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1773 lines
65 KiB
Python
1773 lines
65 KiB
Python
# https://github.com/andrewkdinh/fund-indicators
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# Determine indicators of overperforming mutual funds
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# Andrew Dinh
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# Python 3.6.7
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# PYTHON FILES
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import Functions
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from yahoofinancials import YahooFinancials
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from termcolor import cprint
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# REQUIRED
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import requests_cache
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import os.path
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import re
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import datetime
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import json
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import requests
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from bs4 import BeautifulSoup
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import numpy as np
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# OPTIONAL
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try:
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import matplotlib.pyplot as plt
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except:
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pass
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from halo import Halo
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# FOR ASYNC
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from concurrent.futures import ThreadPoolExecutor as PoolExecutor
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import time
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import random
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import sys
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sys.path.insert(0, './modules')
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requests_cache.install_cache(
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'cache', backend='sqlite', expire_after=43200) # 12 hours
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# API Keys
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apiAV = 'O42ICUV58EIZZQMU'
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# apiBarchart = 'a17fab99a1c21cd6f847e2f82b592838'
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apiBarchart = 'f40b136c6dc4451f9136bb53b9e70ffa'
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apiTiingo = '2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8'
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apiTradier = 'n26IFFpkOFRVsB5SNTVNXicE5MPD'
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apiQuandl = 'KUh3U3hxke9tCimjhWEF'
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# apiIntrinio = 'OmNmN2E5YWI1YzYxN2Q4NzEzZDhhOTgwN2E2NWRhOWNl'
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# If you're going to take these API keys and abuse it, you should really
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# reconsider your life priorities
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'''
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API Keys:
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Alpha Vantage API Key: O42ICUV58EIZZQMU
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Barchart API Key: a17fab99a1c21cd6f847e2f82b592838
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Possible other one? f40b136c6dc4451f9136bb53b9e70ffa
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150 getHistory queries per day
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Tiingo API Key: 2e72b53f2ab4f5f4724c5c1e4d5d4ac0af3f7ca8
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Tradier API Key: n26IFFpkOFRVsB5SNTVNXicE5MPD
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Monthly Bandwidth = 5 GB
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Hourly Requests = 500
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Daily Requests = 20,000
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Symbol Requests = 500
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Quandl API Key: KUh3U3hxke9tCimjhWEF
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Intrinio API Key: OmNmN2E5YWI1YzYxN2Q4NzEzZDhhOTgwN2E2NWRhOWNl
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Mutual funds?
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Yes: Alpha Vantage, Tiingo
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No: IEX, Barchart
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Adjusted?
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Yes: Alpha Vantage, IEX
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No: Tiingo
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'''
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class Stock:
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# GLOBAL VARIABLES
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timeFrame = 0 # Months
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riskFreeRate = 0
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indicator = ''
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# CONFIG
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removeOutliers = True
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sourceList = ['Yahoo', 'Alpha Vantage', 'IEX', 'Tiingo']
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plotIndicatorRegression = False
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config = 'N/A'
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# BENCHMARK VALUES
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benchmarkDates = []
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benchmarkCloseValues = []
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benchmarkAverageMonthlyReturn = 0
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benchmarkStandardDeviation = 0
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# INDICATOR VALUES
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indicatorCorrelation = []
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indicatorRegression = []
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persTimeFrame = 0
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def __init__(self):
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# BASIC DATA
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self.name = '' # Ticker symbol
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self.allDates = []
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self.allCloseValues = []
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self.dates = []
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self.closeValues = []
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self.datesMatchBenchmark = []
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self.closeValuesMatchBenchmark = []
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# CALCULATED RETURN
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self.averageMonthlyReturn = 0
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self.monthlyReturn = []
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self.sharpe = 0
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self.sortino = 0
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self.treynor = 0
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self.alpha = 0
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self.beta = 0
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self.standardDeviation = 0
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self.downsideDeviation = 0
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self.kurtosis = 0
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self.skewness = 0 # Not sure if I need this
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self.correlation = 0
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self.linearRegression = [] # for y=mx+b, this list has [m,b]
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self.indicatorValue = ''
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def setName(self, newName):
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self.name = newName
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def getName(self):
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return self.name
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def getAllDates(self):
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return self.allDates
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def getAllCloseValues(self):
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return self.allCloseValues
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def IEX(self):
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url = ''.join(
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('https://api.iextrading.com/1.0/stock/', self.name, '/chart/5y'))
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# link = "https://api.iextrading.com/1.0/stock/spy/chart/5y"
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cprint("Get:" + url, 'white', attrs=['dark'])
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with Halo(spinner='dots'):
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f = requests.get(url)
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Functions.fromCache(f)
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json_data = f.text
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if json_data == 'Unknown symbol' or f.status_code != 200:
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print("IEX not available")
