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Finished overhaul of version-1
This commit is contained in:
Andrew Dinh
parent
5f63eeb57e
commit
0dcdd1049d
3
.gitignore
vendored
3
.gitignore
vendored
@ -1,8 +1,5 @@
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__pycache__/StockData.cpython-37.pyc
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__pycache__/
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*.pyc
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quickstart.py
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creds.json
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test/
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.vscode/
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listGoogle.py
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@ -12,17 +12,19 @@ import numpy
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from urllib.request import urlopen
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import re
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class ExpenseRatio:
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def __init__(self):
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def main(): # For testing purposes
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def main(): # For testing purposes
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'''
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a = [1,2,3]
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b = [2,4,6]
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c = numpy.corrcoef(a, b)[0, 1]
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print(c)
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a = [1,2,3]
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b = [2,4,6]
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c = numpy.corrcoef(a, b)[0, 1]
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print(c)
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'''
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if __name__ == "__main__":
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main()
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main()
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57
Functions.py
57
Functions.py
@ -1,24 +1,47 @@
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# Python file for general functions
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class Functions:
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def getNearest(items, pivot):
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return min(items, key=lambda x: abs(x - pivot))
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def stringToDate(date):
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from datetime import datetime
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def getNearest(items, pivot):
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return min(items, key=lambda x: abs(x - pivot))
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def stringToDate(date):
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from datetime import datetime
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#datetime_object = datetime.strptime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')
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datetime_object = datetime.strptime(date, '%Y-%m-%d').date()
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return(datetime_object)
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'''
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dateSplit = date.split('-')
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year = int(dateSplit[0])
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month = int(dateSplit[1])
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day = int(dateSplit[2])
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datetime_object = datetime.date(year, month, day)
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'''
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return datetime_object
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def removeExtraDatesAndCloseValues(list1, list2):
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# Returns the two lists but with the extra dates and corresponding close values removed
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# list = [[dates], [close values]]
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newList1 = [[], []]
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newList2 = [[], []]
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for i in range(0, len(list1[0]), 1):
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for j in range(0, len(list2[0]), 1):
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if list1[0][i] == list2[0][j]:
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newList1[0].append(list1[0][i])
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newList2[0].append(list1[0][i])
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newList1[1].append(list1[1][i])
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newList2[1].append(list2[1][j])
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break
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returnList = []
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returnList.append(newList1)
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returnList.append(newList2)
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return returnList
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#datetime_object = datetime.strptime('Jun 1 2005 1:33PM', '%b %d %Y %I:%M%p')
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datetime_object = datetime.strptime(date, '%Y-%m-%d').date()
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return(datetime_object)
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'''
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dateSplit = date.split('-')
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year = int(dateSplit[0])
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month = int(dateSplit[1])
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day = int(dateSplit[2])
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datetime_object = datetime.date(year, month, day)
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'''
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return datetime_object
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def main():
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exit()
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if __name__ == "__main__":
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main()
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main()
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@ -16,6 +16,4 @@ To begin, run
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Some ticker values to try:
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SPY, VFINX, AAPL, GOOGL
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`$ pip install numpy`
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Created by Andrew Dinh from Dr. TJ Owens Gilroy Early College Academy
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915
StockData.py
915
StockData.py
File diff suppressed because it is too large
Load Diff
@ -11,10 +11,11 @@ from StockData import StockData
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import datetime
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from Functions import Functions
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class Return:
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def __init__(self, newListOfReturn = [], newTimeFrame = [], newBeta = 0, newStandardDeviation = 0, newNegativeStandardDeviation = 0, newMarketReturn = 0, newSize = 0, newSizeOfNeg = 0, newFirstLastDates = [], newAllLists = [], newAbsFirstLastDates = ''):
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def __init__(self, newListOfReturn=[], newTimeFrame=[], newBeta=0, newStandardDeviation=0, newNegativeStandardDeviation=0, newMarketReturn=0, newSize=0, newSizeOfNeg=0, newFirstLastDates=[], newAllLists=[], newAbsFirstLastDates=''):
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self.listOfReturn = newListOfReturn
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self.timeFrame = newTimeFrame # [years, months (30 days)]
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self.timeFrame = newTimeFrame # [years, months (30 days)]
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self.beta = newBeta
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self.standardDeviation = newStandardDeviation
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self.negativeStandardDeviation = newNegativeStandardDeviation
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@ -32,12 +33,14 @@ class Return:
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def getFirstLastDates(self, stock):
