Raising investment requirement standards




As mentioned previously, you may find that you want to tweak the standards that make a company of interest to you. This tutorial covers how you might go about doing that.

import pandas as pd
import os
import time
from datetime import datetime

from time import mktime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
style.use("dark_background")

import re


path = "X:/Backups/intraQuarter"

def Key_Stats(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 ']):
    
    statspath = path+'/_KeyStats'
    stock_list = [x[0] for x in os.walk(statspath)]
    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'])

    sp500_df = pd.DataFrame.from_csv("YAHOO-INDEX_GSPC.csv")
    stock_df = pd.DataFrame.from_csv("stock_prices.csv")

    ticker_list = []

    for each_dir in stock_list[1:]:
        each_file = os.listdir(each_dir)
        ticker = each_dir.split("\\")[1]
        ticker_list.append(ticker)

##        starting_stock_value = False
##        starting_sp500_value = False

        
        if len(each_file) > 0:
            for file in each_file:
                date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html')
                unix_time = time.mktime(date_stamp.timetuple())
                full_file_path = each_dir+'/'+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)%?'
                            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)
                            

                            
                    try:
                        sp500_date = datetime.fromtimestamp(unix_time).strftime('%Y-%m-%d')
                        row = sp500_df[(sp500_df.index == sp500_date)]
                        sp500_value = float(row["Adjusted Close"])
                    except:
                        try:
                            sp500_date = datetime.fromtimestamp(unix_time-259200).strftime('%Y-%m-%d')
                            row = sp500_df[(sp500_df.index == sp500_date)]
                            sp500_value = float(row["Adjusted Close"])
                        except Exception as e:
                            print("fapsdolkfhasf;lsak",str(e))


                    one_year_later = int(unix_time + 31536000)

                    try:
                        sp500_1y = datetime.fromtimestamp(one_year_later).strftime('%Y-%m-%d')
                        row = sp500_df[(sp500_df.index == sp500_1y)]
                        sp500_1y_value = float(row["Adjusted Close"])
                    except:
                        try:
                            sp500_1y = datetime.fromtimestamp(one_year_later-259200).strftime('%Y-%m-%d')
                            row = sp500_df[(sp500_df.index == sp500_1y)]
                            sp500_1y_value = float(row["Adjusted Close"])
                        except Exception as e:
                            print("sp500 1 year later issue",str(e))



                    try:
                        stock_price_1y = datetime.fromtimestamp(one_year_later).strftime('%Y-%m-%d')
                        row = stock_df[(stock_df.index == stock_price_1y)][ticker.upper()]
                        

                        stock_1y_value = round(float(row),2)
##                        print(stock_1y_value)
##                        time.sleep(1555)
                    
                    except Exception as e:
                        try:
                            stock_price_1y = datetime.fromtimestamp(one_year_later-259200).strftime('%Y-%m-%d')
                            row = stock_df[(stock_df.index == stock_price_1y)][ticker.upper()]
                            stock_1y_value = round(float(row),2)
                        except Exception as e:
                            print("stock price:",str(e))




                    try:
                        stock_price = datetime.fromtimestamp(unix_time).strftime('%Y-%m-%d')
                        row = stock_df[(stock_df.index == stock_price)][ticker.upper()]
                        stock_price = round(float(row),2)
                    
                    except Exception as e:
                        try:
                            stock_price = datetime.fromtimestamp(unix_time-259200).strftime('%Y-%m-%d')
                            row = stock_df[(stock_df.index == stock_price)][ticker.upper()]
                            stock_price = round(float(row),2)
                        except Exception as e:
                            print("stock price:",str(e))




                    stock_p_change = round((((stock_1y_value - stock_price) / stock_price) * 100),2)
                    sp500_p_change = round((((sp500_1y_value - sp500_value) / sp500_value) * 100),2)

                    
                    difference = stock_p_change-sp500_p_change

                    if difference > 5:
                        status = 1
                    else:
                        status = 0


                    if value_list.count("N/A") > 15:
                        pass
                    else:
                        

                        df = df.append({'Date':date_stamp,
                                            'Unix':unix_time,
                                            'Ticker':ticker,
                                            
                                            'Price':stock_price,
                                            'stock_p_change':stock_p_change,
                                            'SP500':sp500_value,
                                            'sp500_p_change':sp500_p_change,
                                            'Difference':difference,
                                            '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],
                                             'key_stats_acc_perf_NO_NA_enhanced.csv")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':status},
                                           ignore_index=True)
                except Exception as e:
                    pass



    df.to_csv("key_stats_acc_perf_WITH_NA_enhanced.csv")
    
        

    


Key_Stats()
    



		

The next tutorial:





  • Intro to Machine Learning with Scikit Learn and Python
  • Simple Support Vector Machine (SVM) example with character recognition
  • Our Method and where we will be getting our Data
  • Parsing data
  • More Parsing
  • Structuring data with Pandas
  • Getting more data and meshing data sets
  • Labeling of data part 1
  • Labeling data part 2
  • Finally finishing up the labeling
  • Linear SVC Machine learning SVM example with Python
  • Getting more features from our data
  • Linear SVC machine learning and testing our data
  • Scaling, Normalizing, and machine learning with many features
  • Shuffling our data to solve a learning issue
  • Using Quandl for more data
  • Improving our Analysis with a more accurate measure of performance in relation to fundamentals
  • Learning and Testing our Machine learning algorithm
  • More testing, this time including N/A data
  • Back-testing the strategy
  • Pulling current data from Yahoo
  • Building our New Data-set
  • Searching for investment suggestions
  • Raising investment requirement standards
  • Testing raised standards
  • Streamlining the changing of standards