chore: 재구성한 example 삭제

chore: 리팩토링 전 패키지 삭제
feat: 구조 리팩토링
2025-03-30 21:20:59 +09:00 · 2025-03-30 21:20:34 +09:00 · 2025-03-30 21:19:46 +09:00 · 2025-03-30 21:19:41 +09:00
44 changed files with 805 additions and 1240 deletions
--- a/README.md
+++ b/README.md
@ -1,9 +1,117 @@
-# 실행
-streamlit run app.py --server.port=20000
+# 콴트 매니저 (Quant Manager)

-# 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).
+한국 주식 시장을 위한 퀀트 투자 도구입니다.
+
+## 프로젝트 구조
+
+```
+make-quant-py/
+├── data/                     # 데이터 수집 관련 코드
+│   ├── krx.py                # 한국거래소 데이터 수집
+│   ├── prices.py             # 가격 데이터 수집
+│   ├── financial.py          # 재무제표 데이터 수집
+│   └── crawling.py           # 웹 크롤링 유틸리티
+├── db/                       # 데이터베이스 유틸리티
+│   └── common.py             # DB 연결 및 쿼리 공통 기능
+├── strategies/               # 전략 구현
+│   ├── factors/              # 개별 요소 모듈
+│   │   ├── value.py          # 가치 전략
+│   │   ├── quality.py        # 퀄리티 전략
+│   │   ├── momentum.py       # 모멘텀 전략
+│   │   ├── f_score.py        # F-Score 전략
+│   │   └── all_value.py      # 종합 가치 전략
+│   ├── composite/            # 복합 전략 구현
+│   │   ├── magic_formula.py  # 마법공식 전략
+│   │   ├── multi_factor.py   # 멀티팩터 전략
+│   │   ├── super_quality.py  # 슈퍼 퀄리티 전략
+│   │   └── super_value_momentum.py # 슈퍼 밸류 모멘텀 전략
+│   └── utils.py              # 전략 공통 유틸리티
+├── backtest/                 # 백테스트 도구
+│   └── engine.py             # 백테스트 구현
+└── streamlit/                # Streamlit 웹 앱
+    ├── app.py                # 메인 앱
+    ├── pages/                # 개별 페이지
+    │   ├── data_page.py      # 데이터 수집 페이지
+    │   ├── quality_page.py   # 슈퍼 퀄리티 전략 페이지
+    │   └── value_momentum_page.py # 밸류 모멘텀 전략 페이지
+    └── components/           # 재사용 가능한 UI 컴포넌트
+        └── charts.py         # 시각화 컴포넌트
+```
+
+## 설치 및 설정
+
+### 필수 요구사항
+
+- Python 3.8 이상
+- MySQL 또는 MariaDB
+
+Windows에서 필요한 빌드 도구:
+- Visual Studio Build Tools 2017 이상
+  - Desktop development with C++ 워크로드 선택
+  - (선택사항) Python development 워크로드 선택
+
+### 가상환경 설정
+
+가상환경 생성:
+```
+python -m venv .venv
+```
+
+가상환경 활성화:
+- Windows: `.venv\Scripts\activate`
+- Linux/Mac: `. .venv/bin/activate`
+
+패키지 설치:
+```
+pip install -r requirements.txt
+```
+
+가상환경 종료:
+```
+deactivate
+```
+
+### 환경 변수 설정
+
+프로젝트 루트에 `.env` 파일을 생성하고 다음 환경 변수를 설정하세요:
+
+```
+DB_USER=your_db_user
+DB_PW=your_db_password
+DB_HOST=localhost
+DB_PORT=3306
+DB_DB=your_db_name
+```
+
+## 실행 방법
+
+Streamlit 앱 실행:
+```
+streamlit run src/app.py --server.port=20000
+```
+
+## 모듈
+
+### 데이터 수집
+
+- `data/krx.py`: 한국거래소(KRX)에서 주식 데이터를 수집합니다.
+- `data/prices.py`: 네이버 금융에서 주가 데이터를 수집합니다.
+- `data/financial.py`: FnGuide에서 재무제표 데이터를 수집합니다.
+
+### 투자 전략
+
+#### 개별 요소 전략
+- `strategies/factors/value.py`: PER, PBR 등 가치 지표 기반 전략
+- `strategies/factors/quality.py`: ROE, GPA 등 퀄리티 지표 기반 전략
+- `strategies/factors/momentum.py`: 주가 모멘텀 기반 전략
+- `strategies/factors/f_score.py`: Piotroski의 F-Score 계산 전략
+
+#### 복합 전략
+- `strategies/composite/magic_formula.py`: Joel Greenblatt의 마법공식 전략
+- `strategies/composite/multi_factor.py`: 다중 요소 통합 전략
+- `strategies/composite/super_quality.py`: F-Score와 GP/A를 결합한 슈퍼 퀄리티 전략
+- `strategies/composite/super_value_momentum.py`: 가치와 모멘텀을 결합한 전략
+
+### 백테스트
+
+- `backtest/engine.py`: bt 패키지를 사용한 백테스트 기능 구현
--- a/example/10-calculate-stock-value.py
+++ b/example/10-calculate-stock-value.py
@ -1,81 +0,0 @@
-# 패키지 불러오기
-import numpy as np
-import pandas as pd
-
-from streamlit_quant import quantcommon
-
-# DB 연결
-common = quantcommon.QuantCommon()
-engine = common.create_engine()
-con = common.connect()
-
-mycursor = con.cursor()
-# 가치 지표 계산
-# 분기 재무제표 불러오기
-kor_fs = pd.read_sql("""
-select * from kor_fs
-where 공시구분 = 'q'
-and 계정 in ('당기순이익', '자본', '영업활동으로인한현금흐름', '매출액');
-""", con=engine)
-
-# 티커 리스트 불러오기
-ticker_list = pd.read_sql("""
-select * from kor_ticker
-where 기준일 = (select max(기준일) from kor_ticker) 
-and 종목구분 = '보통주';
-""", con=engine)
-
-engine.dispose()
-
-# TTM 구하기
-kor_fs = kor_fs.sort_values(['종목코드', '계정', '기준일'])
-kor_fs['ttm'] = kor_fs.groupby(['종목코드', '계정'], as_index=False)['값'].rolling(
-    window=4, min_periods=4).sum()['값']
-
-# 자본은 평균 구하기
-kor_fs['ttm'] = np.where(kor_fs['계정'] == '자본', kor_fs['ttm'] / 4,
-                         kor_fs['ttm'])
-kor_fs = kor_fs.groupby(['계정', '종목코드']).tail(1)
-
-kor_fs_merge = kor_fs[['계정', '종목코드',
-                       'ttm']].merge(ticker_list[['종목코드', '시가총액', '기준일']],
-                                     on='종목코드')
-kor_fs_merge['시가총액'] = kor_fs_merge['시가총액'] / 100000000
-
-kor_fs_merge['value'] = kor_fs_merge['시가총액'] / kor_fs_merge['ttm']
-kor_fs_merge['value'] = kor_fs_merge['value'].round(4)
-kor_fs_merge['지표'] = np.where(
-    kor_fs_merge['계정'] == '매출액', 'PSR',
-    np.where(
-        kor_fs_merge['계정'] == '영업활동으로인한현금흐름', 'PCR',
-        np.where(kor_fs_merge['계정'] == '자본', 'PBR',
-                 np.where(kor_fs_merge['계정'] == '당기순이익', 'PER', None))))
-
-kor_fs_merge.rename(columns={'value': '값'}, inplace=True)
-kor_fs_merge = kor_fs_merge[['종목코드', '기준일', '지표', '값']]
-kor_fs_merge = kor_fs_merge.replace([np.inf, -np.inf, np.nan], None)
-
-query = """
-    insert into kor_value (종목코드, 기준일, 지표, 값)
-    values (%s,%s,%s,%s) as new
-    on duplicate key update
-    값=new.값
-"""
-
-args_fs = kor_fs_merge.values.tolist()
-mycursor.executemany(query, args_fs)
-con.commit()
-
-ticker_list['값'] = ticker_list['주당배당금'] / ticker_list['종가']
-ticker_list['값'] = ticker_list['값'].round(4)
-ticker_list['지표'] = 'DY'
-dy_list = ticker_list[['종목코드', '기준일', '지표', '값']]
-dy_list = dy_list.replace([np.inf, -np.inf, np.nan], None)
-dy_list = dy_list[dy_list['값'] != 0]
-
-args_dy = dy_list.values.tolist()
-mycursor.executemany(query, args_dy)
-con.commit()
-
-engine.dispose()
-con.close()
--- a/example/10-financial-statements-one.py
+++ b/example/10-financial-statements-one.py
@ -1,112 +0,0 @@
-import re
-
-import pandas as pd
-import requests as rq
-from bs4 import BeautifulSoup
-
-from streamlit_quant import quantcommon
-
-# DB 연결
-common = quantcommon.QuantCommon()
-engine = common.create_engine()
-con = common.connect()
-
-mycursor = con.cursor()
-
-# 재무제표 크롤링
-# 티커리스트 불러오기
-ticker_list = pd.read_sql("""
-select * from kor_ticker
-where 기준일 = (select max(기준일) from kor_ticker) 
-	and 종목구분 = '보통주';
-""", con=engine)
-
-# 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 = []
-
-
-# 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)
-    print(df)
-    df['기준일'] = pd.to_datetime(df['기준일'],
-                               format='%Y/%m') + pd.tseries.offsets.MonthEnd()
-    df['종목코드'] = ticker
-    df['공시구분'] = frequency
-
-    return df
-
-
-i = 0
-ticker = ticker_list['종목코드'][i]
-
-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)
-
-    # print([item.head(3) for item in data])
-    # 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])
-    print(data_fs_bind.head(3))
-
-    # 재무제표 데이터를 DB에 저장
-    args = data_fs_bind.values.tolist()
-    mycursor.executemany(query, args)
-    con.commit()
-
-except Exception as e:
-
-    # 오류 발생시 해당 종목명을 저장하고 다음 루프로 이동
-    print(e)
-    error_list.append(ticker)
-
-# DB 연결 종료
-engine.dispose()
-con.close()
--- a/example/10-financial-statements.py
+++ b/example/10-financial-statements.py
@ -1,120 +0,0 @@
-import re
-import time
-
-import pandas as pd
-import requests as rq
-from bs4 import BeautifulSoup
-from tqdm import tqdm
-
-from streamlit_quant import quantcommon
-
-# src/current-financial-statement.py 로 개선
-# DB 연결