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firstLastDates = []
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timeFrame = self.timeFrame
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firstDate = datetime.datetime.now() - datetime.timedelta(days=timeFrame[0]*365)
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firstDate = datetime.datetime.now(
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) - datetime.timedelta(days=timeFrame[0]*365)
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firstDate = firstDate - datetime.timedelta(days=timeFrame[1]*30)
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firstDate = ''.join((str(firstDate.year),'-', str(firstDate.month), '-', str(firstDate.day)))
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firstDate = ''.join(
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(str(firstDate.year), '-', str(firstDate.month), '-', str(firstDate.day)))
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lastDate = StockData.returnAbsFirstLastDates(stock)[1]
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#print(lastDate)
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# print(lastDate)
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firstLastDates.append(firstDate)
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firstLastDates.append(lastDate)
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return firstLastDates
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@ -60,19 +63,21 @@ class Return:
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if firstDateExists == False:
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print("Could not find first date. Changing first date to closest date")
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tempDate = Functions.stringToDate(firstDate) # Change to datetime
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tempDate = Functions.stringToDate(firstDate) # Change to datetime
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print('Original first date:', tempDate)
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#tempDate = datetime.date(2014,1,17)
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newFirstDate = Functions.getNearest(finalDatesAndClose2[0], tempDate)
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newFirstDate = Functions.getNearest(
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finalDatesAndClose2[0], tempDate)
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print('New first date:', newFirstDate)
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firstDate = str(newFirstDate)
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if lastDateExists == False:
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print("Could not find final date. Changing final date to closest date")
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tempDate2 = Functions.stringToDate(lastDate) # Change to datetime
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tempDate2 = Functions.stringToDate(lastDate) # Change to datetime
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print('Original final date:', tempDate2)
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#tempDate2 = datetime.date(2014,1,17)
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newLastDate = Functions.getNearest(finalDatesAndClose2[0], tempDate2)
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newLastDate = Functions.getNearest(
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finalDatesAndClose2[0], tempDate2)
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print('New final date:', newLastDate)
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lastDate = str(newLastDate)
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@ -97,7 +102,8 @@ class Return:
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print('Close values:', firstClose, '...', lastClose)
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fullUnadjustedReturn = float(lastClose/firstClose)
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unadjustedReturn = fullUnadjustedReturn**(1/(self.timeFrame[0]+(self.timeFrame[1])*.1))
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unadjustedReturn = fullUnadjustedReturn**(
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1/(self.timeFrame[0]+(self.timeFrame[1])*.1))
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return unadjustedReturn
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def getBeta(self):
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@ -113,9 +119,9 @@ class Return:
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for i in range(0, len(finalDates), 1):
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if finalDates[i] == str(firstDate):
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firstClose = finalClose[i]
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55ggbh
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#list1 =
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list2 = [1,2,4,1]
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# list1 =
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list2 = [1, 2, 4, 1]
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print(numpy.corrcoef(list1, list2)[0, 1])
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@ -138,7 +144,7 @@ class Return:
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timeFrameMonth = 0
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print(timeFrameMonth)
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self.timeFrame.append(timeFrameMonth)
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#print(self.timeFrame)
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# print(self.timeFrame)
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self.firstLastDates = Return.getFirstLastDates(self, stock)
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print('Dates: ', self.firstLastDates)
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@ -149,10 +155,10 @@ class Return:
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print('\nGetting unadjusted return')
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unadjustedReturn = Return.getUnadjustedReturn(self, stock)
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self.listOfReturn.append(unadjustedReturn)
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print('Average annual return for the past', self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
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print('Average annual return for the past',
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self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
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print((self.listOfReturn[0]-1)*100, '%', sep='')
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def main(self, stock):
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print('Beginning StockReturn.py')
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@ -169,12 +175,14 @@ class Return:
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print('\nGetting unadjusted return')
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unadjustedReturn = Return.getUnadjustedReturn(self, stock)
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self.listOfReturn.append(unadjustedReturn)
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print('Average annual return for the past', self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
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print('Average annual return for the past',
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self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
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print((self.listOfReturn[0]-1)*100, '%', sep='')
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#print('\nGetting beta')
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#beta = Return.getBeta(self, stock)
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def main():
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stockName = 'spy'
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stock1 = StockData(stockName)
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@ -186,5 +194,6 @@ def main():
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Return.main(stock1Return, stock1)
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if __name__ == "__main__":
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main()
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main()
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@ -1,54 +0,0 @@
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# https://support.google.com/docs/answer/3093281?hl=en
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# Historical data cannot be downloaded or accessed via the Sheets API or Apps Script. If you attempt to do so, you will see a #N/A error in place of the values in the corresponding cells of your spreadsheet.
