From 4f559443c0a1fe2cbacefb6e4402542f09dabf42 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 15:49:21 +0900
Subject: [PATCH 01/16] =?UTF-8?q?feat:=20=EC=A0=9C=EB=AC=B4=EC=9E=AC?=
 =?UTF-8?q?=ED=91=9C,=20=EC=A3=BC=EA=B0=80=20=ED=81=AC=EB=A1=A4=EB=A7=81?=
 =?UTF-8?q?=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/10-financial-statements.py            |   1 +
 example/10-stock-data.py                      |   1 +
 example/10-stock-price.py                     |   2 +
 .../src/current-financial-statements.py       | 130 ++++++++++++++++++
 streamlit-quant/src/current-price.py          | 101 ++++++++++++++
 .../{current_stock.py => current-stock.py}    |   1 +
 6 files changed, 236 insertions(+)
 create mode 100644 streamlit-quant/src/current-financial-statements.py
 create mode 100644 streamlit-quant/src/current-price.py
 rename streamlit-quant/src/{current_stock.py => current-stock.py} (99%)

diff --git a/example/10-financial-statements.py b/example/10-financial-statements.py
index 5b185e0..170aedb 100644
--- a/example/10-financial-statements.py
+++ b/example/10-financial-statements.py
@@ -8,6 +8,7 @@ from tqdm import tqdm
 
 import quantcommon
 
+# src/current-financial-statement.py 로 개선
 # DB 연결
 common = quantcommon.QuantCommon()
 engine = common.create_engine()
diff --git a/example/10-stock-data.py b/example/10-stock-data.py
index 80d9529..398897c 100644
--- a/example/10-stock-data.py
+++ b/example/10-stock-data.py
@@ -10,6 +10,7 @@ from bs4 import BeautifulSoup
 from dotenv import load_dotenv
 import quantcommon
 
+# src/current-stock.py 로 개선
 load_dotenv()
 
 GEN_OTP_URL = 'http://data.krx.co.kr/comm/fileDn/GenerateOTP/generate.cmd'
diff --git a/example/10-stock-price.py b/example/10-stock-price.py
index c5c4d03..050cb74 100644
--- a/example/10-stock-price.py
+++ b/example/10-stock-price.py
@@ -11,6 +11,8 @@ from tqdm import tqdm
 
 import quantcommon
 
+# src/current-price.py 로 개선
+
 # DB 연결
 common = quantcommon.QuantCommon()
 engine = common.create_engine()
diff --git a/streamlit-quant/src/current-financial-statements.py b/streamlit-quant/src/current-financial-statements.py
new file mode 100644
index 0000000..004eb8a
--- /dev/null
+++ b/streamlit-quant/src/current-financial-statements.py
@@ -0,0 +1,130 @@
+import re
+import time
+
+import pandas as pd
+import requests as rq
+from bs4 import BeautifulSoup
+from tqdm import tqdm
+
+import quantcommon
+
+# 재무제표 크롤링
+
+def get_ticker_list():
+    engine = quantcommon.QuantCommon().create_engine()
+    # 티커리스트 불러오기
+    ticker_list = {}
+    try:
+        ticker_list = pd.read_sql("""
+                select * from kor_ticker
+                where 기준일 = (select max(기준일) from kor_ticker) 
+                    and 종목구분 = '보통주';
+        """, con=engine)
+    finally:
+        engine.dispose()
+
+    return ticker_list
+
+
+# 재무제표 클렌징 함수
+def clean_fs(df, ticker, frequency):
+    df = df[~df.loc[:, ~df.columns.isin(['계정'])].isna().all(axis=1)]
+    df = df.drop_duplicates(['계정'], keep='first')
+    df = pd.melt(df, id_vars='계정', var_name='기준일', value_name='값')
+    df = df[~pd.isnull(df['값'])]
+    df['계정'] = df['계정'].replace({'계산에 참여한 계정 펼치기': ''}, regex=True)
+    df['기준일'] = pd.to_datetime(df['기준일'],
+                               format='%Y/%m') + pd.tseries.offsets.MonthEnd()
+    df['종목코드'] = ticker
+    df['공시구분'] = frequency
+
+    return df
+
+
+# ticker 별 재무제표 조회해서 DB에 저장
+def process_for_fs(ticker_list):
+    # DB 연결
+    common = quantcommon.QuantCommon()
+    engine = common.create_engine()
+    con = common.connect()
+    mycursor = con.cursor()
+
+    # DB 저장 쿼리
+    query = """
+        insert into kor_fs (계정, 기준일, 값, 종목코드, 공시구분)
+        values (%s,%s,%s,%s,%s) as new
+        on duplicate key update
+        값=new.값
+    """
+
+    # 오류 발생시 저장할 리스트 생성
+    error_list = []
+
+    # for loop
+    for i in tqdm(range(0, len(ticker_list))):
+
+        # 티커 선택
+        ticker = ticker_list['종목코드'][i]
+
+        # 오류 발생 시 이를 무시하고 다음 루프로 진행
+        try:
+            # url 생성
+            url = f'https://comp.fnguide.com/SVO2/ASP/SVD_Finance.asp?pGB=1&gicode=A{ticker}'
+
+            # 데이터 받아오기
+            data = pd.read_html(url, displayed_only=False)
+
+            # 연간 데이터
+            data_fs_y = pd.concat([
+                data[0].iloc[:, ~data[0].columns.str.contains('전년동기')], data[2],
+                data[4]
+            ])
+            data_fs_y = data_fs_y.rename(columns={data_fs_y.columns[0]: "계정"})
+
+            # 결산년 찾기
+            page_data = rq.get(url)
+            page_data_html = BeautifulSoup(page_data.content, 'html.parser')
+
+            fiscal_data = page_data_html.select('div.corp_group1 > h2')
+            fiscal_data_text = fiscal_data[1].text
+            fiscal_data_text = re.findall('[0-9]+', fiscal_data_text)
+
+            # 결산년에 해당하는 계정만 남기기
+            data_fs_y = data_fs_y.loc[:, (data_fs_y.columns == '계정') | (
+                data_fs_y.columns.str[-2:].isin(fiscal_data_text))]
+
+            # 클렌징
+            data_fs_y_clean = clean_fs(data_fs_y, ticker, 'y')
+
+            # 분기 데이터
+            data_fs_q = pd.concat([
+                data[1].iloc[:, ~data[1].columns.str.contains('전년동기')], data[3],
+                data[5]
+            ])
+            data_fs_q = data_fs_q.rename(columns={data_fs_q.columns[0]: "계정"})
+
+            data_fs_q_clean = clean_fs(data_fs_q, ticker, 'q')
+
+            # 두개 합치기
+            data_fs_bind = pd.concat([data_fs_y_clean, data_fs_q_clean])
+
+            # 재무제표 데이터를 DB에 저장
+            args = data_fs_bind.values.tolist()
+            mycursor.executemany(query, args)
+            con.commit()
+
+        except:
+            # 오류 발생시 해당 종목명을 저장하고 다음 루프로 이동
+            print(ticker)
+            error_list.append(ticker)
+
+        # 타임슬립 적용
+        time.sleep(2)
+
+    # DB 연결 종료
+    engine.dispose()
+    con.close()
+
+if __name__ == '__main__':
+    tickers = get_ticker_list()
+    process_for_fs(tickers)
\ No newline at end of file
diff --git a/streamlit-quant/src/current-price.py b/streamlit-quant/src/current-price.py
new file mode 100644
index 0000000..9b3ed8e
--- /dev/null
+++ b/streamlit-quant/src/current-price.py
@@ -0,0 +1,101 @@
+# 패키지 불러오기
+
+import time
+from datetime import date
+from io import BytesIO
+
+import pandas as pd
+import requests as rq
+from dateutil.relativedelta import relativedelta
+from tqdm import tqdm
+
+import quantcommon
+
+# 주가 크롤링
+
+def get_ticker_list():
+    engine = quantcommon.QuantCommon().create_engine()
+    # 티커리스트 불러오기
+    ticker_list = {}
+    try:
+        ticker_list = pd.read_sql("""
+                select * from kor_ticker
+                where 기준일 = (select max(기준일) from kor_ticker) 
+                    and 종목구분 = '보통주';
+        """, con=engine)
+    finally:
+        engine.dispose()
+
+    return ticker_list
+
+
+def process_for_price(ticker_list):
+    # DB 저장 쿼리
+    query = """
+        insert into kor_price (날짜, 시가, 고가, 저가, 종가, 거래량, 종목코드)
+        values (%s,%s,%s,%s,%s,%s,%s) as new
+        on duplicate key update
+        시가 = new.시가, 고가 = new.고가, 저가 = new.저가,
+        종가 = new.종가, 거래량 = new.거래량;
+    """
+
+    # DB 연결
+    common = quantcommon.QuantCommon()
+    engine = common.create_engine()
+    con = common.connect()
+
+    mycursor = con.cursor()
+    # 오류 발생시 저장할 리스트 생성
+    error_list = []
+
+    # 전종목 주가 다운로드 및 저장
+    for i in tqdm(range(0, len(ticker_list))):
+
+        # 티커 선택
+        ticker = ticker_list['종목코드'][i]
+
+        # 시작일과 종료일
+        # fr = (date.today() + relativedelta(years=-5)).strftime("%Y%m%d")
+        to = (date.today()).strftime("%Y%m%d")
+        fr = '20250125'
+
+        # 오류 발생 시 이를 무시하고 다음 루프로 진행
+        try:
+
+            # url 생성
+            url = f'''https://fchart.stock.naver.com/siseJson.nhn?symbol={ticker}&requestType=1
+            &startTime={fr}&endTime={to}&timeframe=day'''
+
+            # 데이터 다운로드
+            data = rq.get(url).content
+            data_price = pd.read_csv(BytesIO(data))
+
+            # 데이터 클렌징
+            price = data_price.iloc[:, 0:6]
+            price.columns = ['날짜', '시가', '고가', '저가', '종가', '거래량']
+            price = price.dropna()
+            price['날짜'] = price['날짜'].str.extract("(\d+)")
+            price['날짜'] = pd.to_datetime(price['날짜'])
+            price['종목코드'] = ticker
+
+            # 주가 데이터를 DB에 저장
+            args = price.values.tolist()
+            mycursor.executemany(query, args)
+            con.commit()
+
+        except:
+
+            # 오류 발생시 error_list에 티커 저장하고 넘어가기
+            print(ticker)
+            error_list.append(ticker)
+
+        # 타임슬립 적용
+        time.sleep(2)
+
+    # DB 연결 종료
+    engine.dispose()
+    con.close()
+
+if __name__ == '__main__':
+    ticker_list = get_ticker_list()
+    process_for_price(ticker_list)
\ No newline at end of file
diff --git a/streamlit-quant/src/current_stock.py b/streamlit-quant/src/current-stock.py
similarity index 99%
rename from streamlit-quant/src/current_stock.py
rename to streamlit-quant/src/current-stock.py
index 80d9529..0b06390 100644
--- a/streamlit-quant/src/current_stock.py
+++ b/streamlit-quant/src/current-stock.py
@@ -187,6 +187,7 @@ def save_sector(sector):
 
 
 if __name__ == '__main__':
+    # sector와 ticker 갱신
     latest_biz_day = get_latest_biz_day()
     process_for_total_stock(latest_biz_day)
     process_for_wics(latest_biz_day)

