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Andrew Dinh
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README.md
10
README.md
@ -1,23 +1,17 @@
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# Mutual Fund Indicators
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A project to determine indicators of overperforming mutual funds.
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This project is written in Python and will examine market capitalization, persistence, turnover, and expense ratios.
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This project is written in Python 3 and will examine market capitalization, persistence, turnover, and expense ratios.
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### Prerequisites
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`$ pip install -r requirements.txt`
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or
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`$ pip install requests`
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`$ pip install numpy`
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### Quickstart
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To begin, run
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`$ python(3) main.py`
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`$ python main.py`
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Some ticker values to try:
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SPY, VFINX, AAPL, GOOGL
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@ -477,6 +477,8 @@ class StockData:
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return False
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def main(self):
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print('Beginning StockData.py')
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import importlib.util, sys # To check whether a package is installed
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packages = ['requests']
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@ -495,6 +497,7 @@ class StockData:
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self.allLists = []
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print('\nNOTE: Only IEX and Alpha Vantage support adjusted returns')
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print('NOTE: Only Alpha Vantage and Tiingo support mutual fund data')
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# IEX
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print("\nIEX")
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@ -526,7 +529,7 @@ class StockData:
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#print(self.allLists)
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#print(listOfFirstLastDates)
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if (len(self.allLists) > 0):
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print("\n")
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print("\n", end='')
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print(len(self.allLists), "available source(s) for", self.name)
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self.absFirstLastDates = StockData.getFirstLastDate(self, listOfFirstLastDates)
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print("\nThe absolute first date with close values is:", self.absFirstLastDates[0])
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@ -541,7 +544,7 @@ class StockData:
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print(len(finalDates), "unique dates:", finalDates[len(finalDates)-1], "...", finalDates[0])
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print(len(finalClose), "close values:", finalClose[len(finalClose)-1], "...", finalClose[0])
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print("\nConverting list of final dates to datetime")
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print("\nConverting list of final dates to datetime\n")
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self.finalDatesAndClose2 = StockData.datetimeDates(self)
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#print(self.finalDatesAndClose2[0][0])
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@ -4,7 +4,7 @@
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# Description:
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'''
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Calculates return for each stock from the lists from ExpenseRatio.py
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listOfReturn = [Unadjsted Return, Sharpe Ratio, Sortino Ratio, Treynor Ratio, Jensen's Alpha]
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listOfReturn = [Unadjusted Return, Sharpe Ratio, Sortino Ratio, Treynor Ratio, Jensen's Alpha]
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'''
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from StockData import StockData
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@ -14,7 +14,7 @@ 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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self.listOfReturn = newListOfReturn
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self.timeFrame = newTimeFrame # [year, 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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@ -23,6 +23,12 @@ class Return:
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self.sizeOfNeg = newSizeOfNeg
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self.firstLastDates = newFirstLastDates
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def returnTimeFrame(self):
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return self.timeFrame
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def setTimeFrame(self, newTimeFrame):
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self.timeFrame = newTimeFrame
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def getFirstLastDates(self, stock):
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firstLastDates = []
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timeFrame = self.timeFrame
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@ -77,7 +83,6 @@ class Return:
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def getUnadjustedReturn(self, stock):
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finalDatesAndClose = StockData.returnFinalDatesAndClose(stock)
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finalDatesAndClose2 = StockData.returnFinalDatesAndClose2(stock)
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firstDate = self.firstLastDates[0]
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lastDate = self.firstLastDates[1]
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finalDates = finalDatesAndClose[0]
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@ -91,15 +96,34 @@ class Return:
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i = len(finalDates)
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print('Close values:', firstClose, '...', lastClose)
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unadjustedReturn = float(lastClose/firstClose)
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unadjustedReturn = unadjustedReturn * 100
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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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return unadjustedReturn
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# def getBeta(self, timeFrame):
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def getBeta(self):
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# Can be calculated with correlation
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import numpy as np
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finalDatesAndClose = StockData.returnFinalDatesAndClose(stock)
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firstDate = self.firstLastDates[0]
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lastDate = self.firstLastDates[1]
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finalDates = finalDatesAndClose[0]
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finalClose = finalDatesAndClose[1]
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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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print(numpy.corrcoef(list1, list2)[0, 1])
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# def getStandardDeviation(self, timeFrame):
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def main(self, stock):
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def mainBenchmark(self, stock):
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print('Beginning StockReturn.py')
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# Find date to start from and last date
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self.timeFrame = []
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self.listOfReturn = []
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@ -125,8 +149,31 @@ 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(self.listOfReturn[0])
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print(self.listOfReturn[0]/timeFrameYear, '%')
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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((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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# Find date to start from and last date
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self.listOfReturn = []
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self.firstLastDates = Return.getFirstLastDates(self, stock)
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print('Dates: ', self.firstLastDates)
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print('\nMaking sure dates are within list...')
