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oFilt.py
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oFilt.py
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import sys
from PyQt5.QtWidgets import (QApplication, QWidget, QLineEdit, QPushButton, QVBoxLayout, QHBoxLayout, QLabel, QTableView, QMainWindow,\
QDateEdit, QMessageBox, QButtonGroup, QFileDialog)
from PyQt5 import QtCore
from PyQt5.QtCore import Qt
from PyQt5 import QtWidgets, uic
import pandas as pd
import numpy as np
import scipy.stats as stats
import math
from PyQt5.QtCore import QDate
from datetime import datetime, date
from datetime import timedelta
from dateutil import parser
import webbrowser
import subprocess
import calendar
import matplotlib.pyplot as plt
import os
#Entering the Qt Designer file, then connecting to this file
qtcreator_file = "oFilt.ui"
Ui_MainWindow, QtBaseClass = uic.loadUiType(qtcreator_file)
class MainWindow(QMainWindow, Ui_MainWindow):
def __init__(self, *args, obj=None, **kwargs):
super(MainWindow,self).__init__(*args, **kwargs)
QtWidgets.QMainWindow.__init__(self)
Ui_MainWindow.__init__(self)
self.setupUi(self)
#Initializing the layouts for the MainWindow
mainLayout = QVBoxLayout()
horizLay1 = QHBoxLayout()
horizLay2 = QHBoxLayout()
horizLay3 = QHBoxLayout()
horizLay4 = QHBoxLayout()
horizLay5 = QHBoxLayout()
vertLay = QVBoxLayout()
horizLay1.addWidget(self.lbCurPri)
horizLay1.addWidget(self.leCurPri)
horizLay1.addWidget(self.fNameLabel)
horizLay1.addWidget(self.leFileName)
#Adding the five following layouts to horizLay1 Layout
horizLay1.addLayout(mainLayout)
horizLay1.addLayout(horizLay2)
horizLay1.addLayout(horizLay3)
horizLay1.addLayout(horizLay4)
horizLay1.addLayout(vertLay)
horizLay2.addWidget(self.lbStrCurMin)
horizLay2.addWidget(self.leStrCurMin)
horizLay2.addWidget(self.lbBidVolMin)
horizLay2.addWidget(self.leBidVolMin)
horizLay2.addWidget(self.lbAskVolMin)
horizLay2.addWidget(self.leAskVolMin)
horizLay2.addWidget(self.lbDelMin)
horizLay2.addWidget(self.leDelMin)
horizLay3.addWidget(self.lbStrCurMax)
horizLay3.addWidget(self.leStrCurMax)
horizLay3.addWidget(self.lbBidVolMax)
horizLay3.addWidget(self.leBidVolMax)
horizLay3.addWidget(self.lbAskVolMax)
horizLay3.addWidget(self.leAskVolMax)
horizLay3.addWidget(self.lbDelMax)
horizLay3.addWidget(self.leDelMax)
horizLay3.addWidget(self.lbBidAsk)
horizLay3.addWidget(self.leBidAsk)
horizLay3.addWidget(self.pbCSV)
horizLay3.addWidget(self.rbCall)
horizLay3.addWidget(self.rbPut)
horizLay3.addWidget(self.cbFid)
horizLay3.addWidget(self.cbExc)
horizLay4.addWidget(self.pbSpCp)
horizLay4.addWidget(self.pbBidVol)
horizLay4.addWidget(self.pbAskVol)
horizLay4.addWidget(self.pbDeltas)
horizLay4.addWidget(self.pbAskBid)
horizLay5.addWidget(self.pbClose)
horizLay5.addWidget(self.pbGraStrVol)
horizLay5.addWidget(self.pbOptCal)
vertLay.addWidget(self.pbRawData)
vertLay.addWidget(self.pbLoadFile)
vertLay.addWidget(self.pbReload)
vertLay.addWidget(self.pbHiCallVol)
vertLay.addWidget(self.pbLoCallVol)
vertLay.addWidget(self.pbHiPutVol)
vertLay.addWidget(self.pbLoPutVol)
vertLay.addWidget(self.pbTightCalls)
vertLay.addWidget(self.pbTightPuts)
vertLay.addWidget(self.lbHisVol)
vertLay.addWidget(self.pbLoaPri)
vertLay.addWidget(self.leTicker)
vertLay.addWidget(self.lbTicker)
vertLay.addWidget(self.leDays)
vertLay.addWidget(self.lbDays)
vertLay.addWidget(self.lbMax)
vertLay.addWidget(self.pbHisVol)
vertLay.addWidget(self.lbResult)
vertLay.addWidget(self.leResult)
bg = QButtonGroup(self)
bg.addButton(self.rbWeekly)