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return 'N/A'
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loaded_json = json.loads(json_data)
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listIEX = []
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print("\nFinding all dates given")
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allDates = []
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for i in range(0, len(loaded_json), 1): # For oldest first
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# for i in range(len(loaded_json)-1, -1, -1):
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line = loaded_json[i]
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date = line['date']
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allDates.append(date)
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listIEX.append(allDates)
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print(len(listIEX[0]), "dates")
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# print("\nFinding close values for each date")
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values = []
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for i in range(0, len(loaded_json), 1): # For oldest first
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# for i in range(len(loaded_json)-1, -1, -1):
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line = loaded_json[i]
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value = line['close']
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values.append(value)
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listIEX.append(values)
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print(len(listIEX[0]), 'dates and', len(listIEX[1]), "close values")
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return listIEX
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def AV(self):
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listAV = []
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url = ''.join(('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=',
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self.name, '&outputsize=full&apikey=', apiAV))
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# https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=MSFT&outputsize=full&apikey=demo
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cprint("Get:" + url, 'white', attrs=['dark'])
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with Halo(spinner='dots'):
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f = requests.get(url)
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Functions.fromCache(f)
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json_data = f.text
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loaded_json = json.loads(json_data)
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if len(loaded_json) == 1 or f.status_code != 200 or len(loaded_json) == 0:
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print("Alpha Vantage not available")
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return 'N/A'
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dailyTimeSeries = loaded_json['Time Series (Daily)']
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listOfDates = list(dailyTimeSeries)
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# listAV.append(listOfDates)
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listAV.append(list(reversed(listOfDates)))
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# print("\nFinding close values for each date")
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values = []
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for i in range(0, len(listOfDates), 1):
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temp = listOfDates[i]
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loaded_json2 = dailyTimeSeries[temp]
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# value = loaded_json2['4. close']
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value = loaded_json2['5. adjusted close']
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values.append(float(value))
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# listAV.append(values)
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listAV.append(list(reversed(values)))
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print(len(listAV[0]), 'dates and', len(listAV[1]), "close values")
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return listAV
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def Tiingo(self):
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token = ''.join(('Token ', apiTiingo))
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headers = {
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'Content-Type': 'application/json',
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'Authorization': token
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}
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url = ''.join(('https://api.tiingo.com/tiingo/daily/', self.name))
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cprint("Get:" + url, 'white', attrs=['dark'])
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with Halo(spinner='dots'):
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f = requests.get(url, headers=headers)
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Functions.fromCache(f)
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loaded_json = f.json()
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if len(loaded_json) == 1 or f.status_code != 200 or loaded_json['startDate'] is None:
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print("Tiingo not available")
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return 'N/A'
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listTiingo = []
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print("\nFinding first and last date")
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firstDate = loaded_json['startDate']
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lastDate = loaded_json['endDate']
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print(firstDate, '...', lastDate)
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print("\nFinding all dates given", end='')
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dates = []
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values = []
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url2 = ''.join((url, '/prices?startDate=',
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firstDate, '&endDate=', lastDate))
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# https://api.tiingo.com/tiingo/daily/<ticker>/prices?startDate=2012-1-1&endDate=2016-1-1
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cprint("\nGet:" + url2 + '\n', 'white', attrs=['dark'])
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with Halo(spinner='dots'):
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requestResponse2 = requests.get(url2, headers=headers)
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Functions.fromCache(requestResponse2)
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loaded_json2 = requestResponse2.json()
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for i in range(0, len(loaded_json2)-1, 1):
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line = loaded_json2[i]
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dateWithTime = line['date']
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temp = dateWithTime.split('T00:00:00.000Z')
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date = temp[0]
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dates.append(date)
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value = line['close']
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values.append(value)
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listTiingo.append(dates)
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print(len(listTiingo[0]), "dates")
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# print("Finding close values for each date")
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# Used loop from finding dates
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listTiingo.append(values)
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print(len(listTiingo[0]), 'dates and',
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len(listTiingo[1]), "close values")
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return listTiingo
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def Yahoo(self):
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url = ''.join(('https://finance.yahoo.com/quote/',
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self.name, '?p=', self.name))
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cprint('Get:' + url, 'white', attrs=['dark'])
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with Halo(spinner='dots'):
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t = requests.get(url)
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if t.history:
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print('Yahoo Finance does not have data for', self.name)
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print('Yahoo not available')
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return 'N/A'
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else:
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print('Yahoo Finance has data for', self.name)
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ticker = self.name
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firstDate = datetime.datetime.now().date(
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) - datetime.timedelta(days=self.timeFrame*31) # 31 days as a buffer just in case
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with Halo(spinner='dots'):
