
M = M + math.factorial(int(n)) / (math.factorial(int(n - i)) * math.factorial(int(i)))
print ("Number of all Portfolios = " + str(int(M)))
nCols = list(range(1,noOfCols))
newList = []
coun_ter = 0
for rng in range(2,noOfCols):
newList_ = list(it.combinations(nCols, rng)) # creating a list of possible combinations
#=====================================================================================
newList_2=[]
temp = []
for i_ in newList_:
i = list(i_)
temp = i.insert(0,0)
newList_2.append(i)
newList.append(newList_2)
#=====================================================================================
PD_MV_RAR = []
PD_MRAR_RAR = []
df_Portfolios = [] # list of dataframes for all portfolios
for subList in range(0,len(newList)):
for sub_subList in range(0,len(newList[subList])):
df_2 = df_.iloc[: , np.array(newList[subList][sub_subList])].copy()
df = pd.DataFrame.from_records(df_2)
ignore_0, PD_MV_RAR_, PD_MRAR_RAR_ = construct_pd(df,frmtDec);
df_Portfolios.append(df)
PD_MV_RAR.append(PD_MV_RAR_)
PD_MRAR_RAR.append(PD_MRAR_RAR_)
coun_ter += 1;
print("Max of PD_MV_RAR:",np.round(max(PD_MV_RAR),decimals=frmtDec))
print("Max of PD_MRAR_RAR:",np.round(max(PD_MRAR_RAR),decimals=frmtDec))
print("\n")
max_PD_MV_RAR = PD_MV_RAR.index(max(PD_MV_RAR))+1
max_PD_MRAR_RAR = PD_MRAR_RAR.index(max(PD_MRAR_RAR))+1
# print("Index_max of PD_MV_RAR:",PD_MV_RAR.index(max(PD_MV_RAR))+1)
# print("Index_max of PD_MRAR_RAR:",PD_MRAR_RAR.index(max(PD_MRAR_RAR))+1)
print("Index_max of PD_MV_RAR: ",max_PD_MV_RAR)
print("Index_max of PD_MRAR_RAR: ",max_PD_MRAR_RAR)
#--------------------------------------------------------------------------
#self.msgEndNote["text"] = "%s" % (txtEndNote)
#============= Key Description ----------------------------------------
txtKeyDesc = '_______________________\n'
txtKeyDesc = txtKeyDesc + 'Reports in *.txt format are produced in the same folder as this module resides in.\n'
txtKeyDesc = txtKeyDesc + 'PyDDOP_YMD_HMS_P[PFNo].txt\n' # File name: [PyDDOP (title of the code)] _ [Date_Time] _ [P[PFNo] (Portfolio + Number)]
txtKeyDesc = txtKeyDesc + 'PyDDOP_YMD_HMS_All.txt\n'
txtKeyDesc = txtKeyDesc + '_______________________\n'
txtKeyDesc = txtKeyDesc + 'Denotations:\nMV: Minimum Variance approach\nMRAR: Maximum Risk Adjusted Return'
txtKeyDesc = txtKeyDesc + '_______________________\n'
txtKeyDesc = txtKeyDesc + 'MV Portfolio: ' + str(max_PD_MV_RAR) + '\n'
txtKeyDesc = txtKeyDesc + 'MRAR Portfolio: ' + str(max_PD_MRAR_RAR) + '\n'
# self.lblListDesc_Key = tk.Label(self, text=txtKeyDesc, font=("Arial Narrow", 10), justify="left", wraplength=200)
# self.lblListDesc_Key.grid(row=110, column=3, columnspan=2, sticky="SWE")
self.lblListDesc_Key["text"] = "%s" % (txtKeyDesc)
#--------------------------------------------------------------------------
fileNameP1 = fillingTheListBox(fileName_Pt1(),PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,frmtDec,self) # theDF: New DataFrame
print_RARs(fileNameP1,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,frmtDec,self)
printAllPortfolios(fileName_All(fileNameP1),PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,frmtDec,self)
def fileName_Pt1():
now = datetime.now()
fileNameP1 = 'PyDDOP_' + now.strftime("%Y%m%d_%H%M%S") + '_'
return fileNameP1
def fileName_All(fileNameP1):
fileName_All = fileNameP1 + 'All.txt'
return fileName_All
def print_RARs(fileNameP1,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,frmtDec,self):
lblRAR_MV = 'MV\nMinimum Variance Approach'
plotVars = PD_MV_RAR