bg.addButton(self.rbMonthly)
bg2 = QButtonGroup(self)
bg2.addButton(self.rbAll)
bg2.addButton(self.rbCall)
bg2.addButton(self.rbPut)
mainLayout.addWidget(self.table)
self.pbLoadFile.clicked.connect(self.loadData)
self.pbClose.clicked.connect(self.close)
self.pbCSV.clicked.connect(self.csvFiles)
self.cbFid.stateChanged.connect(self.opFid)
self.cbExc.stateChanged.connect(self.opExl)
self.pbRawData.clicked.connect(self.clickRaw)
self.pbRawData.clicked.connect(self.seeAll)
self.pbSpCp.clicked.connect(self.minMaxStrCur)
self.pbBidVol.clicked.connect(self.minMaxBidVol)
self.pbAskVol.clicked.connect(self.minMaxAskVol)
self.pbDeltas.clicked.connect(self.minMaxDelta)
self.pbAskBid.clicked.connect(self.maxAskMinusBid)
self.pbReload.clicked.connect(self.reload)
self.pbHiCallVol.clicked.connect(self.askCallVolAsk_50)
self.pbLoCallVol.clicked.connect(self.bidCallVolBid_40)
self.pbHiPutVol.clicked.connect(self.askPutVolAsk_50)
self.pbLoPutVol.clicked.connect(self.bidPutVolBid_40)
self.pbTightCalls.clicked.connect(self.tightCalls)
self.pbTightPuts.clicked.connect(self.tightPuts)
self.pbLoaPri.clicked.connect(self.loadPrices)
self.pbHisVol.clicked.connect(self.calcVol)
self.leFileName.returnPressed.connect(self.addDat)
self.rbCall.clicked.connect(self.hidePutButtons)
self.rbPut.clicked.connect(self.hideCallButtons)
self.leTicker.editingFinished.connect(self.theMax)
self.pbGraStrVol.clicked.connect(self.graStrVol)
self.pbOptCal.clicked.connect(self.actOptCal)
self.dfR = pd.DataFrame() #Only used for Raw Data display
self.df = pd.DataFrame()
self.df1 = pd.DataFrame()
self.df2 = pd.DataFrame()
self.df3 = pd.DataFrame()
self.df4 = pd.DataFrame()
self.df5 = pd.DataFrame()
self.df6 = pd.DataFrame()
self.df7 = pd.DataFrame()
self.df8 = pd.DataFrame()
self.df9 = pd.DataFrame()
self.df10 = pd.DataFrame()
self.df11 = pd.DataFrame()
self.dfpc = pd.DataFrame()#For the previous dataframe for calls
self.dfpp = pd.DataFrame()#For the previous dataframe for puts
self.dfP = pd.DataFrame()
self.dfP2 = pd.DataFrame()
self.mTime1 = 0.0
self.mTime2 = 0.0
self.mTime3 = 0.0
self.mTime4 = 0.0
self.filNum = 0 #Flag to notify Table mode and widget
self.cflag = False #Flag to signify a copy of dataframe has been made
self.filtName = ""
self.v = 0.10 #Volatility variable
self.fname = ''
self.lbResult.setVisible(False)
self.leResult.setVisible(False)
self.readflag = False #Flag signifying PriceHistory.csv is in self.dfP
self.cdf = '' #name of current dataframe
self.cdf_num = 0 #number of the dataframe in display
#Function to show raw data of Fidelity File.
def seeAll(self):
fileName = self.leFileName.text()
try:
self.dfR = pd.read_csv(fileName)
self.dfR = self.dfR.fillna(0)
self.cdf = self.dfR
self.cdf_num = 0
self.model = TableModel(self.dfR)
self.table.setModel(self.model)
except:
QMessageBox.about(self, "Incorrect File Name", \
"You must enter the correct file name!")
#Error messages for Raw Data
def clickRaw(self):
if len(self.leFileName.text()) == 0:
QMessageBox.about(self, "Enter File Name", \
"You must enter a file name!")
if len(self.leCurPri.text()) == 0:
QMessageBox.about(self, "Enter Current Price", \
"You must enter the current price!")
if self.rbWeekly.isChecked() == False and self.rbMonthly.isChecked() == False:
QMessageBox.about(self, "Weekly or Monthly", "You must select either Weekly or Monthly!")
if self.rbCall.isChecked() == False and self.rbPut.isChecked() == False and \
self.rbAll.isChecked() == False:
QMessageBox.about(self, "All, Calls or Puts", "You must select All, Calls or Puts!")
