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filter_by_EVtoSales.py
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# from selected_paris import selected
# import numpy as np
# import statsmodels.formula.api as smf
# import statsmodels.api as sm
# import pandas as pd
# from yahoofinancials import YahooFinancials
# import seaborn as sns
# import matplotlib.pyplot as plt
# # %%
# def find_fundamental_df(start_number, end_number, tickers):
# ev_sales = []
# margins = []
# beta = []
# g = []
# col_names = []
# for i in tickers[start_number: end_number]:
# try:
# yahoo_financials = YahooFinancials(i)
# ev_sales.append(yahoo_financials.get_key_statistics_data()[
# i]["enterpriseToRevenue"])
# margins.append(yahoo_financials.get_key_statistics_data()[
# i]["profitMargins"])
# beta.append(yahoo_financials.get_key_statistics_data()[i]['beta'])
# g.append(yahoo_financials.get_key_statistics_data()
# [i]['earningsQuarterlyGrowth'])
# col_names.append(i)
# except:
# pass
# df = pd.DataFrame(
# {
# 'ev_sales': ev_sales,
# 'margins': margins,
# 'beta': beta,
# 'g': g,
# },
# index=col_names
# )
# df.to_csv(f'df_{start_number +1}_to_{end_number + 1}.csv', index=True)
# df.columns = ['ticker', 'ev_sales', 'margins', 'beta', 'g']
# return df
# def find_tickers():
# df = pd.read_csv('SP500.csv')
# df.columns = df.iloc[0]
# df = df.drop([0])
# tickers = df['Symbol'].to_list()
# df.to_csv('tickers.csv', index=False)
# return tickers
# # %%
# def find_predicted_ev_sales_model(csvdata):
# mod = smf.ols(formula='ev_sales ~ margins + beta + g', data=csvdata)
# res = mod.fit()
# return res
# def clean_tickers(selected):
# tickers_from_clustering = []
# for i in selected:
# current_0 = i[0].split(' ')[0]
# current_1 = i[1].split(' ')[0]
# current_pair = [current_0, current_1]
# tickers_from_clustering.append(current_pair)
# return tickers_from_clustering
# def find_clean_tickers_set(tickers_from_clustering):
# tickers_selected = clean_tickers(tickers_from_clustering)
# flattened = [val for sublist in tickers_selected for val in sublist]
# return flattened
# def find_undervalued_set(df):
# ev_predicting_model = find_predicted_ev_sales_model(df)
# df['fitted_values'] = ev_predicting_model.fittedvalues
# df_undervalued = df[df['ev_sales'] < df['fitted_values']]
# undervalued_set = set(df_undervalued['ticker'])
# return(undervalued_set)
# def find_selected_SP500(selected_pair_cleaned, SP500_tickers):
# SP500_pair = []
# for pair in selected_pair_cleaned:
# if set(pair) <= set(SP500_tickers):
# SP500_pair.append(pair)
# return SP500_pair
# def find_selected_SP500_undervalued_pair(SP500_pair, undervalued_set):
# SP500_pair_undervalued = []
# for pair in SP500_pair:
# if len(set(pair).intersection(undervalued_set)) == 2:
# SP500_pair_undervalued.append(pair)
# return SP500_pair_undervalued
# def find_dict_selected(selected, selected_clean):
# tickers_from_clustering = []
# for i in range(0, len(selected)):
# dic = dict(zip(selected_clean[i], selected[i]))
# tickers_from_clustering.append(dic)
# return tickers_from_clustering
# def find_final_ticker(dict_selected, selected_SP500_pair_undervalued):
# final_tickers = []
# for i in range(0, len(dict_selected)):
# for ticker in selected_SP500_pair_undervalued:
# if ticker[0] in dict_selected[i].keys() and ticker[1] in dict_selected[i].keys():
# final_tickers.append(
# [dict_selected[i][ticker[0]], dict_selected[i][ticker[1]]])
# return final_tickers
# # %%
# SP500_tickers = find_tickers()
# df = pd.read_csv('full_df_1_to_500.csv')
# ev_model = find_predicted_ev_sales_model(df)
# # %%
# selected_clean = clean_tickers(selected)
# selected_set = find_clean_tickers_set(selected_clean)
# # %%
# undervalued_set = find_undervalued_set(df)
# selected_SP500_pair = find_selected_SP500(selected_clean, SP500_tickers)
# # %%
# selected_SP500_pair_undervalued = find_selected_SP500_undervalued_pair(
# selected_SP500_pair, undervalued_set)
# # %%
# dict_selected = find_dict_selected(selected, selected_clean)
# final_tickers = find_final_ticker(
# dict_selected, selected_SP500_pair_undervalued)
# print(final_tickers)
# # by fundamental data
# further_selected = [
# ['AJG R735QTJ8XC9X', 'MMC R735QTJ8XC9X'],
# ['LNC R735QTJ8XC9X', 'PRU SAI0XJNH6IJP'],
# ['AEE R735QTJ8XC9X', 'AEP R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'DTE R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'CMS R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['DTE R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['DTE R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['ED R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['ED R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['ETR R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['IP R735QTJ8XC9X', 'WRK W1V0C8ZTUBTX'],
# ['DHI R735QTJ8XC9X', 'LEN R735QTJ8XC9X'],
# ['DHI R735QTJ8XC9X', 'PHM R735QTJ8XC9X']
# ]# from selected_paris import selected
