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fund-indicators/main.py
Andrew Dinh d398924c0d Finished basic functionalities
2019-02-14 12:17:22 -08:00

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# https://github.com/andrewkdinh/fund-indicators
# Determine indicators of overperforming mutual funds
# Andrew Dinh
# Python 3.6.7
# Required
import requests
import json
import datetime
import Functions
import numpy as np
# Required for linear regression
import matplotlib.pyplot as plt
import sys
# Optional
import requests_cache
# https://requests-cache.readthedocs.io/en/lates/user_guide.html
requests_cache.install_cache(
'requests_cache', backend='sqlite', expire_after=43200) # 12 hours
# API Keys
apiAV = 'O42ICUV58EIZZQMU'
# apiBarchart = 'a17fab99a1c21cd6f847e2f82b592838'
apiBarchart = 'f40b136c6dc4451f9136bb53b9e70ffa'
apiTiingo = '2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8'
apiTradier = 'n26IFFpkOFRVsB5SNTVNXicE5MPD'
apiQuandl = 'KUh3U3hxke9tCimjhWEF'
# apiIntrinio = 'OmNmN2E5YWI1YzYxN2Q4NzEzZDhhOTgwN2E2NWRhOWNl'
# 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
Quandl API Key: KUh3U3hxke9tCimjhWEF
Intrinio API Key: OmNmN2E5YWI1YzYxN2Q4NzEzZDhhOTgwN2E2NWRhOWNl
Mutual funds?
Yes: Alpha Vantage, Tiingo
No: IEX, Barchart
Adjusted?
Yes: Alpha Vantage, IEX
No: Tiingo
'''
class Stock:
# GLOBAL VARIABLES
timeFrame = 0
riskFreeRate = 0
indicator = ''
# BENCHMARK VALUES
benchmarkDates = []
benchmarkCloseValues = []
benchmarkAverageAnnualReturn = 0
benchmarkStandardDeviation = 0
# INDICATOR VALUES
indicatorCorrelation = []
indicatorRegression = []
def __init__(self):
# BASIC DATA
self.name = '' # Ticker symbol
self.allDates = []
self.allCloseValues = []
self.dates = []
self.closeValues = []
self.datesMatchBenchmark = []
self.closeValuesMatchBenchmark = []
# CALCULATED RETURN
self.averageAnnualReturn = 0
self.annualReturn = []
self.sharpe = 0
self.sortino = 0
self.treynor = 0
self.alpha = 0
self.beta = 0
self.standardDeviation = 0
self.downsideDeviation = 0
self.kurtosis = 0
self.skewness = 0 # Not sure if I need this
self.linearRegression = [] # for y=mx+b, this list has [m,b]
self.indicatorValue = ''
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)
Functions.fromCache(f)
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)
f = requests.get(url)
Functions.fromCache(f)
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(float(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)
Functions.fromCache(f)
loaded_json = f.json()
if len(loaded_json) == 1 or f.status_code == 404 or loaded_json['startDate'] == None:
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)
Functions.fromCache(requestResponse2)
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', 'IEX', 'Tiingo']
# sourceList = ['IEX', 'Tiingo', '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:
if j == len(sourceList)-1:
print('\nNo sources have data for', self.name)
print('Removing', self.name,
'from list of stocks to ensure compatibility later')
return 'Not available'
# 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 datesAndCloseFitTimeFrame(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*365)
print('\n', self.timeFrame, ' years 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 calcAverageAnnualReturn(self): # pylint: disable=E0202
# averageAnnualReturn = (float(self.closeValues[len(self.closeValues)-1]/self.closeValues[0])**(1/(self.timeFrame)))-1
# averageAnnualReturn = averageAnnualReturn * 100
averageAnnualReturn = sum(self.annualReturn)/self.timeFrame
print('Average annual return:', averageAnnualReturn)
return averageAnnualReturn
def calcAnnualReturn(self):
annualReturn = []
# Calculate annual return in order from oldest to newest
annualReturn = []
for i in range(0, self.timeFrame, 1):
firstDate = datetime.datetime.now().date() - datetime.timedelta(
days=(self.timeFrame-i)*365)
secondDate = datetime.datetime.now().date() - datetime.timedelta(
days=(self.timeFrame-i-1)*365)
# Find closest dates to firstDate and lastDate
firstDate = Functions.getNearest(self.dates, firstDate)
secondDate = Functions.getNearest(self.dates, secondDate)
if firstDate == secondDate:
print('Closest date is', firstDate,
'which is after the given time frame.')
