Finished overhaul of version-1

.gitignore

@@ -1,8 +1,5 @@
 __pycache__/StockData.cpython-37.pyc
 __pycache__/
 *.pyc
-quickstart.py
-creds.json
 test/
-.vscode/
+.vscode/
-listGoogle.py

ExpenseRatio.py

@@ -12,17 +12,19 @@ import numpy
 from urllib.request import urlopen
 import re
 
+
 class ExpenseRatio:
     def __init__(self):
 
 
-def main(): # For testing purposes
+def main():  # For testing purposes
     '''
-	a = [1,2,3]
+        a = [1,2,3]
-	b = [2,4,6]
+        b = [2,4,6]
-	c = numpy.corrcoef(a, b)[0, 1]
+        c = numpy.corrcoef(a, b)[0, 1]
-	print(c)
+        print(c)
     '''
 
+
 if __name__ == "__main__":
-  	main()
+    main()

Functions.py

@@ -1,24 +1,47 @@
 # Python file for general functions
-class Functions:
+def getNearest(items, pivot):
-    def getNearest(items, pivot):
+    return min(items, key=lambda x: abs(x - pivot))
-        return min(items, key=lambda x: abs(x - pivot))
+
-    def stringToDate(date):
+def stringToDate(date):
-        from datetime import datetime
+    from datetime import datetime
+
+    #datetime_object = datetime.strptime('Jun 1 2005  1:33PM', '%b %d %Y %I:%M%p')
+    datetime_object = datetime.strptime(date, '%Y-%m-%d').date()
+    return(datetime_object)
+    '''
+    dateSplit = date.split('-')
+    year = int(dateSplit[0])
+    month = int(dateSplit[1])
+    day = int(dateSplit[2])
+    datetime_object = datetime.date(year, month, day)
+    '''
+    return datetime_object
+
+def removeExtraDatesAndCloseValues(list1, list2):
+    # Returns the two lists but with the extra dates and corresponding close values removed
+    # list = [[dates], [close values]]
+
+    newList1 = [[], []]
+    newList2 = [[], []]
+
+    for i in range(0, len(list1[0]), 1):
+        for j in range(0, len(list2[0]), 1):
+            if list1[0][i] == list2[0][j]:
+                newList1[0].append(list1[0][i])
+                newList2[0].append(list1[0][i])
+                newList1[1].append(list1[1][i])
+                newList2[1].append(list2[1][j])
+                break
+
+    returnList = []
+    returnList.append(newList1)
+    returnList.append(newList2)
+    return returnList
 
-        #datetime_object = datetime.strptime('Jun 1 2005  1:33PM', '%b %d %Y %I:%M%p')
-        datetime_object = datetime.strptime(date, '%Y-%m-%d').date()
-        return(datetime_object)
-        '''
-        dateSplit = date.split('-')
-        year = int(dateSplit[0])
-        month = int(dateSplit[1])
-        day = int(dateSplit[2])
-        datetime_object = datetime.date(year, month, day)
-        '''
-        return datetime_object
 
 def main():
     exit()
 
+
 if __name__ == "__main__":
-  	main()
+    main()

README.md

@@ -16,6 +16,4 @@ To begin, run
 Some ticker values to try:
 SPY, VFINX, AAPL, GOOGL
 
-`$ pip install numpy`
-
 Created by Andrew Dinh from Dr. TJ Owens Gilroy Early College Academy

StockReturn.py

@@ -11,10 +11,11 @@ from StockData import StockData
 import datetime
 from Functions import Functions
 