From 4459164fd6a088fe81a730515e8d3543c85c9839 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 17:53:06 +0900
Subject: [PATCH 02/16] =?UTF-8?q?feat:=2013-2-1=20=EB=B2=A0=ED=83=80=20?=
 =?UTF-8?q?=EA=B3=84=EC=82=B0=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/13-2-1-calculate-beta.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 example/13-2-1-calculate-beta.py

diff --git a/example/13-2-1-calculate-beta.py b/example/13-2-1-calculate-beta.py
new file mode 100644
index 0000000..2892fbc
--- /dev/null
+++ b/example/13-2-1-calculate-beta.py
@@ -0,0 +1,28 @@
+import yfinance as yf
+import pandas as pd
+import statsmodels.api as sm
+
+KOSPI_CODE = '^KS11'
+KIIUM = '039490.KS'
+# KOSPI 코드(^KS11), 전통적인 고베타주인 증권주 중 키움증권(039490.KS)
+tickers = [KOSPI_CODE, KIIUM]
+
+all_data = {}
+for ticker in tickers:
+    all_data[ticker] = yf.download(ticker, start="2020-01-01", end="2024-12-31")
+
+# print(all_data)
+# for tic, data in all_data.items():
+#     print(f"{tic}: {type(data)} -> {type(data['Close'])}")
+#     print(data['Close'])
+
+# 종가(Close)에 해당하는 열만 선택해서 데이터프레임으로 가공
+prices = pd.DataFrame({tic: data['Close'].squeeze() for tic, data in all_data.items()})
+# 수익률 계산(pct_change), NA 데이터 삭제(dropna)
+ret = prices.pct_change().dropna()
+
+ret['intercept'] = 1
+reg = sm.OLS(ret[[KIIUM]], ret[[KOSPI_CODE, 'intercept']]).fit()
+
+print(reg.summary())
+print(reg.params)
\ No newline at end of file

From f713c7b917fab3a4fdc1abc934bd9cd9b9bc8513 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 17:53:52 +0900
Subject: [PATCH 03/16] =?UTF-8?q?feat:=20quantcommon=20=ED=8B=B0=EC=BB=A4?=
 =?UTF-8?q?=20=EC=A1=B0=ED=9A=8C,=20value=20=EC=A1=B0=ED=9A=8C=20=EC=B6=94?=
 =?UTF-8?q?=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 quantcommon.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/quantcommon.py b/quantcommon.py
index 3990fa8..4398ad4 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -1,6 +1,7 @@
 import os
 from urllib.parse import quote_plus
 
+import pandas as pd
 import pymysql
 from dotenv import load_dotenv
 from sqlalchemy import create_engine
@@ -26,3 +27,30 @@ class QuantCommon:
                       port=self.port,
                       db=self.db,
                       charset='utf8')
+
+    def get_ticker_list(self):
+        engine = self.create_engine()
+
+        try:
+            ticker_list = pd.read_sql("""
+                        select * from kor_ticker
+                        where 기준일 = (select max(기준일) from kor_ticker) 
+                            and 종목구분 = '보통주';
+                """, con=engine)
+        finally:
+            engine.dispose()
+
+        return ticker_list
+
+    def get_value_list(self):
+        engine = self.create_engine()
+
+        try:
+            value_list = pd.read_sql("""
+            select * from kor_value
+            where 기준일 = (select max(기준일) from kor_value);
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return value_list
\ No newline at end of file

From 0d6b14ca4225498e06c776d3af47d289a4a11afa Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 17:54:11 +0900
Subject: [PATCH 04/16] =?UTF-8?q?feat:=20=EA=B0=80=EC=B9=98=EC=A3=BC(PER,?=
 =?UTF-8?q?=20PBR),=20=EB=AA=A8=EB=93=A0=20=EA=B0=80=EC=B9=98=EC=A3=BC(PER?=
 =?UTF-8?q?,=20PBR,=20PCR,=20PSR,=20DY)=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 streamlit-quant/strategy/all-value.py | 36 +++++++++++++++++++++++++++
 streamlit-quant/strategy/value.py     | 25 +++++++++++++++++++
 2 files changed, 61 insertions(+)
 create mode 100644 streamlit-quant/strategy/all-value.py
 create mode 100644 streamlit-quant/strategy/value.py

diff --git a/streamlit-quant/strategy/all-value.py b/streamlit-quant/strategy/all-value.py
new file mode 100644
index 0000000..16be363
--- /dev/null
+++ b/streamlit-quant/strategy/all-value.py
@@ -0,0 +1,36 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+import quantcommon as qc
+
+#가치주 포트폴리오. PER, PBR, PCR, PSR, DY
+def get_all_value_top(count):
+    ticker_list = qc.QuantCommon().get_ticker_list()
+    value_list = qc.QuantCommon().get_value_list()
+
+    # 가치 지표가 0이하인 경우 nan으로 변경
+    value_list.loc[value_list['값'] <= 0, '값'] = np.nan
+    # 가치지표 테이블을 가로로 긴 형태로 변경
+    value_pivot = value_list.pivot(index='종목코드', columns='지표', values='값')
+    # 티커 테이블과 가치 지표 테이블을 합침
+    data_bind = ticker_list[['종목코드', '종목명']].merge(value_pivot,
+                                                   how='left',
+                                                   on='종목코드')
+    value_list_copy = data_bind.copy()
+    # DY(배당수익률)만 높을수록 좋은 지표라서 역수
+    value_list_copy['DY'] = 1 / value_list_copy['DY']
+    value_list_copy = value_list_copy[['PER', 'PBR', 'PCR', 'PSR', 'DY']]
+    value_rank_all = value_list_copy.rank(axis=0)
+    mask = np.triu(value_rank_all.corr())
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.heatmap(value_rank_all.corr(),annot=True,mask=mask, annot_kws={"size": 16}, vmin=0, vmax=1, center=0.5,cmap='coolwarm',square=True)
+    ax.invert_yaxis()
+    # plt.show()
+    value_sum_all = value_rank_all.sum(axis=1, skipna=False).rank()
+
+    return data_bind.loc[value_sum_all <= count]
+
+if __name__ == '__main__':
+    print(get_all_value_top(20))
\ No newline at end of file
diff --git a/streamlit-quant/strategy/value.py b/streamlit-quant/strategy/value.py
new file mode 100644
index 0000000..efb7a1d
--- /dev/null
+++ b/streamlit-quant/strategy/value.py
@@ -0,0 +1,25 @@
+import pandas as pd
+import numpy as np
+import quantcommon as qc
+
+#가치주 포트폴리오. PER, PBR이 낮은 회사 20개
+def get_value_top(count):
+    ticker_list = qc.QuantCommon().get_ticker_list()
+    value_list = qc.QuantCommon().get_value_list()
+
+    # 가치 지표가 0이하인 경우 nan으로 변경
+    value_list.loc[value_list['값'] <= 0, '값'] = np.nan
+    # 가치지표 테이블을 가로로 긴 형태로 변경
+    value_pivot = value_list.pivot(index='종목코드', columns='지표', values='값')
+    # 티커 테이블과 가치 지표 테이블을 합침
+    data_bind = ticker_list[['종목코드', '종목명']].merge(value_pivot,
+                                                   how='left',
+                                                   on='종목코드')
+    # rank() 함수로 PER, PBR 열의 순위를 구함. axis=0을 입력하여 순위는 열 방향으로 구함.(PER, PBR 각각 순위)
+    value_rank = data_bind[['PER', 'PBR']].rank(axis = 0)
+    # axis=1을 통해서 위에서 구한 순위 랭크를 합침. 합친 것을 다시 rank()
+    value_sum = value_rank.sum(axis = 1, skipna = False).rank()
+    return data_bind.loc[value_sum <= count, ['종목코드', '종목명', 'PER', 'PBR']]
+
+if __name__ == '__main__':
+    print(get_value_top(20))
\ No newline at end of file

From 8ae20546cdaaa05fba61c46a9ff949ffc059bc19 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 17:54:19 +0900
Subject: [PATCH 05/16] =?UTF-8?q?feat:=20=EA=B0=80=EC=B9=98=EC=A3=BC(PER,?=
 =?UTF-8?q?=20PBR),=20=EB=AA=A8=EB=93=A0=20=EA=B0=80=EC=B9=98=EC=A3=BC(PER?=
 =?UTF-8?q?,=20PBR,=20PCR,=20PSR,=20DY)=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/13-kor-value-portfolio.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/example/13-kor-value-portfolio.py b/example/13-kor-value-portfolio.py
index b870b16..59c4a7c 100644
--- a/example/13-kor-value-portfolio.py
+++ b/example/13-kor-value-portfolio.py
@@ -2,6 +2,7 @@ import pandas as pd
 import numpy as np
 import quantcommon
 
+# strategy/value 에서 구현
 
 #가치주 포트폴리오. PER, PBR이 낮은 회사 20개
 # DB 연결

From fa03b48f4780c4236b8721c021ee4c38acbdf3ff Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 17:57:04 +0900
Subject: [PATCH 06/16] =?UTF-8?q?feat:=20quantcommon=20=EA=B0=80=EA=B2=A9?=
 =?UTF-8?q?=20=EC=A1=B0=ED=9A=8C=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 quantcommon.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/quantcommon.py b/quantcommon.py
index 4398ad4..a2cea81 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -53,4 +53,18 @@ class QuantCommon:
         finally:
             engine.dispose()
 
