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main.py
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'''
__ _ _ _ _ _
/ _| | | (_) | (_) | |
| |_ _ _ _ __ __| | ______ _ _ __ __| |_ ___ __ _| |_ ___ _ __ ___
| _| | | | '_ \ / _` | |______| | | '_ \ / _` | |/ __/ _` | __/ _ \| '__/ __|
| | | |_| | | | | (_| | | | | | | (_| | | (_| (_| | || (_) | | \__ \
|_| \__,_|_| |_|\__,_| |_|_| |_|\__,_|_|\___\__,_|\__\___/|_| |___/
Project homepage: https://github.com/andrewkdinh/fund-indicators
Author: Andrew Dinh <[email protected]>
Copyright (C) 2019 Andrew Dinh
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
'''
# PYTHON FILES
import Functions
from yahoofinancials import YahooFinancials
from termcolor import cprint
# REQUIRED
import requests_cache
import os.path
import re
import datetime
import json
import requests
from bs4 import BeautifulSoup
import numpy as np
# OPTIONAL
try:
import matplotlib.pyplot as plt
except:
pass
from halo import Halo
# FOR ASYNC
from concurrent.futures import ThreadPoolExecutor as PoolExecutor
import time
import random
import sys
sys.path.insert(0, './modules')
requests_cache.install_cache(
'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 # Months
riskFreeRate = 0
indicator = ''
# CONFIG
removeOutliers = True
sourceList = ['Yahoo', 'Alpha Vantage', 'IEX', 'Tiingo']
plotIndicatorRegression = False
timePlotIndicatorRegression = 5 # seconds
config = 'N/A'
# BENCHMARK VALUES
benchmarkDates = []
benchmarkCloseValues = []
benchmarkAverageMonthlyReturn = 0
benchmarkStandardDeviation = 0
# INDICATOR VALUES
indicatorCorrelation = []
indicatorRegression = []
persTimeFrame = 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.averageMonthlyReturn = 0
self.monthlyReturn = []
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.correlation = 0
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):
url = ''.join(
('https://api.iextrading.com/1.0/stock/', self.name, '/chart/5y'))
# link = "https://api.iextrading.com/1.0/stock/spy/chart/5y"
cprint("GET:" + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
f = requests.get(url)
Functions.fromCache(f)
json_data = f.text
if json_data == 'Unknown symbol' or f.status_code != 200:
print("IEX not available")
return 'N/A'
loaded_json = json.loads(json_data)
listIEX = []
print("\nFinding all dates given")
allDates = []
for i in range(0, len(loaded_json), 1): # For 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): # For 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[0]), 'dates and', len(listIEX[1]), "close values")
return listIEX
def AV(self):
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
cprint("GET:" + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
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 != 200 or len(loaded_json) == 0:
print("Alpha Vantage not available")
return 'N/A'
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[0]), 'dates and', len(listAV[1]), "close values")
return listAV
def Tiingo(self):
token = ''.join(('Token ', apiTiingo))
headers = {
'Content-Type': 'application/json',
'Authorization': token
}
url = ''.join(('https://api.tiingo.com/tiingo/daily/', self.name))
cprint("GET:" + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
f = requests.get(url, headers=headers)
Functions.fromCache(f)
loaded_json = f.json()
if len(loaded_json) == 1 or f.status_code != 200 or loaded_json['startDate'] is None:
print("Tiingo not available")
return 'N/A'
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
cprint("\nGET:" + url2 + '\n', 'white', attrs=['dark'])
with Halo(spinner='dots'):
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[0]), 'dates and', len(listTiingo[1]), "close values")
return listTiingo
def Yahoo(self):
url = ''.join(('https://finance.yahoo.com/quote/',
self.name, '?p=', self.name))
cprint('GET:' + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
t = requests.get(url)
Functions.fromCache(t)
if t.history:
print('Yahoo Finance does not have data for', self.name)