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yahoo_financials = YahooFinancials(ticker)
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r = yahoo_financials.get_historical_price_data(
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str(firstDate), str(datetime.date.today()), 'daily')
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s = r[self.name]['prices']
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listOfDates = []
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listOfCloseValues = []
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for i in range(0, len(s), 1):
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listOfDates.append(s[i]['formatted_date'])
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listOfCloseValues.append(s[i]['close'])
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listYahoo = [listOfDates, listOfCloseValues]
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# Sometimes close value is a None value
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i = 0
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while i < len(listYahoo[1]):
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if Functions.listIndexExists(listYahoo[1][i]) is True:
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if listYahoo[1][i] is None:
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del listYahoo[1][i]
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del listYahoo[0][i]
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i = i - 1
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i = i + 1
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else:
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break
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print(len(listYahoo[0]), 'dates and',
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len(listYahoo[1]), "close values")
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return listYahoo
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def datesAndClose(self):
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cprint('\n' + str(self.name), 'cyan')
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sourceList = Stock.sourceList
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# Use each source until you get a value
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for j in range(0, len(sourceList), 1):
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source = sourceList[j]
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print('Source being used:', source)
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if source == 'Alpha Vantage':
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datesAndCloseList = Stock.AV(self)
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elif source == 'Yahoo':
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datesAndCloseList = Stock.Yahoo(self)
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elif source == 'IEX':
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datesAndCloseList = Stock.IEX(self)
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elif source == 'Tiingo':
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datesAndCloseList = Stock.Tiingo(self)
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if datesAndCloseList != 'N/A':
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break
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else:
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if j == len(sourceList)-1:
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print('\nNo sources have data for', self.name)
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print('Removing ' + self.name +
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' from list of stocks to ensure compatibility later')
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return 'N/A'
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print('')
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# Convert dates to datetime
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allDates = datesAndCloseList[0]
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for j in range(0, len(allDates), 1):
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allDates[j] = Functions.stringToDate(allDates[j])
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datesAndCloseList[0] = allDates
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# Determine if close value list has value of zero
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# AKA https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=RGN&outputsize=full&apikey=O42ICUV58EIZZQMU
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for i in datesAndCloseList[1]:
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if i == 0:
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print('Found close value of 0. This is likely something like ticker RGN (Daily Time Series with Splits and Dividend Events)')
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print('Removing ' + self.name +
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'from list of stocks to ensure compability later')
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return 'N/A'
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return datesAndCloseList
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def datesAndCloseFitTimeFrame(self):
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print('\nShortening list to fit time frame')
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# 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)
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dates = []
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closeValues = []
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for i in range(0, len(self.allDates), 1):
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dates.append(self.allDates[i])
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closeValues.append(self.allCloseValues[i])
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firstDate = datetime.datetime.now().date() - datetime.timedelta(
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days=self.timeFrame*30)
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print(self.timeFrame, ' months ago: ', firstDate, sep='')
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closestDate = Functions.getNearest(dates, firstDate)
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if closestDate != firstDate:
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print('Closest date available for', self.name, ':', closestDate)
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firstDate = closestDate
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else:
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print(self.name, 'has a close value for', firstDate)
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# Remove dates in list up to firstDate
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while dates[0] != firstDate:
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dates.remove(dates[0])
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# Remove close values until list is same length as dates
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while len(closeValues) != len(dates):
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closeValues.remove(closeValues[0])
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datesAndCloseList2 = []
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datesAndCloseList2.append(dates)
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datesAndCloseList2.append(closeValues)
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print(len(dates), 'dates and', len(closeValues), 'close values')
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return datesAndCloseList2
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def calcAverageMonthlyReturn(self):
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# averageMonthlyReturn = (float(self.closeValues[len(self.closeValues)-1]/self.closeValues[0])**(1/(self.timeFrame)))-1
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# averageMonthlyReturn = averageMonthlyReturn * 100
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averageMonthlyReturn = sum(self.monthlyReturn)/self.timeFrame
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print('Average monthly return:', averageMonthlyReturn)
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return averageMonthlyReturn
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def calcMonthlyReturn(self):
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monthlyReturn = []
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# Calculate monthly return in order from oldest to newest
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monthlyReturn = []
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for i in range(0, self.timeFrame, 1):
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firstDate = datetime.datetime.now().date() - datetime.timedelta(
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days=(self.timeFrame-i)*30)
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secondDate = datetime.datetime.now().date() - datetime.timedelta(
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days=(self.timeFrame-i-1)*30)
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# Find closest dates to firstDate and lastDate
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firstDate = Functions.getNearest(self.dates, firstDate)
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secondDate = Functions.getNearest(self.dates, secondDate)
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if firstDate == secondDate:
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print('Closest date is', firstDate,
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'which is after the given time frame.')