-        return value_list
\ No newline at end of file
+        return value_list
+
+    def get_price_list(self):
+        engine = self.create_engine()
+
+        try:
+            price_list = pd.read_sql("""
+            select 날짜, 종가, 종목코드
+            from kor_price
+            where 날짜 >= (select (select max(날짜) from kor_price) - interval 1 year);
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return price_list
\ No newline at end of file

From 88e6f0eb91c0ef582c34e4c9dbc761b5d84af8bd Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 18:27:00 +0900
Subject: [PATCH 07/16] =?UTF-8?q?feat:=20quantcommon=20=EA=B0=80=EA=B2=A9?=
 =?UTF-8?q?=20=EC=A1=B0=ED=9A=8C=20=EC=8B=9C=20=EA=B8=B0=EA=B0=84(?=
 =?UTF-8?q?=EC=9B=94)=20=ED=8C=8C=EB=9D=BC=EB=AF=B8=ED=84=B0=20=EC=B6=94?=
 =?UTF-8?q?=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 quantcommon.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/quantcommon.py b/quantcommon.py
index a2cea81..fbb8c14 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -55,14 +55,14 @@ class QuantCommon:
 
         return value_list
 
-    def get_price_list(self):
+    def get_price_list(self, interval_month):
         engine = self.create_engine()
 
         try:
-            price_list = pd.read_sql("""
+            price_list = pd.read_sql(f"""
             select 날짜, 종가, 종목코드
             from kor_price
-            where 날짜 >= (select (select max(날짜) from kor_price) - interval 1 year);
+            where 날짜 >= (select (select max(날짜) from kor_price) - interval {interval_month} month);
             """, con=engine)
         finally:
             engine.dispose()

From 470b57b2c934ad2cba035fcc5f85620271679251 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Mon, 10 Mar 2025 18:27:43 +0900
Subject: [PATCH 08/16] =?UTF-8?q?feat:=20=EB=AA=A8=EB=A9=98=ED=85=80,=20k-?=
 =?UTF-8?q?ratio(=EB=AA=A8=EB=A9=98=ED=85=80=20=EC=A4=91=20=EA=BE=B8?=
 =?UTF-8?q?=EC=A4=80=ED=95=98=EA=B2=8C=20=EB=A7=8E=EC=9D=B4=20=EC=83=81?=
 =?UTF-8?q?=EC=8A=B9)=20=EA=B5=AC=ED=98=84?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/13-kor-momentum-portfolio.py |  1 +
 streamlit-quant/strategy/momentum.py | 87 ++++++++++++++++++++++++++++
 2 files changed, 88 insertions(+)
 create mode 100644 streamlit-quant/strategy/momentum.py

diff --git a/example/13-kor-momentum-portfolio.py b/example/13-kor-momentum-portfolio.py
index 8ee6a23..22cc264 100644
--- a/example/13-kor-momentum-portfolio.py
+++ b/example/13-kor-momentum-portfolio.py
@@ -5,6 +5,7 @@ import statsmodels.api as sm
 import numpy as np
 import quantcommon
 
+# strategy/momentum에 구현
 # 모멘텀 포트폴리오. 최근 12개월 수익률이 높은 주식
 engine = quantcommon.QuantCommon().create_engine()
 
diff --git a/streamlit-quant/strategy/momentum.py b/streamlit-quant/strategy/momentum.py
new file mode 100644
index 0000000..3baa50b
--- /dev/null
+++ b/streamlit-quant/strategy/momentum.py
@@ -0,0 +1,87 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import statsmodels.api as sm
+import numpy as np
+import quantcommon
+
+
+def print_graph(values):
+    plt.rc('font', family='Malgun Gothic')
+    g = sns.relplot(data=values,
+                    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()
+
+# strategy/momentum에 구현
+# 모멘텀 포트폴리오. 최근 12개월 수익률이 높은 주식
+def get_momentum_top(count):
+    ticker_list = quantcommon.QuantCommon().get_ticker_list()
+    price_list = quantcommon.QuantCommon().get_price_list(interval_month=12)
+
+    price_pivot = price_list.pivot(index='날짜', columns='종목코드', values='종가')
+
+    # 가격 테이블에서 (가장 끝 행 / 가장 첫 행)으로 각 종목의 12개월 수익률을 구함
+    ret_list = pd.DataFrame(data=(price_pivot.iloc[-1] / price_pivot.iloc[0]) - 1,
+                            columns=['return'])
+    data_bind = ticker_list[['종목코드', '종목명']].merge(ret_list, how='left', on='종목코드')
+
+    # 12개월 수익률 열 순위를 구함. 지표가 높을 수록 좋으니 ascending=False
+    momentum_rank = data_bind['return'].rank(axis=0, ascending=False)
+    # 모멘텀만 가지고 순위 측정
+    price_momentum = price_list[price_list['종목코드'].isin(
+        data_bind.loc[momentum_rank <= count, '종목코드'])]
+    # 해당 종목들(모멘텀 상위 count 개)의 가격 그래프 확인
+    # print_graph(price_momentum)
+
+    # k-ratio(모멘텀의 꾸준함 지표)
+    # pct_change() 함수로 각 종목의 수익률 계산하고 수익률이 곗나되지 않는 첫 번째 행은 제외
+    ret = price_pivot.pct_change().iloc[1:]
+    # 로그 누적 수익률 계산
+    ret_cum = np.log(1 + ret).cumsum()
+
+    # x축은 기간
+    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)
+    momentum_top = data_bind[k_ratio_rank <= count]
+
+    k_ratio_momentum = price_list[price_list['종목코드'].isin(data_bind.loc[k_ratio_rank <= count, '종목코드'])]
+    print_graph(k_ratio_momentum)
+    return momentum_top
+
+if __name__ == '__main__':
+    print(get_momentum_top(20))
\ No newline at end of file

From 694e58189dae07a511c4e3adcb56d9345664e053 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Fri, 14 Mar 2025 15:21:31 +0900
Subject: [PATCH 09/16] =?UTF-8?q?chore:=20quantcommon=20=EC=82=AC=EC=9A=A9?=
 =?UTF-8?q?=20=EB=B0=A9=EC=8B=9D=20=EB=B3=80=EA=B2=BD?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 streamlit-quant/strategy/all-value.py | 8 ++++----
 streamlit-quant/strategy/momentum.py  | 5 +++--
 streamlit-quant/strategy/value.py     | 9 +++++----
 3 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/streamlit-quant/strategy/all-value.py b/streamlit-quant/strategy/all-value.py
index 16be363..38b945a 100644
--- a/streamlit-quant/strategy/all-value.py
+++ b/streamlit-quant/strategy/all-value.py
@@ -1,13 +1,13 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-
-import quantcommon as qc
+import quantcommon
 
 #가치주 포트폴리오. PER, PBR, PCR, PSR, DY
 def get_all_value_top(count):
-    ticker_list = qc.QuantCommon().get_ticker_list()
-    value_list = qc.QuantCommon().get_value_list()
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    value_list = qc.get_value_list()
 
     # 가치 지표가 0이하인 경우 nan으로 변경
     value_list.loc[value_list['값'] <= 0, '값'] = np.nan
diff --git a/streamlit-quant/strategy/momentum.py b/streamlit-quant/strategy/momentum.py
index 3baa50b..05439db 100644
--- a/streamlit-quant/strategy/momentum.py
+++ b/streamlit-quant/strategy/momentum.py
@@ -29,8 +29,9 @@ def print_graph(values):
 # strategy/momentum에 구현
 # 모멘텀 포트폴리오. 최근 12개월 수익률이 높은 주식
 def get_momentum_top(count):
-    ticker_list = quantcommon.QuantCommon().get_ticker_list()
-    price_list = quantcommon.QuantCommon().get_price_list(interval_month=12)
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    price_list = qc.get_price_list(interval_month=12)
 
     price_pivot = price_list.pivot(index='날짜', columns='종목코드', values='종가')
 
diff --git a/streamlit-quant/strategy/value.py b/streamlit-quant/strategy/value.py
index efb7a1d..fd9c2c2 100644
--- a/streamlit-quant/strategy/value.py
+++ b/streamlit-quant/strategy/value.py
@@ -1,11 +1,12 @@
-import pandas as pd
 import numpy as np
-import quantcommon as qc
+
+import quantcommon
 
 #가치주 포트폴리오. PER, PBR이 낮은 회사 20개
 def get_value_top(count):
-    ticker_list = qc.QuantCommon().get_ticker_list()
-    value_list = qc.QuantCommon().get_value_list()
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    value_list = qc.get_value_list()
 
     # 가치 지표가 0이하인 경우 nan으로 변경
     value_list.loc[value_list['값'] <= 0, '값'] = np.nan

From c5f82a819b678e954186addf3820940c58e91e6e Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Fri, 14 Mar 2025 15:47:19 +0900
Subject: [PATCH 10/16] =?UTF-8?q?feat:=20=ED=80=84=EB=A6=AC=ED=8B=B0(?=
 =?UTF-8?q?=EC=9A=B0=EB=9F=89=EC=A3=BC)=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/13-quality-portfolio.py     |  2 +
 quantcommon.py                      | 16 +++++++-
 streamlit-quant/strategy/quality.py | 57 +++++++++++++++++++++++++++++
 3 files changed, 74 insertions(+), 1 deletion(-)
 create mode 100644 streamlit-quant/strategy/quality.py

diff --git a/example/13-quality-portfolio.py b/example/13-quality-portfolio.py
index 4f4b168..7380c64 100644
--- a/example/13-quality-portfolio.py
+++ b/example/13-quality-portfolio.py
@@ -4,6 +4,8 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import quantcommon
 
+# strategy/quality에서 구현
+
 # 퀄리티(우량주) 포트폴리오. 영업수익성이 높은 주식
 engine = quantcommon.QuantCommon().create_engine()
 
diff --git a/quantcommon.py b/quantcommon.py
index fbb8c14..5a2da0b 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -67,4 +67,18 @@ class QuantCommon:
         finally:
             engine.dispose()
 