+
 class Return:
-    def __init__(self, newListOfReturn = [], newTimeFrame = [], newBeta = 0, newStandardDeviation = 0, newNegativeStandardDeviation = 0, newMarketReturn = 0, newSize = 0, newSizeOfNeg = 0, newFirstLastDates = [], newAllLists = [], newAbsFirstLastDates = ''):
+    def __init__(self, newListOfReturn=[], newTimeFrame=[], newBeta=0, newStandardDeviation=0, newNegativeStandardDeviation=0, newMarketReturn=0, newSize=0, newSizeOfNeg=0, newFirstLastDates=[], newAllLists=[], newAbsFirstLastDates=''):
         self.listOfReturn = newListOfReturn
-        self.timeFrame = newTimeFrame # [years, months (30 days)]
+        self.timeFrame = newTimeFrame  # [years, months (30 days)]
         self.beta = newBeta
         self.standardDeviation = newStandardDeviation
         self.negativeStandardDeviation = newNegativeStandardDeviation
@@ -32,12 +33,14 @@ class Return:
     def getFirstLastDates(self, stock):
         firstLastDates = []
         timeFrame = self.timeFrame
-        firstDate = datetime.datetime.now() - datetime.timedelta(days=timeFrame[0]*365)
+        firstDate = datetime.datetime.now(
+        ) - datetime.timedelta(days=timeFrame[0]*365)
         firstDate = firstDate - datetime.timedelta(days=timeFrame[1]*30)
-        firstDate = ''.join((str(firstDate.year),'-', str(firstDate.month), '-', str(firstDate.day)))
+        firstDate = ''.join(
+            (str(firstDate.year), '-', str(firstDate.month), '-', str(firstDate.day)))
 
         lastDate = StockData.returnAbsFirstLastDates(stock)[1]
-        #print(lastDate)
+        # print(lastDate)
         firstLastDates.append(firstDate)
         firstLastDates.append(lastDate)
         return firstLastDates
@@ -60,19 +63,21 @@ class Return:
 
         if firstDateExists == False:
             print("Could not find first date. Changing first date to closest date")
-            tempDate = Functions.stringToDate(firstDate) # Change to datetime
+            tempDate = Functions.stringToDate(firstDate)  # Change to datetime
             print('Original first date:', tempDate)
             #tempDate = datetime.date(2014,1,17)
-            newFirstDate = Functions.getNearest(finalDatesAndClose2[0], tempDate)
+            newFirstDate = Functions.getNearest(
+                finalDatesAndClose2[0], tempDate)
             print('New first date:', newFirstDate)
             firstDate = str(newFirstDate)
 
         if lastDateExists == False:
             print("Could not find final date. Changing final date to closest date")
-            tempDate2 = Functions.stringToDate(lastDate) # Change to datetime
+            tempDate2 = Functions.stringToDate(lastDate)  # Change to datetime
             print('Original final date:', tempDate2)
             #tempDate2 = datetime.date(2014,1,17)
-            newLastDate = Functions.getNearest(finalDatesAndClose2[0], tempDate2)
+            newLastDate = Functions.getNearest(
+                finalDatesAndClose2[0], tempDate2)
             print('New final date:', newLastDate)
             lastDate = str(newLastDate)
 
@@ -97,7 +102,8 @@ class Return:
 
         print('Close values:', firstClose, '...', lastClose)
         fullUnadjustedReturn = float(lastClose/firstClose)
-        unadjustedReturn = fullUnadjustedReturn**(1/(self.timeFrame[0]+(self.timeFrame[1])*.1))
+        unadjustedReturn = fullUnadjustedReturn**(
+            1/(self.timeFrame[0]+(self.timeFrame[1])*.1))
         return unadjustedReturn
 
     def getBeta(self):
@@ -113,9 +119,9 @@ class Return:
         for i in range(0, len(finalDates), 1):
             if finalDates[i] == str(firstDate):
                 firstClose = finalClose[i]
-55ggbh
+
-        #list1 = 
+        # list1 =
-        list2 = [1,2,4,1]
+        list2 = [1, 2, 4, 1]
 
         print(numpy.corrcoef(list1, list2)[0, 1])
 
@@ -138,7 +144,7 @@ class Return:
         timeFrameMonth = 0
         print(timeFrameMonth)
         self.timeFrame.append(timeFrameMonth)
-        #print(self.timeFrame)
+        # print(self.timeFrame)
         self.firstLastDates = Return.getFirstLastDates(self, stock)
         print('Dates: ', self.firstLastDates)
 