-common = quantcommon.QuantCommon()
-engine = common.create_engine()
-con = common.connect()
-
-mycursor = con.cursor()
-
-# 재무제표 크롤링
-# 티커리스트 불러오기
-ticker_list = pd.read_sql("""
-select * from kor_ticker
-where 기준일 = (select max(기준일) from kor_ticker) 
-	and 종목구분 = '보통주';
-""", con=engine)
-
-# 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 = []
-
-
-# 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
-
-
-# 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()
--- a/example/10-stock-data.py
+++ b/example/10-stock-data.py
@ -1,193 +0,0 @@
-import re
-import time
-from io import BytesIO
-
-import numpy as np
-import pandas as pd
-import requests as rq
-from tqdm import tqdm
-from bs4 import BeautifulSoup
-from dotenv import load_dotenv
-from streamlit_quant import quantcommon
-
-# src/current-stock.py 로 개선
-load_dotenv()
-
-GEN_OTP_URL = 'http://data.krx.co.kr/comm/fileDn/GenerateOTP/generate.cmd'
-DOWN_URL = 'http://data.krx.co.kr/comm/fileDn/download_csv/download.cmd'
-
-# 최근 영업일을 가져옴
-def get_latest_biz_day():
-    url = 'https://finance.naver.com/sise/sise_deposit.nhn'
-    data = rq.post(url)
-    data_html = BeautifulSoup(data.content, 'lxml')
-    parse_day = data_html.select_one('div.subtop_sise_graph2 > ul.subtop_chart_note > li > span.tah').text
-    biz_day = re.findall('[0-9]+', parse_day)
-    biz_day = ''.join(biz_day)
-    return biz_day
-
-
-# 업종 분류 현황 가져옴
-def get_stock_data(biz_day, mkt_id):
-    # logging.basicConfig(level=logging.DEBUG)
-    gen_otp_data = {
-        'locale': 'ko_KR',
-        'mktId': mkt_id, # STK: 코스피, KSQ: 코스닥
-        'trdDd': biz_day,
-        'money': '1',
-        'csvxls_isNo': 'false',
-        'name': 'fileDown',
-        'url': 'dbms/MDC/STAT/standard/MDCSTAT03901'
-    }
-    headers = {
-        'Referer': 'http://data.krx.co.kr/contents/MDC/MDI/mdiLoader/index.cmd?menuId=MDC0201050201',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36'
-    }
-
-    otp = rq.post(url=GEN_OTP_URL, data=gen_otp_data, headers=headers, verify=False)
-    # # 요청 디버깅
-    # print("===== Request Details =====")
-    # print(f"Method: {otp.request.method}")
-    # print(f"URL: {otp.request.url}")
-    # print(f"Headers: {otp.request.headers}")
-    # print(f"Body: {otp.request.body}")
-    #
-    # # 응답 디버깅
-    # print("===== Response Details =====")
-    # print(f"Status Code: {otp.status_code}")
-    # print(f"Headers: {otp.headers}")
-    # print(f"Body: {otp.text}")
-
-    down_sector = rq.post(url=DOWN_URL, data={'code': otp.text}, headers=headers)
-    return pd.read_csv(BytesIO(down_sector.content), encoding='EUC-KR')
-
-
-# 개별 지표 조회
-def get_ind_stock_data(biz_day):
-    gen_otp_data = {
-        'locale': 'ko_KR',
-        'searchType': '1',
-        'mktId': 'ALL',
-        'trdDd': biz_day,
-        'csvxls_isNo': 'false',
-        'name': 'fileDown',
-        'url': 'dbms/MDC/STAT/standard/MDCSTAT03501'
-    }
-    headers = {
-        'Referer': 'http://data.krx.co.kr/contents/MDC/MDI/mdiLoader/index.cmd?menuId=MDC0201050201',
-        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36'
-    }
-
-    otp = rq.post(url=GEN_OTP_URL, data=gen_otp_data, headers=headers, verify=False)
-
-    down_ind_sector = rq.post(url=DOWN_URL, data={'code': otp.text}, headers=headers)
-    return pd.read_csv(BytesIO(down_ind_sector.content), encoding='EUC-KR')
-
-
-def process_for_total_stock(biz_day):
-    # 업종 분류 현황(코스피, 코스닥)
-    sector_stk = get_stock_data(biz_day, 'STK')
-    sector_ksq = get_stock_data(biz_day, 'KSQ')
-    # 각각 조회 후 합침
-    krx_sector = pd.concat([sector_stk, sector_ksq]).reset_index(drop=True)
-    krx_sector['종목명'] = krx_sector['종목명'].str.strip()
-    krx_sector['기준일'] = biz_day
-
-    # 개별 지표 조회
-    krx_ind = get_ind_stock_data(biz_day)
-    krx_ind['종목명'] = krx_ind['종목명'].str.strip()
-    krx_ind['기준일'] = biz_day
-
-    # 데이터 정리
-    # 종목, 개별 중 한군데만 있는 데이터 삭제(선박펀드, 광물펀드, 해외종목 등)
-    diff = list(set(krx_sector['종목명']).symmetric_difference(set(krx_ind['종목명'])))
-
-    kor_ticker = pd.merge(krx_sector,
-                          krx_ind,
-                          on=krx_sector.columns.intersection(
-                              krx_ind.columns).tolist(),
-                          how='outer')
-    # 일반적인 종목과 SPAC, 우선주, 리츠, 기타 주식을 구분
-    kor_ticker['종목구분'] = np.where(kor_ticker['종목명'].str.contains('스팩|제[0-9]+호'), '스팩',
-                              np.where(kor_ticker['종목코드'].str[-1:] != '0', '우선주',
-                                       np.where(kor_ticker['종목명'].str.endswith('리츠'), '리츠',
-                                                np.where(kor_ticker['종목명'].isin(diff),  '기타',
-                                                '보통주'))))
-    kor_ticker = kor_ticker.reset_index(drop=True)
-    kor_ticker.columns = kor_ticker.columns.str.replace(' ', '')
-    kor_ticker = kor_ticker[['종목코드', '종목명', '시장구분', '종가',
-                             '시가총액', '기준일', 'EPS', '선행EPS', 'BPS', '주당배당금', '종목구분']]
-    kor_ticker = kor_ticker.replace({np.nan: None})
-    kor_ticker['기준일'] = pd.to_datetime(kor_ticker['기준일'])
-
-    save_ticker(kor_ticker)
-
-
-def save_ticker(ticker):
-    con = quantcommon.QuantCommon().connect()
-
-    mycursor = con.cursor()
-    query = f"""
-        insert into kor_ticker (종목코드,종목명,시장구분,종가,시가총액,기준일,EPS,선행EPS,BPS,주당배당금,종목구분)
-        values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) as new
-        on duplicate key update
-        종목명=new.종목명,시장구분=new.시장구분,종가=new.종가,시가총액=new.시가총액,EPS=new.EPS,선행EPS=new.선행EPS,
-        BPS=new.BPS,주당배당금=new.주당배당금,종목구분 = new.종목구분;
-    """
-
-    args = ticker.values.tolist()
-
-    mycursor.executemany(query, args)
-    con.commit()
-
-    con.close()
-
-
-# WICS 기준 섹터정보 크롤링
-def process_for_wics(biz_day):
-    sector_code = [
-        'G25', 'G35', 'G50', 'G40', 'G10', 'G20', 'G55', 'G30', 'G15', 'G45'
-    ]
-
-    data_sector = []
-
-    # 모든 섹터에 대한 데이터 받아서 가공
-    for i in tqdm(sector_code):
-        url = f'''http://www.wiseindex.com/Index/GetIndexComponets?ceil_yn=0&dt={biz_day}&sec_cd={i}'''
-        data = rq.get(url).json()
-        data_pd = pd.json_normalize(data['list'])
-
-        data_sector.append(data_pd)
-
-        time.sleep(2)
-
-    kor_sector = pd.concat(data_sector, axis=0)
-    kor_sector = kor_sector[['IDX_CD', 'CMP_CD', 'CMP_KOR', 'SEC_NM_KOR']]
-    kor_sector['기준일'] = biz_day
-    kor_sector['기준일'] = pd.to_datetime(kor_sector['기준일'])
-    save_sector(kor_sector)
-
-
-def save_sector(sector):
-    con = quantcommon.QuantCommon().connect()
-
-    mycursor = con.cursor()
-    query = f"""
-        insert into kor_sector (IDX_CD, CMP_CD, CMP_KOR, SEC_NM_KOR, 기준일)
-        values (%s,%s,%s,%s,%s) as new
-        on duplicate key update
-        IDX_CD = new.IDX_CD, CMP_KOR = new.CMP_KOR, SEC_NM_KOR = new.SEC_NM_KOR
-    """
-
-    args = sector.values.tolist()
-
-    mycursor.executemany(query, args)
-    con.commit()
-
-    con.close()
-
-
-if __name__ == '__main__':
-    latest_biz_day = get_latest_biz_day()
-    process_for_total_stock(latest_biz_day)
-    process_for_wics(latest_biz_day)
--- a/example/10-stock-price.py
+++ b/example/10-stock-price.py
@ -1,87 +0,0 @@
-# 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
-
-from streamlit_quant import quantcommon
-
-# src/current-price.py 로 개선
-
-# DB 연결
-common = quantcommon.QuantCommon()
-engine = common.create_engine()
-con = common.connect()
-
-mycursor = con.cursor()
-
-# 티커리스트 불러오기
-ticker_list = pd.read_sql("""