-        return price_list
\ No newline at end of file
+        return price_list
+
+    def get_fs_list(self):
+        engine = self.create_engine()
+
+        try:
+            fs_list = pd.read_sql("""
+            select * from kor_fs
+            where 계정 in ('당기순이익', '매출총이익', '영업활동으로인한현금흐름', '자산', '자본')
+            and 공시구분 = 'q';
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return fs_list
\ No newline at end of file
diff --git a/streamlit-quant/strategy/quality.py b/streamlit-quant/strategy/quality.py
new file mode 100644
index 0000000..28a6e2e
--- /dev/null
+++ b/streamlit-quant/strategy/quality.py
@@ -0,0 +1,57 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import quantcommon
+
+# 퀄리티(우량주) 포트폴리오. 영업수익성이 높은 주식
+def get_quality_top(count):
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    fs_list = qc.get_fs_list()
+
+    fs_list = fs_list.sort_values(['종목코드', '계정', '기준일'])
+    # TTM 값을 구하기 위해서 rolling() 메소드를 통해 4분기 합 구함. 4분기 데이터가 없는 경우 제외하기 위해서 min_periods=4
+    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()
+    # 자산과 자본은 재무상태표 항목이므로 합이 아닌 평균을 구하며, 나머지 항목은 합을 그대로 사용
+    fs_list_clean['ttm'] = np.where(fs_list_clean['계정'].isin(['자산', '자본']),
+                                    fs_list_clean['ttm'] / 4, fs_list_clean['ttm'])
+    # tail(1)을 통해 종목코드와 계정별 최근 데이터만 선택
+    fs_list_clean = fs_list_clean.groupby(['종목코드', '계정']).tail(1)
+
+    fs_list_pivot = fs_list_clean.pivot(index='종목코드', columns='계정', values='ttm')
+    # 수익성 지표에 해당하는 ROE, GPA, CFO 계산
+    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']
+
+    # 티커 테이블과 합침
+    quality_list = ticker_list[['종목코드', '종목명']].merge(fs_list_pivot,
+                                                      how='left',
+                                                      on='종목코드')
+    # quality_list.round(count).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()
+
+    # 위에서 구한 3개 지표들의 순위를 더한 후 다시 순위를 매김
+    quality_sum = quality_rank.sum(axis=1, skipna=False).rank()
+    # 최종 순위가 낮은 종목 선택
+    return quality_list.loc[quality_sum <= count, ['종목코드', '종목명', 'ROE', 'GPA', 'CFO']].round(4)
+
+if __name__ == '__main__':
+    print(get_quality_top(20))
\ No newline at end of file

From 7e31a630323fcdd4739f4badb8443d74c1b5e990 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Fri, 14 Mar 2025 16:16:09 +0900
Subject: [PATCH 11/16] =?UTF-8?q?feat:=20=EB=A7=88=EB=B2=95=20=EA=B3=B5?=
 =?UTF-8?q?=EC=8B=9D=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 quantcommon.py                            | 15 +++++
 streamlit-quant/strategy/magic-formula.py | 79 +++++++++++++++++++++++
 2 files changed, 94 insertions(+)
 create mode 100644 streamlit-quant/strategy/magic-formula.py

diff --git a/quantcommon.py b/quantcommon.py
index 5a2da0b..2cc3ee3 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -81,4 +81,19 @@ class QuantCommon:
         finally:
             engine.dispose()
 
+        return fs_list
+
+    def get_expanded_fs_list(self):
+        engine = self.create_engine()
+
+        try:
+            fs_list = pd.read_sql("""
+            select * from kor_fs
+            where 계정 in ('매출액', '당기순이익', '법인세비용', '이자비용', '현금및현금성자산',
+                '부채', '유동부채', '유동자산', '비유동자산', '감가상각비')
+            and 공시구분 = 'q';
+            """, con=engine)
+        finally:
+            engine.dispose()
+
         return fs_list
\ No newline at end of file
diff --git a/streamlit-quant/strategy/magic-formula.py b/streamlit-quant/strategy/magic-formula.py
new file mode 100644
index 0000000..55f8d8c
--- /dev/null
+++ b/streamlit-quant/strategy/magic-formula.py
@@ -0,0 +1,79 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import quantcommon
+
+# 마법 공식 포트폴리오. 밸류와 퀄리티의 조합. 조엘 그린블라트의 '마법공식'
+def get_magic_formula_top(count):
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    fs_list = qc.get_expanded_fs_list()
+
+    fs_list = fs_list.sort_values(['종목코드', '계정', '기준일'])
+    # TTM 값을 구하기 위해서 rolling() 메소드를 통해 4분기 합 구함. 4분기 데이터가 없는 경우 제외하기 위해서 min_periods=4
+    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()
+    # 재무상태표 현황(부채, 유동부채, 유동자산, 비유동자산)은 평균값 사용
+    fs_list_clean['ttm'] = np.where(
+        fs_list_clean['계정'].isin(['부채', '유동부채', '유동자산', '비유동자산']),
+        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')
+
+    data_bind = ticker_list[['종목코드', '종목명', '시가총액']].merge(fs_list_pivot,
+                                                           how='left',
+                                                           on='종목코드')
+    # 티커 테이블에 있는 시가총액은 원, 제무제표 테이블은 억 단위이므로 단위를 맞춤
+    data_bind['시가총액'] = data_bind['시가총액'] / 100000000
+
+    # 이익수익률 계산식: 이자 및 법인세 차감전 이익(EBIT) / 가입가치(시가총액 + 순차입금)
+    # 분자(EBIT)
+    magic_ebit = data_bind['당기순이익'] + data_bind['법인세비용'] + data_bind['이자비용']
+
+    # 분모
+    magic_cap = data_bind['시가총액']
+    magic_debt = data_bind['부채']
+
+    ## 분모: 여유자금
+    magic_excess_cash = data_bind['유동부채'] - data_bind['유동자산'] + data_bind[
+        '현금및현금성자산']
+    magic_excess_cash[magic_excess_cash < 0] = 0
+    magic_excess_cash_final = data_bind['현금및현금성자산'] - magic_excess_cash
+
+    magic_ev = magic_cap + magic_debt - magic_excess_cash_final
+
+    # 이익수익률
+    magic_ey = magic_ebit / magic_ev
+
+    # 투하자본 수익률
+    magic_ic = (data_bind['유동자산'] - data_bind['유동부채']) + (data_bind['비유동자산'] -
+                                                          data_bind['감가상각비'])
+    magic_roc = magic_ebit / magic_ic
+
+    # 열 입력하기
+    data_bind['이익 수익률'] = magic_ey
+    data_bind['투하자본 수익률'] = magic_roc
+
+    magic_rank = (magic_ey.rank(ascending=False, axis=0) +
+                  magic_roc.rank(ascending=False, axis=0)).rank(axis=0)
+    return data_bind.loc[magic_rank <= 20, ['종목코드', '종목명', '이익 수익률', '투하자본 수익률']].round(4)
+
+    # data_bind['투자구분'] = np.where(magic_rank <= 20, '마법공식', '기타')
+    #
+    # plt.rc('font', family='Malgun Gothic')
+    # plt.subplots(1, 1, figsize=(10, 6))
+    # sns.scatterplot(data=data_bind,
+    #                 x='이익 수익률',
+    #                 y='투하자본 수익률',
+    #                 hue='투자구분',
+    #                 style='투자구분',
+    #                 s=200)
+    # plt.xlim(0, 1)
+    # plt.ylim(0, 1)
+    # plt.show()
+
+if __name__ == '__main__':
+    print(get_magic_formula_top(20))
\ No newline at end of file

From 07e07dfbcd48d1259773541f789adbe375f8a1b7 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Fri, 14 Mar 2025 18:18:39 +0900
Subject: [PATCH 12/16] =?UTF-8?q?feat:=20=EB=A9=80=ED=8B=B0=ED=8C=A9?=
 =?UTF-8?q?=ED=84=B0=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 example/13-multi-factor-portfolio.py     |   2 +
 quantcommon.py                           |  15 +-
 streamlit-quant/strategy/multi-factor.py | 262 +++++++++++++++++++++++
 3 files changed, 278 insertions(+), 1 deletion(-)
 create mode 100644 streamlit-quant/strategy/multi-factor.py

diff --git a/example/13-multi-factor-portfolio.py b/example/13-multi-factor-portfolio.py
index 39d977c..d04f3ed 100644
--- a/example/13-multi-factor-portfolio.py
+++ b/example/13-multi-factor-portfolio.py
@@ -6,6 +6,8 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import quantcommon
 
+# strategy/multi-factor에서 구현
+
 # 멀티 팩터 포트폴리오.
 # 퀄리티: 자기자본이익률(ROE), 매출총이익(GPA), 영업활동현금흐름(CFO)
 # 밸류: PER, PBR, PSR, PCR, DY
diff --git a/quantcommon.py b/quantcommon.py
index 2cc3ee3..8b3f5b7 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -96,4 +96,17 @@ class QuantCommon:
         finally:
             engine.dispose()
 
