import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import quantcommon

# 퀄리티(우량주) 포트폴리오. 영업수익성이 높은 주식
engine = quantcommon.QuantCommon().create_engine()

ticker_list = pd.read_sql("""
select * from kor_ticker
where 기준일 = (select max(기준일) from kor_ticker) 
and 종목구분 = '보통주';
""", con=engine)

fs_list = pd.read_sql("""
select * from kor_fs
where 계정 in ('당기순이익', '매출총이익', '영업활동으로인한현금흐름', '자산', '자본')
and 공시구분 = 'q';
""", con=engine)

engine.dispose()

fs_list = fs_list.sort_values(['종목코드', '계정', '기준일'])
fs_list['ttm'] = fs_list.groupby(['종목코드', '계정'], as_index=False)['값'].rolling(
    window=4, min_periods=4).sum()['값']
fs_list_clean = fs_list.copy()
fs_list_clean['ttm'] = np.where(fs_list_clean['계정'].isin(['자산', '자본']),
                                fs_list_clean['ttm'] / 4, fs_list_clean['ttm'])
fs_list_clean = fs_list_clean.groupby(['종목코드', '계정']).tail(1)

fs_list_pivot = fs_list_clean.pivot(index='종목코드', columns='계정', values='ttm')
fs_list_pivot['ROE'] = fs_list_pivot['당기순이익'] / fs_list_pivot['자본']
fs_list_pivot['GPA'] = fs_list_pivot['매출총이익'] / fs_list_pivot['자산']
fs_list_pivot['CFO'] = fs_list_pivot['영업활동으로인한현금흐름'] / fs_list_pivot['자산']

quality_list = ticker_list[['종목코드', '종목명']].merge(fs_list_pivot,
                                                  how='left',
                                                  on='종목코드')
# print(quality_list.round(4).head())

quality_list_copy = quality_list[['ROE', 'GPA', 'CFO']].copy()
quality_rank = quality_list_copy.rank(ascending=False, axis=0)

mask = np.triu(quality_rank.corr())
fig, ax = plt.subplots(figsize=(10, 6))
sns.heatmap(quality_rank.corr(),
            annot=True,
            mask=mask,
            annot_kws={"size": 16},
            vmin=0,
            vmax=1,
            center=0.5,
            cmap='coolwarm',
            square=True)
ax.invert_yaxis()

quality_sum = quality_rank.sum(axis=1, skipna=False).rank()
print(quality_list.loc[quality_sum <= 20,
                 ['종목코드', '종목명', 'ROE', 'GPA', 'CFO']].round(4))