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self.firstLastDates = Return.getFirstLastDates2(self, stock)
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print('New dates: ', self.firstLastDates)
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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((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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@ -135,6 +182,8 @@ def main():
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StockData.main(stock1)
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stock1Return = Return()
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Return.setTimeFrame(stock1Return, [5, 0])
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Return.main(stock1Return, stock1)
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if __name__ == "__main__":
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73
main.py
73
main.py
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'''
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from StockData import StockData
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from StockReturn import Return
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listOfStocks = []
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numberOfStocks = int(input("How many stocks or mutual funds would you like to analyze? "))
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listOfStocksData = []
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listOfStocksReturn = []
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#numberOfStocks = int(input("How many stocks or mutual funds would you like to analyze? ")) # CHANGE BACK LATER
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numberOfStocks = 1
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for i in range(0, numberOfStocks, 1):
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print("Stock", i+1, ": ", end='')
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stockName = str(input())
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listOfStocks.append(i)
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listOfStocks[i] = StockData()
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listOfStocks[i].setName(stockName)
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#print(listOfStocks[i].name)
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listOfStocksData.append(i)
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listOfStocksData[i] = StockData()
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listOfStocksData[i].setName(stockName)
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# print(listOfStocksData[i].name)
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#listOfStocksReturn.append(i)
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#listOfStocksReturn[i] = StockReturn()
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# Decide on a benchmark
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benchmarkTicker = ''
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while benchmarkTicker == '':
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listOfBenchmarks = ['S&P500', 'DJIA', 'Russell 3000', 'MSCI EAFE']
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listOfBenchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
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print('\nList of benchmarks:', listOfBenchmarks)
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#benchmark = str(input('Benchmark to compare to: '))
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benchmark = 'S&P500'
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for i in range(0,len(listOfBenchmarks), 1):
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if benchmark == listOfBenchmarks[i]:
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benchmarkTicker = listOfBenchmarksTicker[i]
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i = len(listOfBenchmarks)
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if benchmarkTicker == '':
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print('Benchmark not found. Please type in a benchmark from the list')
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print('\n', benchmark, ' (', benchmarkTicker, ')', sep='')
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benchmarkName = str(benchmark)
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benchmark = StockData()
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benchmark.setName(benchmarkName)
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StockData.main(benchmark)
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benchmarkReturn = Return()
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Return.mainBenchmark(benchmarkReturn, benchmark)
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timeFrame = Return.returnTimeFrame(benchmarkReturn)
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print('Time Frame [years, months]:', timeFrame)
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sumOfListLengths = 0
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for i in range(0, numberOfStocks, 1):
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print(listOfStocks[i].name)
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StockData.main(listOfStocks[i])
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print('\n', listOfStocksData[i].name, sep='')
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StockData.main(listOfStocksData[i])
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# Count how many stocks are available
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temp = StockData.returnAllLists(listOfStocks[i])
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sumOfListLengths = sumOfListLengths + len(temp)
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sumOfListLengths = sumOfListLengths + len(StockData.returnAllLists(listOfStocksData[i]))
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if sumOfListLengths == 0:
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print("No sources have data for given stocks")
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exit()
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# Find return over time using either Jensen's Alpha, Sharpe Ratio, Sortino Ratio, or Treynor Ratio
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#from StockReturn import Return
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for i in range(0, numberOfStocks, 1):
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print('\n', listOfStocksData[i].name, sep='')
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#StockReturn.main(listOfStocksReturn[i])
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# Runs correlation or regression study
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#print(listOfStocks[0].name, listOfStocks[0].absFirstLastDates, listOfStocks[0].finalDatesAndClose)
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# print(listOfStocksData[0].name, listOfStocksData[0].absFirstLastDates, listOfStocksData[0].finalDatesAndClose)
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indicatorFound = False
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while indicatorFound == False:
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print("\n1. Expense Ratio\n2. Asset Size\n3. Turnover\n4. Persistence\nWhich indicator would you like to look at? ", end='')
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indicator = str(input())
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print("1. Expense Ratio\n2. Asset Size\n3. Turnover\n4. Persistence\nWhich indicator would you like to look at? ", end='')
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#indicator = str(input()) # CHANGE BACK TO THIS LATER
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indicator = 'Expense Ratio'
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print(indicator, end='')
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indicatorFound = True
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print('\n', end='')
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if indicator == 'Expense Ratio' or indicator == '1' or indicator == 'expense ratio':
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#from ExpenseRatio import ExpenseRatio
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@ -66,7 +109,7 @@ while indicatorFound == False:
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else:
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indicatorFound = False
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print('\nInvalid input, please enter indicator again')
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print('Invalid input, please enter indicator again')
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'''
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stockName = 'IWV'
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