PF_no_MV = PD_MV_RAR.index(max(PD_MV_RAR))
fileNameP1_MV = fileNameP1 + 'MV_'
print_outcome(fileNameP1_MV,lblRAR_MV,plotVars,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,PF_no_MV,frmtDec,self)
lblRAR_MRAR = 'MRAR\nMaximum Risk Adjusted Return Approach'
plotVars = PD_MRAR_RAR
PF_no_MRAR = PD_MRAR_RAR.index(max(PD_MRAR_RAR))
fileNameP1_MRAR = fileNameP1 + 'MRAR_'
print_outcome(fileNameP1_MRAR,lblRAR_MRAR,plotVars,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,PF_no_MRAR,frmtDec,self)
def print_outcome(fileNameP1,lblRAR,plotVars,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,Portfolio_no,frmtDec,self):
plot_Portfolios(lblRAR,plotVars,df_Portfolios,Portfolio_no) # ploting bar plot for portfolios
df_to_print, ignore_1, ignore_2 = construct_pd(df_Portfolios[Portfolio_no],frmtDec);
txtFileName = create_rprt_file(fileNameP1,df_to_print,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,Portfolio_no,frmtDec,self);
EndNote(txtFileName,self);
return
def print_outcome_Selected(fileNameP1,df_Portfolios,Portfolio_no,frmtDec,self):
Portfolio_no = Portfolio_no - 1
df_to_print, ignore_1, ignore_2 = construct_pd(df_Portfolios[Portfolio_no],frmtDec);
PD_MV_RAR = []
PD_MV_RAR = list(range(1,len(df_Portfolios)))
PD_MRAR_RAR = []
PD_MRAR_RAR = PD_MV_RAR
txtFileName = create_rprt_file(fileNameP1,df_to_print,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,Portfolio_no,frmtDec,self);
EndNote(txtFileName,self);
#==========================================================================
# End of Loop
#==========================================================================
def construct_pd(df,frmtDec): # df: DataFrame
x = df.iloc[:, 1:].values;
noOfCols = len(df.iloc[0, :].values);
roa = np.exp(np.diff(np.log(x), axis = 0))-1 # => roa = (b2 - b1)/b1 : Return of Asset
theCov = np.cov(roa, rowvar=False)
Er = np.average(roa, axis=0) # Er: Average Return Values
SD = np.std(roa, axis=0, ddof=1)
RAdjR = np.divide(Er, SD)
#=============== Calculating Hs&K and Cs&E =====================
# Hs: the selected columns from K
# K: calculating values from both matrices of Ev and Cov
# Cs: table of covariance
# E: selected columns from Covariance table
rows = len(theCov) - 1
cols = len(theCov[0])
Cij = np.array([[0 for i in range(cols)] for j in range(rows)], dtype=float)
Kij = np.array([[0 for i in range(cols)] for j in range(rows)], dtype=float)
for iRows in range(0,rows):
for jCols in range(0,cols):
Cij[iRows,jCols] = (2*theCov[iRows+1,jCols])-(2*theCov[iRows, jCols])
Kij[iRows,jCols] = (Er[iRows]*2*theCov[iRows+1,jCols])-(Er[iRows+1]*2*theCov[iRows, jCols])
theOnes = np.ones(rows+1)
Cij = np.vstack([Cij, theOnes])
Kij = np.vstack([Kij, theOnes])
detC = np.linalg.det(Cij)
detK = np.linalg.det(Kij)
#=============== Calculating Ws ===============================
# Es stands for 'W (weight) for MV'
# Ws stands for W (weight) for MRAR
wRows = len(Kij)
wCols = len(Kij[0])
Ei = np.array([0 for i in range(wCols)], dtype=float)
Wi = np.array([0 for i in range(wCols)], dtype=float)
Eij = np.array([[0 for i in range(wCols-1)] for j in range(wRows-1)], dtype=float)
Wij = np.array([[0 for i in range(wCols-1)] for j in range(wRows-1)], dtype=float)
WijCol = 0
for theWiCol in range(0,wCols):
for jwCols in range(0,wCols):
if theWiCol != jwCols:
for iwRows in range(0,wRows-1):
Eij[iwRows,WijCol] = Cij[iwRows, jwCols]
Wij[iwRows,WijCol] = Kij[iwRows, jwCols]
WijCol +=1
sign_p = (theWiCol + 1) + wCols; # One is added to theWiCol becuase the loop start from 0.