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import gspread, time, webbrowser, msvcrt
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from oauth2client.service_account import ServiceAccountCredentials
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def main():
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scope = ['https://spreadsheets.google.com/feeds',
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'https://www.googleapis.com/auth/drive']
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credentials = ServiceAccountCredentials.from_json_keyfile_name('creds.json', scope)
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gc = gspread.authorize(credentials)
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'''
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# Just by ID:
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#sheet = gc.open_by_key('1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM')
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sheet = gc.open_by_url('https://docs.google.com/spreadsheets/d/1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM')
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worksheet = sheet.get_worksheet(0)
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worksheet.update_acell('B1', 'bingo!')
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#worksheet.update_cell(1, 2, 'Bingo!')
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val = worksheet.acell('B1').value
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#val = worksheet.cell(1, 2).value
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print(val)
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'''
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url = 'https://docs.google.com/spreadsheets/d/1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM'
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surl = 'https://www.andrewkdinh.com/u/listGoogle'
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print("Opening", url)
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#webbrowser.open(surl)
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sheet = gc.open_by_url(url)
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worksheet = sheet.get_worksheet(0)
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print('Writing Google Finance function to A1')
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worksheet.update_cell(1, 1, '=GOOGLEFINANCE("GOOG", "price", DATE(2014,1,1), DATE(2014,12,31), "DAILY")')
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print('\nOpening link to the Google Sheet. Please download the file as comma-separated values (.csv) and move it to the directory of this Python file',
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'\nFile > Download as > Comma-separated values(.csv,currentsheet)')
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print("If the link did not open, please go to", surl)
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print("Press any key to continue")
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#time.sleep(45)
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'''
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for i in range(60, 0, -1):
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print(i, end='\r')
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time.sleep(1)
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'''
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waiting = True
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while waiting == True:
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if msvcrt.kbhit():
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waiting = False
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print("e")
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#val = worksheet.acell('A1').value
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#print(val)
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if __name__ == '__main__':
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main()
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567
main.py
567
main.py
@ -1,55 +1,509 @@
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# main.py
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# Andrew Dinh
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# Python 3.6.1
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# Description:
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'''
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Asks users for mutual funds/stocks to compare
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Asks to be compared (expense ratio, turnover, market capitalization, or persistence)
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Asks for time period (Possibly: 1 year, 5 years, 10 years)
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Makes the mutual funds as class Stock
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Gets data from each API
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Compare and contrast dates and end changeOverTime for set time period
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NOTES: Later can worry about getting close values to make a graph or something
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Gives correlation value using equation at the end (from 0 to 1)
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# Python 3.6.7
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FIRST TESTING WITH EXPENSE RATIO
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import requests
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import json
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import datetime
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import numpy
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import Functions
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# API Keys
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apiAV = 'O42ICUV58EIZZQMU'
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# apiBarchart = 'a17fab99a1c21cd6f847e2f82b592838'
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apiBarchart = 'f40b136c6dc4451f9136bb53b9e70ffa'
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apiTiingo = '2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8'
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apiTradier = 'n26IFFpkOFRVsB5SNTVNXicE5MPD'
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# If you're going to take these API keys and abuse it, you should really reconsider your life priorities
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'''
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API Keys:
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Alpha Vantage API Key: O42ICUV58EIZZQMU
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Barchart API Key: a17fab99a1c21cd6f847e2f82b592838
