2023-05-17 11:42:56 +00:00
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import sys
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sys.path.append('../group-1')
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import pandas as pd
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from scraper.top100_extractor import programming_crime_list
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def stock_price_time_series(ticker: str):
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# Read market price csv
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market_prices = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}_price_history.csv')
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return market_prices
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def eps_bar_chart(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')
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earnings = earnings[['symbol', 'epsActual', 'quarter']]
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return earnings
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def concatenate_price_history():
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# Declare empty dataframe
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unified_price_history = pd.DataFrame()
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for ticker in programming_crime_list:
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prices = stock_price_time_series(ticker)
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unified_price_history = pd.concat([prices, unified_price_history], ignore_index=True, axis=0)
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unified_price_history.to_csv('Elaborated_Data/price_history_data.csv')
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def concatenate_eps():
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# Declare empty dataframe
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eps_df = pd.DataFrame()
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for ticker in programming_crime_list:
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eps = eps_bar_chart(ticker)
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eps_df = pd.concat([eps, eps_df], ignore_index=True, axis=0)
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eps_df.to_csv('Elaborated_Data/eps_quarterly_bar_chart.csv')
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2023-05-29 13:56:15 +00:00
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def eps_comparison():
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# Read earnings csv
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eps_comp_df = pd.DataFrame()
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for ticker in programming_crime_list:
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earnings = pd.read_csv(f'Companies_Data/{ticker}_Data/{ticker}earnings.csv')
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earnings = earnings[['symbol', 'epsActual', 'epsEstimate', 'quarter']]
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earnings['epsDifferential'] = ((earnings['epsActual'] - earnings['epsEstimate']) / earnings['epsEstimate']) * 100
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earnings = earnings.drop(columns=['epsActual', 'epsEstimate'])
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earnings = earnings.rename(columns={'symbol': 'Ticker'})
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eps_comp_df = pd.concat([eps_comp_df, earnings], ignore_index=True, axis=0)
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# Save the earnings df to Elaborated_Data
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eps_comp_df.to_csv(f'Elaborated_Data/eps_comparison.csv')
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return earnings
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2023-05-17 11:42:56 +00:00
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if __name__ == '__main__':
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concatenate_price_history()
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2023-05-29 13:56:15 +00:00
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concatenate_eps()
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eps_comparison()
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