sign_ = (-1)**sign_p;
Ei[theWiCol] = sign_ * (np.linalg.det(Eij) / detC)
Wi[theWiCol] = sign_ * (np.linalg.det(Wij) / detK)
WijCol =0
PD_MV_AR = np.sum(np.array(Er) * np.array(Ei))
PD_MRAR_AR = np.sum(np.array(Er) * np.array(Wi))
noOfAssets = noOfCols - 1;
sigma_p_ = 0
sigma_p_c_ = 0
for sd_i in range(0,noOfAssets):
for sd_j in range(0,noOfAssets):
w_sd_i = Ei[sd_i];
w_sd_j = Ei[sd_j];
w_D_sd_i = Wi[sd_i];
w_D_sd_j = Wi[sd_j];
sigma_p_ = sigma_p_ + (w_sd_i * w_sd_j * theCov[sd_i, sd_j]);
sigma_p_c_ = sigma_p_c_ + (w_D_sd_i * w_D_sd_j * theCov[sd_i, sd_j]);
PD_MV_sigma_p = np.sqrt(sigma_p_);
PD_MRAR_sigma_p_c = np.sqrt(sigma_p_c_);
PD_MV_RAR = PD_MV_AR / PD_MV_sigma_p;
PD_MRAR_RAR = PD_MRAR_AR / PD_MRAR_sigma_p_c;
MV = [np.round(PD_MV_AR,decimals=frmtDec),np.round(PD_MV_sigma_p,decimals=frmtDec),np.round(PD_MV_RAR,decimals=frmtDec),'','']
MRAR = [np.round(PD_MRAR_AR,decimals=frmtDec),np.round(PD_MRAR_sigma_p_c,decimals=frmtDec),np.round(PD_MRAR_RAR,decimals=frmtDec),'','']
Asset_df = []
Er_df = []
SD_df = []
RAdjR_df = []
Ei_df = []
Wi_df = []
PD_MV = []
PD_MRAR = []
Asset_df = 'Assets'
Er_lbl = 'E(r)'
SD_df = 'Standard Deviation'
RAdjR_df = 'Risk Adjusted Return'
Ei_df = 'w for MV'
Wi_df = 'w for MRAR'
PD_MV = 'Portfolio - Minimum Variance (MV)'
PD_MRAR = 'Portfolio - Maximum Risk Adjusted Return (MRAR)'
Asset_df = np.append(Asset_df,df.columns[1:])
Er_df = np.append(Er_lbl,Er)
Er_df = np.append(Er_lbl,np.round(Er,decimals=frmtDec))
SD_df = np.append(SD_df,np.round(SD,decimals=frmtDec))
RAdjR_df = np.append(RAdjR_df,np.round(RAdjR,decimals=frmtDec))
Ei_df = np.append(Ei_df,np.round(Ei,decimals=frmtDec))
Wi_df = np.append(Wi_df,np.round(Wi,decimals=frmtDec))
PD_MV = np.append(PD_MV,MV)
PD_MRAR = np.append(PD_MRAR,MRAR)
estResult=[]
estResult = Asset_df
estResult = np.vstack((estResult,Er_df))
estResult = np.vstack((estResult,SD_df))
estResult = np.vstack((estResult,RAdjR_df))
estResult = np.vstack((estResult,Ei_df))
estResult = np.vstack((estResult,Wi_df))
estResult2 = estResult.transpose()
estResult4 = np.vstack((estResult2,PD_MV))
df_to_print = np.vstack((estResult4,PD_MRAR))
return df_to_print,PD_MV_RAR,PD_MRAR_RAR;
################################################
def create_rprt_file(fileNameP1,theDF,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,Portfolio_no,frmtDec,self): # theDF: New DataFrame
txtFileName = fileNameP1 + 'P' +str(Portfolio_no+1) + '.txt'
output_rprt_file = open(txtFileName,'w')
output_rprt_file.write('#############################################################################\n')
output_rprt_file.write('# #\n')
output_rprt_file.write('# DETERMINING THE DIMENSION OF THE OPTIMAL PORTFOLIO #\n')
output_rprt_file.write('# #\n')
output_rprt_file.write('#############################################################################\n')
output_rprt_file.write(tabulate(theDF))
output_rprt_file.write('\n')
output_rprt_file.close
return(txtFileName)
def fillingTheListBox(fileNameP1,PD_MV_RAR,PD_MRAR_RAR,df_Portfolios,frmtDec,self): # theDF: New DataFrame
values2 = []
# length of RAR is used to find the total number of portfolios
Portfolio_no = len(PD_MV_RAR)
#Adding values to the Listbox
for i in range(1,Portfolio_no+1):
listAssets = df_Portfolios[i-1].columns[1:]
NoOfSubPortfolios = str(len(df_Portfolios[i-1].columns[1:]))
values2.append('Portfolio ' + str(i) + ' [' + NoOfSubPortfolios + '] ' + str(fDcml(PD_MV_RAR[i-1],frmtDec)) + str(' ') + str(fDcml(PD_MRAR_RAR[i-1],frmtDec)) + str(' ') + str(listAssets.tolist()))
values = tk.StringVar(value=values2)
self.listbox["listvariable"] = values
self.btnConstSP["command"] = lambda: selected_item(fileNameP1,df_Portfolios,frmtDec,self)
self.btnConstSP['state'] = 'normal'
return fileNameP1