return 'Not available'
# Get corresponding close values and calculate annual return
for i in range(0, len(self.dates), 1):
if self.dates[i] == firstDate:
firstClose = self.closeValues[i]
elif self.dates[i] == secondDate:
secondClose = self.closeValues[i]
break
annualReturnTemp = (secondClose/firstClose)-1
annualReturnTemp = annualReturnTemp * 100
annualReturn.append(annualReturnTemp)
print('Annual return over the past',
self.timeFrame, 'years:', annualReturn)
return annualReturn
def calcCorrelation(self, closeList):
correlation = np.corrcoef(
self.closeValuesMatchBenchmark, closeList)[0, 1]
print('Correlation with benchmark:', correlation)
return correlation
def calcStandardDeviation(self):
numberOfValues = self.timeFrame
mean = self.averageAnnualReturn
standardDeviation = (
(sum((self.annualReturn[x]-mean)**2 for x in range(0, numberOfValues, 1)))/(numberOfValues-1))**(1/2)
print('Standard Deviation:', standardDeviation)
return standardDeviation
def calcDownsideDeviation(self):
numberOfValues = self.timeFrame
targetReturn = self.averageAnnualReturn
downsideDeviation = (
(sum(min(0, (self.annualReturn[x]-targetReturn))**2 for x in range(0, numberOfValues, 1)))/(numberOfValues-1))**(1/2)
print('Downside Deviation:', downsideDeviation)
return downsideDeviation
def calcKurtosis(self):
numberOfValues = self.timeFrame
mean = self.averageAnnualReturn
kurtosis = (sum((self.annualReturn[x]-mean)**4 for x in range(
0, numberOfValues, 1)))/((numberOfValues-1)*(self.standardDeviation ** 4))
print('Kurtosis:', kurtosis)
return kurtosis
def calcSkewness(self):
numberOfValues = self.timeFrame
mean = self.averageAnnualReturn
skewness = (sum((self.annualReturn[x]-mean)**3 for x in range(
0, numberOfValues, 1)))/((numberOfValues-1)*(self.standardDeviation ** 3))
print('Skewness:', skewness)
return skewness
def calcBeta(self):
beta = self.correlation * \
(self.standardDeviation/Stock.benchmarkStandardDeviation)
print('Beta:', beta)
return beta
def calcAlpha(self):
alpha = self.averageAnnualReturn - \
(Stock.riskFreeRate+((Stock.benchmarkAverageAnnualReturn -
Stock.riskFreeRate) * self.beta))
print('Alpha:', alpha)
return alpha
def calcSharpe(self):
sharpe = (self.averageAnnualReturn - Stock.riskFreeRate) / \
self.standardDeviation
print('Sharpe Ratio:', sharpe)
return sharpe
def calcSortino(self):
sortino = (self.averageAnnualReturn - self.riskFreeRate) / \
self.downsideDeviation
print('Sortino Ratio:', sortino)
return sortino
def calcTreynor(self):
treynor = (self.averageAnnualReturn - Stock.riskFreeRate)/self.beta
print('Treynor Ratio:', treynor)
return treynor
def calcLinearRegression(self):
dates = self.dates
y = self.closeValues
# First change dates to integers (days from first date)
x = datesToDays(dates)
x = np.array(x)
y = np.array(y)
# Estimate coefficients
# number of observations/points
n = np.size(x)
# mean of x and y vector
m_x, m_y = np.mean(x), np.mean(y)
# calculating cross-deviation and deviation about x
SS_xy = np.sum(y*x) - n*m_y*m_x
SS_xx = np.sum(x*x) - n*m_x*m_x
# calculating regression coefficients
b_1 = SS_xy / SS_xx
b_0 = m_y - b_1*m_x
b = [b_0, b_1]
formula = ''.join(
('y = ', str(round(float(b[0]), 2)), 'x + ', str(round(float(b[1]), 2))))
print('Linear regression formula:', formula)
# Stock.plot_regression_line(self, x, y, b)
regression = []
regression.append(b[0])
regression.append(b[1])
return regression
def plot_regression_line(self, x, y, b):
# plotting the actual points as scatter plot
plt.scatter(self.dates, y, color="m",