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return 'N/A'
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# Get corresponding close values and calculate monthly return
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for i in range(0, len(self.dates), 1):
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if self.dates[i] == firstDate:
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firstClose = self.closeValues[i]
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elif self.dates[i] == secondDate:
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secondClose = self.closeValues[i]
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break
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monthlyReturnTemp = (secondClose/firstClose)-1
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monthlyReturnTemp = monthlyReturnTemp * 100
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monthlyReturn.append(monthlyReturnTemp)
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# print('Monthly return over the past', self.timeFrame, 'months:', monthlyReturn)
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return monthlyReturn
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def calcCorrelation(self, closeList):
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correlation = np.corrcoef(
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self.closeValuesMatchBenchmark, closeList)[0, 1]
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print('Correlation with benchmark:', correlation)
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return correlation
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def calcStandardDeviation(self):
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numberOfValues = self.timeFrame
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mean = self.averageMonthlyReturn
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standardDeviation = (
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(sum((self.monthlyReturn[x]-mean)**2 for x in range(0, numberOfValues, 1)))/(numberOfValues-1))**(1/2)
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print('Standard Deviation:', standardDeviation)
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return standardDeviation
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def calcDownsideDeviation(self):
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numberOfValues = self.timeFrame
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targetReturn = self.averageMonthlyReturn
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downsideDeviation = (
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(sum(min(0, (self.monthlyReturn[x]-targetReturn))**2 for x in range(0, numberOfValues, 1)))/(numberOfValues-1))**(1/2)
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print('Downside Deviation:', downsideDeviation)
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return downsideDeviation
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def calcKurtosis(self):
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numberOfValues = self.timeFrame
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mean = self.averageMonthlyReturn
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kurtosis = (sum((self.monthlyReturn[x]-mean)**4 for x in range(
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0, numberOfValues, 1)))/((numberOfValues-1)*(self.standardDeviation ** 4))
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print('Kurtosis:', kurtosis)
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return kurtosis
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def calcSkewness(self):
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numberOfValues = self.timeFrame
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mean = self.averageMonthlyReturn
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skewness = (sum((self.monthlyReturn[x]-mean)**3 for x in range(
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0, numberOfValues, 1)))/((numberOfValues-1)*(self.standardDeviation ** 3))
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print('Skewness:', skewness)
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return skewness
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def calcBeta(self):
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beta = self.correlation * \
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(self.standardDeviation/Stock.benchmarkStandardDeviation)
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print('Beta:', beta)
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return beta
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def calcAlpha(self):
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alpha = self.averageMonthlyReturn - \
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(Stock.riskFreeRate+((Stock.benchmarkAverageMonthlyReturn -
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Stock.riskFreeRate) * self.beta))
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print('Alpha:', alpha)
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return alpha
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def calcSharpe(self):
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sharpe = (self.averageMonthlyReturn - Stock.riskFreeRate) / \
