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va-project/indexer/indexer.py

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Python
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import sys
sys.path.append('../group-1')
import pandas as pd
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from scraper.top100_extractor import programming_crime_list
from sklearn import preprocessing
pd.set_option('display.max_rows', 500)
def get_peg(ticker: str):
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# Read current ratios .csv. Check if it exists
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
current_ratios['asOfDate'] = pd.to_datetime(current_ratios['asOfDate'])
# Sorting per Date
current_ratios = current_ratios.sort_values('asOfDate', ascending=False)
# Drop NaN pandas values
current_ratios = current_ratios.dropna()
# Take first value (the last peg ratio)
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# If it does not exist, it returns 0
print(ticker)
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):
# Read balance sheet .csv
balance_sheet = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_balance_sheet_4Y+4Q.csv', index_col=[0])
# Convert Object to DateTime
balance_sheet['asOfDate'] = pd.to_datetime(balance_sheet['asOfDate'])
# Sorting per Date
balance_sheet = balance_sheet.sort_values('asOfDate', ascending=False)
# Drop NaN pandas values
balance_sheet = balance_sheet.dropna()
# Create financial health column
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try:
balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets']
except KeyError:
return "NoDebt"
# Get financial health
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financial_health = balance_sheet['financial_health'].iloc[:1]
return financial_health.values[0]
def estimated_growth(ticker: str):
# Read 5 years growth estimates
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
def past_performance_earnings(ticker: str):
# Read earnings csv
earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv', index_col=[0])
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# Performance
performance_index = round((earnings['epsActual'].sum() - earnings['epsEstimate'].sum()) / earnings['epsEstimate'].sum() * 100, 2)
return performance_index
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def normalizer():
# Read Not_normalized .csv
not_normalized = pd.read_csv('Elaborated_Data/Not_Normalized.csv')
# Takes values for Valuation and compute normalization
v_low, v_up = not_normalized['Valuation'].min(), not_normalized['Valuation'].max()
# v_values = (100 - 0) * ((not_normalized['Valuation'] - v_low) / v_up - v_low) + 0
v_values = 240 / not_normalized['Valuation']
not_normalized['Valuation'] = v_values
# # Takes values for financial health and compute normalization
# fh_low, fh_up = not_normalized['Financial Health'],min(), not_normalized['Financial Health'].max()
# fh_values = (100 - 0) * ((not_normalized['Financial Health'] - fh_low) / fh_up - fh_low) + 0
# not_normalized['Financial Health'] = fh_values
# eg_low, eg_up = not_normalized['Estimated Growth'],min(), not_normalized['Estimated Growth'].max()
# eg_values = (100 - 0) * ((not_normalized['Financial Health'] - fh_low) / fh_up - fh_low) + 0
print(not_normalized)
def create_df(companies_list):
# Dictionary
d = {
'Ticker': [],
'Valuation' : [],
'Financial Health': [],
'Estimated Growth': [],
'Past Performance': []
}
# Loop to get all the data
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))
# Dataframe
df = pd.DataFrame(data=d)
# Save to csv
df.to_csv("Elaborated_Data/Not_Normalized.csv")
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def main():
# create_df(programming_crime_list)
normalizer()
# print(get_peg('GOOGL')) # < 1 ( GREEN); > 1 (RED); = 1 (ORANGE)
# print(get_financial_health('GOOGL')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)
# print(estimated_growth('GOOGL')) # < 0 (RED); 0 < x < 8% (ORANGE); < 8 % (GREEN)
# print(past_performance_earnings('GOOGL'), "%") # -100 < x < 0 (RED); = 0 (ORANGE); 0 < x < 100 (GREEN)
if __name__ == '__main__':
main()