-        return fs_list
\ No newline at end of file
+        return fs_list
+
+    def get_sector_list(self):
+        engine = self.create_engine()
+
+        try:
+            sector_list = pd.read_sql("""
+            select * from kor_sector
+            where 기준일 = (select max(기준일) from kor_sector);	
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return sector_list
\ No newline at end of file
diff --git a/streamlit-quant/strategy/multi-factor.py b/streamlit-quant/strategy/multi-factor.py
new file mode 100644
index 0000000..e5a3c57
--- /dev/null
+++ b/streamlit-quant/strategy/multi-factor.py
@@ -0,0 +1,262 @@
+import pandas as pd
+import numpy as np
+import statsmodels.api as sm
+from scipy.stats import zscore
+import matplotlib.pyplot as plt
+import seaborn as sns
+import quantcommon
+
+# 멀티 팩터 포트폴리오.
+# 퀄리티: 자기자본이익률(ROE), 매출총이익(GPA), 영업활동현금흐름(CFO)
+# 밸류: PER, PBR, PSR, PCR, DY
+# 모멘텀: 12개월 수익률, K-Ratio
+engine = quantcommon.QuantCommon().create_engine()
+
+# 각 섹터별 아웃라이어를 제거한 후 순위와 z-score를 구하는 함수
+def col_clean(df, cutoff=0.01, asc=False):
+
+    q_low = df.quantile(cutoff)
+    q_hi = df.quantile(1 - cutoff)
+
+    # 이상치 데이터 제거
+    df_trim = df[(df > q_low) & (df < q_hi)]
+
+    df_z_score = df_trim.rank(axis=0, ascending=asc).apply(
+        zscore, nan_policy='omit')
+
+    return df_z_score
+
+
+def plot_rank(df):
+    ax = sns.relplot(data=df,
+                     x='rank',
+                     y=1,
+                     col='variable',
+                     hue='invest',
+                     size='size',
+                     sizes=(10, 100),
+                     style='invest',
+                     markers={'Y': 'X','N': 'o'},
+                     palette={'Y': 'red','N': 'grey'},
+                     kind='scatter')
+    ax.set(xlabel=None)
+    ax.set(ylabel=None)
+
+    plt.show()
+
+
+def get_multi_factor_top(count):
+    qc = quantcommon.QuantCommon()
+    ticker_list = qc.get_ticker_list()
+    fs_list = qc.get_fs_list()
+    value_list =  qc.get_value_list()
+    price_list = qc.get_price_list(12)
+    sector_list = qc.get_sector_list()
+
+    # 퀄리티 지표 계산
+    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']
+
+    fs_list_pivot.round(4).head()
+
+    value_list.loc[value_list['값'] <= 0, '값'] = np.nan
+    value_pivot = value_list.pivot(index='종목코드', columns='지표', values='값')
+
+    value_pivot.head()
+
+    # 가치 지표 계산
+    # 음수를 제거하고 행으로 긴 형태로 변경
+    price_pivot = price_list.pivot(index='날짜', columns='종목코드', values='종가')
+    ret_list = pd.DataFrame(data=(price_pivot.iloc[-1] / price_pivot.iloc[0]) - 1,
+                            columns=['12M'])
+
+    ret = price_pivot.pct_change().iloc[1:]
+    ret_cum = np.log(1 + ret).cumsum()
+
+    x = np.array(range(len(ret)))
+    k_ratio = {}
+
+    for i in range(0, len(ticker_list)):
+        ticker = ticker_list.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()
+
+    # 가격 테이블을 이용해서 최근 12개월 수익률을 구하고
+    # 로그 누적 수익률을 통해 각 종목별 K-Ratio를 계산
+    data_bind = ticker_list[['종목코드', '종목명']].merge(
+        sector_list[['CMP_CD', 'SEC_NM_KOR']],
+        how='left',
+        left_on='종목코드',
+        right_on='CMP_CD').merge(
+            fs_list_pivot[['ROE', 'GPA', 'CFO']], how='left',
+            on='종목코드').merge(value_pivot, how='left',
+                             on='종목코드').merge(ret_list, how='left',
+                                              on='종목코드').merge(k_ratio_bind,
+                                                               how='left',
+                                                               on='종목코드')
+
+    data_bind.loc[data_bind['SEC_NM_KOR'].isnull(), 'SEC_NM_KOR'] = '기타'
+    data_bind = data_bind.drop(['CMP_CD'], axis=1)
+
+    data_bind.round(4).head()
+
+    # 종목코드와 섹터정보(SEC_NM_KOR)를 인덱스로 설정한 후, 섹터에 따른 그룹을 묶어준다.
+    data_bind_group = data_bind.set_index(['종목코드',
+                                           'SEC_NM_KOR']).groupby('SEC_NM_KOR', as_index=False)
+
+    data_bind_group.head(1).round(4)
+
+    # 퀄리티 지표의 z-score를 계산
+    # 퀄리티 지표에 해당하는 열(ROE, GPA, CFO) 선택해서 col_clean() 적용한 후 순위의 z-score 계산
+    # sum() 함수를 통해 z-score의 합을 구하며, to_frame() 메소드를 통해 데이터프레임 형태로 변경
+    z_quality = data_bind_group[['ROE', 'GPA', 'CFO'
+                                 ]].apply(lambda x: col_clean(x, 0.01, False)).sum(
+                                     axis=1, skipna=False).to_frame('z_quality')
+    # data_bind 테이블과 합치며, z_quality 열에는 퀄리티 지표의 z-score가 표시
+    data_bind = data_bind.merge(z_quality, how='left', on=['종목코드', 'SEC_NM_KOR'])
+
+    data_bind.round(4).head()
+
+    # 가치 지표의 z-score 계산
+    # 가치 지표에 해당하는 열(PBR, PCR, PER, PSR) 선택해서 col_clean() 적용, 오름차순
+    value_1 = data_bind_group[['PBR', 'PCR', 'PER',
+                               'PSR']].apply(lambda x: col_clean(x, 0.01, True))
+    # DY(배당수익률)의 경우 내림차순으로 계산
+    value_2 = data_bind_group[['DY']].apply(lambda x: col_clean(x, 0.01, False))
+
+    # 두 결과를 합쳐 z-score의 합을 구한 후, 데이터프레임 형태로 변경
+    z_value = value_1.merge(value_2, on=['종목코드', 'SEC_NM_KOR'
+                                         ]).sum(axis=1,
+                                                skipna=False).to_frame('z_value')
+    # data_bind 테이블과 합치며, z_value 열에는 밸류 지표의 z-score가 표시
+    data_bind = data_bind.merge(z_value, how='left', on=['종목코드', 'SEC_NM_KOR'])
+
+    data_bind.round(4).head()
+
+    # 모멘텀 지표의 z-score 계산
+    # 모멘텀 지표에 해당하는 열(12M, K_ratio) 선택 후 col_clean() 적용
+    z_momentum = data_bind_group[[
+        '12M', 'K_ratio'
+    ]].apply(lambda x: col_clean(x, 0.01, False)).sum(
+        axis=1, skipna=False).to_frame('z_momentum')
+    # data_bind 테이블과 합치며, z_momentum 열에는 모멘텀 지표의 z-score가 표시
+    data_bind = data_bind.merge(z_momentum, how='left', on=['종목코드', 'SEC_NM_KOR'])
+
+    data_bind.round(4).head()
+
+    # 팩터 분포를 시각화
+    data_z = data_bind[['z_quality', 'z_value', 'z_momentum']].copy()
+
+    fig, axes = plt.subplots(3, 1, figsize=(10, 6), sharex=True, sharey=True)
+    for n, ax in enumerate(axes.flatten()):
+        ax.hist(data_z.iloc[:, n])
+        ax.set_title(data_z.columns[n], size=12)
+    fig.tight_layout()
+    # plt.show()
+
+    # 팩터 분포가 동일하지 않으니 z-score를 다시 계산해서 분포의 넓이를 비슷하게 맞춤
+    # 종목 코드와 각 팩터의 z-score만 선택한 후, 종목 코드를 인덱스로 설정
+    # apply()를 통해서 z-score 다시 계산
+    data_bind_final = data_bind[['종목코드', 'z_quality', 'z_value', 'z_momentum'
+                                 ]].set_index('종목코드').apply(zscore,
+                                                            nan_policy='omit')
+    # 열 이름 설정
+    data_bind_final.columns = ['quality', 'value', 'momentum']
+
+    plt.rc('font', family='Malgun Gothic')
+    plt.rc('axes', unicode_minus=False)
+    fig, axes = plt.subplots(3, 1, figsize=(10, 6), sharex=True, sharey=True)
+    for n, ax in enumerate(axes.flatten()):
+        ax.hist(data_bind_final.iloc[:, n])
+        ax.set_title(data_bind_final.columns[n], size=12)
+    fig.tight_layout()
+    # plt.show()
+
+    # 각 팩터간 상관 관계 확인
+    mask = np.triu(data_bind_final.corr())
+    fig, ax = plt.subplots(figsize=(10, 6))
+    sns.heatmap(data_bind_final.corr(),
+                annot=True,
+                mask=mask,
+                annot_kws={"size": 16},
+                vmin=0,
+                vmax=1,
+                center=0.5,
+                cmap='coolwarm',
+                square=True)
+    ax.invert_yaxis()
+    # plt.show()
+
+    # 각 팩터간 상관관계가 매우 낮으며, 여러 팩터를 동시에 고려함으로써 분산효과를 기대할 수 있다.
+    # 이제 계산된 팩터들을 토대로 최종 포트폴리오를 구성해 보자.
+    # 각 팩터를 동일 비중으로 설정. 0.2, 0.4, 0.4 등 중요하다고 생각되는 팩터에 비중을 다르게도 지정할 수 있음
+    wts = [0.3, 0.3, 0.3]
+    # 위에서 설정한 비율을 반영해서 데이터프레임 형태로 변경
+    data_bind_final_sum = (data_bind_final * wts).sum(axis=1,
+                                                      skipna=False).to_frame()
+    data_bind_final_sum.columns = ['qvm']
+    # 기본 테이블(data_bind)과 합침
+    port_qvm = data_bind.merge(data_bind_final_sum, on='종목코드')
+    # 최종 z-score의 합(qvm) 기준 순위가 20위 이내인 경우 투자 종목에 해당하니 Y로 표시, 나머진 N
+    port_qvm['invest'] = np.where(port_qvm['qvm'].rank() <= 20, 'Y', 'N')
+
+    # round()는 DataFrame 객체 내의 요소를 반올림하는 메서드
+    return port_qvm[port_qvm['invest'] == 'Y'].round(4)
+
+    # 이하 선택된 종목과 선택되지 않은 종목들 간의 특성을 그리기 위한 코드
+    # data_melt = port_qvm.melt(id_vars='invest',
+    #                           value_vars=[
+    #                               'ROE', 'GPA', 'CFO', 'PER', 'PBR', 'PCR', 'PSR',
+    #                               'DY', '12M', 'K_ratio'
+    #                           ])
+    #
+    # data_melt['size'] = data_melt['invest'].map({'Y': 100, 'N': 10})
+    # data_melt.head()
+    #
+    # hist_quality = data_melt[data_melt['variable'].isin(['ROE', 'GPA',
+    #                                                      'CFO'])].copy()
+    # hist_quality['rank'] = hist_quality.groupby('variable')['value'].rank(
+    #     ascending=False)
+    # plot_rank(hist_quality)
+    #
+    # hist_value = data_melt[data_melt['variable'].isin(
+    #     ['PER', 'PBR', 'PCR', 'PSR', 'DY'])].copy()
+    # hist_value['value'] = np.where(hist_value['variable'] == 'DY',
+    #                                1 / hist_value['value'], hist_value['value'])
+    # hist_value['rank'] = hist_value.groupby('variable')['value'].rank()
+    # plot_rank(hist_value)
+    #
+    # hist_momentum = data_melt[data_melt['variable'].isin(['12M', 'K_ratio'])].copy()
+    # hist_momentum['rank'] = hist_momentum.groupby('variable')['value'].rank(ascending = False)
+    # plot_rank(hist_momentum)
+    #
+    # port_qvm[port_qvm['invest'] == 'Y']['종목코드'].to_excel('model.xlsx', index=False)
+
+
+if __name__ == '__main__':
+    top = get_multi_factor_top(20)
+    # head() 상위 n개 반환. 기본값은 5
+    print(top.head())
+    top['종목코드'].to_excel('model.xlsx', index=False)
\ No newline at end of file