def loadData(self):
#Reading the file copied from Fidelity or MarketWatch
if len(self.leStrCurMax.text()) > 0 or \
len(self.leStrCurMin.text()) > 0 or \
len(self.leBidVolMin.text()) > 0 or \
len(self.leBidVolMax.text()) > 0 or \
len(self.leAskVolMin.text()) > 0 or \
len(self.leAskVolMax.text()) > 0 or \
len(self.leDelMin.text()) > 0 or \
len(self.leDelMax.text()) > 0 or \
len(self.leBidAsk.text()) > 0:
self.leStrCurMin.clear()
self.leStrCurMax.clear()
self.leBidVolMin.clear()
self.leBidVolMax.clear()
self.leAskVolMin.clear()
self.leAskVolMax.clear()
self.leDelMin.clear()
self.leDelMax.clear()
self.leBidAsk.clear()
fileName = self.leFileName.text()
try:
self.df = pd.read_csv(fileName)
self.df = self.df.fillna(0)
except:
QMessageBox.about(self, "Incorrect File Name", \
"You must enter the correct file name!")
self.df.replace(',','', regex=True, inplace = True)
self.cflag = False
if self.rbWeekly.isChecked() == False and self.rbMonthly.isChecked() == False:
QMessageBox.about(self, "Weekly or Monthly", "You must select either Weekly or Monthly!")
if self.rbCall.isChecked() == False and self.rbPut.isChecked() == False and \
self.rbAll.isChecked() == False:
QMessageBox.about(self, "All, Calls or Puts", "You must select All, Calls or Puts!")
#Finding number of rows - rs - and columns - cs - in the table
rs = self.df.shape[0]
#Finding the (num) number of numbers in a file name,
file_name = (self.leFileName.text())
num = ''
for element in file_name:
if element.isnumeric():
num = num + element
#Creating the price based upon the number of digits in the file name.
#x deletes the 6 digit date at the end of the file name
#y collects the digits in the price
#z is the length of the price of 1,345.67 and would have a length of 6
#z is the length of the price of 102.45 and would have a length of 5
#z is the length of the price of 67.78 would have a length of 4
x = num[-6:]
y = num[:-6]
z = len(y)
if z == 3:
a = y[0:1]
b = y[-2:]
p = a + "." + b
self.leCurPri.setText(p)
elif z == 4:
a = y[0:2]
b = y[-2:]
p = a + "." + b
self.leCurPri.setText(p)
elif z == 5:
a = y[0:3]
b = y[-2:]
p = a + "." + b
self.leCurPri.setText(p)
elif z == 6:
a = y[0:4]
b = y[-2:]
p = a + "." + b
self.leCurPri.setText(p)
if len(self.leCurPri.text()) == 0:
QMessageBox.about(self, "Enter Current Price", "The stock's current price must be entered!")
self.df = self.df.rename(columns=({'Delta.1': 'Delta_1', 'Ask.1': 'Ask_1', 'Bid.1': 'Bid_1'}))
#Drop the following columns none of which will be used in filters
self.df.drop('Change', inplace=True, axis=1)
self.df.drop('Volume', inplace=True, axis=1)
self.df.drop('Open Int', inplace=True, axis=1)
self.df.drop('Imp Vol', inplace=True, axis=1)
self.df.drop('Last', inplace=True, axis=1)
self.df.drop('Action', inplace=True, axis=1)
self.df.drop('Action.1', inplace=True, axis=1)
self.df.drop('Last.1', inplace=True, axis=1)
self.df.drop('Change.1', inplace=True, axis=1)
self.df.drop('Volume.1', inplace=True, axis=1)
self.df.drop('Open Int.1', inplace=True, axis=1)
self.df.drop('Imp Vol.1', inplace=True, axis=1)
#Making a new column for current price and fill with Current Price
self.df["Current"] = float(self.leCurPri.text())
self.df['Strike'] = self.df['Strike'].astype('float')
#Adding ExpDate column
rs = self.df.shape[0]
exd = []
for i in range(0, rs):
exd.append("")
i += i +1
#Create and populate column ExpDate to be Column #1
self.df.insert(0,"ExpDate", exd)
#Populate ExpDate column for weekly options then for Monthly options
if (self.rbWeekly.isChecked() == True):
#Determination of days from today to maturity date
self.days = self.calDayDiff()
self.eDate1 = date.today() + timedelta(self.days)
