example/13-kor-momentum-portfolio.py

@@ -1,8 +1,6 @@
 import pandas as pd
 import matplotlib.pyplot as plt
 import seaborn as sns
-import statsmodels.api as sm
-import numpy as np
 import quantcommon
 
 # 모멘텀 포트폴리오. 최근 12개월 수익률이 높은 주식
@@ -53,60 +51,4 @@ g.set(ylabel=None)
 g.fig.set_figwidth(15)
 g.fig.set_figheight(8)
 plt.subplots_adjust(wspace=0.5, hspace=0.2)
-# plt.show()
-
-# k-ratio(모멘텀의 꾸준함 지표)
-ret = price_pivot.pct_change().iloc[1:]
-ret_cum = np.log(1 + ret).cumsum()
-
-x = np.array(range(len(ret)))
-y = ret_cum.iloc[:, 0].values
-
-reg = sm.OLS(y, x).fit()
-reg.summary()
-
-x = np.array(range(len(ret)))
-k_ratio = {}
-
-for i in range(0, len(ticker_list)):
-
-    ticker = data_bind.loc[i, '종목코드']
-
-    try:
-        y = ret_cum.loc[:, price_pivot.columns == ticker]
-        reg = sm.OLS(y, x).fit()
-        res = float(reg.params / reg.bse)
-    except:
-        res = np.nan
-
-    k_ratio[ticker] = res
-
-k_ratio_bind = pd.DataFrame.from_dict(k_ratio, orient='index').reset_index()
-k_ratio_bind.columns = ['종목코드', 'K_ratio']
-
-k_ratio_bind.head()
-
-data_bind = data_bind.merge(k_ratio_bind, how='left', on='종목코드')
-k_ratio_rank = data_bind['K_ratio'].rank(axis=0, ascending=False)
-print(data_bind[k_ratio_rank <= 20])
-
-k_ratio_momentum = price_list[price_list['종목코드'].isin(data_bind.loc[k_ratio_rank <= 20, '종목코드'])]
-
-plt.rc('font', family='Malgun Gothic')
-g = sns.relplot(data=k_ratio_momentum,
-                x='날짜',
-                y='종가',
-                col='종목코드',
-                col_wrap=5,
-                kind='line',
-                facet_kws={
-                    'sharey': False,
-                    'sharex': True
-                })
-g.set(xticklabels=[])
-g.set(xlabel=None)
-g.set(ylabel=None)
-g.fig.set_figwidth(15)
-g.fig.set_figheight(8)
-plt.subplots_adjust(wspace=0.5, hspace=0.2)
 plt.show()

example/13-quality-portfolio.py

@@ -1,60 +0,0 @@
-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))