-select * from kor_ticker
-where 기준일 = (select max(기준일) from kor_ticker) 
-	and 종목구분 = '보통주';
-""", con=engine)
-
-# 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;
-"""
-
-# 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")
-
-    # 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()
--- a/example/13-kor-momentum-portfolio.py
+++ b/example/13-kor-momentum-portfolio.py
@ -1,113 +0,0 @@
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-import statsmodels.api as sm
-import numpy as np
-from streamlit_quant import quantcommon
-
-# strategy/momentum에 구현
-# 모멘텀 포트폴리오. 최근 12개월 수익률이 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)
-
-
-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)
-
-engine.dispose()
-
-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=['return'])
-data_bind = ticker_list[['종목코드', '종목명']].merge(ret_list, how='left', on='종목코드')
-
-momentum_rank = data_bind['return'].rank(axis=0, ascending=False)
-
-price_momentum = price_list[price_list['종목코드'].isin(
-    data_bind.loc[momentum_rank <= 20, '종목코드'])]
-
-plt.rc('font', family='Malgun Gothic')
-g = sns.relplot(data=price_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()
-
-# 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()
--- a/example/13-kor-value-portfolio.py
+++ b/example/13-kor-value-portfolio.py
@ -1,32 +0,0 @@
-import pandas as pd
-import numpy as np
-from streamlit_quant import quantcommon
-
-# strategy/value 에서 구현
-
-#가치주 포트폴리오. PER, PBR이 낮은 회사 20개
-# DB 연결
-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)
-
-value_list = pd.read_sql("""
-select * from kor_value
-where 기준일 = (select max(기준일) from kor_value);
-""", con=engine)
-
-engine.dispose()
-
-value_list.loc[value_list['값'] <= 0, '값'] = np.nan
-value_pivot = value_list.pivot(index='종목코드', columns='지표', values='값')
-data_bind = ticker_list[['종목코드', '종목명']].merge(value_pivot,
-                                               how='left',
-                                               on='종목코드')
-# print(data_bind.head())
-value_rank = data_bind[['PER', 'PBR']].rank(axis = 0)
-value_sum = value_rank.sum(axis = 1, skipna = False).rank()
-print(data_bind.loc[value_sum <= 20, ['종목코드', '종목명', 'PER', 'PBR']])
--- a/example/13-magic-formula-portfolio.py
+++ b/example/13-magic-formula-portfolio.py
@ -1,84 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from streamlit_quant import quantcommon
-
-# 마법 공식 포트폴리오. 밸류와 퀄리티의 조합. 조엘 그린블라트의 '마법공식
-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 ('매출액', '당기순이익', '법인세비용', '이자비용', '현금및현금성자산',
-'부채', '유동부채', '유동자산', '비유동자산', '감가상각비')
-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(['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')
-data_bind['시가총액'] = data_bind['시가총액'] / 100000000
-
-# 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)
-print(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()
--- a/example/13-multi-factor-portfolio.py
+++ b/example/13-multi-factor-portfolio.py
@ -1,248 +0,0 @@
-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
-from streamlit_quant import quantcommon
-
-# strategy/multi-factor에서 구현
-
-# 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()
-
-def col_clean(df, cutoff=0.01, asc=False):
-
-    q_low = df.quantile(cutoff)
-    q_hi = df.quantile(1 - cutoff)
-
-    df_trim = df[(df > q_low) & (df < q_hi)]
-
-    if asc == False:
-        df_z_score = df_trim.rank(axis=0, ascending=False).apply(
-            zscore, nan_policy='omit')
-    if asc == True:
-        df_z_score = df_trim.rank(axis=0, ascending=True).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()
-
-
-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)
-
-value_list = pd.read_sql("""
-select * from kor_value
-where 기준일 = (select max(기준일) from kor_value);
-""", con=engine)
-
-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)
-
-sector_list = pd.read_sql("""
-select * from kor_sector
-where 기준일 = (select max(기준일) from kor_sector);	
-""", 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']
-
-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()
-
-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()
-
-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()
-
-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)
-
-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 = data_bind.merge(z_quality, how='left', on=['종목코드', 'SEC_NM_KOR'])
-
-data_bind.round(4).head()
-
-value_1 = data_bind_group[['PBR', 'PCR', 'PER',
-                           'PSR']].apply(lambda x: col_clean(x, 0.01, True))
-value_2 = data_bind_group[['DY']].apply(lambda x: col_clean(x, 0.01, False))
-
-z_value = value_1.merge(value_2, on=['종목코드', 'SEC_NM_KOR'
-                                     ]).sum(axis=1,
-                                            skipna=False).to_frame('z_value')
-data_bind = data_bind.merge(z_value, how='left', on=['종목코드', 'SEC_NM_KOR'])
-
-data_bind.round(4).head()
-
-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 = data_bind.merge(z_momentum, how='left', on=['종목코드', 'SEC_NM_KOR'])
-
-print(data_bind.round(4).head())
-
-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()
-
-data_bind_final = data_bind[['종목코드', 'z_quality', 'z_value', 'z_momentum'
-                             ]].set_index('종목코드').apply(zscore,
-                                                        nan_policy='omit')
-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()
-
-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()
-
-wts = [0.3, 0.3, 0.3]
-data_bind_final_sum = (data_bind_final * wts).sum(axis=1,
-                                                  skipna=False).to_frame()
-data_bind_final_sum.columns = ['qvm']
-port_qvm = data_bind.merge(data_bind_final_sum, on='종목코드')
-port_qvm['invest'] = np.where(port_qvm['qvm'].rank() <= 20, 'Y', 'N')
-
-port_qvm[port_qvm['invest'] == 'Y'].round(4)
-
-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)
--- a/example/13-quality-portfolio.py
+++ b/example/13-quality-portfolio.py
@ -1,62 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from streamlit_quant import quantcommon
-
-# strategy/quality에서 구현
-
-# 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))
--- a/src/app.py
+++ b/src/app.py
@ -0,0 +1,44 @@
+"""
+Main Streamlit application for the Quant Manager.
+"""
+import streamlit as st
+from ui.pages.data_page import render_data_page
+from ui.pages.quality_page import render_quality_page
+from ui.pages.value_momentum_page import render_value_momentum_page
+
+# Configure the application
+st.set_page_config(
+    page_title="콴트 매니저",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Define the sidebar navigation
+def main():
+    """Main application function."""
+    # Create sidebar navigation
+    st.sidebar.title("콴트 매니저")
+    
+    # Navigation options
+    pages = {
+        "데이터 수집": render_data_page,
+        "슈퍼 퀄리티 전략": render_quality_page,
+        "슈퍼 밸류 모멘텀 전략": render_value_momentum_page
+    }
+    
+    # Select page
+    selection = st.sidebar.radio("메뉴", list(pages.keys()))
+    
+    # Render the selected page
+    pages[selection]()
+    
+    # Footer
+    st.sidebar.markdown("---")
+    st.sidebar.info(
+        "© 2023-2025 콴트 매니저\n\n"
+        "한국 주식 시장을 위한 퀀트 투자 도구"
+    )
+
+if __name__ == "__main__":
+    main()
--- a/src/backtest/init.py
+++ b/src/backtest/init.py
--- a/streamlit_quant/backtest/backtest-1.py
+++ b/streamlit_quant/backtest/backtest-1.py
@ -3,12 +3,12 @@ import matplotlib.pyplot as plt