################################################
def printAllPortfolios(FileNameAll,PD_MV_RAR_0,PD_MRAR_RAR_0,df_Portfolios,frmtDec,self):
lblPF = []
allPortfolios = []
lblPF = list(range(1,len(PD_MV_RAR_0) +1))
lblPF = ['Portfolio ' + str(x) for x in lblPF]
listAssets_ = []
for aPF in range(0, len(df_Portfolios)):
listAssets_.append(df_Portfolios[aPF].columns[1:])
listAssets = np.asanyarray(listAssets_,dtype=object)
PD_MV_RAR = np.round(PD_MV_RAR_0,decimals=frmtDec)
PD_MRAR_RAR = np.round(PD_MRAR_RAR_0,decimals=frmtDec)
allPortfolios = np.vstack((PD_MV_RAR,PD_MRAR_RAR))
allPortfolios = np.vstack((allPortfolios,listAssets))
allPortfolios = allPortfolios.transpose()
allPortfolios = np.asanyarray(allPortfolios)
col_lbl = ['MV','MRAR','List of Assets']
theDF_All = pd.DataFrame(allPortfolios,index=lblPF,columns=col_lbl)
tabulate_ = tabulate(theDF_All, headers=col_lbl)
#=============== creating Report File for All Portfolios ==================
output_rprt_file = open(FileNameAll,'w')
# output_rprt_file.write('#############################################################################\n')
# output_rprt_file.write('# #\n')
# output_rprt_file.write('# DETERMINING THE DIMENSION OF THE OPTIMAL PORTFOLIO #\n')
# output_rprt_file.write('# #\n')
# output_rprt_file.write('#############################################################################\n')
output_rprt_file.write(tabulate_)
output_rprt_file.write('\n')
output_rprt_file.close
################################################
def EndNote(fileName,self):
output_rprt_file = open(fileName,'a');
output_rprt_file.write('\n');
output_rprt_file.write('==========================================================================================\n');
output_rprt_file.write('| REFERENCES |\n');
output_rprt_file.write('| - Markowitz H. (1952) Portfolio Selection, Journal of Finance, vol. 7(1), 77-91. |\n');
output_rprt_file.write('| - Hatemi-J A. and El-Khatib Y. (2015) Portfolio Selection: An Alternative Approach, |\n');
output_rprt_file.write('| Economics Letters, vol. 135, 141-143. |\n');
output_rprt_file.write('| - Hatemi-J A., Hajji, M.A. and El-Khatib Y. (2022) Exact solution for the portfolio |\n');
output_rprt_file.write('| diversification problem based on maximizing the risk adjusted return. Research in |\n');
output_rprt_file.write('| International Business and Finance, 59, 101548. |\n');
output_rprt_file.write('| |\n');
output_rprt_file.write('==========================================================================================\n');
output_rprt_file.write('\n');
output_rprt_file.write('==========================================================================================\n');
output_rprt_file.write('| ADDITIONAL INFORMATION |\n');
output_rprt_file.write('| |\n');
output_rprt_file.write('| This program code is the copyright of the authors. Applications are allowed |\n');
output_rprt_file.write('| only if proper reference and acknowledgments are |\n');
output_rprt_file.write('| provided. For non-Commercial applications only. No performance guarantee is |\n');
output_rprt_file.write('| made. Bug reports are welcome. If this code is used for research or in any |\n');
output_rprt_file.write('| other code, proper attribution needs to be included. |\n');
output_rprt_file.write('| |\n');
output_rprt_file.write('| © 2023 Dr. Alan Mustafa and Prof. Abdulnasser Hatemi-J |\n');
output_rprt_file.write('==========================================================================================\n');
output_rprt_file.close;
# --------------------------------------------------------------------------
txtEndNote = "";
txtEndNote = txtEndNote + """REFERENCES:
- Markowitz H. (1952) Portfolio Selection, Journal of Finance, vol. 7(1), 77-91.
