From 18e085fd3c677d889aa9f6fcb856c49b7d3d1b6f Mon Sep 17 00:00:00 2001
From: ayuriel <zephyrdark@gmail.com>
Date: Fri, 31 Jan 2025 17:19:55 +0900
Subject: [PATCH] =?UTF-8?q?chore:=20=ED=85=8C=EC=8A=A4=ED=8A=B8=EC=9A=A9?=
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---
 example/10-financial-statements-one.py | 114 +++++++++++++++++++++++++
 example/10-financial-statements.py     |   6 +-
 2 files changed, 117 insertions(+), 3 deletions(-)
 create mode 100644 example/10-financial-statements-one.py

diff --git a/example/10-financial-statements-one.py b/example/10-financial-statements-one.py
new file mode 100644
index 0000000..ded0d36
--- /dev/null
+++ b/example/10-financial-statements-one.py
@@ -0,0 +1,114 @@
+import re
+import time
+
+import pandas as pd
+import requests as rq
+from bs4 import BeautifulSoup
+from tqdm import tqdm
+
+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()
\ No newline at end of file
diff --git a/example/10-financial-statements.py b/example/10-financial-statements.py
index 2eded4c..5b185e0 100644
--- a/example/10-financial-statements.py
+++ b/example/10-financial-statements.py
@@ -15,7 +15,7 @@ con = common.connect()
 
 mycursor = con.cursor()
 
-# 제무재표 크롤링
+# 재무제표 크롤링
 # 티커리스트 불러오기
 ticker_list = pd.read_sql("""
 select * from kor_ticker
@@ -44,7 +44,7 @@ def clean_fs(df, ticker, frequency):
     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()
+                               format='%Y/%m') + pd.tseries.offsets.MonthEnd()
     df['종목코드'] = ticker
     df['공시구분'] = frequency
 
@@ -61,7 +61,7 @@ for i in tqdm(range(0, len(ticker_list))):
     try:
 
         # url 생성
-        url = f'http://comp.fnguide.com/SVO2/ASP/SVD_Finance.asp?pGB=1&gicode=A{ticker}'
+        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)