marker="o", s=30)
# predicted response vector
y_pred = b[0] + b[1]*x
# plotting the regression line
plt.plot(self.dates, y_pred, color="g")
# putting labels
plt.title(self.name)
plt.xlabel('Dates')
plt.ylabel('Close Values')
# function to show plot
plt.show(block=False)
for i in range(3, 0, -1):
if i == 1:
sys.stdout.write('Keeping plot open for ' +
str(i) + ' second \r')
else:
sys.stdout.write('Keeping plot open for ' +
str(i) + ' seconds \r')
plt.pause(1)
sys.stdout.flush()
plt.close()
def datesToDays(dates):
days = []
firstDate = dates[0]
days.append(0)
for i in range(1, len(dates), 1):
# Calculate days from first date to current date
daysDiff = (dates[i]-firstDate).days
days.append(daysDiff)
return days
def isConnected():
import socket # To check internet connection
print('Checking 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:')
for i in range(0, len(benchmarks), 1):
print(str(i+1) + '. ' +
benchmarks[i] + ' (' + benchmarksTicker[i] + ')')
benchmark = str(input('Please choose a benchmark from the list: '))
# benchmark = 'SPY' # TESTING
if Functions.stringIsInt(benchmark) == True:
if int(benchmark) <= len(benchmarks):
benchmarkInt = int(benchmark)
benchmark = benchmarks[benchmarkInt-1]
benchmarkTicker = benchmarksTicker[benchmarkInt-1]
else:
for i in range(0, len(benchmarks), 1):
if benchmark == benchmarks[i]:
benchmarkTicker = benchmarksTicker[i]
break
if benchmark == benchmarksTicker[i] or benchmark == benchmarksTicker[i].lower():
benchmark = benchmarks[i]
benchmarkTicker = benchmarksTicker[i]
break
if benchmarkTicker == '':
print('Benchmark not found. Please use a benchmark from the list')
print(benchmark, ' (', benchmarkTicker, ')', sep='')
benchmark = Stock()
benchmark.setName(benchmarkTicker)
return benchmark
def stocksInit():
listOfStocks = []
isInteger = False
while isInteger == False:
temp = input('\nNumber of stocks to analyze (2 minimum): ')
isInteger = Functions.stringIsInt(temp)
if isInteger == True:
numberOfStocks = int(temp)
else:
print('Please type an integer')
# numberOfStocks = 5 # TESTING
# print('How many stocks would you like to analyze? ', numberOfStocks)
print('\nThis program can analyze stocks (GOOGL), mutual funds (VFINX), and ETFs (SPY)')
print('For simplicity, all of them will be referred to as "stock"\n')
# listOfGenericStocks = ['googl', 'aapl', 'vfinx', 'tsla', 'vthr']
for i in range(0, numberOfStocks, 1):
print('Stock', i + 1, end=' ')
stockName = str(input('ticker: '))
# stockName = listOfGenericStocks[i]
# print(':', stockName)
stockName = stockName.upper()
listOfStocks.append(stockName)
listOfStocks[i] = Stock()
listOfStocks[i].setName(stockName)
return listOfStocks
def timeFrameInit():
isInteger = False
while isInteger == False:
print(
'\nPlease enter the time frame in years (10 years or less recommended):', end='')
temp = input(' ')
isInteger = Functions.stringIsInt(temp)
if isInteger == True:
years = int(temp)
else:
print('Please type an integer')
# years = 5 # TESTING
# print('Years:', years)
timeFrame = years
return timeFrame
def dataMain(listOfStocks):
print('\nGathering dates and close values')
i = 0
while i < len(listOfStocks):
datesAndCloseList = Stock.datesAndClose(listOfStocks[i])
if datesAndCloseList == 'Not available':
del listOfStocks[i]
if len(listOfStocks) == 0:
print('No stocks to analyze. Ending program')
exit()
else:
listOfStocks[i].allDates = datesAndCloseList[0]
listOfStocks[i].allCloseValues = datesAndCloseList[1]