 import pandas as pd

-from streamlit_quant import quantcommon
+from db.common import DBManager
 import streamlit_quant.strategy.multi_factor as multi_factor
 import streamlit_quant.strategy.magic_formula as magic_formula


-qc = quantcommon.QuantCommon()
+qc = DBManager()
 mf = multi_factor.get_multi_factor_top(qc, 20)
 magic_formula = magic_formula.get_magic_formula_top(20)

--- a/src/data/init.py
+++ b/src/data/init.py
@ -0,0 +1 @@
+__all__ = ['financial', 'krx', 'prices']
--- a/streamlit_quant/src/current-financial-statements.py
+++ b/streamlit_quant/src/current-financial-statements.py
@ -6,27 +6,10 @@ import requests as rq
 from bs4 import BeautifulSoup
 from tqdm import tqdm

-from streamlit_quant import quantcommon
+from db.common import DBManager


 # 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)]
@ -43,11 +26,12 @@ def clean_fs(df, ticker, frequency):


 # ticker 별 재무제표 조회해서 DB에 저장
-def process_for_fs(ticker_list):
+def process_for_fs():
+    ticker_list = DBManager().get_ticker_list()
    # DB 연결
-    common = quantcommon.QuantCommon()
-    engine = common.create_engine()
-    con = common.connect()
+    db_manager = DBManager()
+    engine = db_manager.create_engine()
+    con = db_manager.connect()
    mycursor = con.cursor()

    # DB 저장 쿼리
@ -127,5 +111,4 @@ def process_for_fs(ticker_list):
    con.close()

 if __name__ == '__main__':
-    tickers = get_ticker_list()
-    process_for_fs(tickers)
+    process_for_fs()
--- a/streamlit_quant/src/current-stock.py
+++ b/streamlit_quant/src/current-stock.py
@ -8,7 +8,7 @@ import requests as rq
 from tqdm import tqdm
 from bs4 import BeautifulSoup
 from dotenv import load_dotenv
-from streamlit_quant import quantcommon
+from db.common import DBManager

 load_dotenv()

@ -123,7 +123,7 @@ def process_for_total_stock(biz_day):


 def save_ticker(ticker):
-    con = quantcommon.QuantCommon().connect()
+    con = DBManager().connect()

    mycursor = con.cursor()
    query = f"""
@ -168,7 +168,7 @@ def process_for_wics(biz_day):


 def save_sector(sector):
-    con = quantcommon.QuantCommon().connect()
+    con = DBManager().connect()

    mycursor = con.cursor()
    query = f"""
--- a/streamlit_quant/src/current-price.py
+++ b/streamlit_quant/src/current-price.py
@ -1,5 +1,3 @@
-# 패키지 불러오기
-
 import time
 from datetime import date
 from io import BytesIO
@ -8,28 +6,12 @@ import pandas as pd
 import requests as rq
 from tqdm import tqdm

-from streamlit_quant import quantcommon
+from db.common import DBManager


 # 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):
+def process_for_price():
+    ticker_list = DBManager().get_ticker_list()
    # DB 저장 쿼리
    query = """
        insert into kor_price (날짜, 시가, 고가, 저가, 종가, 거래량, 종목코드)
@ -40,7 +22,7 @@ def process_for_price(ticker_list):
    """