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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
|
|
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 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 is True:
|
|
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 (to work with linear regression module):', marketCap)
|
|
return marketCap
|
|
|
|
elif Stock.indicator == 'Turnover':
|
|
if stockType == 'Stock':
|
|
print(self.name, 'is a stock, and therefore does not have turnover')
|
|
return 'Stock'
|
|
|
|
if 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
|
|
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'):
|
|
raw_html = requests.get(url).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
|
|
|
|
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 indicatorValueFound is False:
|
|
if Stock.indicator == 'Expense Ratio':
|
|
indicatorValue = str(
|
|
input(Stock.indicator + ' for ' + 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: '))
|
|
elif Stock.indicator == 'Market Capitalization':
|
|
indicatorValue = str(
|
|
input(Stock.indicator + ' of ' + self.name + ': '))
|
|
else:
|
|
# print('Something is wrong. Indicator was not found. Ending program.')
|
|
cprint(
|
|
'Something is wrong. Indicator was not found. Ending program.', 'white', 'on_red')
|
|
exit()
|
|
|
|
if Functions.strintIsFloat(indicatorValue) is True:
|
|
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) is True:
|
|
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. Please use a benchmark from the list')
|
|
|
|
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
|
|
while found is False:
|
|
print('\nMethods:')
|
|
method = 0
|
|
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)',
|
|
'Yahoo top mutual funds (25)']
|
|
|
|
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) is True:
|
|
method = int(method)
|
|
if method == 0 or method > len(methods):
|
|
print('Please choose a valid method')
|
|
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', '_test_runner.py',
|
|
'CODE-OF-CONDUCT.md']
|
|
# 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 stocksFound is False:
|
|
fileName = str(input('What is the file number/name? '))
|
|
if Functions.stringIsInt(fileName) is True:
|
|
if int(fileName) < len(listOfFiles)+1 and int(fileName) > 0:
|
|
fileName = listOfFiles[int(fileName)-1]
|
|
print(fileName)
|
|
if Functions.fileExists(fileName) is True:
|
|
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 isInteger is False:
|
|
temp = input('Number of stocks to analyze (2 minimum): ')
|
|
isInteger = Functions.stringIsInt(temp)
|
|
if isInteger is True:
|
|
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)
|
|
|
|
print('\n' + str(len(listOfStocks)) + ' mutual funds total')
|
|
|
|
elif method == 4:
|
|
listOfStocks = []
|
|
url = 'https://www.thestreet.com/topic/21421/top-rated-mutual-funds.html'
|
|
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('thestreet-stocks.txt', 'w')
|
|
r = soup.find_all('a')
|
|
for k in r:
|
|
if len(k.text.strip()) == 5:
|
|
n = re.findall(r'^/quote/.*\.html', k['href'])
|
|
if len(n) != 0:
|
|
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)
|
|
|
|
print('\n' + str(len(listOfStocks)) + ' mutual funds total')
|
|
|
|
elif method == 5:
|
|
listOfStocks = []
|
|
url = 'http://money.com/money/4616747/best-mutual-funds-etfs-money-50/'
|
|
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('money.com-stocks.txt', 'w')
|
|
r = soup.find_all('td')
|
|
|
|
for k in r:
|
|
t = k.text.strip()
|
|
if '(' in t and ')' in t:
|
|
t = t.split('(')[1]
|
|
t = t.split(')')[0]
|
|
print(t, end=' ')
|
|
listOfStocks.append(t)
|
|
file.write(str(t + '\n'))
|
|
file.close()
|
|
|
|
for i in range(0, len(listOfStocks), 1):
|
|
stockName = listOfStocks[i].upper()
|
|
listOfStocks[i] = Stock()
|
|
listOfStocks[i].setName(stockName)
|
|
|
|
print('\n' + str(len(listOfStocks)) + ' mutual funds total')
|
|
|
|
elif method == 6:
|
|
listOfStocks = []
|
|
listOfStocksOriginal = []
|
|
url = 'https://www.investors.com/etfs-and-funds/mutual-funds/best-mutual-funds-beating-sp-500-over-last-1-3-5-10-years/'
|
|
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('investors-stocks.txt', 'w')
|
|
r = soup.find_all('td')
|
|
|
|
for k in r:
|
|
t = k.text.strip()
|
|
if len(t) == 5 and Functions.strintIsFloat(t) is False:
|
|
if t not in listOfStocksOriginal or listOfStocksOriginal == []:
|
|
if t[-1] != '%':
|
|
listOfStocksOriginal.append(t)
|
|
print(t, 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)
|
|
|
|
print('\n' + str(len(listOfStocks)) + ' mutual funds total')
|
|
|
|
elif method == 7:
|
|
listOfStocks = []
|
|
url = 'https://finance.yahoo.com/screener/predefined/top_mutual_funds/'
|
|
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('yahoo-stocks.txt', 'w')
|
|
r = soup.find_all('a', attrs={'class': 'Fw(600)'})
|
|
|
|
for k in r:
|
|
t = k.text.strip()
|
|
if len(t) == 5 and t == t.upper():
|
|
print(t, 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)
|
|
|
|
print('\n' + str(len(listOfStocks)) + ' mutual funds total')
|
|
|
|
if len(listOfStocks) < 2:
|
|
print('Please choose another method')
|
|
else:
|
|
found = True
|
|
|
|
return listOfStocks
|
|
|
|
|
|
def asyncData(benchmark, listOfStocks):
|
|
# Make list of urls to send requests to
|
|
urlList = []
|
|
# Benchmark
|
|
url = ''.join(('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=',