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Possible other one? f40b136c6dc4451f9136bb53b9e70ffa
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150 getHistory queries per day
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Tiingo API Key: 2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8
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Tradier API Key: n26IFFpkOFRVsB5SNTVNXicE5MPD
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Monthly Bandwidth = 5 GB
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Hourly Requests = 500
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Daily Requests = 20,000
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Symbol Requests = 500
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Mutual funds:
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Yes: Alpha Vantage, Tiingo
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No: IEX, Barchart
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'''
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class Stock:
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# GLOBAL VARIABLES
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timeFrame = []
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benchmarkDates = []
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benchmarkCloseValues = []
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benchmarkUnadjustedReturn = 0
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def __init__(self):
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# BASIC DATA
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self.name = '' # Ticker symbol
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self.allDates = []
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self.allCloseValues = []
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self.dates = []
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self.closeValues = []
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self.datesMatchBenchmark = []
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self.closeValuesMatchBenchmark = []
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# CALCULATED RETURN
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self.unadjustedReturn = 0
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self.sortino = 0
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self.sharpe = 0
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self.treynor = 0
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self.alpha = 0
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self.beta = 0
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self.standardDeviation = 0
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self.negStandardDeviation = 0
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# INDICATOR VALUES
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self.expenseRatio = 0
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self.assetSize = 0
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self.turnover = 0
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self.persistence = [] # [Years, Months]
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# CALCULATED VALUES FOR INDICATORS
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self.correlation = 0
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self.regression = 0
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def setName(self, newName):
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self.name = newName
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def getName(self):
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return self.name
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def getAllDates(self):
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return self.allDates
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def getAllCloseValues(self):
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return self.allCloseValues
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def IEX(self):
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print('IEX')
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url = ''.join(
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('https://api.iextrading.com/1.0/stock/', self.name, '/chart/5y'))
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#link = "https://api.iextrading.com/1.0/stock/spy/chart/5y"
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print("\nSending request to:", url)
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f = requests.get(url)
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json_data = f.text
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if json_data == 'Unknown symbol' or f.status_code == 404:
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print("IEX not available")
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return 'Not available'
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loaded_json = json.loads(json_data)
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listIEX = []
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print("\nFinding all dates given")
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allDates = []
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for i in range(0, len(loaded_json), 1): # If you want to do oldest first
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# for i in range(len(loaded_json)-1, -1, -1):
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line = loaded_json[i]
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date = line['date']
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allDates.append(date)
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listIEX.append(allDates)
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print(len(listIEX[0]), "dates")
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print("\nFinding close values for each date")
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values = []
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for i in range(0, len(loaded_json), 1): # If you want to do oldest first
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# for i in range(len(loaded_json)-1, -1, -1):
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line = loaded_json[i]
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value = line['close']
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values.append(value)
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listIEX.append(values)
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print(len(listIEX[1]), "close values")
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return listIEX
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def AV(self):
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print('Alpha Vantage')
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listAV = []
