56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
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import pandas as pd
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def get_peg(ticker: str):
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# Read current ratios .csv
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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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peg_ratio = current_ratios['PegRatio'][:1]
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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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balance_sheet['financial_health'] = balance_sheet['TotalDebt'] / balance_sheet['TotalAssets']
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# Get financial health
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financial_health = balance_sheet['financial_health'][: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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if __name__ == '__main__':
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print(get_peg('MCD')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)
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print(get_financial_health('MCD')) # < 1 (GREEN); > 1 (RED); = 1 (ORANGE)
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print(estimated_growth('MCD')) # < 0 (RED); 0 < x < 8% (ORANGE); < 8 % (GREEN)
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