#print('Data type of eDate1: ', type(self.eDate1))
#print('Data type of rbCall: ', type(self.rbCall))
self.days2 = 7
self.days3 = 14
self.days4 = 21
#Determination of actual maturity dates
self.eDate2 = self.eDate1 + timedelta(self.days2)
self.eDate3 = self.eDate1 + timedelta(self.days3)
self.eDate4 = self.eDate1 + timedelta(self.days4)
index = 0
self.ct = 0
self.df['Strike'] = self.df['Strike'].replace(np.nan, 0)
for ind in self.df.index:
if self.ct == 0:
self.df.at[ind, 'ExpDate'] = self.eDate1
elif self.ct == 1:
self.df.at[ind, 'ExpDate'] = self.eDate2
elif self.ct == 2:
self.df.at[ind, 'ExpDate'] = self.eDate3
elif self.ct == 3:
self.df.at[ind, 'ExpDate'] = self.eDate4
if self.df['Strike'][ind] == 0.00:
self.df = self.df.drop(index=ind)
self.ct = self.ct + 1
elif self.rbMonthly.isChecked() == True:
self.df['Strike'] = self.df['Strike'].replace(np.nan, 0)
c = calendar.Calendar(firstweekday=calendar.SUNDAY)
current_date = datetime.now()
year = current_date.year
month = current_date.month
day = current_date.day
monthcal = c.monthdatescalendar(year, month)
self.ct = 0
third_friday = [day for week in monthcal for day in week if
day.weekday() == calendar.FRIDAY and day.month == month][2]
if (current_date.day > third_friday.day):
self.eDate1 = self.fridayEd1nm()
self.eDate2 = self.fridayEd2nm()
else:
self.eDate1 = self.fridayEd1cm()
self.eDate2 = self.fridayEd2cm()
for ind in self.df.index:
if self.ct == 0:
self.df.at[ind, 'ExpDate'] = self.eDate1
elif self.ct == 1:
self.df.at[ind, 'ExpDate'] = self.eDate2
if self.df['Strike'][ind] == 0.00:
self.df = self.df.drop(index=ind)
self.ct = self.ct + 1
#self.df["Bid"] = pd.to_numeric(self.df["Bid"], downcast="float")
self.df['Bid'] = self.df['Bid'].astype('float')
#self.df["Ask"] = pd.to_numeric(self.df["Ask"], downcast="float")
self.df['Ask'] = self.df['Ask'].astype('float')
#self.df["Bid_1"] = pd.to_numeric(self.df["Bid_1"], downcast="float")
self.df['Bid_1'] = self.df['Bid_1'].astype('float')
#self.df["Ask_1"] = pd.to_numeric(self.df["Ask_1"], downcast="float")
self.df['Ask_1'] = self.df['Ask_1'].astype('float')
#Create array to make Volatility-Bid Column for Calls and then Puts
rs = self.df.shape[0]
if self.rbCall.isChecked() == True:
vol = []
for i in range(0, rs):
vol.append("")
i = i +1
self.df.insert(8, 'VolatBid', vol)
elif self.rbPut.isChecked() == True:
vol_p = []
for i in range(0, rs):
vol_p.append("")
i = i +1
self.df.insert(8, 'VolatBid_1', vol_p)
#Create array to make Volatility-Ask Column for Calls and then Puts
if self.rbCall.isChecked() == True:
volA = []
for i in range(0, rs):
volA.append("")
i = i +1
self.df.insert(9, 'Volat-Ask', volA)
elif self.rbPut.isChecked() == True:
volB = []
for i in range(0, rs):
volB.append("")
i = i +1
self.df.insert(9, 'Volat-Ask_1', volB)
#Adding Result of Strike Price divided by current price
self.df["StrkCurr"] = self.df["Strike"].astype('float') / self.df["Current"].astype('float')
self.df['StrkCurr'] = pd.Series(["{0:.1f}%".format(val * 100) for val in self.df['StrkCurr']], index = self.df.index)
#Conversion of self.df[Bid] from object to float for Calls and Puts
if self.rbCall.isChecked() == True:
self.df['Bid'] = self.df['Bid'].astype('float', errors = 'raise')
self.df["Ask-Bid"] = (self.df["Ask"] - self.df["Bid"])
self.df['Ask-Bid'] = pd.Series(["{0:.2f}".format(val) for val in self.df['Ask-Bid']], index = self.df.index)
elif self.rbPut.isChecked() == True:
self.df['Bid_1'] = self.df['Bid_1'].astype('float', errors = 'raise')
self.df["Ask_1-Bid_1"] = (self.df["Ask_1"] - self.df["Bid_1"])
self.df['Ask_1-Bid_1'] = pd.Series(["{0:.2f}".format(val) for val in self.df['Ask_1-Bid_1']], index = self.df.index)
#Deletion of rows where Bid + Strike < current price