From 679eb4fe0b327e161c4e07b35674b050a21c246b Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Sat, 15 Mar 2025 01:24:46 +0900
Subject: [PATCH 13/16] =?UTF-8?q?feat:=20=ED=8C=8C=EC=9D=BC=20=EC=9D=B4?=
 =?UTF-8?q?=EB=A6=84=20=EB=B3=80=EA=B2=BD,=20=EB=B0=B1=ED=85=8C=EC=8A=A4?=
 =?UTF-8?q?=ED=8A=B8=20=EC=83=98=ED=94=8C=20=EC=B6=94=EA=B0=80,=20?=
 =?UTF-8?q?=EB=94=94=EB=A0=89=ED=86=A0=EB=A6=AC=20=ED=8C=A8=ED=82=A4?=
 =?UTF-8?q?=EC=A7=80=ED=99=94?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 README.md                                     |  7 +++
 quantcommon.py                                | 13 ++++++
 streamlit_quant/__init__.py                   |  1 +
 {streamlit-quant => streamlit_quant}/app.py   |  0
 streamlit_quant/backtest/backtest-1.py        | 46 +++++++++++++++++++
 .../pages/crawling.py                         |  0
 .../pages/super_quality.py                    |  0
 .../pages/super_value_momentum.py             |  0
 .../src/current-financial-statements.py       |  0
 .../src/current-price.py                      |  0
 .../src/current-stock.py                      |  0
 streamlit_quant/strategy/__init__.py          |  1 +
 .../strategy/all_value.py                     |  0
 .../strategy/f_score.py                       |  1 -
 .../strategy/magic_formula.py                 |  0
 .../strategy/momentum.py                      |  0
 .../strategy/multi_factor.py                  | 17 ++-----
 .../strategy/quality.py                       |  0
 .../strategy/value.py                         |  0
 19 files changed, 71 insertions(+), 15 deletions(-)
 create mode 100644 streamlit_quant/__init__.py
 rename {streamlit-quant => streamlit_quant}/app.py (100%)
 create mode 100644 streamlit_quant/backtest/backtest-1.py
 rename {streamlit-quant => streamlit_quant}/pages/crawling.py (100%)
 rename {streamlit-quant => streamlit_quant}/pages/super_quality.py (100%)
 rename {streamlit-quant => streamlit_quant}/pages/super_value_momentum.py (100%)
 rename {streamlit-quant => streamlit_quant}/src/current-financial-statements.py (100%)
 rename {streamlit-quant => streamlit_quant}/src/current-price.py (100%)
 rename {streamlit-quant => streamlit_quant}/src/current-stock.py (100%)
 create mode 100644 streamlit_quant/strategy/__init__.py
 rename streamlit-quant/strategy/all-value.py => streamlit_quant/strategy/all_value.py (100%)
 rename streamlit-quant/strategy/f-score.py => streamlit_quant/strategy/f_score.py (95%)
 rename streamlit-quant/strategy/magic-formula.py => streamlit_quant/strategy/magic_formula.py (100%)
 rename {streamlit-quant => streamlit_quant}/strategy/momentum.py (100%)
 rename streamlit-quant/strategy/multi-factor.py => streamlit_quant/strategy/multi_factor.py (96%)
 rename {streamlit-quant => streamlit_quant}/strategy/quality.py (100%)
 rename {streamlit-quant => streamlit_quant}/strategy/value.py (100%)

diff --git a/README.md b/README.md
index b451cc3..45ea151 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,9 @@
 # 실행
 streamlit run .\streamlit-quant\app.py --server.port 20000
+
+# pre-
+Go to Build Tools for Visual Studio 2017
+Select free download under Visual Studio Community 2017. This will download the installer. Run the installer.
+Select what you need under workload tab:
+a. Under Windows, there are three choices. Only check Desktop development with C++.
+b. Under Web & Cloud, there are seven choices. Only check Python development (I believe this is optional, but I have done it).
\ No newline at end of file
diff --git a/quantcommon.py b/quantcommon.py
index 8b3f5b7..2df5794 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -69,6 +69,19 @@ class QuantCommon:
 
         return price_list
 
+    def get_price_list_by_code(self, codes):
+        engine = self.create_engine()
+
+        try:
+            price_list = pd.read_sql(f"""
+            select * from kor_price
+            where 종목코드 in ({codes});
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return price_list
+
     def get_fs_list(self):
         engine = self.create_engine()
 
diff --git a/streamlit_quant/__init__.py b/streamlit_quant/__init__.py
new file mode 100644
index 0000000..04bd080
--- /dev/null
+++ b/streamlit_quant/__init__.py
@@ -0,0 +1 @@
+__all__ = ['backtest', 'strategy']
\ No newline at end of file
diff --git a/streamlit-quant/app.py b/streamlit_quant/app.py
similarity index 100%
rename from streamlit-quant/app.py
rename to streamlit_quant/app.py
diff --git a/streamlit_quant/backtest/backtest-1.py b/streamlit_quant/backtest/backtest-1.py
new file mode 100644
index 0000000..9b62d7a
--- /dev/null
+++ b/streamlit_quant/backtest/backtest-1.py
@@ -0,0 +1,46 @@
+import bt
+import matplotlib.pyplot as plt
+
+import pandas as pd
+
+import quantcommon
+import streamlit_quant.strategy.multi_factor as multi_factor
+
+
+qc = quantcommon.QuantCommon()
+mf = multi_factor.get_multi_factor_top(qc, 20)
+
+codes = ','.join(mf['종목코드'].array)
+price = qc.get_price_list_by_code(codes)
+
+# price = price.set_index(['날짜'])
+# price.rename(columns={"날짜": "Date"})
+price["Date"] = pd.to_datetime(price["날짜"])
+
+pivot_df = price.pivot(index="Date", columns="종목코드", values="종가")
+
+# print(pivot_df.tail)
+
+strategy = bt.Strategy("Asset_EW", [
+    bt.algos.SelectAll(),    # 모든 데이터 사용
+    bt.algos.WeighEqually(), # 동일 비중 투자
+    bt.algos.RunMonthly(),   # 매 월말 리밸런싱
+    bt.algos.Rebalance()     # 계산된 비중에 따라 리밸런싱
+])
+
+# 가격 데이터 중 시작 시점이 모두 다르므로, dropna() 함수를 통해 NA를 모두 제거하여 시작 시점을 맞춤
+pivot_df.dropna(inplace=True)
+
+# 백테스트 생성
+backtest = bt.Backtest(strategy, pivot_df)
+
+# 백테스트 실행
+result = bt.run(backtest)
+# prices: 누적 수익률이 데이터프레임 형태로 나타나며, 시작 시점을 100으로 환산하여 계산
+# to_returns: 수익률 계산
+# print(result.prices.to_returns())
+
+result.plot(figsize=(10, 6), legend=False)
+plt.show()
+
+result.display()
\ No newline at end of file
diff --git a/streamlit-quant/pages/crawling.py b/streamlit_quant/pages/crawling.py
similarity index 100%
rename from streamlit-quant/pages/crawling.py
rename to streamlit_quant/pages/crawling.py
diff --git a/streamlit-quant/pages/super_quality.py b/streamlit_quant/pages/super_quality.py
similarity index 100%
rename from streamlit-quant/pages/super_quality.py
rename to streamlit_quant/pages/super_quality.py
diff --git a/streamlit-quant/pages/super_value_momentum.py b/streamlit_quant/pages/super_value_momentum.py
similarity index 100%
rename from streamlit-quant/pages/super_value_momentum.py
rename to streamlit_quant/pages/super_value_momentum.py
diff --git a/streamlit-quant/src/current-financial-statements.py b/streamlit_quant/src/current-financial-statements.py
similarity index 100%
rename from streamlit-quant/src/current-financial-statements.py
rename to streamlit_quant/src/current-financial-statements.py
diff --git a/streamlit-quant/src/current-price.py b/streamlit_quant/src/current-price.py
similarity index 100%
rename from streamlit-quant/src/current-price.py
rename to streamlit_quant/src/current-price.py
diff --git a/streamlit-quant/src/current-stock.py b/streamlit_quant/src/current-stock.py
similarity index 100%
rename from streamlit-quant/src/current-stock.py
rename to streamlit_quant/src/current-stock.py
diff --git a/streamlit_quant/strategy/__init__.py b/streamlit_quant/strategy/__init__.py
new file mode 100644
index 0000000..cb7180d
--- /dev/null
+++ b/streamlit_quant/strategy/__init__.py
@@ -0,0 +1 @@
+__all__ = ['multi_factor']
\ No newline at end of file
diff --git a/streamlit-quant/strategy/all-value.py b/streamlit_quant/strategy/all_value.py
similarity index 100%
rename from streamlit-quant/strategy/all-value.py
rename to streamlit_quant/strategy/all_value.py
diff --git a/streamlit-quant/strategy/f-score.py b/streamlit_quant/strategy/f_score.py
similarity index 95%
rename from streamlit-quant/strategy/f-score.py
rename to streamlit_quant/strategy/f_score.py
index 64f5393..66b2fc6 100644
--- a/streamlit-quant/strategy/f-score.py
+++ b/streamlit_quant/strategy/f_score.py
@@ -1,5 +1,4 @@
 import pandas as pd
-import numpy as np
 import quantcommon
 
 # DB 연결
diff --git a/streamlit-quant/strategy/magic-formula.py b/streamlit_quant/strategy/magic_formula.py
similarity index 100%
rename from streamlit-quant/strategy/magic-formula.py
rename to streamlit_quant/strategy/magic_formula.py
diff --git a/streamlit-quant/strategy/momentum.py b/streamlit_quant/strategy/momentum.py
similarity index 100%
rename from streamlit-quant/strategy/momentum.py
rename to streamlit_quant/strategy/momentum.py
diff --git a/streamlit-quant/strategy/multi-factor.py b/streamlit_quant/strategy/multi_factor.py
similarity index 96%
rename from streamlit-quant/strategy/multi-factor.py
rename to streamlit_quant/strategy/multi_factor.py
index e5a3c57..a0d9139 100644
--- a/streamlit-quant/strategy/multi-factor.py
+++ b/streamlit_quant/strategy/multi_factor.py
@@ -4,14 +4,11 @@ import statsmodels.api as sm
 from scipy.stats import zscore
 import matplotlib.pyplot as plt
 import seaborn as sns
-import quantcommon
 