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url = ''.join(('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=',
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self.name, '&outputsize=full&apikey=', apiAV))
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# https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=MSFT&outputsize=full&apikey=demo
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||||
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||||
print("\nSending request to:", url)
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||||
print("(This will take a while)")
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||||
f = requests.get(url)
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||||
json_data = f.text
|
||||
loaded_json = json.loads(json_data)
|
||||
|
||||
if len(loaded_json) == 1 or f.status_code == 404:
|
||||
print("Alpha Vantage not available")
|
||||
return 'Not available'
|
||||
|
||||
dailyTimeSeries = loaded_json['Time Series (Daily)']
|
||||
listOfDates = list(dailyTimeSeries)
|
||||
# listAV.append(listOfDates)
|
||||
listAV.append(list(reversed(listOfDates)))
|
||||
|
||||
print("\nFinding close values for each date")
|
||||
values = []
|
||||
for i in range(0, len(listOfDates), 1):
|
||||
temp = listOfDates[i]
|
||||
loaded_json2 = dailyTimeSeries[temp]
|
||||
#value = loaded_json2['4. close']
|
||||
value = loaded_json2['5. adjusted close']
|
||||
values.append(value)
|
||||
# listAV.append(values)
|
||||
listAV.append(list(reversed(values)))
|
||||
print(len(listAV[1]), "close values")
|
||||
|
||||
return listAV
|
||||
|
||||
def Tiingo(self):
|
||||
print('Tiingo')
|
||||
token = ''.join(('Token ', apiTiingo))
|
||||
headers = {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': token
|
||||
}
|
||||
url = ''.join(('https://api.tiingo.com/tiingo/daily/', self.name))
|
||||
print("\nSending request to:", url)
|
||||
f = requests.get(url, headers=headers)
|
||||
loaded_json = f.json()
|
||||
if len(loaded_json) == 1 or f.status_code == 404:
|
||||
print("Tiingo not available")
|
||||
return 'Not available'
|
||||
|
||||
listTiingo = []
|
||||
|
||||
print("\nFinding first and last date")
|
||||
firstDate = loaded_json['startDate']
|
||||
lastDate = loaded_json['endDate']
|
||||
print(firstDate, '...', lastDate)
|
||||
|
||||
print("\nFinding all dates given", end='')
|
||||
dates = []
|
||||
values = []
|
||||
url2 = ''.join((url, '/prices?startDate=',
|
||||
firstDate, '&endDate=', lastDate))
|
||||
# https://api.tiingo.com/tiingo/daily/<ticker>/prices?startDate=2012-1-1&endDate=2016-1-1
|
||||
print("\nSending request to:", url2, '\n')
|
||||
requestResponse2 = requests.get(url2, headers=headers)
|
||||
loaded_json2 = requestResponse2.json()
|
||||
for i in range(0, len(loaded_json2)-1, 1):
|
||||
line = loaded_json2[i]
|
||||
dateWithTime = line['date']
|
||||
temp = dateWithTime.split('T00:00:00.000Z')
|
||||
date = temp[0]
|
||||
dates.append(date)
|
||||
|
||||
value = line['close']
|
||||
values.append(value)
|
||||
listTiingo.append(dates)
|
||||
print(len(listTiingo[0]), "dates")
|
||||
|
||||
print("Finding close values for each date")
|
||||
# Used loop from finding dates
|
||||
listTiingo.append(values)
|
||||
print(len(listTiingo[1]), "close values")
|
||||
|
||||
return listTiingo
|
||||
|
||||
def datesAndClose(self):
|
||||
print('\n', Stock.getName(self), sep='')
|
||||
|
||||
# sourceList = ['AV', 'Tiingo', 'IEX'] # Change back to this later
|
||||
sourceList = ['Tiingo', 'IEX', 'AV']
|
||||
# Use each source until you get a value
|
||||
for j in range(0, len(sourceList), 1):
|
||||
source = sourceList[j]
|
||||
print('\nSource being used: ', source)
|
||||
|
||||
if source == 'AV':
|
||||
datesAndCloseList = Stock.AV(self)
|
||||
elif source == 'Tiingo':
|
||||
datesAndCloseList = Stock.Tiingo(self)
|
||||
elif source == 'IEX':
|
||||
datesAndCloseList = Stock.IEX(self)
|
||||
|
||||
if datesAndCloseList != 'Not available':
|
||||
break
|
||||
else:
|
||||
#print(sourceList[j], 'does not have data available')
|
||||
if j == len(sourceList)-1:
|
||||
print('\nNo sources have data for', self.name)
|
||||
return
|
||||
# FIGURE OUT WHAT TO DO HERE
|
||||
|
||||
# Convert dates to datetime
|
||||
allDates = datesAndCloseList[0]
|
||||
for j in range(0, len(allDates), 1):
|
||||
allDates[j] = Functions.stringToDate(allDates[j])
|
||||
datesAndCloseList[0] = allDates
|
||||
|
||||
return datesAndCloseList
|
||||
|
||||
def datesAndClose2(self):
|
||||
print('Shortening list to fit time frame')
|
||||
# Have to do this because if I just make dates = self.allDates & closeValues = self.allCloseValues, then deleting from dates & closeValues also deletes it from self.allDates & self.allCloseValues (I'm not sure why)
|
||||
dates = []
|
||||
closeValues = []
|
||||
for i in range(0, len(self.allDates), 1):
|
||||
dates.append(self.allDates[i])
|
||||
closeValues.append(self.allCloseValues[i])
|
||||
|
||||
firstDate = datetime.datetime.now().date() - datetime.timedelta(
|
||||
days=self.timeFrame[0]*365) - datetime.timedelta(days=self.timeFrame[1]*30)
|
||||
print('\n', self.timeFrame[0], ' years and ',
|
||||
self.timeFrame[1], ' months ago: ', firstDate, sep='')
|
||||
closestDate = Functions.getNearest(dates, firstDate)
|
||||
if closestDate != firstDate:
|
||||
print('Closest date available for', self.name, ':', closestDate)
|
||||
firstDate = closestDate
|
||||
else:
|
||||
print(self.name, 'has a close value for', firstDate)
|
||||
|
||||
# Remove dates in list up to firstDate
|
||||
while dates[0] != firstDate:
|
||||
dates.remove(dates[0])
|
||||
|
||||
# Remove close values until list is same length as dates
|
||||
while len(closeValues) != len(dates):
|
||||
closeValues.remove(closeValues[0])
|
||||
|
||||
datesAndCloseList2 = []
|
||||
datesAndCloseList2.append(dates)
|
||||
datesAndCloseList2.append(closeValues)
|
||||
|
||||
print(len(dates), 'dates')
|
||||
print(len(closeValues), 'close values')
|
||||
|
||||
return datesAndCloseList2
|
||||
|
||||
def unadjustedReturn(self):
|
||||
unadjustedReturn = (float(self.closeValues[len(
|
||||
self.closeValues)-1]/self.closeValues[0])**(1/(self.timeFrame[0]+(self.timeFrame[1])*.1)))-1
|
||||
print('Annual unadjusted return:', unadjustedReturn)
|
||||
return unadjustedReturn
|
||||
|
||||
def beta(self, benchmarkMatchDatesAndCloseValues):
|
||||
beta = numpy.corrcoef(self.closeValuesMatchBenchmark,
|
||||
benchmarkMatchDatesAndCloseValues[1])[0, 1]
|
||||
print('Beta:', beta)
|
||||
return beta
|
||||
|
||||
|
||||
def isConnected():
|
||||
import socket # To check internet connection
|
||||
try:
|
||||
# connect to the host -- tells us if the host is actually reachable
|
||||
socket.create_connection(("www.andrewkdinh.com", 80))
|
||||
print('Internet connection is good!')
|
||||
return True
|
||||
except OSError:
|
||||
# pass
|
||||
print("No internet connection!")
