160 lines
5.4 KiB
Python
160 lines
5.4 KiB
Python
import sys
|
|
sys.path.append('../group-1')
|
|
import math
|
|
import pandas as pd
|
|
import os
|
|
from scraper.top100_extractor import programming_crime_list
|
|
import numpy as np
|
|
from sklearn import preprocessing
|
|
import random
|
|
|
|
|
|
pd.set_option('display.max_rows', 500)
|
|
|
|
def get_peg(ticker: str):
|
|
|
|
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
|
|
|
|
current_ratios['asOfDate'] = pd.to_datetime(current_ratios['asOfDate']) #Convert Object to DateTime
|
|
|
|
current_ratios = current_ratios.sort_values('asOfDate', ascending=False) # Sorting per Date
|
|
current_ratios = current_ratios.dropna()
|
|
|
|
# Take first value (the last peg ratio)
|
|
# If it does not exist, it returns 0
|
|
try:
|
|
if len(current_ratios['PegRatio']) > 0:
|
|
peg_ratio = current_ratios['PegRatio'].iloc[:1]
|
|
else:
|
|
return 0.0
|
|
except KeyError:
|
|
return 0.0
|
|
return peg_ratio.values[0]
|
|
|
|
|
|
|
|
def get_financial_health(ticker: str):
|
|
|
|
balance_sheet = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_balance_sheet_4Y+4Q.csv', index_col=[0]) # Read balance sheet .csv
|
|
|
|
balance_sheet['asOfDate'] = pd.to_datetime(balance_sheet['asOfDate']) # Convert Object to DateTime
|
|
|
|
balance_sheet = balance_sheet.sort_values('asOfDate', ascending=False) # Sorting per Date
|
|
|
|
balance_sheet = balance_sheet.dropna()
|
|
|
|
# Create financial health column
|
|
try:
|
|
balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets']
|
|
except KeyError:
|
|
return 2.0
|
|
|
|
# Get financial health
|
|
financial_health = balance_sheet['financial_health'].iloc[:1]
|
|
|
|
return financial_health.values[0]
|
|
|
|
|
|
|
|
def estimated_growth(ticker: str):
|
|
|
|
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
|
|
|
|
return growth_estimated
|
|
|
|
|
|
def employees_over_time(ticker: str):
|
|
|
|
employee_df = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}meta_data.csv') #get df to retrieve employee number of the company
|
|
|
|
employee_number = employee_df.at[0, 'number_employees']
|
|
|
|
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
|
|
# previous years, using uniform distribution subtraction with the number at i. This makes so that the trend, once you reverse the list, is
|
|
# growing over the years with some random fluctuations (just like how the number of employees grows over time, it's not like y=x)
|
|
|
|
for i in range(0, 11):
|
|
|
|
lst.append(lst[i] + random.uniform(-0.2*lst[i], 0.1*lst[i]))
|
|
|
|
lst.reverse()
|
|
|
|
return lst
|
|
|
|
|
|
def past_performance_earnings(ticker: str):
|
|
|
|
earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv', index_col=[0]) # Read earnings csv
|
|
|
|
performance_index = round((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2) #Performance
|
|
|
|
return performance_index
|
|
|
|
|
|
|
|
def normalizer():
|
|
''' Normalize the dataframe columns to a range between 0 and 200'''
|
|
|
|
not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv') # Read Not_normalized .csv
|
|
|
|
v_values = (200/(1+math.e**( 0.2*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
|
|
not_normalized['Valuation'] = v_values
|
|
|
|
fh_values= (80/not_normalized['Financial Health'].mean())*not_normalized['Financial Health'] #FINANCIAL HEALTH STAT
|
|
not_normalized['Financial Health'] = fh_values
|
|
|
|
not_normalized['Estimated Growth'] = not_normalized['Estimated Growth'].str.strip("%").astype("float")
|
|
eg_values= (200/(1+math.e**( 0.08*(not_normalized['Estimated Growth'].mean()-not_normalized['Estimated Growth'])))) #ESTIMATED GROWTH STAT
|
|
for i in range(len(eg_values)):
|
|
eg_values[i] = float(round(eg_values[i],2))
|
|
not_normalized['Estimated Growth']= eg_values
|
|
|
|
pf_values = (200/(1+math.e**( 0.08*(not_normalized['Past Performance'].mean()-not_normalized['Past Performance'])))) #PAST PERFORMANCE
|
|
not_normalized['Past Performance'] = pf_values
|
|
|
|
# Create normalized dataframe for main page
|
|
not_normalized.to_csv(r'Elaborated_Data/normalized_data.csv')
|
|
|
|
def create_df(companies_list):
|
|
|
|
d = {
|
|
'Ticker': [],
|
|
'Valuation' : [],
|
|
'Financial Health': [],
|
|
'Estimated Growth': [],
|
|
'Past Performance': []
|
|
}
|
|
|
|
d_emp = {
|
|
'Ticker': [],
|
|
'Employees_over_time': []
|
|
}
|
|
|
|
for company in companies_list:
|
|
d['Ticker'].append(company)
|
|
d['Valuation'].append(get_peg(company))
|
|
d['Financial Health'].append(get_financial_health(company))
|
|
d['Estimated Growth'].append(estimated_growth(company))
|
|
d['Past Performance'].append(past_performance_earnings(company))
|
|
d_emp['Ticker'].append(company)
|
|
d_emp['Employees_over_time'].append(employees_over_time(company))
|
|
|
|
df = pd.DataFrame(data=d)
|
|
df.to_csv("Elaborated_Data/Not_Normalized.csv")
|
|
|
|
df_employees = pd.DataFrame(data=d_emp)
|
|
df_employees.to_csv(r"Elaborated_Data/employees_over_time.csv")
|
|
|
|
|
|
|
|
def main():
|
|
|
|
if not os.path.exists(r"Elaborated_Data"):
|
|
os.mkdir(r"Elaborated_Data")
|
|
|
|
create_df(programming_crime_list)
|
|
normalizer()
|
|
|
|
if __name__ == '__main__':
|
|
main()
|
|
|