 # 멀티 팩터 포트폴리오.
 # 퀄리티: 자기자본이익률(ROE), 매출총이익(GPA), 영업활동현금흐름(CFO)
 # 밸류: PER, PBR, PSR, PCR, DY
 # 모멘텀: 12개월 수익률, K-Ratio
-engine = quantcommon.QuantCommon().create_engine()
-
 # 각 섹터별 아웃라이어를 제거한 후 순위와 z-score를 구하는 함수
 def col_clean(df, cutoff=0.01, asc=False):
 
@@ -45,8 +42,7 @@ def plot_rank(df):
     plt.show()
 
 
-def get_multi_factor_top(count):
-    qc = quantcommon.QuantCommon()
+def get_multi_factor_top(qc, count):
     ticker_list = qc.get_ticker_list()
     fs_list = qc.get_fs_list()
     value_list =  qc.get_value_list()
@@ -220,7 +216,7 @@ def get_multi_factor_top(count):
     # 기본 테이블(data_bind)과 합침
     port_qvm = data_bind.merge(data_bind_final_sum, on='종목코드')
     # 최종 z-score의 합(qvm) 기준 순위가 20위 이내인 경우 투자 종목에 해당하니 Y로 표시, 나머진 N
-    port_qvm['invest'] = np.where(port_qvm['qvm'].rank() <= 20, 'Y', 'N')
+    port_qvm['invest'] = np.where(port_qvm['qvm'].rank() <= count, 'Y', 'N')
 
     # round()는 DataFrame 객체 내의 요소를 반올림하는 메서드
     return port_qvm[port_qvm['invest'] == 'Y'].round(4)
@@ -252,11 +248,4 @@ def get_multi_factor_top(count):
     # hist_momentum['rank'] = hist_momentum.groupby('variable')['value'].rank(ascending = False)
     # plot_rank(hist_momentum)
     #
-    # port_qvm[port_qvm['invest'] == 'Y']['종목코드'].to_excel('model.xlsx', index=False)
-
-
-if __name__ == '__main__':
-    top = get_multi_factor_top(20)
-    # head() 상위 n개 반환. 기본값은 5
-    print(top.head())
-    top['종목코드'].to_excel('model.xlsx', index=False)
\ No newline at end of file
+    # port_qvm[port_qvm['invest'] == 'Y']['종목코드'].to_excel('model.xlsx', index=False)
\ No newline at end of file
diff --git a/streamlit-quant/strategy/quality.py b/streamlit_quant/strategy/quality.py
similarity index 100%
rename from streamlit-quant/strategy/quality.py
rename to streamlit_quant/strategy/quality.py
diff --git a/streamlit-quant/strategy/value.py b/streamlit_quant/strategy/value.py
similarity index 100%
rename from streamlit-quant/strategy/value.py
rename to streamlit_quant/strategy/value.py

From 5666ab567fba7c48c4258507d78f706e737f0f32 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Sun, 16 Mar 2025 12:50:27 +0900
Subject: [PATCH 14/16] =?UTF-8?q?feat:=20=EB=B0=B1=ED=85=8C=EC=8A=A4?=
 =?UTF-8?q?=ED=8A=B8=20=EC=B6=94=EA=B0=80(=EA=B3=BC=EA=B1=B0=EC=97=90=20FS?=
 =?UTF-8?q?=20=EB=8D=B0=EC=9D=B4=ED=84=B0=EA=B0=80=20=EC=97=86=EC=96=B4?=
 =?UTF-8?q?=EC=84=9C=20=EC=A0=9C=EB=8C=80=EB=A1=9C=EB=90=9C=20=ED=85=8C?=
 =?UTF-8?q?=EC=8A=A4=ED=8A=B8=20=EC=A7=84=ED=96=89=20=EB=B6=88=EA=B0=80)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 streamlit_quant/backtest/backtest-1.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/streamlit_quant/backtest/backtest-1.py b/streamlit_quant/backtest/backtest-1.py
index 9b62d7a..791d7b5 100644
--- a/streamlit_quant/backtest/backtest-1.py
+++ b/streamlit_quant/backtest/backtest-1.py
@@ -5,12 +5,14 @@ import pandas as pd
 
 import quantcommon
 import streamlit_quant.strategy.multi_factor as multi_factor
+import streamlit_quant.strategy.magic_formula as magic_formula
 
 
 qc = quantcommon.QuantCommon()
 mf = multi_factor.get_multi_factor_top(qc, 20)
+magic_formula = magic_formula.get_magic_formula_top(20)
 
-codes = ','.join(mf['종목코드'].array)
+codes = ','.join(magic_formula['종목코드'].array)
 price = qc.get_price_list_by_code(codes)
 
 # price = price.set_index(['날짜'])

From da6349dc35121948caa8972ca94e0ffb0ff42287 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Sun, 16 Mar 2025 12:51:14 +0900
Subject: [PATCH 15/16] =?UTF-8?q?feat:=20=EC=8B=A0F-=EC=8A=A4=EC=BD=94?=
 =?UTF-8?q?=EC=96=B4=20+=20GP/A=20=EC=88=9C=EC=9C=84=20+=20=EC=86=8C?=
 =?UTF-8?q?=ED=98=95=EC=A3=BC=20=EC=A0=84=EB=9E=B5=20=EC=B6=94=EA=B0=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 quantcommon.py                      |  15 ++++
 streamlit_quant/strategy/f_score.py | 127 +++++++++++++++++++++++++---
 2 files changed, 130 insertions(+), 12 deletions(-)

diff --git a/quantcommon.py b/quantcommon.py
index 2df5794..27179c3 100644
--- a/quantcommon.py
+++ b/quantcommon.py
@@ -96,6 +96,21 @@ class QuantCommon:
 
         return fs_list
 
+    def get_fs_list_by_account_and_date(self, account, date):
+        engine = self.create_engine()
+
+        try:
+            fs_list = pd.read_sql(f"""
+            select * from kor_fs
+            where 계정 in ({account})
+            and 기준일 in ({date})
+            and 공시구분 = 'y';
+            """, con=engine)
+        finally:
+            engine.dispose()
+
+        return fs_list
+
     def get_expanded_fs_list(self):
         engine = self.create_engine()
 
diff --git a/streamlit_quant/strategy/f_score.py b/streamlit_quant/strategy/f_score.py
index 66b2fc6..a59c50a 100644
--- a/streamlit_quant/strategy/f_score.py
+++ b/streamlit_quant/strategy/f_score.py
@@ -1,18 +1,121 @@
+from datetime import datetime
 import pandas as pd
 import quantcommon
 
-# DB 연결
-engine = quantcommon.QuantCommon().create_engine()
+# 흑자 기업이면 1점(당기순이익)
+def calc_net_income(qc, ticker_list, base_date):
+    net_income_list = qc.get_fs_list_by_account_and_date("'당기순이익'", f"'{base_date}'")
+    net_income_list['score1'] = (net_income_list['값'] > 0).astype(int)
+    # print(net_income_list)
 
-ticker_list = pd.read_sql("""
-select * from kor_ticker
-where 기준일 = (select max(기준일) from kor_ticker) 
-	and 종목구분 = '보통주';
-""", con=engine)
+    result_df = net_income_list[['종목코드', 'score1']]
 
-value_list = pd.read_sql("""
-select * from kor_value
-where 기준일 = (select max(기준일) from kor_value);
-""", con=engine)
+    # 원본 데이터프레임에 score 병합
+    final_df = ticker_list[['종목코드', '종목명', '분류']].merge(result_df, on='종목코드', how='left')
 