print('Yahoo not available')
return 'N/A'
else:
print('Yahoo Finance has data for', self.name)
ticker = self.name
firstDate = datetime.datetime.now().date(
) - datetime.timedelta(days=self.timeFrame*31) # 31 days as a buffer just in case
with Halo(spinner='dots'):
yahoo_financials = YahooFinancials(ticker)
r = yahoo_financials.get_historical_price_data(
str(firstDate), str(datetime.date.today()), 'daily')
s = r[self.name]['prices']
listOfDates = []
listOfCloseValues = []
for i in range(0, len(s), 1):
listOfDates.append(s[i]['formatted_date'])
listOfCloseValues.append(s[i]['close'])
listYahoo = [listOfDates, listOfCloseValues]
# Sometimes close value is a None value
i = 0
while i < len(listYahoo[1]):
if Functions.listIndexExists(listYahoo[1][i]):
if listYahoo[1][i] is None:
del listYahoo[1][i]
del listYahoo[0][i]
i -= 1
i += 1
else:
break
# print(len(listYahoo[0]), 'dates and', len(listYahoo[1]), "close values")
return listYahoo
def datesAndClose(self):
cprint('\n' + str(self.name), 'cyan')
sourceList = Stock.sourceList
# Use each source until you get a value
for j in range(0, len(sourceList), 1):
source = sourceList[j]
print('Source being used:', source)
if source == 'Alpha Vantage':
datesAndCloseList = Stock.AV(self)
elif source == 'Yahoo':
datesAndCloseList = Stock.Yahoo(self)
elif source == 'IEX':
datesAndCloseList = Stock.IEX(self)
elif source == 'Tiingo':
datesAndCloseList = Stock.Tiingo(self)
if datesAndCloseList != 'N/A':
break
else:
if j == len(sourceList)-1:
print('\nNo sources have data for', self.name)
cprint('Removing ' + self.name +
' because no data was found', 'yellow')
return 'N/A'
print('')
# Convert dates to datetime
allDates = datesAndCloseList[0]
for j in range(0, len(allDates), 1):
allDates[j] = Functions.stringToDate(allDates[j])
datesAndCloseList[0] = allDates
# Determine if close value list has value of zero
# AKA https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=RGN&outputsize=full&apikey=O42ICUV58EIZZQMU
for i in datesAndCloseList[1]:
if i == 0:
print('Found close value of 0. This is likely something like ticker RGN (Daily Time Series with Splits and Dividend Events)')
cprint('Removing ' + self.name +
'from list of stocks to ensure compability later', 'yellow')
return 'N/A'
return datesAndCloseList
def datesAndCloseFitTimeFrame(self):
# print('\nShortening 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 = list(self.allDates)
closeValues = list(self.allCloseValues)
firstDate = datetime.datetime.now().date() - datetime.timedelta(
days=self.timeFrame*30)
# print(self.timeFrame, ' months ago: ', firstDate, sep='')
closestDate = Functions.getNearest(dates, firstDate)
if closestDate != firstDate:
# print('Closest date available to ' + str(self.timeFrame) + ' months ago: ' + str(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 = [dates, closeValues]
print(len(dates), 'dates and', len(closeValues), 'close values')
return datesAndCloseList2
def calcAverageMonthlyReturn(self):
# averageMonthlyReturn = (float(self.closeValues[len(self.closeValues)-1]/self.closeValues[0])**(1/(self.timeFrame)))-1
# averageMonthlyReturn = averageMonthlyReturn * 100
averageMonthlyReturn = sum(self.monthlyReturn)/self.timeFrame
print('Average monthly return:', averageMonthlyReturn)
return averageMonthlyReturn
def calcMonthlyReturn(self):
monthlyReturn = []
# Calculate monthly return in order from oldest to newest
monthlyReturn = []
for i in range(0, self.timeFrame, 1):
firstDate = datetime.datetime.now().date() - datetime.timedelta(
days=(self.timeFrame-i)*30)
secondDate = datetime.datetime.now().date() - datetime.timedelta(
days=(self.timeFrame-i-1)*30)
# 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 ' + str(firstDate) +
', which is after the given time frame')
return 'N/A'
# Get corresponding close values and calculate monthly 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
monthlyReturnTemp = (secondClose/firstClose)-1