rs = self.df.shape[0]
self.df["Bid"] = self.df.Bid.astype('float')
#The below code will discard Delta values of '--' and create a new dataframe - NOT A COPY
self.df = self.df.loc[self.df['Delta'] != '--']
self.df = self.df.loc[self.df['Delta'] != -1]
self.df = self.df.loc[self.df['Delta_1'] != '-']
self.df['Delta_1'] = self.df.Delta_1.astype('float')
self.df['Delta'] = self.df.Delta.astype('float')
self.df.reset_index(inplace = True, drop = True)
self.df['Bid'] = self.df.Bid.astype('float')
self.df['Ask'] = self.df.Ask.astype('float')
rs = self.df.shape[0]
self.df.reset_index(inplace = True, drop = True)
#Creating table to accomdate Calls
if self.rbCall.isChecked() == True:
self.filNum = self.filNum + 1
self.df.drop('Bid_1', inplace=True, axis=1)
self.df.drop('Ask_1', inplace=True, axis=1)
self.df.drop('Delta_1', inplace=True, axis=1)
#Creating table to accomdate Puts
elif self.rbPut.isChecked() == True:
self.filNum = self.filNum + 1
self.df.drop('Bid', inplace=True, axis=1)
self.df.drop('Ask', inplace=True, axis=1)
self.df.drop('Delta', inplace=True, axis=1)
self.df.reset_index(inplace = True, drop = True)
#Calculation of expected call bid volatility
if (len(self.leBidVolMin.text()) == 0 and (len(self.leBidVolMax.text())) == 0) \
and (self.rbCall.isChecked() == True):
index = 0
for index, row in self.df.iterrows():
premK = float(self.df.loc[index, 'Bid'])
premUK = 0.07
vF = 0.0
self.v = 0.05
sp = self.leCurPri.text()
S = float(sp)
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
K = float(self.df.loc[index, 'Strike'])
r = 1.5
r = r / 100
t = self.matTime(index)
self.v = self.v + .05
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = d1
firstFactor = S * stats.norm.cdf(x)
secondFactor = K * math.exp(-r*t) * stats.norm.cdf(d2)
premium = firstFactor - secondFactor
premUK = premium
premUK = 0.07
self.v = self.v - .05
while(premUK < premK):
K = float(self.df.loc[index, 'Strike'])
r = 1.5
r = r / 100
self.v = self.v + 0.001
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = d1
firstFactor = S * stats.norm.cdf(x)
secondFactor = K * math.exp(-r*t) * stats.norm.cdf(d2)
premium = firstFactor - secondFactor
premUK = premium
vF = int(self.v * 100)
vF = format(vF, ".2f")
vF = float(self.v * 100)
vF = round(vF, 2)
rs = self.df.shape[0]
self.df.loc[index, 'VolatBid'] = (str(vF) + "%")
index = index + 1
#Calculation of expected call Ask volatility
if (len(self.leAskVolMin.text()) == 0 and (len(self.leAskVolMax.text())) == 0) \
and (self.rbCall.isChecked() == True):
index = 0
for index, row in self.df.iterrows():
premK = float(self.df.loc[index, 'Ask'])
premUK = 0.07
vF = 0.0
self.v = 0.05
S = float(self.leCurPri.text())
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
K = float(self.df.loc[index, 'Strike'])
r = 1.5
r = r / 100
self.v = self.v + .05
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = d1
firstFactor = S * stats.norm.cdf(x)
secondFactor = K * math.exp(-r*t) * stats.norm.cdf(d2)
premium = firstFactor - secondFactor
premUK = premium
premUK = 0.07
self.v = self.v - .05
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
K = float(self.df.loc[index, 'Strike'])
r = 1.5
r = r / 100
self.v = self.v + 0.001
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = d1
firstFactor = S * stats.norm.cdf(x)
secondFactor = K * math.exp(-r*t) * stats.norm.cdf(d2)
premium = firstFactor - secondFactor
premUK = premium
vF = int(self.v * 100)
vF = format(vF, ".2f")
vF = float(self.v * 100)
vF = round(vF, 2)
rs = self.df.shape[0]
self.df.loc[index, 'Volat-Ask'] = (str(vF) + "%")
index = index + 1
#calculation of Bid volatilities for Puts
if (len(self.leBidVolMin.text()) == 0 and (len(self.leBidVolMax.text())) == 0) \