-engine.dispose()
+    # score 값이 NaN인 경우 기본값 0으로 채우기
+    final_df['score1'] = final_df['score1'].fillna(0).astype(int)
+
+    return final_df
+
+# CFO(영업활동현금흐름) 흑자 기업이면 1점
+def calc_cfo(qc, ticker_list, base_date):
+    cfo_list = qc.get_fs_list_by_account_and_date("'*영업에서창출된현금흐름'", f"'{base_date}'")
+    cfo_list['score2'] = (cfo_list['값'] > 0).astype(int)
+    # print(cfo_list)
+
+    result_df = cfo_list[['종목코드', 'score2']]
+
+    # 원본 데이터프레임에 score 병합
+    final_df = ticker_list[['종목코드', '종목명', '분류', 'score1']].merge(result_df, on='종목코드', how='left')
+
+    # score 값이 NaN인 경우 기본값 0으로 채우기
+    final_df['score2'] = final_df['score2'].fillna(0).astype(int)
+
+    return final_df
+
+
+# 신규 주식 발행(유상증사): 전년 없음인 경우 1점
+# 제작년과 작년 자본금 변화가 없는 경우로 체크
+def calc_capital(qc, ticker_list, base_date):
+    last_year = datetime(base_date.year - 1, base_date.month, base_date.day).date()
+    capital_date = f"'{last_year}', '{base_date}'"
+
+    # 자본금
+    capital_list = qc.get_fs_list_by_account_and_date("'자본금'", capital_date)
+
+    # 기준일별로 피벗 테이블 생성
+    pivot_df = capital_list.pivot_table(
+        values='값',
+        index='종목코드',
+        columns='기준일',
+        aggfunc='first'
+    )
+    pivot_df = pivot_df.dropna()
+    # print(pivot_df)
+
+    # 값 차이 계산 및 score 부여
+    pivot_df['diff'] = pivot_df[base_date] - pivot_df[last_year]
+    pivot_df['score3'] = (pivot_df['diff'] == 0).astype(int)
+
+    # 결과 정리
+    result_df = pivot_df.reset_index()[['종목코드', 'score3']]
+
+    # 원본 데이터프레임에 score 병합
+    final_df = ticker_list[['종목코드', '종목명', '분류', 'score1', 'score2']].merge(result_df, on='종목코드', how='left')
+
+    # score 값이 NaN인 경우 기본값 0으로 채우기
+    final_df['score3'] = final_df['score3'].fillna(0).astype(int)
+
+    # score_1_df = final_df[final_df['score'] == 1]
+    return final_df
+
+def calc_gpa(qc, ticker_list, base_date):
+    fs_list = qc.get_fs_list_by_account_and_date("'매출총이익', '자산'", f"'{base_date}'")
+    fs_list_pivot = fs_list.pivot(index='종목코드', columns='계정', values='값')
+    fs_list_pivot['GPA'] = fs_list_pivot['매출총이익'] / fs_list_pivot['자산']
+
+    # 결과 정리
+    result_df = fs_list_pivot.reset_index()[['종목코드', 'GPA']]
+
+    # 티커 테이블과 합침
+    final_df = ticker_list[['종목코드', '종목명', '분류', 'f-score']].merge(result_df,
+                                                      how='left',
+                                                      on='종목코드')
+    final_df['GPA'] = final_df['GPA'].fillna(-1).astype(float)
+    return final_df
+
+def get_ticker_list(qc):
+    ticker_list = qc.get_ticker_list()
+    # 시가총액을 기준으로 정렬
+    ticker_list['분류'] = pd.qcut(ticker_list['시가총액'],
+                       q=[0, 0.2, 0.8, 1.0],  # 0-20%, 20-80%, 80-100% 구간
+                       labels=['소형주', '중형주', '대형주'])
+
+    return ticker_list
+
+def get_f_score(qc, base_date):
+    ticker_list = get_ticker_list(qc)
+    apply_net_income = calc_net_income(qc, ticker_list, base_date)
+    apply_cfo = calc_cfo(qc, apply_net_income, base_date)
+    apply_capital = calc_capital(qc, apply_cfo, base_date)
+
+    # 개별 점수들로 신f-score 계산
+    apply_capital['f-score'] = apply_capital['score1'] + apply_capital['score2'] + apply_capital['score3']
+
+    apply_gpa = calc_gpa(qc, apply_capital, base_date)
+
+    f_score3 = apply_gpa[apply_gpa['f-score'] == 3]
+    result = f_score3[f_score3['분류'] == '소형주'].sort_values('GPA', ascending=False)
+
+    # print(f_score3)
+    # fs_list_copy = f_score3[['GPA']].copy()
+    # # print(fs_list_copy)
+    # fs_rank = fs_list_copy.rank(ascending=False, axis=0)
+    # # print(fs_rank)
+    # return f_score3.loc[fs_rank['GPA'] <= 20, ['종목코드', '종목명', '분류', 'f-score', 'GPA']].round(4)
+    return result
+
+if __name__ == '__main__':
+    date = datetime(2024, 12, 31).date()
+    print(get_f_score(quantcommon.QuantCommon(), date).head(30))
\ No newline at end of file

From 04d1557a9d29b2d97504a9af06c274e1fcffcc58 Mon Sep 17 00:00:00 2001
From: Ayuriel <zephyrdark@gmail.com>
Date: Sun, 16 Mar 2025 18:23:24 +0900
Subject: [PATCH 16/16] =?UTF-8?q?feat:=20f-score=20=EB=A6=AC=ED=8C=A9?=
 =?UTF-8?q?=ED=86=A0=EB=A7=81?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 streamlit_quant/strategy/f_score.py | 78 +++++++++++------------------
 1 file changed, 30 insertions(+), 48 deletions(-)

diff --git a/streamlit_quant/strategy/f_score.py b/streamlit_quant/strategy/f_score.py
index a59c50a..7970925 100644
--- a/streamlit_quant/strategy/f_score.py
+++ b/streamlit_quant/strategy/f_score.py
@@ -3,41 +3,24 @@ import pandas as pd
 import quantcommon
 
 # 흑자 기업이면 1점(당기순이익)
-def calc_net_income(qc, ticker_list, base_date):
+def calc_net_income(qc, base_date):
     net_income_list = qc.get_fs_list_by_account_and_date("'당기순이익'", f"'{base_date}'")
     net_income_list['score1'] = (net_income_list['값'] > 0).astype(int)
-    # print(net_income_list)
 
-    result_df = net_income_list[['종목코드', 'score1']]
+    return net_income_list[['종목코드', 'score1']]
 
-    # 원본 데이터프레임에 score 병합
-    final_df = ticker_list[['종목코드', '종목명', '분류']].merge(result_df, on='종목코드', how='left')
-
-    # score 값이 NaN인 경우 기본값 0으로 채우기
-    final_df['score1'] = final_df['score1'].fillna(0).astype(int)
-
-    return final_df
 
 # CFO(영업활동현금흐름) 흑자 기업이면 1점
-def calc_cfo(qc, ticker_list, base_date):
+def calc_cfo(qc, base_date):
     cfo_list = qc.get_fs_list_by_account_and_date("'*영업에서창출된현금흐름'", f"'{base_date}'")
     cfo_list['score2'] = (cfo_list['값'] > 0).astype(int)
-    # print(cfo_list)
 
-    result_df = cfo_list[['종목코드', 'score2']]
-
-    # 원본 데이터프레임에 score 병합
-    final_df = ticker_list[['종목코드', '종목명', '분류', 'score1']].merge(result_df, on='종목코드', how='left')
-
-    # score 값이 NaN인 경우 기본값 0으로 채우기
-    final_df['score2'] = final_df['score2'].fillna(0).astype(int)
-
-    return final_df
+    return cfo_list[['종목코드', 'score2']]
 
 
 # 신규 주식 발행(유상증사): 전년 없음인 경우 1점
 # 제작년과 작년 자본금 변화가 없는 경우로 체크
-def calc_capital(qc, ticker_list, base_date):
+def calc_capital(qc, base_date):
     last_year = datetime(base_date.year - 1, base_date.month, base_date.day).date()
     capital_date = f"'{last_year}', '{base_date}'"
 
@@ -52,38 +35,22 @@ def calc_capital(qc, ticker_list, base_date):
         aggfunc='first'
     )
     pivot_df = pivot_df.dropna()
-    # print(pivot_df)
 
     # 값 차이 계산 및 score 부여
     pivot_df['diff'] = pivot_df[base_date] - pivot_df[last_year]
     pivot_df['score3'] = (pivot_df['diff'] == 0).astype(int)
 
     # 결과 정리
-    result_df = pivot_df.reset_index()[['종목코드', 'score3']]
+    return pivot_df.reset_index()[['종목코드', 'score3']]
 
-    # 원본 데이터프레임에 score 병합
-    final_df = ticker_list[['종목코드', '종목명', '분류', 'score1', 'score2']].merge(result_df, on='종목코드', how='left')
 
-    # score 값이 NaN인 경우 기본값 0으로 채우기
-    final_df['score3'] = final_df['score3'].fillna(0).astype(int)
-
-    # score_1_df = final_df[final_df['score'] == 1]
-    return final_df
-
-def calc_gpa(qc, ticker_list, base_date):
+def calc_gpa(qc, base_date):
     fs_list = qc.get_fs_list_by_account_and_date("'매출총이익', '자산'", f"'{base_date}'")
     fs_list_pivot = fs_list.pivot(index='종목코드', columns='계정', values='값')
     fs_list_pivot['GPA'] = fs_list_pivot['매출총이익'] / fs_list_pivot['자산']
 
     # 결과 정리
-    result_df = fs_list_pivot.reset_index()[['종목코드', 'GPA']]
-
-    # 티커 테이블과 합침
-    final_df = ticker_list[['종목코드', '종목명', '분류', 'f-score']].merge(result_df,
-                                                      how='left',
-                                                      on='종목코드')
-    final_df['GPA'] = final_df['GPA'].fillna(-1).astype(float)
-    return final_df
+    return fs_list_pivot.reset_index()[['종목코드', 'GPA']]
 
 def get_ticker_list(qc):
     ticker_list = qc.get_ticker_list()
@@ -92,20 +59,35 @@ def get_ticker_list(qc):
                        q=[0, 0.2, 0.8, 1.0],  # 0-20%, 20-80%, 80-100% 구간
                        labels=['소형주', '중형주', '대형주'])
 
-    return ticker_list
+    return ticker_list[['종목코드', '종목명', '분류', '종가']]
 
 def get_f_score(qc, base_date):
     ticker_list = get_ticker_list(qc)
-    apply_net_income = calc_net_income(qc, ticker_list, base_date)
-    apply_cfo = calc_cfo(qc, apply_net_income, base_date)
-    apply_capital = calc_capital(qc, apply_cfo, base_date)
+    score1_list = calc_net_income(qc, base_date)
+    score2_list = calc_cfo(qc, base_date)
+    score3_list = calc_capital(qc, base_date)
+    gpa_list = calc_gpa(qc, base_date)
+
+    # score 1 병합 + NaN인 경우 기본값 0
+    merge_score1 = ticker_list.merge(score1_list, on='종목코드', how='left')
+    merge_score1['score1'] = merge_score1['score1'].fillna(0).astype(int)
+
+    # score 2 병합 + NaN인 경우 기본값 0
+    merge_score2 = merge_score1.merge(score2_list, on='종목코드', how='left')
+    merge_score2['score2'] = merge_score2['score2'].fillna(0).astype(int)
+
+    # score 3 병합 + NaN인 경우 기본값 0
+    merge_score3 = merge_score2.merge(score3_list, on='종목코드', how='left')
+    merge_score3['score3'] = merge_score3['score3'].fillna(0).astype(int)
 
     # 개별 점수들로 신f-score 계산
-    apply_capital['f-score'] = apply_capital['score1'] + apply_capital['score2'] + apply_capital['score3']
+    merge_score3['f-score'] = merge_score3['score1'] + merge_score3['score2'] + merge_score3['score3']
 
-    apply_gpa = calc_gpa(qc, apply_capital, base_date)
+    # GPA 병합 + NaN인 경우 기본 값 -1(내림차순 정렬 시에 하위 순위를 받게 하려고)
+    final_df = merge_score3.merge(gpa_list, on='종목코드', how='left')
+    final_df['GPA'] = final_df['GPA'].fillna(-1).astype(float)
 
-    f_score3 = apply_gpa[apply_gpa['f-score'] == 3]
+    f_score3 = final_df[final_df['f-score'] == 3].round(4)
     result = f_score3[f_score3['분류'] == '소형주'].sort_values('GPA', ascending=False)
 
     # print(f_score3)