160 lines
5.6 KiB
Python
160 lines
5.6 KiB
Python
import sys
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sys.path.append('../group-1')
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import math
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import pandas as pd
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import os
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from scraper.top100_extractor import programming_crime_list
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import numpy as np
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import random
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pd.set_option('display.max_rows', 500)
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def get_peg(ticker: str):
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current_ratios = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_current_ratios.csv', index_col=[0]) #Read current ratios .csv. Check if it exists
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current_ratios['asOfDate'] = pd.to_datetime(current_ratios['asOfDate']) #Convert Object to DateTime
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current_ratios = current_ratios.sort_values('asOfDate', ascending=False) # Sorting per Date
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current_ratios = current_ratios.dropna()
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# Take first value (the last peg ratio)
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# If it does not exist, it returns 0
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try:
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if len(current_ratios['PegRatio']) > 0:
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peg_ratio = current_ratios['PegRatio'].iloc[:1]
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else:
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return 0.0
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except KeyError:
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return 0.0
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return peg_ratio.values[0]
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def get_financial_health(ticker: str):
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balance_sheet = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_balance_sheet_4Y+4Q.csv', index_col=[0]) # Read balance sheet .csv
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balance_sheet['asOfDate'] = pd.to_datetime(balance_sheet['asOfDate']) # Convert Object to DateTime
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balance_sheet = balance_sheet.sort_values('asOfDate', ascending=False) # Sorting per Date
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balance_sheet = balance_sheet.dropna()
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# Create financial health column
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try:
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balance_sheet['financial_health'] = balance_sheet['TotalAssets'] / balance_sheet['TotalDebt']
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except KeyError:
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return 2.0
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# Get financial health
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financial_health = balance_sheet['financial_health'].iloc[:1]
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return financial_health.values[0]
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def estimated_growth(ticker: str):
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growth_estimated = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}5YGrowthEstimates.csv', index_col=[0])['5Y Growth estimate'].values[0] # Read 5 years growth estimates
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return growth_estimated
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def employees_over_time(ticker: str):
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employee_df = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}meta_data.csv') #get df to retrieve employee number of the company
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employee_number = employee_df.at[0, 'number_employees']
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lst = [employee_number] #What does this loop do? --> you start from the actual value of employee number of the company and then create absolutely false values for
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# previous years, using uniform distribution subtraction with the number at i. This makes so that the trend, once you reverse the list, is
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# growing over the years with some random fluctuations (just like how the number of employees grows over time, it's not like y=x)
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for i in range(0, 11):
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lst.append(lst[i] + random.uniform(-0.2*lst[i], 0.1*lst[i]))
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lst.reverse()
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return lst
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def past_performance_earnings(ticker: str):
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earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv', index_col=[0]) # Read earnings csv
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performance_index = round((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2) #Performance
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return performance_index
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def normalizer():
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''' Normalize the dataframe columns to a range between 0 and 200'''
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not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv') # Read Not_normalized .csv
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# v_values = (200/(1+math.e**( 0.1*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
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v_values = (200/(1+(1/9*not_normalized['Valuation']**2))) # VALUATION STAT
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not_normalized['Valuation'] = v_values
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# fh_values= (200/(1+math.e**( -0.1*(-not_normalized['Financial Health'].mean()+not_normalized['Financial Health'])))) #FINANCIAL HEALTH STAT
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fh_values = (200-200*math.e**(-0.138*not_normalized['Financial Health'])) #FINANCIAL HEALTH STAT
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not_normalized['Financial Health'] = fh_values
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not_normalized['Estimated Growth'] = not_normalized['Estimated Growth'].str.strip("%").astype("float")
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eg_values= (200/(1+math.e**( -0.1*(-not_normalized['Estimated Growth'].mean()+not_normalized['Estimated Growth'])))) #ESTIMATED GROWTH STAT
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for i in range(len(eg_values)):
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eg_values[i] = float(round(eg_values[i],2))
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not_normalized['Estimated Growth']= eg_values
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pf_values = (200/(1+math.e**( -0.05*(-not_normalized['Past Performance'].mean()+not_normalized['Past Performance'])))) #PAST PERFORMANCE
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not_normalized['Past Performance'] = pf_values
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# Create normalized dataframe for main page
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not_normalized.to_csv(r'Elaborated_Data/normalized_data.csv')
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def create_df(companies_list):
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d = {
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'Ticker': [],
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'Valuation' : [],
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'Financial Health': [],
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'Estimated Growth': [],
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'Past Performance': []
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}
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d_emp = {
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'Ticker': [],
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'Employees_over_time': []
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}
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for company in companies_list:
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d['Ticker'].append(company)
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d['Valuation'].append(get_peg(company))
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d['Financial Health'].append(get_financial_health(company))
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d['Estimated Growth'].append(estimated_growth(company))
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d['Past Performance'].append(past_performance_earnings(company))
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d_emp['Ticker'].append(company)
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d_emp['Employees_over_time'].append(employees_over_time(company))
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df = pd.DataFrame(data=d)
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df.to_csv("Elaborated_Data/Not_Normalized.csv")
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df_employees = pd.DataFrame(data=d_emp)
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df_employees.to_csv(r"Elaborated_Data/employees_over_time.csv")
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def main():
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if not os.path.exists(r"Elaborated_Data"):
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os.mkdir(r"Elaborated_Data")
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create_df(programming_crime_list)
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normalizer()
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if __name__ == '__main__':
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main()
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