and (self.rbPut.isChecked() == True):
index = 0
for index, row in self.df.iterrows():
premK = float(self.df.loc[index, 'Bid_1'])
premUK = 0.07
self.v = 0.05
vF = 0.0
S = float(self.leCurPri.text())
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
K = float(self.df.loc[index, 'Strike'])
r = 1.5
r = r / 100
self.v = self.v + .05
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = -d2
y = -d1
factorOne = stats.norm.cdf(x) * K * math.exp(-r*t)
factorTwo = stats.norm.cdf(y) * S
premiumP = factorOne - factorTwo
premUK = premiumP
vF = 0.0
premUK = 0.07
self.v = self.v - .05
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
S = float(self.leCurPri.text())
K = float(self.df.loc[index, 'Strike'])
r = 0.005
r = r / 100
self.v = self.v + 0.001
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = -d2
y = -d1
factorOne = stats.norm.cdf(x) * K * math.exp(-r*t)
factorTwo = stats.norm.cdf(y) * S
premiumP = factorOne - factorTwo
premUK = premiumP
vF = int(self.v * 100)
vF = format(vF, ".2f")
vF = float(self.v * 100)
vF = round(vF, 2)
rs = self.df.shape[0]
self.df.loc[index, 'VolatBid_1'] = (str(vF) + "%")
#calculation of Ask volatilities for Puts
if (len(self.leAskVolMin.text()) == 0 and (len(self.leAskVolMax.text())) == 0) \
and (self.rbPut.isChecked() == True):
index = 0
for index, row in self.df.iterrows():
premK = float(self.df.loc[index, 'Ask_1'])
premUK = 0.07
self.v = 0.05
vF = 0.0
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
S = float(self.leCurPri.text())
K = float(self.df.loc[index, 'Strike'])
r = 0.005
r = r / 100
self.v = self.v + .05
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = -d2
y = -d1
factorOne = stats.norm.cdf(x) * K * math.exp(-r*t)
factorTwo = stats.norm.cdf(y) * S
premiumP = factorOne - factorTwo
premUK = premiumP
premUK = 0.07
self.v = self.v - .05
vF = 0.0
while (premUK < premK):
#Calculations on options with a very low premium don't give a significant answer
S = float(self.leCurPri.text())
K = float(self.df.loc[index, 'Strike'])
r = 0.005
r = r / 100
self.v = self.v + 0.001
t = self.matTime(index)
d1_numerator = np.log(S/K) + (r + ((self.v * self.v)/2)) * t
d1_denominator = self.v * math.sqrt(t)
d1 = d1_numerator/d1_denominator
d2 = d1 - self.v * math.sqrt(t)
x = -d2
y = -d1
factorOne = stats.norm.cdf(x) * K * math.exp(-r*t)
factorTwo = stats.norm.cdf(y) * S
premiumP = factorOne - factorTwo
premUK = premiumP
vF = int(self.v * 100)
vF = format(vF, ".2f")
vF = float(self.v * 100)
vF = round(vF, 2)
rs = self.df.shape[0]
self.df.loc[index, 'Volat-Ask_1'] = (str(vF) + "%")
#Drop string '0.0%' from VolatBid in order to delete Bid < 0.1% AND Ask < 0.1
#all of which in the same row
if self.rbCall.isChecked() == True:
self.df = self.df.loc[self.df['VolatBid'] != '0.0%']
self.df.reset_index(inplace = True, drop = True)
self.df = self.df.loc[self.df['Volat-Ask'] != '0.0%']
self.df.reset_index(inplace = True, drop = True)
#Drop string '0.0%' from VolatBid-1 in order to delete Bid < 0.1% AND Ask < 0.1
#all of which in the same row
if self.rbPut.isChecked() == True:
self.df = self.df.loc[self.df['Volat-Ask_1'] != '0.0%']
self.df.reset_index(inplace = True, drop = True)
self.df = self.df.loc[self.df['VolatBid_1'] != '0.0%']
self.df.reset_index(inplace = True, drop = True)
#Delete rows with 5.1% VoltBid or VoltBid_1; In these rows the Bid +
#the Strike value will be < the Current value
if self.rbPut.isChecked() == True:
self.df = self.df.loc[self.df['VolatBid_1'] != '5.1%']
elif self.rbCall.isChecked() == True:
self.df = self.df.loc[self.df['VolatBid'] != '5.1%']
self.df.reset_index(inplace = True, drop = True)
#Determining which dataframe will be shown.