|
||||
return False
|
||||
|
||||
|
||||
def checkPackages():
|
||||
import importlib.util
|
||||
import sys
|
||||
|
||||
packagesInstalled = True
|
||||
packages = ['requests', 'numpy']
|
||||
for i in range(0, len(packages), 1):
|
||||
package_name = packages[i]
|
||||
spec = importlib.util.find_spec(package_name)
|
||||
if spec is None:
|
||||
print(
|
||||
package_name +
|
||||
" is not installed\nPlease type in 'pip install -r requirements.txt' to install all required packages")
|
||||
packagesInstalled = False
|
||||
return packagesInstalled
|
||||
|
||||
|
||||
def benchmarkInit():
|
||||
# Treat benchmark like stock
|
||||
benchmarkTicker = ''
|
||||
while benchmarkTicker == '':
|
||||
benchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
|
||||
benchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
|
||||
print('\nList of benchmarks:', benchmarks)
|
||||
|
||||
# benchmark = str(input('Benchmark to compare to: '))
|
||||
benchmark = 'S&P500'
|
||||
|
||||
for i in range(0, len(benchmarks), 1):
|
||||
if benchmark == benchmarks[i]:
|
||||
benchmarkTicker = benchmarksTicker[i]
|
||||
|
||||
if benchmarkTicker == '':
|
||||
print('Benchmark not found. Please type in a benchmark from the list')
|
||||
|
||||
print(benchmark, ' (', benchmarkTicker, ')', sep='')
|
||||
|
||||
benchmark = Stock()
|
||||
benchmark.setName(benchmarkTicker)
|
||||
|
||||
return benchmark
|
||||
|
||||
|
||||
def stocksInit():
|
||||
listOfStocks = []
|
||||
|
||||
# numberOfStocks = int(input('\nHow many stocks/mutual funds/ETFs would you like to analyze? '))
|
||||
numberOfStocks = 1
|
||||
|
||||
print('\nHow many stocks/mutual funds/ETFs would you like to analyze? ', numberOfStocks)
|
||||
|
||||
for i in range(0, numberOfStocks, 1):
|
||||
print('Stock', i + 1, ': ', end='')
|
||||
#stockName = str(input())
|
||||
|
||||
stockName = 'FBGRX'
|
||||
print(stockName)
|
||||
|
||||
listOfStocks.append(stockName)
|
||||
listOfStocks[i] = Stock()
|
||||
listOfStocks[i].setName(stockName)
|
||||
|
||||
return listOfStocks
|
||||
|
||||
|
||||
def timeFrameInit():
|
||||
print('\nPlease enter the time frame in years and months (30 days)')
|
||||
print("Years: ", end='')
|
||||
#years = int(input())
|
||||
years = 5
|
||||
print(years)
|
||||
print("Months: ", end='')
|
||||
#months = int(input())
|
||||
months = 0
|
||||
print(months)
|
||||
|
||||
timeFrame = []
|
||||
timeFrame.append(years)
|
||||
timeFrame.append(months)
|
||||
return timeFrame
|
||||
|
||||
|
||||
def dataMain(listOfStocks):
|
||||
print('\nGathering dates and close values')
|
||||
for i in range(0, len(listOfStocks), 1):
|
||||
|
||||
datesAndCloseList = Stock.datesAndClose(listOfStocks[i])
|
||||
listOfStocks[i].allDates = datesAndCloseList[0]
|
||||
listOfStocks[i].allCloseValues = datesAndCloseList[1]
|
||||
|
||||
# Clip list to fit time frame
|
||||
datesAndCloseList2 = Stock.datesAndClose2(listOfStocks[i])
|
||||
listOfStocks[i].dates = datesAndCloseList2[0]
|
||||
listOfStocks[i].closeValues = datesAndCloseList2[1]
|
||||
|
||||
|
||||
def returnMain(benchmark, listOfStocks):
|
||||
print('\nCalculating unadjusted return, Sharpe ratio, Sortino ratio, and Treynor ratio\n')
|
||||
print(benchmark.name)
|
||||
benchmark.unadjustedReturn = Stock.unadjustedReturn(benchmark)
|
||||
|
||||
# Make benchmark data global
|
||||
# Maybe remove this later
|
||||
Stock.benchmarkDates = benchmark.dates
|
||||
Stock.benchmarkCloseValues = benchmark.closeValues
|
||||
Stock.benchmarkUnadjustedReturn = benchmark.unadjustedReturn
|
||||
|
||||
for i in range(0, len(listOfStocks), 1):
|
||||
print(listOfStocks[i].name)
|
||||
|
||||
# Make sure each date has a value for both the benchmark and the stock
|
||||
list1 = []
|
||||
list2 = []
|
||||
list1.append(listOfStocks[i].dates)
|
||||
list1.append(listOfStocks[i].closeValues)