    # DB 연결
-    common = quantcommon.QuantCommon()
+    common = DBManager()
    engine = common.create_engine()
    con = common.connect()

@ -54,6 +36,7 @@ def process_for_price(ticker_list):
        # 티커 선택
        ticker = ticker_list['종목코드'][i]

+        # todo: 날짜 범위 수정
        # 시작일과 종료일
        # fr = (date.today() + relativedelta(years=-5)).strftime("%Y%m%d")
        to = (date.today()).strftime("%Y%m%d")
@ -97,5 +80,4 @@ def process_for_price(ticker_list):
    con.close()

 if __name__ == '__main__':
-    ticker_list = get_ticker_list()
-    process_for_price(ticker_list)
+    process_for_price()
--- a/src/db/init.py
+++ b/src/db/init.py
@ -0,0 +1 @@
+__all__ = ['common']
--- a/streamlit_quant/quantcommon.py
+++ b/streamlit_quant/quantcommon.py
@ -7,7 +7,7 @@ from dotenv import load_dotenv
 from sqlalchemy import create_engine


-class QuantCommon:
+class DBManager:
    def __init__(self):
        load_dotenv()
        self.user = os.getenv('DB_USER')
--- a/src/strategies/init.py
+++ b/src/strategies/init.py
@ -0,0 +1 @@
+__all__ = ['composite', 'factors', 'utils']
--- a/src/strategies/composite/init.py
+++ b/src/strategies/composite/init.py
@ -0,0 +1 @@
+__all__ = ['multi_factor', 'multi_factor', 'super_quality', 'super_value_momentum']
--- a/src/strategies/composite/magic_formula.py
+++ b/src/strategies/composite/magic_formula.py
@ -1,12 +1,12 @@
 import numpy as np
-from streamlit_quant import quantcommon
+from db.common import DBManager


 # 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()
+    db = DBManager()
+    ticker_list = db.get_ticker_list()
+    fs_list = db.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
--- a/src/strategies/composite/multi_factor.py
+++ b/src/strategies/composite/multi_factor.py
--- a/src/strategies/composite/super_quality.py
+++ b/src/strategies/composite/super_quality.py
@ -1,8 +1,8 @@
 from datetime import datetime
-import streamlit as st
+from src import ui as st

-from strategy import f_score
-import quantcommon
+from strategies.factors import f_score
+from db.common import DBManager


 # st.write("""
@ -60,7 +60,7 @@ def get_last_year_end():
 st.write("투자 전략: 강환국 슈퍼 퀄리티 전략 2.0")

 date = get_last_year_end()
-data = f_score.get_f_score(quantcommon.QuantCommon(), date)
+data = f_score.get_f_score(DBManager(), date)

 config = {}

--- a/src/strategies/composite/super_value_momentum.py
+++ b/src/strategies/composite/super_value_momentum.py
@ -1,3 +1,3 @@
-import streamlit as st
+from src import ui as st

 st.write("슈퍼 밸류 모멘텀 전략 2.0")
--- a/src/strategies/factors/init.py
+++ b/src/strategies/factors/init.py
@ -0,0 +1 @@
+__all__ = ['all_value', 'f_score', 'momentum', 'quality', 'value']
--- a/streamlit_quant/strategy/all_value.py
+++ b/streamlit_quant/strategy/all_value.py
@ -1,14 +1,14 @@
 import matplotlib.pyplot as plt
 import numpy as np
 import seaborn as sns
-from streamlit_quant import quantcommon
+from db.common import DBManager


 #가치주 포트폴리오. PER, PBR, PCR, PSR, DY
 def get_all_value_top(count):
-    qc = quantcommon.QuantCommon()
-    ticker_list = qc.get_ticker_list()
-    value_list = qc.get_value_list()
+    db = DBManager()
+    ticker_list = db.get_ticker_list()
+    value_list = db.get_value_list()

    # 가치 지표가 0이하인 경우 nan으로 변경
    value_list.loc[value_list['값'] <= 0, '값'] = np.nan
--- a/streamlit_quant/strategy/f_score.py
+++ b/streamlit_quant/strategy/f_score.py
@ -1,6 +1,6 @@
 from datetime import datetime
 import pandas as pd
-from streamlit_quant import quantcommon
+from db.common import DBManager


 # 흑자 기업이면 1점(당기순이익)
@ -101,4 +101,4 @@ def get_f_score(qc, base_date):

 if __name__ == '__main__':
    date = datetime(2024, 12, 31).date()
-    print(get_f_score(quantcommon.QuantCommon(), date).head(30))
+    print(get_f_score(DBManager(), date).head(30))
--- a/streamlit_quant/strategy/momentum.py
+++ b/streamlit_quant/strategy/momentum.py
@ -3,7 +3,7 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 import statsmodels.api as sm
 import numpy as np
-from streamlit_quant import quantcommon
+from db.common import DBManager


 def print_graph(values):
@ -29,9 +29,9 @@ def print_graph(values):
 # strategy/momentum에 구현
 # 모멘텀 포트폴리오. 최근 12개월 수익률이 높은 주식
 def get_momentum_top(count):
-    qc = quantcommon.QuantCommon()
-    ticker_list = qc.get_ticker_list()
-    price_list = qc.get_price_list(interval_month=12)
+    db = DBManager()
+    ticker_list = db.get_ticker_list()
+    price_list = db.get_price_list(interval_month=12)

    price_pivot = price_list.pivot(index='날짜', columns='종목코드', values='종가')

--- a/streamlit_quant/strategy/quality.py
+++ b/streamlit_quant/strategy/quality.py
@ -1,12 +1,12 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
-from streamlit_quant import quantcommon
+from db.common import DBManager


 # 퀄리티(우량주) 포트폴리오. 영업수익성이 높은 주식
 def get_quality_top(count):
-    qc = quantcommon.QuantCommon()
+    qc = DBManager()
    ticker_list = qc.get_ticker_list()
    fs_list = qc.get_fs_list()

--- a/streamlit_quant/strategy/value.py
+++ b/streamlit_quant/strategy/value.py
@ -1,13 +1,13 @@
 import numpy as np

-from streamlit_quant import quantcommon
+from db.common import DBManager


 #가치주 포트폴리오. PER, PBR이 낮은 회사 20개
 def get_value_top(count):
-    qc = quantcommon.QuantCommon()
-    ticker_list = qc.get_ticker_list()
-    value_list = qc.get_value_list()
+    db = DBManager()
+    ticker_list = db.get_ticker_list()
+    value_list = db.get_value_list()

