Main_Indicators.py

"""

5/29/2019
Robert Sloan, Bill Paceman
replicating Al Zmyslowski's Technical and Economic Indicators
Based on Al Zmyslowski's Stock Market Review - Technical & Economic Indicators
from the American Association of Individual Investors www.aaii.com

TO DO

  Store stock data in a way that calculations could be accuratly recreated
  Put Spreadsheet in Al's order
  for the purpose of the graphs I may prefer to have more than a years worth of data

"""
# Import libraries
import pandas as pd
pd.set_option('display.max_rows', 100)
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 400)
import numpy as np
import os # misc operating system interfaces
from glob import glob # unix pathname functions


#def loadTickers(start_date,record_date,todays_date,tickers,path=None):  # not sure what to use path for
def loadTickers(start_date,record_date,todays_date,tickers):
  #print(" in loadTickers")
  from distutils.version import LooseVersion, StrictVersion
  from pandas_datareader import data
  if StrictVersion(pd.__version__) < StrictVersion(u'0.23.4'):
    print ("Error: Old version of pandas used in loadTickers gives columns of objects, not floats")
  print ("Pandas ",pd.__version__ , " Numpy ", np.__version__)
  if not os.path.exists('Figures'):
    os.makedirs('Figures')
  if not os.path.exists('Spreadsheets'):
    os.makedirs('Spreadsheets')

  return data.DataReader(tickers,
                       start=start_date,
                       end=record_date,
                       data_source='yahoo')['Adj Close']


def allIndicators(moduleList):
  import datetime
  #print(" in allIndicators ")
  # create a date range
  currentDT = datetime.datetime.now()
  todays_date = str(currentDT.year) + "-" + str(currentDT.month) + "-"+  str(currentDT.day)
  # NOTE: can make the record_date a date in the past to generate previous Technical and Economic indicators
  record_date = todays_date
  #oneYearAgo = (currentDT - datetime.timedelta(366))
  # two years of data is needed to calculate 12M Gain and Fund X Score indicators
  twoYearsAgo = (currentDT - datetime.timedelta(731))
  start_date = str(twoYearsAgo.year) + "-" + str(twoYearsAgo.month) + "-"+  str(twoYearsAgo.day)

  # Load the tickers for date range
  data = loadTickers(start_date,record_date,todays_date,["SPY","BIL"])
  #print(" exited loadTickers")
  # Determine last market day of last month
  # Get the last day of last month by taking the first day of this month
  # and subtracting 1 day.
  lastDay = datetime.date(currentDT.year, currentDT.month, 1) - datetime.timedelta(1)
  # Set the day to 1 gives us the start of the month
  firstDay = lastDay.replace(day=1)
  last_EOM_date = data["SPY"].loc[firstDay:lastDay].last('1D').index # get the last market day last month

  strLast_EOM_date = last_EOM_date.strftime("%Y-%m-%d") # get the last market day last month
  print("Last Market Day last Month", str(strLast_EOM_date[0]))
  # print("type(strLast_EOM_date) = ", type(str(strLast_EOM_date[0])))
  # last_EOM_date = str(strLast_EOM_date[0])
  # Determine last market day of the month before last
  # Get the last day of last month by taking the first day of last month
  # and subtracting 1 day.
  # Set the day to 1 gives us the start of the month

  if (currentDT.month > 1):
    lastDay = datetime.date(currentDT.year, currentDT.month-1, 1) - datetime.timedelta(1)
  else:
    lastDay = datetime.date(currentDT.year-1, currentDT.month+11, 1) - datetime.timedelta(1)

  firstDay = lastDay.replace(day=1)
  previous_EOM_date = data["SPY"].loc[firstDay:lastDay].last('1D').index

  # get the last market day last month

  strPrevious_EOM_date = previous_EOM_date.strftime('%Y-%m-%d') # and month before last
  print("Last Market Day Month before last", str(strPrevious_EOM_date[0]))

  # the following line is equivolent to :
  # spreadsheetData = []
  #for for module in moduleList: spreadsheetData.append(__import__(module).Indicator(data))

  spreadsheetList = [__import__(module).Indicator(data, record_date, last_EOM_date, previous_EOM_date) for module in moduleList]

  # print(" done with streadsheetList ")
  # Turn aggegated results into spreadsheet.
  # print("type(spreadsheetData) = ", type(spreadsheetData))
  # print (spreadsheetData)
  print("\n")
  spreadsheet = pd.DataFrame(spreadsheetList, columns = ['Technical Indicator','Frequency', 'MonthBeforeLast', 'LastMonth','Comment'])
  print(spreadsheet)

  writer = pd.ExcelWriter(str('Spreadsheets/'+record_date +'_indicator_sheet.xlsx'))

  spreadsheet.to_excel(writer,'Indicators', index=False)
  writer.save()


#===========================================================================================================
# the following line is to allow the calling of this code from a command line
if __name__ == '__main__':
  #print("... starting ...")
  modules =[name[:-3] for name in glob("i*.py")] #i.e.i01_10MSMASPY.py
  modules.sort()
  allIndicators(modules)