|
||||
list2.append(Stock.benchmarkDates)
|
||||
list2.append(Stock.benchmarkCloseValues)
|
||||
temp = Functions.removeExtraDatesAndCloseValues(list1, list2)
|
||||
listOfStocks[i].datesMatchBenchmark = temp[0][0]
|
||||
listOfStocks[i].closeValuesMatchBenchmark = temp[0][1]
|
||||
benchmarkMatchDatesAndCloseValues = temp[1]
|
||||
|
||||
listOfStocks[i].unadjustedReturn = Stock.unadjustedReturn(
|
||||
listOfStocks[i])
|
||||
listOfStocks[i].beta = Stock.beta(
|
||||
listOfStocks[i], benchmarkMatchDatesAndCloseValues)
|
||||
|
||||
|
||||
def main():
|
||||
# Test internet connection
|
||||
internetConnection = isConnected()
|
||||
if not internetConnection:
|
||||
return
|
||||
|
||||
# Check that all required packages are installed
|
||||
packagesInstalled = checkPackages()
|
||||
if not packagesInstalled:
|
||||
return
|
||||
|
||||
# Choose benchmark and makes it class Stock
|
||||
benchmark = benchmarkInit()
|
||||
# Add it to a list to work with other functions
|
||||
benchmarkAsList = []
|
||||
benchmarkAsList.append(benchmark)
|
||||
|
||||
# Asks for stock(s) ticker and makes them class Stock
|
||||
listOfStocks = stocksInit()
|
||||
|
||||
# Determine time frame [Years, Months]
|
||||
timeFrame = timeFrameInit()
|
||||
Stock.timeFrame = timeFrame # Needs to be a global variable for all stocks
|
||||
|
||||
# Gather data for benchmark and stock(s)
|
||||
dataMain(benchmarkAsList)
|
||||
dataMain(listOfStocks)
|
||||
|
||||
# Calculate return for benchmark and stock(s)
|
||||
returnMain(benchmark, listOfStocks)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
|
||||
'''
|
||||
from StockData import StockData
|
||||
from StockReturn import Return
|
||||
|
||||
listOfStocksData = []
|
||||
listOfStocksReturn = []
|
||||
#numberOfStocks = int(input("How many stocks or mutual funds would you like to analyze? ")) # CHANGE BACK LATER
|
||||
# numberOfStocks = int(input("How many stocks or mutual funds would you like to analyze? ")) # CHANGE BACK LATER
|
||||
numberOfStocks = 1
|
||||
for i in range(0, numberOfStocks, 1):
|
||||
print("Stock", i+1, ": ", end='')
|
||||
stockName = str(input())
|
||||
listOfStocksData.append(i)
|
||||
listOfStocksData[i] = StockData()
|
||||
listOfStocksData[i].setName(stockName)
|
||||
# print(listOfStocksData[i].name)
|
||||
print("Stock", i+1, ": ", end='')
|
||||
stockName = str(input())
|
||||
listOfStocksData.append(i)
|
||||
listOfStocksData[i] = StockData()
|
||||
listOfStocksData[i].setName(stockName)
|
||||
# print(listOfStocksData[i].name)
|
||||
|
||||
#listOfStocksReturn.append(i)
|
||||
#listOfStocksReturn[i] = StockReturn()
|
||||
# listOfStocksReturn.append(i)
|
||||
# listOfStocksReturn[i] = StockReturn()
|
||||
|
||||
|
||||
# Decide on a benchmark
|
||||
benchmarkTicker = ''
|
||||
while benchmarkTicker == '':
|
||||
listOfBenchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
|
||||
listOfBenchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
|
||||
print('\nList of benchmarks:', listOfBenchmarks)
|
||||
#benchmark = str(input('Benchmark to compare to: '))
|
||||
benchmark = 'S&P500'
|
||||
listOfBenchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
|
||||
listOfBenchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
|
||||
print('\nList of benchmarks:', listOfBenchmarks)
|
||||
# benchmark = str(input('Benchmark to compare to: '))
|
||||
benchmark = 'S&P500'
|
||||
|
||||
for i in range(0,len(listOfBenchmarks), 1):
|
||||
if benchmark == listOfBenchmarks[i]:
|
||||
benchmarkTicker = listOfBenchmarksTicker[i]
|
||||
i = len(listOfBenchmarks)
|
||||
for i in range(0,len(listOfBenchmarks), 1):
|
||||
if benchmark == listOfBenchmarks[i]:
|
||||
benchmarkTicker = listOfBenchmarksTicker[i]
|
||||
i = len(listOfBenchmarks)
|
||||
|
||||
if benchmarkTicker == '':
|
||||
print('Benchmark not found. Please type in a benchmark from the list')