@@ -149,10 +155,10 @@ class Return:
         print('\nGetting unadjusted return')
         unadjustedReturn = Return.getUnadjustedReturn(self, stock)
         self.listOfReturn.append(unadjustedReturn)
-        print('Average annual return for the past', self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
+        print('Average annual return for the past',
+              self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
         print((self.listOfReturn[0]-1)*100, '%', sep='')
 
-
     def main(self, stock):
         print('Beginning StockReturn.py')
 
@@ -169,12 +175,14 @@ class Return:
         print('\nGetting unadjusted return')
         unadjustedReturn = Return.getUnadjustedReturn(self, stock)
         self.listOfReturn.append(unadjustedReturn)
-        print('Average annual return for the past', self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
+        print('Average annual return for the past',
+              self.timeFrame[0], 'years and', self.timeFrame[1], 'months: ', end='')
         print((self.listOfReturn[0]-1)*100, '%', sep='')
 
         #print('\nGetting beta')
         #beta = Return.getBeta(self, stock)
 
+
 def main():
     stockName = 'spy'
     stock1 = StockData(stockName)
@@ -186,5 +194,6 @@ def main():
 
     Return.main(stock1Return, stock1)
 
+
 if __name__ == "__main__":
-  	main()
+    main()

listGoogle.py

@@ -1,54 +0,0 @@
-# https://support.google.com/docs/answer/3093281?hl=en
-# 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.
-
-import gspread, time, webbrowser, msvcrt
-from oauth2client.service_account import ServiceAccountCredentials
-
-def main():
-    scope = ['https://spreadsheets.google.com/feeds',
-             'https://www.googleapis.com/auth/drive']
-
-    credentials = ServiceAccountCredentials.from_json_keyfile_name('creds.json', scope)
-
-    gc = gspread.authorize(credentials)
-    '''
-    # Just by ID:
-    #sheet = gc.open_by_key('1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM')
-    sheet = gc.open_by_url('https://docs.google.com/spreadsheets/d/1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM')
-    worksheet = sheet.get_worksheet(0)
-    worksheet.update_acell('B1', 'bingo!')
-    #worksheet.update_cell(1, 2, 'Bingo!')
-    val = worksheet.acell('B1').value
-    #val = worksheet.cell(1, 2).value
-    print(val)
-    '''
-    url = 'https://docs.google.com/spreadsheets/d/1YS8qBQCXKNfSgQgXeUdSGOd6lM2wm-inV0_1YE36vQM'
-    surl = 'https://www.andrewkdinh.com/u/listGoogle'
-    print("Opening", url)
-    #webbrowser.open(surl)
-    sheet = gc.open_by_url(url)
-    worksheet = sheet.get_worksheet(0)
-    print('Writing Google Finance function to A1')
-    worksheet.update_cell(1, 1, '=GOOGLEFINANCE("GOOG", "price", DATE(2014,1,1), DATE(2014,12,31), "DAILY")')
-    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', 
-        '\nFile > Download as > Comma-separated values(.csv,currentsheet)')
-    print("If the link did not open, please go to", surl)
-    print("Press any key to continue")
-    #time.sleep(45)
-    '''
-    for i in range(60, 0, -1):
-        print(i, end='\r')
-        time.sleep(1)
-    '''
-    waiting = True
-    while waiting == True:
-        if msvcrt.kbhit():
-            waiting = False
-            
-    print("e")
-
-    #val = worksheet.acell('A1').value
-    #print(val)
-
-if __name__ == '__main__':
-    main()

main.py

@@ -1,55 +1,509 @@
 # main.py
 # Andrew Dinh
-# Python 3.6.1
+# Python 3.6.7
-# Description:
-'''
-Asks users for mutual funds/stocks to compare
-Asks to be compared (expense ratio, turnover, market capitalization, or persistence)
-Asks for time period (Possibly: 1 year, 5 years, 10 years)
-Makes the mutual funds as class Stock
-Gets data from each API
-Compare and contrast dates and end changeOverTime for set time period
-  	NOTES: Later can worry about getting close values to make a graph or something
-Gives correlation value using equation at the end (from 0 to 1)
 