    # 가치 지표가 0이하인 경우 nan으로 변경
    value_list.loc[value_list['값'] <= 0, '값'] = np.nan
--- a/src/strategies/utils.py
+++ b/src/strategies/utils.py
@ -0,0 +1,80 @@
+"""
+Common utilities for quantitative investment strategies.
+"""
+import pandas as pd
+from db.common import DBManager
+
+
+def get_sector_data():
+    """Get sector data for all stocks."""
+    db = DBManager()
+    return db.get_sector_list()
+
+
+def get_stock_data():
+    """Get stock data for all available stocks."""
+    db = DBManager()
+    return db.get_ticker_list()
+
+
+def get_financial_data():
+    """Get financial statement data."""
+    db = DBManager()
+    return db.get_fs_list()
+
+
+def get_expanded_financial_data():
+    """Get expanded financial statement data."""
+    db = DBManager()
+    return db.get_expanded_fs_list()
+
+
+def get_price_data(interval_months=60):
+    """Get price data for the specified interval."""
+    db = DBManager()
+    return db.get_price_list(interval_months)
+
+
+def get_value_metrics():
+    """Get value metrics for all stocks."""
+    db = DBManager()
+    return db.get_value_list()
+
+
+def calculate_zscore(series):
+    """Calculate z-score for a pandas series."""
+    return (series - series.mean()) / series.std()
+
+
+def rank_by_metric(df, metric, ascending=True):
+    """Rank stocks by a metric."""
+    return df.sort_values(by=metric, ascending=ascending)
+
+
+def rank_combined(df, metrics, weights=None, ascending=True):
+    """Rank stocks by combined metrics.
+    
+    Args:
+        df: DataFrame with stock data
+        metrics: List of column names to use for ranking
+        weights: Optional weights for each metric
+        ascending: Direction for ranking (True = smaller is better)
+    
+    Returns:
+        DataFrame with combined rank
+    """
+    if weights is None:
+        weights = [1] * len(metrics)
+    
+    # Calculate rank for each metric
+    ranks = pd.DataFrame(index=df.index)
+    for i, metric in enumerate(metrics):
+        ranks[f'rank_{metric}'] = df[metric].rank(ascending=ascending) * weights[i]
+    
+    # Calculate combined rank
+    ranks['combined_rank'] = ranks.mean(axis=1)
+    
+    # Combine with original data
+    result = pd.concat([df, ranks['combined_rank']], axis=1)
+    
+    return result.sort_values('combined_rank')
--- a/src/ui/init.py
+++ b/src/ui/init.py
@ -0,0 +1 @@
+__all__ = ['components', 'pages']
--- a/src/ui/components/init.py
+++ b/src/ui/components/init.py
@ -0,0 +1 @@
+__all__ = ['charts']
--- a/src/ui/components/charts.py
+++ b/src/ui/components/charts.py
@ -0,0 +1,189 @@
+"""
+Charting components for the Streamlit Quant application.
+"""
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from datetime import datetime, timedelta
+
+def plot_stock_price(price_data, ticker_column='종목코드', date_column='날짜', 
+                     price_column='종가', name_column='종목명'):
+    """
+    Plot stock price chart for selected stocks.
+    
+    Args:
+        price_data: DataFrame with price data
+        ticker_column: Column name for ticker code
+        date_column: Column name for date
+        price_column: Column name for price
+        name_column: Column name for stock name
+    """
+    if price_data.empty:
+        st.warning("가격 데이터가 없습니다.")
+        return
+    
+    # Get unique tickers
+    tickers = price_data[ticker_column].unique()
+    
+    if len(tickers) == 0:
+        st.warning("표시할 종목이 없습니다.")
+        return
+    
+    # Create figure
+    fig = go.Figure()
+    
+    # Add traces
+    for ticker in tickers:
+        ticker_data = price_data[price_data[ticker_column] == ticker]
+        
+        name = ticker
+        if name_column in ticker_data.columns:
+            name = ticker_data[name_column].iloc[0] if not ticker_data.empty else ticker
+            name = f"{name} ({ticker})"
+        
+        fig.add_trace(
+            go.Scatter(
+                x=ticker_data[date_column],
+                y=ticker_data[price_column],
+                mode='lines',
+                name=name
+            )
+        )
+    
+    # Update layout
+    fig.update_layout(
+        title='주가 차트',
+        xaxis_title='날짜',
+        yaxis_title='가격',
+        legend_title='종목',
+        hovermode='x unified'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_returns_comparison(returns_data, benchmark_data=None, 
+                           date_column='날짜', returns_column='누적수익률'):
+    """
+    Plot returns comparison chart for strategy vs benchmark.
+    
+    Args:
+        returns_data: DataFrame with strategy returns
+        benchmark_data: DataFrame with benchmark returns (optional)
+        date_column: Column name for date
+        returns_column: Column name for returns
+    """
+    if returns_data.empty:
+        st.warning("수익률 데이터가 없습니다.")
+        return
+    
+    # Create figure
+    fig = go.Figure()
+    
+    # Add strategy trace
+    fig.add_trace(
+        go.Scatter(
+            x=returns_data[date_column],
+            y=returns_data[returns_column],
+            mode='lines',
+            name='전략'
+        )
+    )
+    
+    # Add benchmark trace if provided
+    if benchmark_data is not None and not benchmark_data.empty:
+        fig.add_trace(
+            go.Scatter(
+                x=benchmark_data[date_column],
+                y=benchmark_data[returns_column],
+                mode='lines',
+                name='벤치마크'
+            )
+        )
+    
+    # Update layout
+    fig.update_layout(
+        title='수익률 비교',
+        xaxis_title='날짜',
+        yaxis_title='누적수익률 (%)',
+        legend_title='전략',
+        hovermode='x unified'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_factor_distribution(data, factor_column, title=None):
+    """
+    Plot histogram for factor distribution.
+    
+    Args:
+        data: DataFrame with factor data
+        factor_column: Column name for factor
+        title: Chart title (optional)
+    """
+    if data.empty:
+        st.warning("데이터가 없습니다.")
+        return
+    
+    if factor_column not in data.columns:
+        st.warning(f"{factor_column} 칼럼이, 데이터에 없습니다.")
+        return
+    
+    # Create histogram
+    fig = px.histogram(
+        data, 
+        x=factor_column,
+        nbins=30,
+        marginal="box",
+        title=title or f"{factor_column} 분포"
+    )
+    
+    # Update layout
+    fig.update_layout(
+        xaxis_title=factor_column,
+        yaxis_title='종목 수'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_scatter_matrix(data, columns, color_column=None, size_column=None, title=None):
+    """
+    Plot scatter matrix for multiple factors.
+    
+    Args:
+        data: DataFrame with factor data
+        columns: List of column names to plot
+        color_column: Column name for color (optional)
+        size_column: Column name for point size (optional)
+        title: Chart title (optional)
+    """
+    if data.empty:
+        st.warning("데이터가 없습니다.")
+        return
+    
+    # Validate columns
+    missing_cols = [col for col in columns if col not in data.columns]
+    if missing_cols:
+        st.warning(f"다음 칼럼이 데이터에 없습니다: {', '.join(missing_cols)}")
+        return
+    
+    # Create scatter matrix
+    fig = px.scatter_matrix(
+        data,
+        dimensions=columns,
+        color=color_column if color_column in data.columns else None,
+        size=size_column if size_column in data.columns else None,
+        title=title or "팩터 상관관계"
+    )
+    
+    # Update layout
+    fig.update_layout(
+        height=600,
+        width=800
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
--- a/src/ui/pages/init.py
+++ b/src/ui/pages/init.py
@ -0,0 +1 @@
+__all__ = ['data_page', 'quality_page', 'value_momentum_page']
--- a/src/ui/pages/data_page.py
+++ b/src/ui/pages/data_page.py
@ -0,0 +1,60 @@
+"""
+Data collection page for the Streamlit Quant application.
+"""
+import streamlit as st
+from data import financial, krx, prices
+
+
+def render_data_page():
+    """Render the data collection page interface."""
+    st.title("데이터 수집")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        if st.button(label='주식 데이터 수집', help="KOSPI, KOSDAQ 주식 데이터 수집"):
+            with st.spinner('주식 데이터 수집중...'):
+                try:
+                    # Call the krx data collection function
+                    biz_day = krx.get_latest_biz_day()
+                    krx.process_for_total_stock(biz_day)
+                    krx.process_for_wics(biz_day)
+                    # Show success message
+                    st.success('종목 데이터 수집 완료')
+                except Exception as e:
+                    st.error(f"데이터 수집 실패: {e}")
+        
+        if st.button(label='가격 데이터 수집', help="주가 데이터 수집"):
+            with st.spinner('가격 데이터 수집중...'):
+                try:
+                    # Call the price data collection function
+                    prices.process_for_price()
+                    # Show success message
+                    st.success(f'가격 데이터 수집 완료')
+                except Exception as e:
+                    st.error(f"데이터 수집 실패: {e}")
+    
+    with col2:
+        if st.button(label='재무제표 데이터 수집', help="재무제표 데이터 수집"):
+            with st.spinner('재무제표 데이터 수집중...'):
+                try:
+                    # Call the financial data collection function
+                    financial.process_for_fs()
+                    # Show success message
+                    st.success(f'재무제표 데이터 수집 완료')
+                except Exception as e:
+                    st.error(f"데이터 수집 실패: {e}")
+
+        if st.button(label='데이터 업데이트', help="모든 데이터 업데이트"):
+            with st.spinner('데이터 업데이트중...'):
+                try:
+                    # Call all data collection functions
+                    # biz_day = krx.get_latest_biz_day()
+                    # stock_data = krx.get_stock_data(biz_day)
+                    # sector_data = krx.get_sector_data(biz_day)
+                    # price_data = prices.crawl_price()
+                    # financial_data = financial.crawl_fs()
+                    # Show success message
+                    st.success('모든 데이터 업데이트 완료')
+                except Exception as e:
+                    st.error(f"데이터 업데이트 실패: {e}")
--- a/src/ui/pages/quality_page.py
+++ b/src/ui/pages/quality_page.py
@ -0,0 +1,118 @@
+"""
+Super Quality strategy page for the Streamlit Quant application.
+"""
+import streamlit as st
+from datetime import datetime
+from strategies.factors.f_score import get_f_score
+from db.common import DBManager
+
+def render_quality_page():
+    """Render the Super Quality strategy page."""
+    st.title("슈퍼 퀄리티 전략")
+    
+    with st.expander("전략 설명", expanded=False):
+        st.write("""
+        '신F-스코어 3점 + 고GP/A 전략'을 '강환국 슈퍼 퀄리티 전략'이라 명명한다.
+        이 전략은 신F-스코어가 3점인 종목을 매수하되, GP/A로 순위를 매겨서 순위가 높은 종목만 매수하는 것이다.
+        이 경우 한국에서 수익이 어땠을지 분석해보자.
+        연도별로 신F-스코어 3점을 충족하는 종목은 600-700개였다.
+
+        신F-스코어 3점 기업 내에서도 GP/A가 높은 종목이 3점 종목 평균보다 CAGR 기준으로 3-4% 더 높았다.
+        반대로 GP/A가 낮은 종목의 수익률은 상대적으로 저조했다.
+
+        ---
+        투자 전략: 강환국 슈퍼 퀄리티 전략 1.0  
+        레벨: 초, 중급  
+        스타일: 퀄리티  
+        기대 CAGR: 약 20%  
+        매수 전략:
+        - 신F-스코어 3점 종목만 매수
+        - 여기에 GP/A 순위를 부여, 순위 높은 20-30종목을 매수
+        매도 전략: 연 1회 리밸런싱
+
+        ---
+        지금까지 소개한 거의 모든 전략에서 소형주 전략이 전체 주식 수익률보다 높았다.
+        시가총액 하위 20% 종목의 CAGR을 분석해보았다.
+
+        ---
+        투자 전략: 강환국 슈퍼 퀄리티 전략 2.0  
+        레벨: 초, 중급  
+        스타일: 퀄리티  
+        기대 CAGR: 20% 이상  
+        매수 전략:  
+        아래 조건을 만족하는 20-30종목 매수
+        - 신F-스코어 3점 종목만 매수
+        - 여기에 GP/A 순위를 부여, 순위 높은 종목만 매수
+        - 단, 소형주(시가총액 최저 20%)만 매수
+        매도 전략: 연 1회 리밸런싱
+        ---
+        소형주 중 신F-스코어가 3점인 종목을 찾아보니 2004-2016년 구간에 80-100개 종목이 남았다.
+        그 주식들을 통째로 매수해도 CAGR 34.55%를 벌수 있었다!
+        정말 상당한 수익이다.
+        이 종목들을 다 샀으면 총 1,159개 종목 중 14개가 파산했다.(1.2%)
+        또 1년간 마이너스 수익을 기록한 종목이 29.7%였다.
+
+        신F-스코어가 3점인 종목 중 GP/A가 높은 종목 위주로 매수했으면 (1) CAGR도 조금 개선되고 (2) 최상 30개 종목을 매수했을 경우 선택받은 종목 360개 중 파산한 기업은 단 1개였다.
+        F-스코어와 GP/A는 엄청난 잠재력을 지닌 콤비네이션임이 분명하다.
+        """)
+    
+    # Strategy implementation
+    st.write("## 슈퍼 퀄리티 전략 2.0 포트폴리오")
+    
+    # Get data
+    date = get_last_year_end()
+    db = DBManager()
+    data = get_f_score(db, date)
+    
+    # Display options
+    col1, col2 = st.columns([1, 2])
+    
+    with col1:
+        st.write("### 설정")
+        min_f_score = st.slider("최소 F-스코어", min_value=0, max_value=3, value=3)
+        include_small_caps = st.checkbox("소형주만 포함", value=True)
+        num_stocks = st.slider("포트폴리오 종목수", min_value=5, max_value=50, value=20)
+    
+    # Filter data
+    filtered_data = data[data['f-score'] >= min_f_score].copy()
+    
+    if include_small_caps:
+        # Sort by market cap and keep only the bottom 20%
+        filtered_data = filtered_data.sort_values('시가총액')
+        filtered_data = filtered_data.head(int(len(filtered_data) * 0.2))
+    
+    # Sort by GP/A in descending order
+    filtered_data = filtered_data.sort_values('GP/A', ascending=False)
+    
+    # Get top N stocks
+    portfolio = filtered_data.head(num_stocks)
+    
+    # Display portfolio
+    with col2:
+        st.write(f"### 선택된 {len(portfolio)} 종목")
+        st.dataframe(portfolio, use_container_width=True)
+    
+    # Display metrics
+    st.write("### 포트폴리오 지표")
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.metric(label="평균 F-스코어", value=f"{portfolio['f-score'].mean():.2f}")
+    
+    with col2:
+        st.metric(label="평균 GP/A", value=f"{portfolio['GP/A'].mean():.2f}%")
+    
+    with col3:
+        avg_market_cap = portfolio['시가총액'].mean() / 1_000_000_000
+        st.metric(label="평균 시가총액", value=f"{avg_market_cap:.1f}십억원")
+    
+    with col4:
+        st.metric(label="종목 수", value=len(portfolio))
+
+
+def get_last_year_end():