# Clip list to fit time frame
datesAndCloseList2 = Stock.datesAndCloseFitTimeFrame(
listOfStocks[i])
listOfStocks[i].dates = datesAndCloseList2[0]
listOfStocks[i].closeValues = datesAndCloseList2[1]
i += 1
def riskFreeRate():
print('Quandl')
url = ''.join(
('https://www.quandl.com/api/v3/datasets/USTREASURY/LONGTERMRATES.json?api_key=', apiQuandl))
# https://www.quandl.com/api/v3/datasets/USTREASURY/LONGTERMRATES.json?api_key=KUh3U3hxke9tCimjhWEF
print("\nSending request to:", url)
f = requests.get(url)
Functions.fromCache(f)
json_data = f.text
loaded_json = json.loads(json_data)
riskFreeRate = (loaded_json['dataset']['data'][0][1])/100
riskFreeRate = riskFreeRate * 100
riskFreeRate = round(riskFreeRate, 2)
print('Risk-free rate:', riskFreeRate, end='\n\n')
if f.status_code == 404:
print("Quandl not available")
print('Returning 2.50 as risk-free rate', end='\n\n')
# return 0.0250
return 2.50
return riskFreeRate
def returnMain(benchmark, listOfStocks):
print('\nCalculating unadjusted return, Sharpe ratio, Sortino ratio, and Treynor ratio\n')
print('Getting risk-free rate from current 10-year treasury bill rates', end='\n\n')
Stock.riskFreeRate = riskFreeRate()
print(benchmark.name, end='\n\n')
benchmark.annualReturn = Stock.calcAnnualReturn(benchmark)
if benchmark.annualReturn == 'Not available':
print('Please use a lower time frame\nEnding program')
exit()
benchmark.averageAnnualReturn = Stock.calcAverageAnnualReturn(benchmark)
benchmark.standardDeviation = Stock.calcStandardDeviation(benchmark)
# Make benchmark data global
Stock.benchmarkDates = benchmark.dates
Stock.benchmarkCloseValues = benchmark.closeValues
Stock.benchmarkAverageAnnualReturn = benchmark.averageAnnualReturn
Stock.benchmarkStandardDeviation = benchmark.standardDeviation
i = 0
while i < len(listOfStocks):
print('\n' + listOfStocks[i].name, end='\n\n')
# 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]
# Calculate everything for each stock
listOfStocks[i].annualReturn = Stock.calcAnnualReturn(listOfStocks[i])
if listOfStocks[i].annualReturn == 'Not available':
print('Removing', listOfStocks[i].name, 'from list of stocks')
del listOfStocks[i]
if len(listOfStocks) == 0:
print('No stocks to analyze. Ending program')
exit()
else:
listOfStocks[i].averageAnnualReturn = Stock.calcAverageAnnualReturn(
listOfStocks[i])
listOfStocks[i].correlation = Stock.calcCorrelation(
listOfStocks[i], benchmarkMatchDatesAndCloseValues[1])
listOfStocks[i].standardDeviation = Stock.calcStandardDeviation(
listOfStocks[i])
listOfStocks[i].downsideDeviation = Stock.calcDownsideDeviation(
listOfStocks[i])
listOfStocks[i].kurtosis = Stock.calcKurtosis(
listOfStocks[i])
listOfStocks[i].skewness = Stock.calcSkewness(
listOfStocks[i])
listOfStocks[i].beta = Stock.calcBeta(listOfStocks[i])
listOfStocks[i].alpha = Stock.calcAlpha(listOfStocks[i])
listOfStocks[i].sharpe = Stock.calcSharpe(listOfStocks[i])
listOfStocks[i].sortino = Stock.calcSortino(listOfStocks[i])
listOfStocks[i].treynor = Stock.calcTreynor(listOfStocks[i])
listOfStocks[i].linearRegression = Stock.calcLinearRegression(
listOfStocks[i])
i += 1
print('\nNumber of stocks from original list that fit time frame:',
len(listOfStocks))
def indicatorInit():
# Runs correlation or regression study
indicatorFound = False
listOfIndicators = ['Expense Ratio',
'Market Capitalization', 'Turnover', 'Persistence']
print('\n', end='')
while indicatorFound == False:
print('List of indicators:')
for i in range(0, len(listOfIndicators), 1):
print(str(i + 1) + '. ' + listOfIndicators[i])
