p22.py

import pandas as pd
import os
import time
from datetime import datetime
import re
from time import mktime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
style.use("dark_background")

def Forward(
    gather=[
      "Total Debt/Equity",
      'Trailing P/E',
      'Price/Sales',
      'Price/Book',
      'Profit Margin',
      'Operating Margin',
      'Return on Assets',
      'Return on Equity',
      'Revenue Per Share',
      'Market Cap',
      'Enterprise Value',
      'Forward P/E',
      'PEG Ratio',
      'Enterprise Value/Revenue',
      'Enterprise Value/EBITDA',
      'Revenue',
      'Gross Profit',
      'EBITDA',
      'Net Income Avl to Common ',
      'Diluted EPS',
      'Earnings Growth',
      'Revenue Growth',
      'Total Cash',
      'Total Cash Per Share',
      'Total Debt',
      'Current Ratio',
      'Book Value Per Share',
      'Cash Flow',
      'Beta',
      'Held by Insiders',
      'Held by Institutions',
      'Shares Short (as of',
      'Short Ratio',
      'Short % of Float',
      'Shares Short (prior '
    ]
  ):
  
  df = pd.DataFrame(
    columns = [
      'Date',
      'Unix',
      'Ticker',
      'Price',
      'stock_p_change',
      'SP500',
      'sp500_p_change',
      'Difference',
      ##############
      'DE Ratio',
      'Trailing P/E',
      'Price/Sales',
      'Price/Book',
      'Profit Margin',
      'Operating Margin',
      'Return on Assets',
      'Return on Equity',
      'Revenue Per Share',
      'Market Cap',
      'Enterprise Value',
      'Forward P/E',
      'PEG Ratio',
      'Enterprise Value/Revenue',
      'Enterprise Value/EBITDA',
      'Revenue',
      'Gross Profit',
      'EBITDA',
      'Net Income Avl to Common ',
      'Diluted EPS',
      'Earnings Growth',
      'Revenue Growth',
      'Total Cash',
      'Total Cash Per Share',
      'Total Debt',
      'Current Ratio',
      'Book Value Per Share',
      'Cash Flow',
      'Beta',
      'Held by Insiders',
      'Held by Institutions',
      'Shares Short (as of',
      'Short Ratio',
      'Short % of Float',
      'Shares Short (prior ',                                
      ##############
      'Status'
    ]
  )

  file_list = os.listdir("forward")
  for each_file in file_list[1:]:
    ticker = each_file.split(".html")[0]
    full_file_path = "forward/"+each_file
    source = open(full_file_path,'r').read()
    try:
      value_list = []
      for each_data in gather:
        try:
          regex = re.escape(each_data) + r'.*?(\d{1,8}\.\d{1,8}M?B?|N/A)%?</td>'
          value = re.search(regex, source)
          value = (value.group(1))
          if "B" in value:
            value = float(value.replace("B",''))*1000000000
          elif "M" in value:
            value = float(value.replace("M",''))*1000000
          value_list.append(value)
        except Exception as e:
          value = "N/A"
          value_list.append(value)

      # if value_list.count("N/A") > 0:
      if value_list.count("N/A") > 15:
        pass
      else:
        df = df.append(
          {
            'Date':"N/A",
            'Unix':"N/A",
            'Ticker':ticker,
            'Price':"N/A",
            'stock_p_change':"N/A",
            'SP500':"N/A",
            'sp500_p_change':"N/A",
            'Difference':"N/A",
            'DE Ratio':value_list[0],
            #'Market Cap':value_list[1],
            'Trailing P/E':value_list[1],
            'Price/Sales':value_list[2],
            'Price/Book':value_list[3],
            'Profit Margin':value_list[4],
            'Operating Margin':value_list[5],
            'Return on Assets':value_list[6],
            'Return on Equity':value_list[7],
            'Revenue Per Share':value_list[8],
            'Market Cap':value_list[9],
            'Enterprise Value':value_list[10],
            'Forward P/E':value_list[11],
            'PEG Ratio':value_list[12],
            'Enterprise Value/Revenue':value_list[13],
            'Enterprise Value/EBITDA':value_list[14],
            'Revenue':value_list[15],
            'Gross Profit':value_list[16],
            'EBITDA':value_list[17],
            'Net Income Avl to Common ':value_list[18],
            'Diluted EPS':value_list[19],
            'Earnings Growth':value_list[20],
            'Revenue Growth':value_list[21],
            'Total Cash':value_list[22],
            'Total Cash Per Share':value_list[23],
            'Total Debt':value_list[24],
            'Current Ratio':value_list[25],
            'Book Value Per Share':value_list[26],
            'Cash Flow':value_list[27],
            'Beta':value_list[28],
            'Held by Insiders':value_list[29],
            'Held by Institutions':value_list[30],
            'Shares Short (as of':value_list[31],
            'Short Ratio':value_list[32],
            'Short % of Float':value_list[33],
            'Shares Short (prior ':value_list[34],
            'Status':"N/A"
          },
          ignore_index=True)
    except Exception as e:
      pass

  # df.to_csv("forward_sample_NO_NA.csv")
  df.to_csv("forward_sample_WITH_NA.csv")

Forward()