if self.filNum > 0:
self.df = self.df.dropna()
self.cdf = self.df
self.cdf_num = 1
self.model = TableModel(self.df)
self.table.setModel(self.model)
else:
self.df = self.df.dropna()
self.cdf = self.df
self.cdf_num = 1
self.model = TableModel(self.df)
self.table.setModel(self.model)
#Functioin for pbCSV to show csv files in current directory
def csvFiles(self):
#fname = QFileDialog.getOpenFileName(self, "Open csv File", "users/jamesbotts/oFiltStable", "CSV Files (*.csv)")
self.fname = QFileDialog.getOpenFileName(self, "csv Files", "", "csv(*.csv)")
#When the rbCall button is selected, the buttons acting on Puts will hide
def hidePutButtons(self):
self.pbTightPuts.setVisible(False)
self.pbLoPutVol.setVisible(False)
self.pbHiPutVol.setVisible(False)
self.pbTightCalls.setVisible(True)
self.pbLoCallVol.setVisible(True)
self.pbHiCallVol.setVisible(True)
self.lbDefFil.move(1379,249)
self.pbSpCp.setText("Strike/Current for Calls")
self.pbBidVol.setText("Bid Volatilities for Calls")
self.pbAskVol.setText("Ask Volatilities for Calls")
self.pbDeltas.setText("Deltas for Calls")
self.pbAskBid.setText("Ask-Bids for Calls")
#When the rbPut button is selected, the buttons acting on Calls will hide
def hideCallButtons(self):
self.pbTightPuts.setVisible(True)
self.pbLoPutVol.setVisible(True)
self.pbHiPutVol.setVisible(True)
self.pbTightCalls.setVisible(False)
self.pbLoCallVol.setVisible(False)
self.pbHiCallVol.setVisible(False)
self.lbDefFil.move(1379,400)
self.pbSpCp.setText("Strike/Current for Puts")
self.pbBidVol.setText("Bid Volatilities for Puts")
self.pbAskVol.setText("Ask Volatilities for Puts")
self.pbDeltas.setText("Deltas for Puts")
self.pbAskBid.setText("Ask-Bid for Puts")
#The number of days until Friday from the current date. Day 4 = Friday and
#Day 0 is Monday
def calDayDiff(self):
if ((datetime.today().weekday()) == 0):
daydiff = 4
elif((datetime.today().weekday()) == 1):
daydiff = 3
elif((datetime.today().weekday()) == 2):
daydiff = 2
elif((datetime.today().weekday()) == 3):
daydiff = 1
elif((datetime.today().weekday()) == 4):
daydiff = 7
elif((datetime.today().weekday()) == 5):
daydiff = 6
elif((datetime.today().weekday()) == 6):
daydiff = 5
return daydiff
def calc_mat_time1(self):
if self.rbWeekly.isChecked() == True :
self.mTime1 = self.calDayDiff() / 365.25
return self.mTime1
elif self.rbMonthly.isChecked() == True:
d1 = datetime.now().date()
d2 = self.eDate1
delta = d2 - d1
self.mTime1 = delta.days / 365.25
return self.mTime1
def calc_mat_time2(self):
if self.rbWeekly.isChecked() == True:
self.mTime2 = (self.calDayDiff() + 7) / 365.25
return self.mTime2
elif self.rbMonthly.isChecked() == True:
d1 = datetime.now().date()
d2 = self.eDate2
delta = d2 - d1
self.mTime2 = delta.days / 365.25
return self.mTime2
def calc_mat_time3(self):
self.mTime3 = (self.calDayDiff() + 14) / 365.25
return self.mTime3
def calc_mat_time4(self):
self.mTime4 = (self.calDayDiff() + 21) / 365.25
return self.mTime4
#User in the current month determining eDate1
#cm = current month
def fridayEd1cm(self):
c = calendar.Calendar(firstweekday=calendar.SUNDAY)
current_date = datetime.now()
year = current_date.year
month = current_date.month
day = current_date.day
monthcal = c.monthdatescalendar(year, month)
try:
third_friday = [day for week in monthcal for day in week if
day.weekday() == calendar.FRIDAY and day.month == month][2]
eDate1 = third_friday
return eDate1
except IndexError:
print('No date found')
#User in the current month determining eDate2
#cm = current month
def fridayEd2cm(self):
c = calendar.Calendar(firstweekday=calendar.SUNDAY)
current_date = datetime.now()
year = current_date.year
month = current_date.month + 1
day = current_date.day
monthcal = c.monthdatescalendar(year, month)
try:
third_friday = [day for week in monthcal for day in week if
day.weekday() == calendar.FRIDAY and day.month == month][2]
eDate2 = third_friday
return eDate2
except IndexError:
print('No date found')
#User in before options for edate1 determining determining eDate1
#nm = next month
def fridayEd1nm(self):
c = calendar.Calendar(firstweekday=calendar.SUNDAY)
current_date = datetime.now()
year = current_date.year
month = current_date.month + 1
day = current_date.day
monthcal = c.monthdatescalendar(year, month)
try:
third_friday = [day for week in monthcal for day in week if
day.weekday() == calendar.FRIDAY and day.month == month][2]