indicator = str(input('Choose an indicator from the list: '))
# indicator = 'expense ratio' # TESTING
if Functions.stringIsInt(indicator) == True:
if int(indicator) <= 4:
indicator = listOfIndicators[int(indicator)-1]
indicatorFound = True
else:
indicatorFormats = [
indicator.upper(), indicator.lower(), indicator.title()]
for i in range(0, len(indicatorFormats), 1):
for j in range(0, len(listOfIndicators), 1):
if listOfIndicators[j] == indicatorFormats[i]:
indicator = listOfIndicators[j]
indicatorFound = True
break
if indicatorFound == False:
print('Please choose an indicator from the list')
return indicator
def calcIndicatorCorrelation(listOfIndicatorValues, listOfReturns):
correlationList = []
for i in range(0, len(listOfReturns), 1):
correlation = np.corrcoef(
listOfIndicatorValues, listOfReturns[i])[0, 1]
correlationList.append(correlation)
return correlationList
def calcIndicatorRegression(listOfIndicatorValues, listOfReturns):
regressionList = []
x = np.array(listOfIndicatorValues)
for i in range(0, len(listOfReturns), 1):
y = np.array(listOfReturns[i])
# Estimate coefficients
# number of observations/points
n = np.size(x)
# mean of x and y vector
m_x, m_y = np.mean(x), np.mean(y)
# calculating cross-deviation and deviation about x
SS_xy = np.sum(y*x) - n*m_y*m_x
SS_xx = np.sum(x*x) - n*m_x*m_x
# calculating regression coefficients
b_1 = SS_xy / SS_xx
b_0 = m_y - b_1*m_x
b = [b_0, b_1]
regression = []
regression.append(b[0])
regression.append(b[1])
regressionList.append(regression)
# plot_regression_line(x, y, b, i)
return regressionList
def plot_regression_line(x, y, b, i):
# plotting the actual points as scatter plot
plt.scatter(x, y, color="m",
marker="o", s=30)
# predicted response vector
y_pred = b[0] + b[1]*x
# plotting the regression line
plt.plot(x, y_pred, color="g")
# putting labels
listOfReturnStrings = ['Average Annual Return',
'Sharpe Ratio', 'Sortino Ratio', 'Treynor Ratio', 'Alpha']
plt.title(Stock.indicator + ' and ' + listOfReturnStrings[i])
if Stock.indicator == 'Expense Ratio':
plt.xlabel(Stock.indicator + ' (%)')
else:
plt.xlabel(Stock.indicator)
if i == 0:
plt.ylabel(listOfReturnStrings[i] + ' (%)')
else:
plt.ylabel(listOfReturnStrings[i])
# function to show plot
plt.show(block=False)
for i in range(2, 0, -1):
if i == 1:
sys.stdout.write('Keeping plot open for ' +
str(i) + ' second \r')
else:
sys.stdout.write('Keeping plot open for ' +
str(i) + ' seconds \r')
plt.pause(1)
sys.stdout.flush()
sys.stdout.write(
' \r')
sys.stdout.flush()
plt.close()
def indicatorMain(listOfStocks):
Stock.indicator = indicatorInit()
print(Stock.indicator, end='\n\n')
# indicatorValuesGenericExpenseRatio = [2.5, 4.3, 3.1, 2.6, 4.2] # TESTING
listOfStocksIndicatorValues = []
for i in range(0, len(listOfStocks), 1):
indicatorValueFound = False
while indicatorValueFound == False:
if Stock.indicator == 'Expense Ratio':
indicatorValue = str(
input(Stock.indicator + ' for ' + listOfStocks[i].name + ' (%): '))
elif Stock.indicator == 'Persistence':
indicatorValue = str(
input(Stock.indicator + ' for ' + listOfStocks[i].name + ' (years): '))
elif Stock.indicator == 'Turnover':
indicatorValue = str(input(
Stock.indicator + ' for ' + listOfStocks[i].name + ' in the last ' + str(Stock.timeFrame) + ' years: '))
elif Stock.indicator == 'Market Capitalization':
indicatorValue = str(
input(Stock.indicator + ' of ' + listOfStocks[i].name + ': '))
else:
print('Something is wrong. Indicator was not found. Ending program.')