monthlyReturnTemp = monthlyReturnTemp * 100
monthlyReturn.append(monthlyReturnTemp)
# print('Monthly return over the past', self.timeFrame, 'months:', monthlyReturn)
return monthlyReturn
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.averageMonthlyReturn
standardDeviation = (
(sum((self.monthlyReturn[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.averageMonthlyReturn
downsideDeviation = (
(sum(min(0, (self.monthlyReturn[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.averageMonthlyReturn
kurtosis = (sum((self.monthlyReturn[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.averageMonthlyReturn
skewness = (sum((self.monthlyReturn[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.averageMonthlyReturn - \
(Stock.riskFreeRate+((Stock.benchmarkAverageMonthlyReturn -
Stock.riskFreeRate) * self.beta))
print('Alpha:', alpha)
return alpha
def calcSharpe(self):
sharpe = (self.averageMonthlyReturn - Stock.riskFreeRate) / \
self.standardDeviation
print('Sharpe Ratio:', sharpe)
return sharpe
def calcSortino(self):
sortino = (self.averageMonthlyReturn - self.riskFreeRate) / \
self.downsideDeviation
print('Sortino Ratio:', sortino)
return sortino
def calcTreynor(self):
treynor = (self.averageMonthlyReturn - 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
try:
t = Stock.timePlotIndicatorRegression
plt.show(block=False)
for i in range(t, 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()
except:
sys.stdout.write(
' \r')
sys.stdout.flush()
def scrapeYahooFinance(self):
# Determine if ETF, Mutual fund, or stock
url = ''.join(('https://finance.yahoo.com/quote/',
self.name, '?p=', self.name))
cprint('GET:' + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
t = requests.get(url)
Functions.fromCache(t)
if t.history:
print('Yahoo Finance does not have data for', self.name)
return 'N/A'
else:
print('Yahoo Finance has data for', self.name)
stockType = ''
url2 = ''.join(('https://finance.yahoo.com/lookup?s=', self.name))
cprint('GET:' + url2, 'white', attrs=['dark'])
with Halo(spinner='dots'):
x = requests.get(url2)
raw_html = x.text
Functions.fromCache(x)
soup2 = BeautifulSoup(raw_html, 'html.parser')
# Type (Stock, ETF, Mutual Fund)
r = soup2.find_all(
'td', attrs={'class': 'data-col4 Ta(start) Pstart(20px) Miw(30px)'})
u = soup2.find_all('a', attrs={'class': 'Fw(b)'}) # Name and class
z = soup2.find_all('td', attrs={
'class': 'data-col1 Ta(start) Pstart(10px) Miw(80px)'}) # Name of stock
listNames = []
for i in u:
if i.text.strip() == i.text.strip().upper():
listNames.append(i.text.strip())
'''
if len(i.text.strip()) < 6:
listNames.append(i.text.strip())
elif '.' in i.text.strip():
listNames.append(i.text.strip()) # Example: TSNAX (TSN.AX)
#! If having problems later, separate them by Industries (Mutual funds and ETF's are always N/A)
'''
for i in range(0, len(listNames), 1):
if listNames[i] == self.name:
break
r = r[i].text.strip()
z = z[i].text.strip()
print('Name:', z)
if r == 'ETF':
stockType = 'ETF'
elif r == 'Stocks':
stockType = 'Stock'
elif r == 'Mutual Fund':
stockType = 'Mutual Fund'
else:
print('Could not determine fund type')
return 'N/A'
print('Type:', stockType)
if Stock.indicator == 'Expense Ratio':
if stockType == 'Stock':
print(
self.name, 'is a stock, and therefore does not have an expense ratio')
return 'Stock'
raw_html = t.text
soup = BeautifulSoup(raw_html, 'html.parser')
r = soup.find_all('span', attrs={'class': 'Trsdu(0.3s)'})
if r == []:
print('Something went wrong with scraping expense ratio')
return('N/A')
if stockType == 'ETF':
for i in range(len(r)-1, 0, -1):
s = r[i].text.strip()
if s[-1] == '%':
break
elif stockType == 'Mutual Fund':
count = 0 # Second in set
for i in range(0, len(r)-1, 1):
s = r[i].text.strip()
if s[-1] == '%' and count == 0:
count += 1
elif s[-1] == '%' and count == 1:
break
if s[-1] == '%':
expenseRatio = float(s.replace('%', ''))
else:
print('Something went wrong with scraping expense ratio')
return 'N/A'
print(Stock.indicator + ': ', end='')
