147 lines
5.0 KiB
Python
147 lines
5.0 KiB
Python
import sys
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sys.path.append('../VISUAL-AN-PROJECT')
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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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from sklearn import preprocessing
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pd.set_option('display.max_rows', 500)
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def get_peg(ticker: str):
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# Read current ratios .csv. Check if it exists
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current_ratios = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_current_ratios.csv', index_col=[0])
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# Convert Object to DateTime
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current_ratios['asOfDate'] = pd.to_datetime(current_ratios['asOfDate'])
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# Sorting per Date
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current_ratios = current_ratios.sort_values('asOfDate', ascending=False)
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# Drop NaN pandas values
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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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# Read balance sheet .csv
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balance_sheet = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_balance_sheet_4Y+4Q.csv', index_col=[0])
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# Convert Object to DateTime
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balance_sheet['asOfDate'] = pd.to_datetime(balance_sheet['asOfDate'])
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# Sorting per Date
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balance_sheet = balance_sheet.sort_values('asOfDate', ascending=False)
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# Drop NaN pandas values
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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['TotalDebt'] / balance_sheet['TotalAssets']
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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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# Read 5 years growth estimates
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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]
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return growth_estimated
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def past_performance_earnings(ticker: str):
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# Read earnings csv
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earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv', index_col=[0])
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# Performance
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performance_index = round((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2)
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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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# Read Not_normalized .csv
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not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')
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# Elaborate Valuation column
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v_values = (200/(1+math.e**( 0.2*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
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not_normalized['Valuation'] = v_values
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# Elaborate Financial health column
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fh_values= (80/not_normalized['Financial Health'].mean())*not_normalized['Financial Health'] #FINANCIAL HEALTH STAT
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not_normalized['Financial Health'] = fh_values
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# Elaborate Estimated Growth column
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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**( 0.08*(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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# Elaborate Past Performance Column
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pf_values = (200/(1+math.e**( 0.08*(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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# Dictionary
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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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# Loop to get all the data
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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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# Dataframe
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df = pd.DataFrame(data=d)
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# Save to csv
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df.to_csv("Elaborated_Data/Not_Normalized.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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# print(get_peg('GOOGL')) # < 1 ( GREEN); > 1 (RED); = 1 (ORANGE)
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# print(get_financial_health('GOOGL')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)
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# print(estimated_growth('GOOGL')) # < 0 (RED); 0 < x < 8% (ORANGE); < 8 % (GREEN)
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# print(past_performance_earnings('GOOGL'), "%") # -100 < x < 0 (RED); = 0 (ORANGE); 0 < x < 100 (GREEN)
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if __name__ == '__main__':
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main()
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