exit()
if Functions.strintIsFloat(indicatorValue) == True:
listOfStocks[i].indicatorValue = float(indicatorValue)
indicatorValueFound = True
else:
print('Please enter a number')
# listOfStocks[i].indicatorValue = indicatorValuesGenericExpenseRatio[i] # TESTING
listOfStocksIndicatorValues.append(listOfStocks[i].indicatorValue)
listOfReturns = [] # A list that matches the above list with return values [[averageAnnualReturn1, aAR2, aAR3], [sharpe1, sharpe2, sharpe3], etc.]
tempListOfReturns = []
for i in range(0, len(listOfStocks), 1):
tempListOfReturns.append(listOfStocks[i].averageAnnualReturn)
listOfReturns.append(tempListOfReturns)
tempListOfReturns = []
for i in range(0, len(listOfStocks), 1):
tempListOfReturns.append(listOfStocks[i].sharpe)
listOfReturns.append(tempListOfReturns)
tempListOfReturns = []
for i in range(0, len(listOfStocks), 1):
tempListOfReturns.append(listOfStocks[i].sortino)
listOfReturns.append(tempListOfReturns)
tempListOfReturns = []
for i in range(0, len(listOfStocks), 1):
tempListOfReturns.append(listOfStocks[i].treynor)
listOfReturns.append(tempListOfReturns)
tempListOfReturns = []
for i in range(0, len(listOfStocks), 1):
tempListOfReturns.append(listOfStocks[i].alpha)
listOfReturns.append(tempListOfReturns)
# Create list of each indicator (e.g. expense ratio)
listOfIndicatorValues = []
for i in range(0, len(listOfStocks), 1):
listOfIndicatorValues.append(listOfStocks[i].indicatorValue)
Stock.indicatorCorrelation = calcIndicatorCorrelation(
listOfIndicatorValues, listOfReturns)
listOfReturnStrings = ['Average Annual Return',
'Sharpe Ratio', 'Sortino Ratio', 'Treynor Ratio', 'Alpha']
print('\n', end='')
for i in range(0, len(Stock.indicatorCorrelation), 1):
print('Correlation with ' + Stock.indicator.lower() + ' and ' +
listOfReturnStrings[i].lower() + ': ' + str(Stock.indicatorCorrelation[i]))
Stock.indicatorRegression = calcIndicatorRegression(
listOfIndicatorValues, listOfReturns)
print('\n', end='')
for i in range(0, len(Stock.indicatorCorrelation), 1):
formula = ''.join(
('y = ', str(round(float(Stock.indicatorRegression[i][0]), 2)), 'x + ', str(round(float(Stock.indicatorRegression[i][1]), 2))))
print('Linear regression equation for ' + Stock.indicator.lower() + ' and ' +
listOfReturnStrings[i].lower() + ': ' + formula)
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)
# Choose indicator and calculate correlation with indicator
indicatorMain(listOfStocks)
exit()
if __name__ == "__main__":
main()
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