print(str(expenseRatio) + '%')
return expenseRatio
elif Stock.indicator == 'Market Capitalization':
somethingWrong = False
raw_html = t.text
soup = BeautifulSoup(raw_html, 'html.parser')
r = soup.find_all(
'span', attrs={'class': 'Trsdu(0.3s)'})
if r == []:
somethingWrong = True
else:
marketCap = 0
for t in r:
s = t.text.strip()
if s[-1] == 'B':
print(Stock.indicator + ': ', end='')
print(s, end='')
s = s.replace('B', '')
marketCap = float(s) * 1000000000 # 1 billion
break
elif s[-1] == 'M':
print(Stock.indicator + ': ', end='')
print(s, end='')
s = s.replace('M', '')
marketCap = float(s) * 1000000 # 1 million
break
elif s[-1] == 'K':
print(Stock.indicator + ': ', end='')
print(s, end='')
s = s.replace('K', '')
marketCap = float(s) * 1000 # 1 thousand
break
if marketCap == 0:
somethingWrong = True
if somethingWrong:
ticker = self.name
yahoo_financials = YahooFinancials(ticker)
marketCap = yahoo_financials.get_market_cap()
if marketCap is not None:
print('(Taken from yahoofinancials)')
print(marketCap)
return int(marketCap)
else:
print(
'Was not able to scrape or get market capitalization from yahoo finance')
return 'N/A'
marketCap = int(marketCap)
return marketCap
print(' =', marketCap)
marketCap = marketCap / 1000000
print(
'Dividing marketCap by 1 million:', marketCap)
return marketCap
elif Stock.indicator == 'Turnover':
if stockType == 'ETF':
url = ''.join(('https://finance.yahoo.com/quote/',
self.name, '/profile?p=', self.name))
# https://finance.yahoo.com/quote/SPY/profile?p=SPY
cprint('GET:' + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
t = requests.get(url)
Functions.fromCache(t)
raw_html = t.text
soup = BeautifulSoup(raw_html, 'html.parser')
r = soup.find_all(
'span', attrs={'class': 'W(20%) D(b) Fl(start) Ta(e)'})
if r == []:
print('Something went wrong without scraping turnover')
return 'N/A'
turnover = 0
for i in range(len(r)-1, 0, -1):
s = r[i].text.strip()
if s[-1] == '%':
turnover = float(s.replace('%', ''))
break
elif stockType == 'Mutual Fund':
raw_html = t.text
soup = BeautifulSoup(raw_html, 'html.parser')
r = soup.find_all(
'span', attrs={'class': 'Trsdu(0.3s)'})
if r == []:
print('Something went wrong without scraping turnover')
return 'N/A'
turnover = 0
for i in range(len(r)-1, 0, -1):
s = r[i].text.strip()
if s[-1] == '%':
turnover = float(s.replace('%', ''))
break
elif stockType == 'Stock':
print(self.name, 'is a stock, and therefore does not have turnover')
return 'Stock'
if turnover == 0:
print('Something went wrong with scraping turnover')
return 'N/A'
print(Stock.indicator + ': ', end='')
print(str(turnover) + '%')
return turnover
def indicatorManual(self):
indicatorValueFound = False
while not indicatorValueFound:
if Stock.indicator == 'Expense Ratio':
indicatorValue = str(
input(Stock.indicator + ' for ' + self.name + ' (%): '))
elif Stock.indicator == 'Market Capitalization':
indicatorValue = str(
input(Stock.indicator + ' of ' + self.name + ': '))
elif Stock.indicator == 'Persistence':
indicatorValue = str(
input(Stock.indicator + ' for ' + self.name + ' (years): '))
elif Stock.indicator == 'Turnover':
indicatorValue = str(input(
Stock.indicator + ' for ' + self.name + ' in the last ' + str(Stock.timeFrame) + ' years: '))
if Functions.strintIsFloat(indicatorValue):
indicatorValueFound = True
return float(indicatorValue)
else:
print('Please enter a number')
def calcPersistence(self):
persistenceFirst = (sum(self.monthlyReturn[i] for i in range(
0, Stock.persTimeFrame, 1))) / Stock.persTimeFrame
persistenceSecond = self.averageMonthlyReturn
persistence = persistenceSecond-persistenceFirst
print('Change (difference) in average monthly return:', persistence)
return persistence
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 benchmarkInit():
# Treat benchmark like stock
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] + ')')
while benchmarkTicker == '':
benchmark = str(input('Please choose a benchmark from the list: '))