eDate1 = third_friday
return eDate1
except IndexError:
print('No date found')
#User in before options for edate1 determining determining eDate2
#nm = next month
def fridayEd2nm(self):
c = calendar.Calendar(firstweekday=calendar.SUNDAY)
current_date = datetime.now()
year = current_date.year
month = current_date.month + 2
day = current_date.day
monthcal = c.monthdatescalendar(year, month)
try:
third_friday = [day for week in monthcal for day in week if
day.weekday() == calendar.FRIDAY and day.month == month][2]
eDate2 = third_friday
return eDate2
except IndexError:
print('No date found')
def matTime(self, index):
if self.df.loc[index, 'ExpDate'] == self.eDate1:
self.mTime1 = self.calc_mat_time1()
return self.mTime1
elif self.df.loc[index, 'ExpDate'] == self.eDate2:
self.mTime2 = self.calc_mat_time2()
return self.mTime2
elif self.df.loc[index, 'ExpDate'] == self.eDate3:
self.mTime3 = self.calc_mat_time3()
return self.mTime3
elif self.df.loc[index, 'ExpDate'] == self.eDate4:
self.mTime3 = self.calc_mat_time4()
return self.mTime4
def minMaxStrCur(self):
#If the user has been running filters and has not filtered data from
#StrkCurr, then the code in if below will use filtered data from prior
#filters and reduce the number of rows further
#dfp stands for "dataframe previous" and cflag is for "copy flag";
if self.cflag == True and self.filtName != 'mmStrCur':
if self.rbCall.isChecked():
self.df1 = self.dfpc.copy()
elif self.rbPut.isChecked():
self.df1 = self.dfpp.copy()
#In the elif below the user has filtered StrkCurr before and has returned
#to do additional filtering of StrkCurr
elif (self.cflag == True and self.filtName == 'mmStrCur'):
if self.rbCall.isChecked():
self.df1 = self.dfpc.copy()
elif self.rbPut.isChecked():
self.df1 = self.dfpp.copy()
elif self.cflag == False:
self.df1 = self.df.copy()
self.filtName = 'mmStrCur'
rs = self.df1.shape[0]
if self.rbCall.isChecked():
if (len(self.leStrCurMin.text()) > 0 and (len(self.leStrCurMax.text())) > 0) or \
(len(self.leStrCurMin.text()) == 0 and (len(self.leStrCurMax.text())) > 0) or \
(len(self.leStrCurMin.text()) > 0 and (len(self.leStrCurMax.text())) == 0):
self.filNum = self.filNum + 1
#Change values in StrkCurr from percentages to floats
self.df1['StrkCurr'] = self.df1['StrkCurr'].str.rstrip("%").astype('float')
#Filter done using floats
if len(self.leStrCurMin.text()) > 0:
scMin = self.leStrCurMin.text()
else:
scMin = '0.00'
scMin = float(scMin)
if len(self.leStrCurMax.text()) > 0:
scMax = self.leStrCurMax.text()
else:
scMax = '200.00'
scMax = float(scMax)
self.df1 = (self.df1.loc[self.df1['StrkCurr'] >= float(scMin)])
self.df1 = (self.df1.loc[self.df1["StrkCurr"] <= float(scMax)])
self.df1 = self.df1.sort_values(by = ['ExpDate', 'Strike'], ascending = [True, True])
#Strings returned to Strkurr will now be converted to floats, then to percentages
self.df1["StrkCurr"] = pd.to_numeric(self.df1["StrkCurr"], downcast="float")
self.df1['StrkCurr'] = pd.Series(["{0:.1f}%".format(val) for val in self.df1['StrkCurr']], index = self.df1.index)
self.df1.reset_index(drop = True, inplace = True)
self.cdf = self.df1
self.cdf_num = 2
self.model = TableModel(self.df1)
self.table.setModel(self.model)
if self.rbPut.isChecked():
if (len(self.leStrCurMin.text()) > 0 and (len(self.leStrCurMax.text())) > 0) or \
(len(self.leStrCurMin.text()) == 0 and (len(self.leStrCurMax.text())) > 0) or \
(len(self.leStrCurMin.text()) > 0 and (len(self.leStrCurMax.text())) == 0):
self.filNum = self.filNum + 1
#Change values in StrkCurr from percentages to floats
self.df1['StrkCurr'] = self.df1['StrkCurr'].str.rstrip("%").astype('float')
#Filter done using floats
scMin = self.leStrCurMin.text() or '0.00'
scMax = self.leStrCurMax.text() or '200.0'
self.df1 = (self.df1.loc[self.df1['StrkCurr'] >= float(scMin)])
self.df1 = (self.df1.loc[self.df1["StrkCurr"] <= float(scMax)])
self.df1 = self.df1.sort_values(by = ['ExpDate', 'Strike'], ascending = [True, True])
#Strings returned to Strkurr will now be converted to floats, then to percentages
self.df1["StrkCurr"] = pd.to_numeric(self.df1["StrkCurr"], downcast="float")
self.df1['StrkCurr'] = pd.Series(["{0:.1f}%".format(val) for val in self.df1['StrkCurr']], index = self.df1.index)
self.df1.reset_index(drop = True, inplace = True)
self.cdf = self.df1
self.cdf_num = 2
self.model = TableModel(self.df1)
self.table.setModel(self.model)