-FIRST TESTING WITH EXPENSE RATIO
+import requests
+import json
+import datetime
+import numpy
+import Functions
+
+# API Keys
+apiAV = 'O42ICUV58EIZZQMU'
+# apiBarchart = 'a17fab99a1c21cd6f847e2f82b592838'
+apiBarchart = 'f40b136c6dc4451f9136bb53b9e70ffa'
+apiTiingo = '2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8'
+apiTradier = 'n26IFFpkOFRVsB5SNTVNXicE5MPD'
+# If you're going to take these API keys and abuse it, you should really reconsider your life priorities
+
+'''
+API Keys:
+    Alpha Vantage API Key: O42ICUV58EIZZQMU
+    Barchart API Key: a17fab99a1c21cd6f847e2f82b592838 
+        Possible other one? f40b136c6dc4451f9136bb53b9e70ffa
+        150 getHistory queries per day
+    Tiingo API Key: 2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8
+    Tradier API Key: n26IFFpkOFRVsB5SNTVNXicE5MPD
+        Monthly Bandwidth = 5 GB
+        Hourly Requests = 500
+        Daily Requests = 20,000
+        Symbol Requests = 500
+
+    Mutual funds:
+    Yes: Alpha Vantage, Tiingo
+    No: IEX, Barchart
 '''
 
+
+class Stock:
+
+    # GLOBAL VARIABLES
+    timeFrame = []
+    benchmarkDates = []
+    benchmarkCloseValues = []
+    benchmarkUnadjustedReturn = 0
+
+    def __init__(self):
+        # BASIC DATA
+        self.name = ''  # Ticker symbol
+        self.allDates = []
+        self.allCloseValues = []
+        self.dates = []
+        self.closeValues = []
+        self.datesMatchBenchmark = []
+        self.closeValuesMatchBenchmark = []
+
+        # CALCULATED RETURN
+        self.unadjustedReturn = 0
+        self.sortino = 0
+        self.sharpe = 0
+        self.treynor = 0
+        self.alpha = 0
+        self.beta = 0
+        self.standardDeviation = 0
+        self.negStandardDeviation = 0
+
+        # INDICATOR VALUES
+        self.expenseRatio = 0
+        self.assetSize = 0
+        self.turnover = 0
+        self.persistence = []  # [Years, Months]
+
+        # CALCULATED VALUES FOR INDICATORS
+        self.correlation = 0
+        self.regression = 0
+
+    def setName(self, newName):
+        self.name = newName
+
+    def getName(self):
+        return self.name
+
+    def getAllDates(self):
+        return self.allDates
+
+    def getAllCloseValues(self):
+        return self.allCloseValues
+
+    def IEX(self):
+        print('IEX')
+        url = ''.join(
+            ('https://api.iextrading.com/1.0/stock/', self.name, '/chart/5y'))
+        #link = "https://api.iextrading.com/1.0/stock/spy/chart/5y"
+        print("\nSending request to:", url)
+        f = requests.get(url)
+        json_data = f.text
+        if json_data == 'Unknown symbol' or f.status_code == 404:
+            print("IEX not available")
+            return 'Not available'
+        loaded_json = json.loads(json_data)
+        listIEX = []
+
+        print("\nFinding all dates given")
+        allDates = []
+        for i in range(0, len(loaded_json), 1):  # If you want to do oldest first
+            # for i in range(len(loaded_json)-1, -1, -1):
+            line = loaded_json[i]
+            date = line['date']
+            allDates.append(date)
+        listIEX.append(allDates)
+        print(len(listIEX[0]), "dates")
+
+        print("\nFinding close values for each date")
+        values = []
+        for i in range(0, len(loaded_json), 1):  # If you want to do oldest first
+            # for i in range(len(loaded_json)-1, -1, -1):
+            line = loaded_json[i]
+            value = line['close']
+            values.append(value)
+        listIEX.append(values)
+        print(len(listIEX[1]), "close values")
+
+        return listIEX
+
+    def AV(self):
+        print('Alpha Vantage')
+        listAV = []
+        url = ''.join(('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=',
+                       self.name, '&outputsize=full&apikey=', apiAV))
+        # https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=MSFT&outputsize=full&apikey=demo
+
+        print("\nSending request to:", url)
+        print("(This will take a while)")
+        f = requests.get(url)
+        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='')
+      print("Stock", i+1, ": ", end='')
-  	stockName = str(input())
+      stockName = str(input())
-  	listOfStocksData.append(i)
+      listOfStocksData.append(i)
-  	listOfStocksData[i] = StockData()
+      listOfStocksData[i] = StockData()
-  	listOfStocksData[i].setName(stockName)
+      listOfStocksData[i].setName(stockName)
-	# print(listOfStocksData[i].name)
+    # print(listOfStocksData[i].name)
 