# benchmark = 'SPY' # TESTING
if Functions.stringIsInt(benchmark):
if int(benchmark) <= len(benchmarks) and int(benchmark) > 0:
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')
print(benchmark, ' (', benchmarkTicker, ')', sep='')
benchmark = Stock()
benchmark.setName(benchmarkTicker)
return benchmark
def stocksInit():
listOfStocks = []
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"')
found = False
methods = ['Read from a file', 'Enter manually',
'Kiplinger top-performing funds (50)',
'TheStreet top-rated mutual funds (20)',
'Money best mutual funds (50)',
'Investors Business Daily best mutual funds (~45)']
while not found:
print('\nMethods:')
method = 0
for i in range(0, len(methods), 1):
print('[' + str(i+1) + '] ' + methods[i])
while method == 0 or method > len(methods):
method = str(input('Which method? '))
if Functions.stringIsInt(method):
method = int(method)
if method == 0 or method > len(methods):
print('Please choose a number from 1 to', len(methods))
else:
method = 0
print('Please choose a number')
print('')
if method == 1:
defaultFiles = ['.gitignore', 'LICENSE', 'main.py', 'Functions.py',
'README.md', 'requirements.txt', 'cache.sqlite',
'config.json', 'CONTRIBUTING.md',
'config.example.json', 'CODE-OF-CONDUCT.md',
'_test_runner.py', 'poetry.lock', 'pyproject.toml']
# Added by repl.it for whatever reason
stocksFound = False
print('Files in current directory (without default files): ')
listOfFilesTemp = [f for f in os.listdir() if os.path.isfile(f)]
listOfFiles = []
for files in listOfFilesTemp:
if files[0] != '.' and any(x in files for x in defaultFiles) is not True:
listOfFiles.append(files)
for i in range(0, len(listOfFiles), 1):
if listOfFiles[i][0] != '.':
print('[' + str(i+1) + '] ' + listOfFiles[i])
while not stocksFound:
fileName = str(input('What is the file number/name? '))
if (
Functions.stringIsInt(fileName)
and int(fileName) < len(listOfFiles) + 1
and int(fileName) > 0
):
fileName = listOfFiles[int(fileName)-1]
print(fileName)
if Functions.fileExists(fileName):
listOfStocks = []
file = open(fileName, 'r')
n = file.read()
file.close()
s = re.findall(r'[^,;\s]+', n)
for i in s:
if str(i) != '' and Functions.hasNumbers(str(i)) is False:
listOfStocks.append(str(i).upper())
stocksFound = True
else:
print('File not found')
for i in range(0, len(listOfStocks), 1):
stockName = listOfStocks[i].upper()
listOfStocks[i] = Stock()
listOfStocks[i].setName(stockName)
for k in listOfStocks:
print(k.name, end=' ')
print('\n' + str(len(listOfStocks)) + ' stocks total')
elif method == 2:
isInteger = False
while not isInteger:
temp = input('Number of stocks to analyze (2 minimum): ')
isInteger = Functions.stringIsInt(temp)
if isInteger:
if int(temp) >= 2:
numberOfStocks = int(temp)
else:
print('Please type a number greater than or equal to 2')
isInteger = False
else:
print('Please type an integer')
i = 0
while i < numberOfStocks:
print('Stock', i + 1, end=' ')
stockName = str(input('ticker: '))
if stockName != '' and Functions.hasNumbers(stockName) is False:
stockName = stockName.upper()
listOfStocks.append(stockName)
listOfStocks[i] = Stock()
listOfStocks[i].setName(stockName)
i += 1
else:
print('Invalid ticker')
elif method == 3:
listOfStocks = []
url = 'https://www.kiplinger.com/tool/investing/T041-S001-top-performing-mutual-funds/index.php'
headers = {
'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36'}
cprint('GET:' + url, 'white', attrs=['dark'])
with Halo(spinner='dots'):
f = requests.get(url, headers=headers)
Functions.fromCache(f)
raw_html = f.text
soup = BeautifulSoup(raw_html, 'html.parser')
file = open('kiplinger-stocks.txt', 'w')
r = soup.find_all('a', attrs={'style': 'font-weight:700;'})
for k in r:
print(k.text.strip(), end=' ')
listOfStocks.append(k.text.strip())
file.write(str(k.text.strip()) + '\n')
file.close()
for i in range(0, len(listOfStocks), 1):
stockName = listOfStocks[i].upper()
listOfStocks[i] = Stock()
listOfStocks[i].setName(stockName)