quantpython/python_quant.py at master · bjpublic/quantpython · GitHub

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# -*- coding: utf-8 -*-
importpandasaspd
importnumpyasnp


# 재무 관련 데이터 전처리하는 함수
defget_finance_data(path):
data_path=path
raw_data=pd.read_excel(data_path)
big_col=list(raw_data.columns)
small_col=list(raw_data.iloc[0])

new_big_col= []
fornum, colinenumerate(big_col):
if'Unnamed'incol:
new_big_col.append(new_big_col[num-1])
else:
new_big_col.append(big_col[num])

raw_data.columns= [new_big_col, small_col]
clean_df=raw_data.loc[ raw_data.index.dropna() ]

returnclean_df

# N/A(IFRS)'를 NaN으로 바꾸기
defcheck_IFRS(x):
ifx=='N/A(IFRS)':
returnnp.NaN
else:
returnx

# PER기준으로 오름차순으로 정렬하여 주는 함수
deflow_per(invest_df, index_date, num):
invest_df[(index_date, 'PER')] =pd.to_numeric(invest_df[(index_date, 'PER')])
per_sorted=invest_df.sort_values(by=(index_date, 'PER'))
returnper_sorted[index_date][:num]


# ROA기준으로 내림차순으로 정렬하여 주는 함수
defhigh_roa(fr_df, index_date, num):
fr_df[(index_date, 'ROA')] =fr_df[(index_date, 'ROA')].apply(check_IFRS)
fr_df[(index_date, 'ROA')] =pd.to_numeric(fr_df[(index_date, 'ROA')] )
sorted_roa=fr_df.sort_values(by=(index_date, 'ROA'), ascending=False)
returnsorted_roa[index_date][:num]

# 마법공식
defmagic_formula(fr_df, invest_df, index_date, num):
per=low_per(invest_df, index_date, None)
roa=high_roa(fr_df, index_date, None)
per['per순위'] =per['PER'].rank()
roa['roa순위'] =roa['ROA'].rank(ascending=False)
magic=pd.merge(per, roa, how='outer', left_index=True, right_index=True)
magic['마법공식 순위'] = (magic['per순위'] +magic['roa순위']).rank().sort_values()
magic=magic.sort_values(by='마법공식 순위')
returnmagic[:num]

# 저평가 지수를 기준으로 정렬하여 순위 만들어 주는 함수
defget_value_rank(invest_df, value_type, index_date, num):
invest_df[(index_date, value_type)] =pd.to_numeric(invest_df[(index_date, value_type)])
value_sorted=invest_df.sort_values(by=(index_date, value_type))[index_date]
value_sorted[ value_type+'순위'] =value_sorted[value_type].rank()
returnvalue_sorted[[value_type, value_type+'순위']][:num]

# 저평가 지표 조합 함수
defmake_value_combo(value_list, invest_df, index_date, num):

fori, valueinenumerate(value_list):
temp_df=get_value_rank(invest_df, value, index_date, None)
ifi==0:
value_combo_df=temp_df
rank_combo=temp_df[value+'순위']
else:
value_combo_df=pd.merge(value_combo_df, temp_df, how='outer', left_index=True, right_index=True)
rank_combo=rank_combo+temp_df[value+'순위']

value_combo_df['종합순위'] =rank_combo.rank()
value_combo_df=value_combo_df.sort_values(by='종합순위')

returnvalue_combo_df[:num]

# F-score
defget_fscore(fs_df, index_date, num):
fscore_df=fs_df[index_date]
fscore_df['당기순이익점수'] =fscore_df['당기순이익'] >0
fscore_df['영업활동점수'] =fscore_df['영업활동으로인한현금흐름'] >0
fscore_df['더큰영업활동점수'] =fscore_df['영업활동으로인한현금흐름'] >fscore_df['당기순이익']
fscore_df['종합점수'] =fscore_df[['당기순이익점수', '영업활동점수', '더큰영업활동점수']].sum(axis=1)
fscore_df=fscore_df[fscore_df['종합점수'] ==3]
returnfscore_df[:num]

# 모멘텀 함수
defget_momentum_rank(price_df, index_date, date_range, num):
momentum_df=pd.DataFrame(price_df.pct_change(date_range).loc[index_date])
momentum_df.columns= ['모멘텀']
momentum_df['모멘텀순위'] =momentum_df['모멘텀'].rank(ascending=False)
momentum_df=momentum_df.sort_values(by='모멘텀순위')
returnmomentum_df[:num]

# 저평가 + F-score
defget_value_quality(invest_df, fs_df, index_date, num):
value=make_value_combo(['PER', 'PBR', 'PSR', 'PCR'], invest_df, index_date, None)
quality=get_fscore(fs_df, index_date, None)
value_quality=pd.merge(value, quality, how='outer', left_index=True, right_index=True)
value_quality_filtered=value_quality[value_quality['종합점수'] ==3]
vq_df=value_quality_filtered.sort_values(by='종합순위')
returnvq_df[:num]