- Hatemi-J A. and El-Khatib Y. (2015) Portfolio Selection: An Alternative Approach, Economics Letters, vol. 135, 141-143.
- Hatemi-J A., Hajji, M.A. and El-Khatib Y. (2022) Exact solution for the portfolio diversification problem based on maximizing the risk adjusted return. Research in International Business and Finance, 59, 101548.
ADDITIONAL INFORMATION:
This program code is the copyright of the authors. Applications are allowed only if proper reference and acknowledgments are provided. For non-Commercial
applications only. No performance guarantee is made. Bug reports are welcome. If this code is used for research or in any other code, proper attribution
needs to be included.
© 2023 Dr. Alan Mustafa and Prof. Abdulnasser Hatemi-J
"""
self.msgEndNote["text"] = "%s" % (txtEndNote)
return;
###############################################################################
# End of Calculations: Constrction of Portfolios #
###############################################################################
###############################################################################
# Start of Plotting Portfolios #
###############################################################################
def plot_Portfolios(lblRAR,plotVars,df_Portfolios,Portfolio_no): # ploting a barchart for portfolios):
portfolios = list(range(1,len(plotVars)+1))
values = list(plotVars)
fig, ax2 = plt.subplots()
clrs = ['lightblue' if (x < max(values)) else 'red' for x in values ]
plt.bar(portfolios, values, color =clrs)
ax2.grid(axis='y')
ax2.set_axisbelow(True)
idx_maxValues = values.index(max(values))
for i in range(len(portfolios)):
if (i == idx_maxValues):
lblText = 'Portfolio ' + str(i + 1)
else:
lblText = ''
plt.text(i+1, values[i], lblText, ha = 'center', va='bottom')
plt.xlabel("Portfolio Number")
plt.ylabel("Risk Adjusted Return")
plt.title(lblRAR,)
plt.show()
listAssets = df_Portfolios[Portfolio_no-1].columns[1:]
print("List of assets: ",listAssets.tolist())
###############################################################################
# End of Plotting Portfolios #
###############################################################################
###############################################################################
# Start of Formating Numbers #
###############################################################################
def fDcml(value,frmtDec): # fDcml = Format the value with Decimal
return (round(value, frmtDec))
###############################################################################
# End of Formating Numbers #
###############################################################################
def getDecNo(theDecNo,self):
loadSampleData(theDecNo,self)
return
def activateTxtBox(self):
self.tbx_DecNoValue['state'] = 'normal'
def activateBtnCalcPD(self):
if self.tbx_DatasetFile.get() != '':
self.btnCalcPD['state'] = 'normal'
self.btnLoadSmplData['state'] = 'disable'
# self.btnConstSP['state'] = 'normal'
else:
self.btnCalcPD['state'] = 'disable'
self.btnLoadSmplData['state'] = 'disable'
self.btnConstSP['state'] = 'disable'
def disableBtnSelectDataFile(self):
self.btnSelectDataFile['state'] = 'disable'
def indicatorActive(self):
self.lblIndicator['fg'] = '#e52b50'
self.lblIndicator['text'] = ' ' + chr(9608) + ' in progress ...'
self.update()
def indicatorEnd(self):
self.lblIndicator['fg'] = '#53af9b'
self.lblIndicator['text'] = ' ' + chr(9608) + ' Complete! '
self.update()
def showInfoOnSampleData():
txtShowInfo = "A copy of sample data has been created in the same folder as this module resides in, labeled as PyDDOP_SampleData.csv\nMake sure your dataset is in the same format."
tk.messagebox.showinfo("Info on sample data", txtShowInfo)
#In the main function, create the GUI and pass it to the App class
def main():
window2= tk.Tk()
window2.title("PyDDOP-v1")
# window2.geometry('950x900')
window2.geometry('850x700+10+10')
create_window_menu_UI2(window2).grid(row=0, column=0, columnspan=1, sticky="W")
window2.mainloop()
#Run the main function
if __name__ == "__main__":
main()