|
|
benchmark.name, '&outputsize=full&apikey=', apiAV))
|
|
urlList.append(url)
|
|
|
|
# Stocks
|
|
for i in range(0, len(listOfStocks), 1):
|
|
# Alpha Vantage
|
|
url = ''.join(('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=',
|
|
listOfStocks[i].name, '&outputsize=full&apikey=', apiAV))
|
|
urlList.append(url)
|
|
|
|
# Risk-free rate
|
|
url = ''.join(
|
|
('https://www.quandl.com/api/v3/datasets/USTREASURY/LONGTERMRATES.json?api_key=', apiQuandl))
|
|
urlList.append(url)
|
|
|
|
# Yahoo Finance
|
|
for i in range(0, len(listOfStocks), 1):
|
|
url = ''.join(('https://finance.yahoo.com/quote/',
|
|
listOfStocks[i].name, '?p=', listOfStocks[i].name))
|
|
urlList.append(url)
|
|
for i in range(0, len(listOfStocks), 1):
|
|
url = ''.join(
|
|
('https://finance.yahoo.com/lookup?s=', listOfStocks[i].name))
|
|
urlList.append(url)
|
|
|
|
# Send async requests
|
|
print('\nSending async requests (Assuming Alpha Vantage is first choice)')
|
|
with PoolExecutor(max_workers=3) as executor:
|
|
for _ in executor.map(sendAsync, urlList):
|
|
pass
|
|
|
|
return
|
|
|
|
|
|
def sendAsync(url):
|
|
time.sleep(random.randrange(0, 2))
|
|
cprint('Get:' + url, 'white', attrs=['dark'])
|
|
requests.get(url)
|
|
return
|
|
|
|
|
|
def timeFrameInit():
|
|
isInteger = False
|
|
while isInteger is False:
|
|
print(
|
|
'\nPlease enter the time frame in months (<60 recommended):', end='')
|
|
temp = input(' ')
|
|
isInteger = Functions.stringIsInt(temp)
|
|
if isInteger is True:
|
|
if int(temp) > 1 and int(temp) < 1000:
|
|
months = int(temp)
|
|
else:
|
|
print('Please enter a number greater than 1')
|
|
isInteger = False
|
|
else:
|
|
print('Please type an integer')
|
|
|
|
timeFrame = months
|
|
return timeFrame
|
|
|
|
|
|
def dataMain(listOfStocks):
|
|
i = 0
|
|
while i < len(listOfStocks):
|
|
|
|
try:
|
|
datesAndCloseList = Stock.datesAndClose(listOfStocks[i])
|
|
except:
|
|
print('Error retrieving data')
|
|
datesAndCloseList = 'N/A'
|
|
if datesAndCloseList == 'N/A':
|
|
del listOfStocks[i]
|
|
if len(listOfStocks) == 0:
|
|
# print('No stocks to analyze. Ending program')
|
|
cprint('No stocks to analyze. Ending program', 'white', 'on_red')
|
|
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
|
|
|
|
cprint('\nGet:' + 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)
|
|
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 != 200:
|
|
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):
|
|
cprint('\nCalculating return statistics\n', 'white', attrs=['underline'])
|
|
print('Getting risk-free rate from current 10-year treasury bill rates', end='\n\n')
|
|
Stock.riskFreeRate = riskFreeRate()
|
|
cprint(benchmark.name, 'cyan')
|
|
benchmark.monthlyReturn = Stock.calcMonthlyReturn(benchmark)
|
|
if benchmark.monthlyReturn == 'N/A':
|
|
# print('Please use a lower time frame\nEnding program')
|
|
cprint('Please use a lower time frame. Ending program', 'white', 'on_red')
|
|
exit()
|
|
benchmark.averageMonthlyReturn = Stock.calcAverageMonthlyReturn(benchmark)
|
|
benchmark.standardDeviation = Stock.calcStandardDeviation(benchmark)
|
|
|
|
# Make benchmark data global
|
|
Stock.benchmarkDates = benchmark.dates
|
|
Stock.benchmarkCloseValues = benchmark.closeValues
|
|
Stock.benchmarkAverageMonthlyReturn = benchmark.averageMonthlyReturn
|
|
Stock.benchmarkStandardDeviation = benchmark.standardDeviation
|
|
|
|
i = 0
|
|
while i < len(listOfStocks):
|
|
cprint('\n' + listOfStocks[i].name, 'cyan')
|
|
|
|
# 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].monthlyReturn = Stock.calcMonthlyReturn(
|
|
listOfStocks[i])
|
|
if listOfStocks[i].monthlyReturn == 'N/A':
|
|
print('Removing ' + listOfStocks[i].name + ' from list of stocks')
|
|
del listOfStocks[i]
|
|
if len(listOfStocks) == 0:
|
|
print('No stocks fit time frame. Ending program')
|
|
cprint('No stocks fit time frame. Ending program',
|
|
'white', 'on_red')
|
|
exit()
|
|
else:
|
|
listOfStocks[i].averageMonthlyReturn = Stock.calcAverageMonthlyReturn(
|
|
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
|
|
|
|
cprint('\nNumber of stocks that fit time frame: ' +
|
|
str(len(listOfStocks)), 'green')
|
|
if len(listOfStocks) < 2:
|
|
# print('Cannot proceed to the next step. Exiting program.')
|
|
cprint('Cannot proceed to the next step. Exiting program.',
|
|
'white', 'on_red')
|
|
exit()
|
|
|
|
|
|
def outlierChoice():
|
|
print('\nWould you like to remove indicator outliers?')
|
|
print('[1] Yes\n[2] No')
|
|
found = False
|
|
while found is False:
|
|
outlierChoice = str(input('Choice: '))
|
|
if Functions.stringIsInt(outlierChoice):
|
|
if int(outlierChoice) == 1:
|
|
return True
|
|
elif int(outlierChoice) == 2:
|
|
return False
|
|
else:
|
|
print('Please enter 1 or 2')
|
|
elif outlierChoice.lower() == 'yes':
|
|
return True
|
|
elif outlierChoice.lower() == 'no':
|
|
return False
|
|
else:
|
|
print('Not valid. Please enter a number or yes or no.')
|
|
|
|
|
|
def indicatorInit():
|
|
# Runs correlation or regression study
|
|
indicatorFound = False
|
|
listOfIndicators = ['Expense Ratio',
|
|
'Market Capitalization', 'Turnover', 'Persistence']
|
|
print('\n', end='')
|
|
print('List of indicators:')
|
|
for i in range(0, len(listOfIndicators), 1):
|
|
print('[' + str(i + 1) + '] ' + listOfIndicators[i])
|
|
while indicatorFound is False:
|
|
indicator = str(input('Choose an indicator from the list: '))
|
|
|
|