-	#listOfStocksReturn.append(i)
+    # listOfStocksReturn.append(i)
-	#listOfStocksReturn[i] = StockReturn()
+    # listOfStocksReturn[i] = StockReturn()
 
 
 # Decide on a benchmark
 benchmarkTicker = ''
 while benchmarkTicker == '':
-	listOfBenchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
+    listOfBenchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
-	listOfBenchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
+    listOfBenchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
-	print('\nList of benchmarks:', listOfBenchmarks)
+    print('\nList of benchmarks:', listOfBenchmarks)
-	#benchmark = str(input('Benchmark to compare to: '))
+    # benchmark = str(input('Benchmark to compare to: '))
-	benchmark = 'S&P500'
+    benchmark = 'S&P500'
 
-	for i in range(0,len(listOfBenchmarks), 1):
+    for i in range(0,len(listOfBenchmarks), 1):
-		if benchmark == listOfBenchmarks[i]:
+        if benchmark == listOfBenchmarks[i]:
-			benchmarkTicker = listOfBenchmarksTicker[i]
+            benchmarkTicker = listOfBenchmarksTicker[i]
-			i = len(listOfBenchmarks)
+            i = len(listOfBenchmarks)
 
-	if benchmarkTicker == '':
+    if benchmarkTicker == '':
-		print('Benchmark not found. Please type in a benchmark from the list')
+        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='')
+      print('\n', listOfStocksData[i].name, sep='')
-  	StockData.main(listOfStocksData[i])
+      StockData.main(listOfStocksData[i])
-  	# Count how many stocks are available
+      # Count how many stocks are available
-  	sumOfListLengths = sumOfListLengths + len(StockData.returnAllLists(listOfStocksData[i]))
+      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='')
+    print('\n', listOfStocksData[i].name, sep='')
-	#StockReturn.main(listOfStocksReturn[i])
+    # 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='')
 
-	indicatorFound = True
+    # indicator = str(input()) # CHANGE BACK TO THIS LATER
-	print('\n', end='')
+    indicator = 'Expense Ratio'
+    print(indicator, end='')
 
-	if indicator == 'Expense Ratio' or indicator == '1' or indicator == 'expense ratio':
+    indicatorFound = True
-        #from ExpenseRatio import ExpenseRatio
+    print('\n', end='')
-		print('\nExpense Ratio')
 
-	elif indicator == 'Asset Size' or indicator == '2' or indicator == 'asset size':
+    if indicator == 'Expense Ratio' or indicator == '1' or indicator == 'expense ratio':
-		print('\nAsset Size')
+        # from ExpenseRatio import ExpenseRatio
+        print('\nExpense Ratio')
 
-	elif indicator == 'Turnover' or indicator == '3' or indicator == 'turnover':
+    elif indicator == 'Asset Size' or indicator == '2' or indicator == 'asset size':
-		print('\nTurnover')
+        print('\nAsset Size')
 
-	elif indicator == 'Persistence' or indicator == '4' or indicator == 'persistence':
+    elif indicator == 'Turnover' or indicator == '3' or indicator == 'turnover':
-		print('\nPersistence')
+        print('\nTurnover')
 
-	else:
+    elif indicator == 'Persistence' or indicator == '4' or indicator == 'persistence':
-		indicatorFound = False
+        print('\nPersistence')
-		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)

requirements.txt

@@ -1,2 +1,2 @@
-requests==2.21.0
+requests~=2.21.0
-numpy==1.15.4
+numpy~=1.15.4