+    """Get the last year's end date."""
+    today = datetime.now()
+    last_year = today.year - 1
+    last_year_end = datetime(last_year, 12, 31)
+    return last_year_end.date()
--- a/src/ui/pages/value_momentum_page.py
+++ b/src/ui/pages/value_momentum_page.py
@ -0,0 +1,146 @@
+"""
+Super Value Momentum strategy page for the Streamlit Quant application.
+"""
+import streamlit as st
+import pandas as pd
+from db.common import DBManager
+from strategies.factors.value import get_value_top
+from strategies.factors.momentum import get_momentum_top
+
+
+def render_value_momentum_page():
+    """Render the Super Value Momentum strategy page."""
+    st.title("슈퍼 밸류 모멘텀 전략")
+    
+    with st.expander("전략 설명", expanded=False):
+        st.write("""
+        슈퍼 밸류 모멘텀 전략은 밸류와 모멘텀 요소를 결합한 전략입니다.
+        
+        - 밸류 요소: PER, PBR과 같은 가치 지표가 낮은 종목 선택
+        - 모멘텀 요소: 최근 가격 상승세가 강한 종목 선택
+        
+        두 요소를 결합함으로써 저평가된 종목 중에서도 상승 모멘텀이 있는 종목만 선택하여
+        가치 함정(value trap)을 피하고 더 나은 성과를 기대할 수 있습니다.
+        
+        **밸류 요소 지표**
+        - PER (주가수익비율): 주가 / 주당순이익
+        - PBR (주가순자산비율): 주가 / 주당순자산
+        
+        **모멘텀 요소 지표**
+        - 6개월 수익률
+        - 12개월 수익률
+        """)
+    
+    # Strategy implementation
+    st.write("## 슈퍼 밸류 모멘텀 포트폴리오")
+    
+    # Settings
+    col1, col2 = st.columns([1, 2])
+    
+    with col1:
+        st.write("### 설정")
+        value_weight = st.slider("밸류 가중치 (%)", min_value=0, max_value=100, value=50)
+        momentum_weight = 100 - value_weight
+        st.write(f"모멘텀 가중치: {momentum_weight}%")
+        
+        num_stocks = st.slider("포트폴리오 종목수", min_value=5, max_value=50, value=20)
+        
+        min_market_cap = st.number_input("최소 시가총액 (억원)", min_value=0, value=500)
+        
+        exclude_industries = st.multiselect(
+            "제외할 산업",
+            options=["금융업", "보험", "은행", "증권", "지주사"],
+            default=["금융업", "보험", "은행", "증권"]
+        )
+    
+    # Get data
+    db = DBManager()
+    
+    try:
+        with st.spinner("데이터 로딩 중..."):
+            # Get value portfolio
+            value_portfolio = get_value_top(20)
+            
+            # Get momentum portfolio
+            momentum_portfolio = get_momentum_top(20)
+            
+            # Get stock info for filtering
+            stocks = db.get_ticker_list()
+            sectors = db.get_sector_list()
+    except Exception as e:
+        st.error(f"데이터 로딩 실패: {e}")
+        return
+    
+    # Merge data
+    stocks = pd.merge(stocks, sectors, on='종목코드', how='left')
+    
+    # Filter by market cap
+    stocks = stocks[stocks['시가총액'] >= min_market_cap * 100000000]
+    
+    # Filter by industry
+    if exclude_industries:
+        excluded_mask = stocks['산업'].isin(exclude_industries)
+        stocks = stocks[~excluded_mask]
+    
+    # Combine value and momentum scores
+    combined_df = pd.DataFrame(index=stocks['종목코드'])
+    
+    # Add value rank (normalized to 0-100, lower is better)
+    if not value_portfolio.empty:
+        value_ranks = value_portfolio.set_index('종목코드')['rank']
+        max_rank = value_ranks.max()
+        combined_df['value_score'] = value_ranks.reindex(combined_df.index).fillna(max_rank)
+        combined_df['value_score'] = 100 - (combined_df['value_score'] / max_rank * 100)
+    
+    # Add momentum rank (normalized to 0-100, higher is better)
+    if not momentum_portfolio.empty:
+        momentum_ranks = momentum_portfolio.set_index('종목코드')['rank']
+        max_rank = momentum_ranks.max()
+        combined_df['momentum_score'] = momentum_ranks.reindex(combined_df.index).fillna(max_rank)
+        combined_df['momentum_score'] = 100 - (combined_df['momentum_score'] / max_rank * 100)
+    
+    # Calculate combined score
+    combined_df['combined_score'] = (
+        combined_df['value_score'] * (value_weight / 100) + 
+        combined_df['momentum_score'] * (momentum_weight / 100)
+    )
+    
+    # Sort by combined score
+    combined_df = combined_df.sort_values('combined_score', ascending=False)
+    
+    # Get top N stocks
+    top_stocks = combined_df.head(num_stocks)
+    
+    # Merge with stock info
+    portfolio = pd.merge(
+        top_stocks.reset_index(), 
+        stocks, 
+        on='종목코드', 
+        how='left'
+    )
+    
+    # Display portfolio
+    with col2:
+        st.write(f"### 선택된 {len(portfolio)} 종목")
+        display_cols = ['종목코드', '종목명', '현재가', '시가총액', 'PER', 'PBR', 
+                        'value_score', 'momentum_score', 'combined_score']
+        st.dataframe(portfolio[display_cols], use_container_width=True)
+    
+    # Display metrics
+    st.write("### 포트폴리오 지표")
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        avg_per = portfolio['PER'].replace([float('inf'), -float('inf')], float('nan')).mean()
+        st.metric(label="평균 PER", value=f"{avg_per:.2f}")
+    
+    with col2:
+        avg_pbr = portfolio['PBR'].replace([float('inf'), -float('inf')], float('nan')).mean()
+        st.metric(label="평균 PBR", value=f"{avg_pbr:.2f}")
+    
+    with col3:
+        avg_market_cap = portfolio['시가총액'].mean() / 1_000_000_000
+        st.metric(label="평균 시가총액", value=f"{avg_market_cap:.1f}십억원")
+    
+    with col4:
+        st.metric(label="종목 수", value=len(portfolio))
--- a/streamlit_quant/init.py
+++ b/streamlit_quant/init.py
@ -1 +0,0 @@
-__all__ = ['backtest', 'strategy', 'quantcommon']
--- a/streamlit_quant/app.py
+++ b/streamlit_quant/app.py
@ -1,13 +0,0 @@
-import streamlit as st
-
-crawling_page = st.Page("crawling.py", title="크롤링")
-super_quality_page = st.Page("super_quality.py", title="슈퍼 퀄리티 전략")
-super_value_momentum_page = st.Page("super_value_momentum.py", title="슈퍼 밸류 모멘텀 전략")
-
-pg = st.navigation({
-    '크롤링': [crawling_page],
-    '전략': [super_quality_page, super_value_momentum_page],
-})
-
-st.set_page_config(page_title="콴트 매니저", page_icon=":material/edit:")
-pg.run()
--- a/streamlit_quant/crawling.py
+++ b/streamlit_quant/crawling.py
@ -1,6 +0,0 @@
-import streamlit as st
-
-st.button(label='동작1')
-st.button(label='동작2')
-st.button(label='동작3')
-st.button(label='동작4')
--- a/streamlit_quant/strategy/init.py
+++ b/streamlit_quant/strategy/init.py
@ -1 +0,0 @@
-__all__ = ['multi_factor', 'f_score']
Author	SHA1	Message	Date
Ayuriel	218bf72874	chore: 재구성한 example 삭제 All checks were successful CI Build / build (push) Successful in 5s Details	2025-03-30 21:20:59 +09:00
Ayuriel	57ba71cb3b	chore: 리팩토링 전 패키지 삭제	2025-03-30 21:20:34 +09:00
Ayuriel	23cada68f7	feat: 구조 리팩토링	2025-03-30 21:19:46 +09:00
Ayuriel	67a9b23fa5	feat: 구조 리팩토링	2025-03-30 21:19:41 +09:00
				`@ -0,0 +1 @@`
				`__all__ = ['multi_factor', 'multi_factor', 'super_quality', 'super_value_momentum']`
				`@ -0,0 +1 @@`
				`__all__ = ['all_value', 'f_score', 'momentum', 'quality', 'value']`
				`@ -0,0 +1 @@`
				`__all__ = ['data_page', 'quality_page', 'value_momentum_page']`
				`@ -1 +0,0 @@`
				`__all__ = ['backtest', 'strategy', 'quantcommon']`