|
||||
if benchmarkTicker == '':
|
||||
print('Benchmark not found. Please type in a benchmark from the list')
|
||||
|
||||
print('\n', benchmark, ' (', benchmarkTicker, ')', sep='')
|
||||
|
||||
@ -66,10 +520,10 @@ print('Time Frame [years, months]:', timeFrame)
|
||||
|
||||
sumOfListLengths = 0
|
||||
for i in range(0, numberOfStocks, 1):
|
||||
print('\n', listOfStocksData[i].name, sep='')
|
||||
StockData.main(listOfStocksData[i])
|
||||
# Count how many stocks are available
|
||||
sumOfListLengths = sumOfListLengths + len(StockData.returnAllLists(listOfStocksData[i]))
|
||||
print('\n', listOfStocksData[i].name, sep='')
|
||||
StockData.main(listOfStocksData[i])
|
||||
# Count how many stocks are available
|
||||
sumOfListLengths = sumOfListLengths + len(StockData.returnAllLists(listOfStocksData[i]))
|
||||
|
||||
if sumOfListLengths == 0:
|
||||
print("No sources have data for given stocks")
|
||||
@ -77,41 +531,40 @@ if sumOfListLengths == 0:
|
||||
|
||||
# Find return over time using either Jensen's Alpha, Sharpe Ratio, Sortino Ratio, or Treynor Ratio
|
||||
for i in range(0, numberOfStocks, 1):
|
||||
print('\n', listOfStocksData[i].name, sep='')
|
||||
#StockReturn.main(listOfStocksReturn[i])
|
||||
print('\n', listOfStocksData[i].name, sep='')
|
||||
# StockReturn.main(listOfStocksReturn[i])
|
||||
|
||||
|
||||
# Runs correlation or regression study
|
||||
# print(listOfStocksData[0].name, listOfStocksData[0].absFirstLastDates, listOfStocksData[0].finalDatesAndClose)
|
||||
indicatorFound = False
|
||||
while indicatorFound == False:
|
||||
print("1. Expense Ratio\n2. Asset Size\n3. Turnover\n4. Persistence\nWhich indicator would you like to look at? ", end='')
|
||||
print("1. Expense Ratio\n2. Asset Size\n3. Turnover\n4. Persistence\nWhich indicator would you like to look at? ", end='')
|
||||
|
||||
#indicator = str(input()) # CHANGE BACK TO THIS LATER
|
||||
indicator = 'Expense Ratio'
|
||||
print(indicator, end='')
|
||||
# indicator = str(input()) # CHANGE BACK TO THIS LATER
|
||||
indicator = 'Expense Ratio'
|
||||
print(indicator, end='')
|
||||
|
||||
indicatorFound = True
|
||||
print('\n', end='')
|
||||
indicatorFound = True
|
||||
print('\n', end='')
|
||||
|
||||
if indicator == 'Expense Ratio' or indicator == '1' or indicator == 'expense ratio':
|
||||
#from ExpenseRatio import ExpenseRatio
|
||||
print('\nExpense Ratio')
|
||||
if indicator == 'Expense Ratio' or indicator == '1' or indicator == 'expense ratio':
|
||||
# from ExpenseRatio import ExpenseRatio
|
||||
print('\nExpense Ratio')
|
||||
|
||||
elif indicator == 'Asset Size' or indicator == '2' or indicator == 'asset size':
|
||||
print('\nAsset Size')
|
||||
elif indicator == 'Asset Size' or indicator == '2' or indicator == 'asset size':
|
||||
print('\nAsset Size')
|
||||
|
||||
elif indicator == 'Turnover' or indicator == '3' or indicator == 'turnover':
|
||||
print('\nTurnover')
|
||||
elif indicator == 'Turnover' or indicator == '3' or indicator == 'turnover':
|
||||
print('\nTurnover')
|
||||
|
||||
elif indicator == 'Persistence' or indicator == '4' or indicator == 'persistence':
|
||||
print('\nPersistence')
|
||||
elif indicator == 'Persistence' or indicator == '4' or indicator == 'persistence':
|
||||
print('\nPersistence')
|
||||
|
||||
else:
|
||||
indicatorFound = False
|
||||
print('Invalid input, please enter indicator again')
|
||||
else:
|
||||
indicatorFound = False
|
||||
print('Invalid input, please enter indicator again')
|
||||
|
||||
'''
|
||||
stockName = 'IWV'
|
||||
stock1 = Stock(stockName)
|
||||
print("Finding available dates and close values for", stock1.name)
|
||||
|
||||
@ -1,2 +1,2 @@
|
||||
requests==2.21.0
|
||||
numpy==1.15.4
|
||||
requests~=2.21.0
|
||||
numpy~=1.15.4
|
||||
Loading…
Reference in New Issue
Block a user