# import numpy as np
# import statsmodels.formula.api as smf
# import statsmodels.api as sm
# import pandas as pd
# from yahoofinancials import YahooFinancials
# import seaborn as sns
# import matplotlib.pyplot as plt
# # %%
# def find_fundamental_df(start_number, end_number, tickers):
# ev_sales = []
# margins = []
# beta = []
# g = []
# col_names = []
# for i in tickers[start_number: end_number]:
# try:
# yahoo_financials = YahooFinancials(i)
# ev_sales.append(yahoo_financials.get_key_statistics_data()[
# i]["enterpriseToRevenue"])
# margins.append(yahoo_financials.get_key_statistics_data()[
# i]["profitMargins"])
# beta.append(yahoo_financials.get_key_statistics_data()[i]['beta'])
# g.append(yahoo_financials.get_key_statistics_data()
# [i]['earningsQuarterlyGrowth'])
# col_names.append(i)
# except:
# pass
# df = pd.DataFrame(
# {
# 'ev_sales': ev_sales,
# 'margins': margins,
# 'beta': beta,
# 'g': g,
# },
# index=col_names
# )
# df.to_csv(f'df_{start_number +1}_to_{end_number + 1}.csv', index=True)
# df.columns = ['ticker', 'ev_sales', 'margins', 'beta', 'g']
# return df
# def find_tickers():
# df = pd.read_csv('SP500.csv')
# df.columns = df.iloc[0]
# df = df.drop([0])
# tickers = df['Symbol'].to_list()
# df.to_csv('tickers.csv', index=False)
# return tickers
# # %%
# def find_predicted_ev_sales_model(csvdata):
# mod = smf.ols(formula='ev_sales ~ margins + beta + g', data=csvdata)
# res = mod.fit()
# return res
# def clean_tickers(selected):
# tickers_from_clustering = []
# for i in selected:
# current_0 = i[0].split(' ')[0]
# current_1 = i[1].split(' ')[0]
# current_pair = [current_0, current_1]
# tickers_from_clustering.append(current_pair)
# return tickers_from_clustering
# def find_clean_tickers_set(tickers_from_clustering):
# tickers_selected = clean_tickers(tickers_from_clustering)
# flattened = [val for sublist in tickers_selected for val in sublist]
# return flattened
# def find_undervalued_set(df):
# ev_predicting_model = find_predicted_ev_sales_model(df)
# df['fitted_values'] = ev_predicting_model.fittedvalues
# df_undervalued = df[df['ev_sales'] < df['fitted_values']]
# undervalued_set = set(df_undervalued['ticker'])
# return(undervalued_set)
# def find_selected_SP500(selected_pair_cleaned, SP500_tickers):
# SP500_pair = []
# for pair in selected_pair_cleaned:
# if set(pair) <= set(SP500_tickers):
# SP500_pair.append(pair)
# return SP500_pair
# def find_selected_SP500_undervalued_pair(SP500_pair, undervalued_set):
# SP500_pair_undervalued = []
# for pair in SP500_pair:
# if len(set(pair).intersection(undervalued_set)) == 2:
# SP500_pair_undervalued.append(pair)
# return SP500_pair_undervalued
# def find_dict_selected(selected, selected_clean):
# tickers_from_clustering = []
# for i in range(0, len(selected)):
# dic = dict(zip(selected_clean[i], selected[i]))
# tickers_from_clustering.append(dic)
# return tickers_from_clustering
# def find_final_ticker(dict_selected, selected_SP500_pair_undervalued):
# final_tickers = []
# for i in range(0, len(dict_selected)):
# for ticker in selected_SP500_pair_undervalued:
# if ticker[0] in dict_selected[i].keys() and ticker[1] in dict_selected[i].keys():
# final_tickers.append(
# [dict_selected[i][ticker[0]], dict_selected[i][ticker[1]]])
# return final_tickers
# # %%
# SP500_tickers = find_tickers()
# df = pd.read_csv('full_df_1_to_500.csv')
# ev_model = find_predicted_ev_sales_model(df)
# # %%
# selected_clean = clean_tickers(selected)
# selected_set = find_clean_tickers_set(selected_clean)
# # %%
# undervalued_set = find_undervalued_set(df)
# selected_SP500_pair = find_selected_SP500(selected_clean, SP500_tickers)
# # %%
# selected_SP500_pair_undervalued = find_selected_SP500_undervalued_pair(
# selected_SP500_pair, undervalued_set)
# # %%
# dict_selected = find_dict_selected(selected, selected_clean)
# final_tickers = find_final_ticker(
# dict_selected, selected_SP500_pair_undervalued)
# print(final_tickers)
# # by fundamental data
# further_selected = [
# ['AJG R735QTJ8XC9X', 'MMC R735QTJ8XC9X'],
# ['LNC R735QTJ8XC9X', 'PRU SAI0XJNH6IJP'],
# ['AEE R735QTJ8XC9X', 'AEP R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'DTE R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['AEE R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'CMS R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['AEP R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'ED R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['CMS R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['DTE R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['DTE R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['ED R735QTJ8XC9X', 'ETR R735QTJ8XC9X'],
# ['ED R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['ETR R735QTJ8XC9X', 'WEC R735QTJ8XC9X'],
# ['IP R735QTJ8XC9X', 'WRK W1V0C8ZTUBTX'],
# ['DHI R735QTJ8XC9X', 'LEN R735QTJ8XC9X'],
# ['DHI R735QTJ8XC9X', 'PHM R735QTJ8XC9X']
# ]