# indicator = 'expense ratio' # TESTING
|
|
|
|
if Functions.stringIsInt(indicator) is True:
|
|
if int(indicator) <= 4 and int(indicator) > 0:
|
|
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 is False:
|
|
print('Please choose an indicator from the list\n')
|
|
|
|
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)
|
|
|
|
if Stock.plotIndicatorRegression is True:
|
|
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 Monthly Return',
|
|
'Sharpe Ratio', 'Sortino Ratio', 'Treynor Ratio', 'Alpha']
|
|
|
|
plt.title(Stock.indicator + ' and ' + listOfReturnStrings[i])
|
|
if Stock.indicator == 'Expense Ratio' or Stock.indicator == 'Turnover':
|
|
plt.xlabel(Stock.indicator + ' (%)')
|
|
elif Stock.indicator == 'Persistence':
|
|
plt.xlabel(Stock.indicator + ' (Difference in average monthly return)')
|
|
elif Stock.indicator == 'Market Capitalization':
|
|
plt.xlabel(Stock.indicator + ' (millions)')
|
|
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(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()
|
|
sys.stdout.write(
|
|
' \r')
|
|
sys.stdout.flush()
|
|
plt.close()
|
|
|
|
|
|
def persistenceTimeFrame():
|
|
print('\nTime frame you chose was', Stock.timeFrame, 'months')
|
|
persTimeFrameFound = False
|
|
while persTimeFrameFound is False:
|
|
persistenceTimeFrame = str(
|
|
input('Please choose how many months to measure persistence: '))
|
|
if Functions.stringIsInt(persistenceTimeFrame) is True:
|
|
if int(persistenceTimeFrame) > 0 and int(persistenceTimeFrame) < Stock.timeFrame - 1:
|
|
persistenceTimeFrame = int(persistenceTimeFrame)
|
|
persTimeFrameFound = True
|
|
else:
|
|
print('Please choose a number between 0 and',
|
|
Stock.timeFrame, end='\n')
|
|
else:
|
|
print('Please choose an integer between 0 and',
|
|
Stock.timeFrame, end='\n')
|
|
|
|
return persistenceTimeFrame
|
|
|
|
|
|
def indicatorMain(listOfStocks):
|
|
cprint('\n' + str(Stock.indicator) + '\n', 'white', attrs=['underline'])
|
|
|
|
listOfStocksIndicatorValues = []
|
|
for i in range(0, len(listOfStocks), 1):
|
|
cprint(listOfStocks[i].name, 'cyan')
|
|
if Stock.indicator == 'Persistence':
|
|
listOfStocks[i].indicatorValue = Stock.calcPersistence(
|
|
listOfStocks[i])
|
|
else:
|
|
try:
|
|
listOfStocks[i].indicatorValue = Stock.scrapeYahooFinance(
|
|
listOfStocks[i])
|
|
except:
|
|
print('Error retrieving indicator data')
|
|
listOfStocks[i].indicatorValue = 'N/A'
|
|
print('')
|
|
|
|
if listOfStocks[i].indicatorValue == 'N/A':
|
|
listOfStocks[i].indicatorValue = Stock.indicatorManual(
|
|
listOfStocks[i])
|
|
elif listOfStocks[i].indicatorValue == 'Stock':
|
|
print('Removing ' + listOfStocks[i].name + ' from list of stocks')
|
|
del listOfStocks[i]
|
|
if len(listOfStocks) < 2:
|
|
# print('Not able to go to the next step. Ending program')
|
|
cprint('Not able to go to the next step. Ending program',
|
|
'white', 'on_red')
|
|
exit()
|
|
|
|
listOfStocksIndicatorValues.append(listOfStocks[i].indicatorValue)
|
|
|
|
# Remove outliers
|
|
if Stock.removeOutliers is True:
|
|
cprint('\nRemoving outliers\n', 'white', attrs=['underline'])
|
|
temp = Functions.removeOutliers(listOfStocksIndicatorValues)
|
|
if temp[0] == listOfStocksIndicatorValues:
|
|
print('No indicator outliers\n')
|
|
else:
|
|
print('First quartile:', temp[2], ', Median:', temp[3],
|
|
', Third quartile:', temp[4], 'Interquartile range:', temp[5])
|
|
# print('Original list:', listOfStocksIndicatorValues)
|
|
listOfStocksIndicatorValues = temp[0]
|
|
i = 0
|
|
while i < len(listOfStocks)-1:
|
|
for j in temp[1]:
|
|
if listOfStocks[i].indicatorValue == j:
|
|
print('Removing', listOfStocks[i].name, 'because it has a',
|
|
Stock.indicator.lower(), 'value of', listOfStocks[i].indicatorValue)
|
|
del listOfStocks[i]
|
|
i = i - 1
|
|
break
|
|
i += 1
|
|
# print('New list:', listOfStocksIndicatorValues, '\n')
|
|
print('')
|
|
|
|
# Calculate data
|
|
cprint('Calculating correlation and linear regression\n',
|
|
'white', attrs=['underline'])
|
|
|
|
listOfReturns = [] # A list that matches the above list with return values [[averageMonthlyReturn1, aAR2, aAR3], [sharpe1, sharpe2, sharpe3], etc.]
|
|
tempListOfReturns = []
|
|
for i in range(0, len(listOfStocks), 1):
|
|
tempListOfReturns.append(listOfStocks[i].averageMonthlyReturn)
|
|
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 Monthly Return',
|
|
'Sharpe Ratio', 'Sortino Ratio', 'Treynor Ratio', 'Alpha']
|
|
for i in range(0, len(Stock.indicatorCorrelation), 1):
|
|
print('Correlation for ' + 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 checkConfig(fileName):
|
|
if Functions.fileExists(fileName) is False:
|
|
return 'N/A'
|
|
file = open(fileName, 'r')
|
|
n = file.read()
|
|
file.close()
|
|
if Functions.validateJson(n) is False:
|
|
print('Config file is not valid')
|
|
return 'N/A'
|
|
t = json.loads(n)
|
|
r = t['Config']
|
|
return r
|
|
|
|
|
|
def continueProgram():
|
|
found = False
|
|
print('Would you like to rerun the program?')
|
|
return Functions.trueOrFalse()
|
|
|
|
|
|
def plotIndicatorRegression():
|
|
if Functions.detectDisplay() is True:
|
|
if Functions.checkPackage('matplotlib') is False:
|
|
print(
|
|
'matplotlib is not installed. \nIf you would like to install' +
|
|
' it (and have a display), run `pip install matplotlib`')
|
|
return False
|
|
else:
|
|
print('\nWould you like to plot indicator linear regression '
|
|
'results?')
|
|
plotLinear = Functions.trueOrFalse()
|
|
if plotLinear is True:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
|
|
def main():
|
|
'''
|
|
Check config file for errors and if not, then use values
|
|
#! Only use this if you know it is exactly correct. I haven't spent much
|
|
#! time debugging this
|
|
'''
|
|
Stock.config = checkConfig('config.json')
|
|
|
|
runningProgram = True
|
|
while runningProgram is True:
|
|
|
|
if Stock.config == 'N/A':
|
|
# Check that all required packages are installed
|
|
packagesInstalled = Functions.checkPackages(
|
|
['numpy', 'requests', 'bs4', 'requests_cache', 'halo'])
|
|
if not packagesInstalled:
|
|
exit()
|
|
else:
|
|
print('All required packages are installed')
|
|
|
|
# Check python version is above 3.3
|
|
pythonVersionGood = Functions.checkPythonVersion()
|
|
if not pythonVersionGood:
|
|
exit()
|
|
|
|
# Test internet connection
|
|
internetConnection = Functions.isConnected()
|
|
if not internetConnection:
|
|
exit()
|
|
else:
|
|
Functions.getJoke()
|
|
# Functions.getWeather()
|
|
|
|
# Choose benchmark and makes it class Stock
|
|
benchmark = benchmarkInit()
|
|
# Add it to a list to work with other functions
|
|
benchmarkAsList = [benchmark]
|
|
|
|
# Asks for stock(s) ticker and makes them class Stock
|
|
listOfStocks = stocksInit()
|
|
|
|
# Determine time frame (Years)
|
|
timeFrame = timeFrameInit()
|
|
Stock.timeFrame = timeFrame
|
|
|
|
# Choose indicator
|
|
Stock.indicator = indicatorInit()
|
|
# Choose time frame for initial persistence
|
|
if Stock.indicator == 'Persistence':
|
|
Stock.persTimeFrame = persistenceTimeFrame()
|
|
|
|
# Choose whether to remove outliers or not
|
|
Stock.removeOutliers = outlierChoice()
|
|
|
|
# Check if matplotlib is installed and if so, ask user if
|
|
# they want to plot
|
|
Stock.plotIndicatorRegression = plotIndicatorRegression()
|
|
|
|
else:
|
|
if Stock.config['Check Packages'] is not False:
|
|
packagesInstalled = Functions.checkPackages(
|
|
['numpy', 'requests', 'bs4', 'requests_cache', 'halo'])
|
|
if not packagesInstalled:
|
|
exit()
|
|
else:
|
|
print('All required packages are installed')
|
|
|
|
if Stock.config['Check Python Version'] is not False:
|
|
pythonVersionGood = Functions.checkPythonVersion()
|
|
if not pythonVersionGood:
|
|
exit()
|
|
|
|
if Stock.config['Check Internet Connection'] is not False:
|
|
internetConnection = Functions.isConnected()
|
|
if not internetConnection:
|
|
exit()
|
|
if Stock.config['Get Joke'] is not False:
|
|
Functions.getJoke()
|
|
|
|
benchmarksTicker = ['SPY', 'DJIA', 'VTHR', 'EFT']
|
|
if Stock.config['Benchmark'] in benchmarksTicker:
|
|
benchmark = Stock()
|
|
benchmark.setName(str(Stock.config['Benchmark']))
|
|
benchmarkAsList = [benchmark]
|
|
else:
|
|
benchmark = benchmarkInit()
|
|
benchmarkAsList = [benchmark]
|
|
|
|
listOfStocks = stocksInit()
|
|
|
|
if int(Stock.config['Time Frame']) >= 2:
|
|
timeFrame = int(Stock.config['Time Frame'])
|
|
else:
|
|
timeFrame = timeFrameInit()
|
|
Stock.timeFrame = timeFrame # Needs to be a global variable for all stocks
|
|
|
|
indicators = ['Expense Ratio',
|
|
'Market Capitalization', 'Turnover', 'Persistence']
|
|
if Stock.config['Indicator'] in indicators:
|
|
Stock.indicator = Stock.config['Indicator']
|
|
else:
|
|
Stock.indicator = indicatorInit()
|
|
|
|
if Stock.indicator == 'Persistence':
|
|
Stock.persTimeFrame = persistenceTimeFrame()
|
|
|
|
# Choose whether to remove outliers or not
|
|
if Stock.config['Remove Outliers'] is not False:
|
|
Stock.removeOutliers = True
|
|
else:
|
|
Stock.removeOutliers = outlierChoice()
|
|
|
|
# Send async request to AV for listOfStocks and benchmark
|
|
# asyncData(benchmark, listOfStocks)
|
|
|
|
# Gather data for benchmark and stock(s)
|
|
cprint('\nGathering data', 'white', attrs=['underline'])
|
|
dataMain(benchmarkAsList)
|
|
dataMain(listOfStocks)
|
|
|
|
# Calculate return for benchmark and stock(s)
|
|
returnMain(benchmark, listOfStocks)
|
|
|
|
# Choose indicator and calculate correlation with indicator
|
|
indicatorMain(listOfStocks)
|
|
|
|
# Decide if running program again
|
|
print('')
|
|
runningProgram = continueProgram()
|
|
print('')
|
|
|
|
exit()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|
|
|
|
'''
|
|
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/>.
|
|
'''
|