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한국투자증권(API)/examples_llm/elw/updown_rate/chk_updown_rate.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on 2025-06-18
"""
import sys
import logging
import pandas as pd
sys.path.extend(['../..', '.']) # kis_auth 파일 경로 추가
import kis_auth as ka
from updown_rate import updown_rate
# 로깅 설정
logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
##############################################################################################
# [국내주식] ELW시세 - ELW 상승률순위[국내주식-167]
##############################################################################################
COLUMN_MAPPING = {
'hts_kor_isnm': 'HTS한글종목명',
'elw_shrn_iscd': 'ELW단축종목코드',
'elw_prpr': 'ELW현재가',
'prdy_vrss': '전일대비',
'prdy_vrss_sign': '전일대비부호',
'prdy_ctrt': '전일대비율',
'acml_vol': '누적거래량',
'stck_sdpr': '주식기준가',
'sdpr_vrss_prpr_sign': '기준가대비현재가부호',
'sdpr_vrss_prpr': '기준가대비현재가',
'sdpr_vrss_prpr_rate': '기준가대비현재가비율',
'stck_oprc': '주식시가2',
'oprc_vrss_prpr_sign': '시가2대비현재가부호',
'oprc_vrss_prpr': '시가2대비현재가',
'oprc_vrss_prpr_rate': '시가2대비현재가비율',
'stck_hgpr': '주식최고가',
'stck_lwpr': '주식최저가',
'prd_rsfl_sign': '기간등락부호',
'prd_rsfl': '기간등락',
'prd_rsfl_rate': '기간등락비율',
'stck_cnvr_rate': '주식전환비율',
'hts_rmnn_dynu': 'HTS잔존일수',
'acpr': '행사가',
'unas_isnm': '기초자산명',
'unas_shrn_iscd': '기초자산코드',
'lp_hldn_rate': 'LP보유비율',
'prit': '패리티',
'prls_qryr_stpr_prc': '손익분기주가가격',
'delta_val': '델타값',
'theta': '세타',
'prls_qryr_rate': '손익분기비율',
'stck_lstn_date': '주식상장일자',
'stck_last_tr_date': '주식최종거래일자',
'hts_ints_vltl': 'HTS내재변동성',
'lvrg_val': '레버리지값'
}
NUMERIC_COLUMNS = [
'ELW현재가', '전일대비', '전일대비율', '누적거래량', '주식기준가', '기준가대비현재가', '기준가대비현재가비율',
'주식시가2', '시가2대비현재가', '시가2대비현재가비율', '주식최고가', '주식최저가', '기간등락', '기간등락비율',
'주식전환비율', 'HTS잔존일수', '행사가', 'LP보유비율', '패리티', '손익분기주가가격', '델타값', '세타', '손익분기비율',
'HTS내재변동성', '레버리지값'
]
def main():
"""
[국내주식] ELW시세
ELW 상승률순위[국내주식-167]
ELW 상승률순위 테스트 함수
Parameters:
- fid_cond_mrkt_div_code (str): 사용자권한정보 (시장구분코드 (W))
- fid_cond_scr_div_code (str): 거래소코드 (Unique key(20277))
- fid_unas_input_iscd (str): 상승율/하락율 구분 ('000000(전체), 2001(코스피200) , 3003(코스닥150), 005930(삼성전자) ')
- fid_input_iscd (str): N일자값 ('00000(전체), 00003(한국투자증권) , 00017(KB증권), 00005(미래에셋주식회사)')
- fid_input_rmnn_dynu_1 (str): 거래량조건 ('0(전체), 1(1개월이하), 2(1개월~2개월), 3(2개월~3개월), 4(3개월~6개월), 5(6개월~9개월),6(9개월~12개월), 7(12개월이상)')
- fid_div_cls_code (str): NEXT KEY BUFF (0(전체), 1(콜), 2(풋))
- fid_input_price_1 (str): 사용자권한정보 ()
- fid_input_price_2 (str): 거래소코드 ()
- fid_input_vol_1 (str): 상승율/하락율 구분 ()
- fid_input_vol_2 (str): N일자값 ()
- fid_input_date_1 (str): 거래량조건 ()
- fid_rank_sort_cls_code (str): NEXT KEY BUFF ('0(상승율), 1(하락율), 2(시가대비상승율) , 3(시가대비하락율), 4(변동율)')
- fid_blng_cls_code (str): 사용자권한정보 (0(전체))
- fid_input_date_2 (str): 거래소코드 ()
Returns:
- DataFrame: ELW 상승률순위 결과
Example:
>>> df = updown_rate(fid_cond_mrkt_div_code="W", fid_cond_scr_div_code="20277", fid_unas_input_iscd="000000", fid_input_iscd="00000", fid_input_rmnn_dynu_1="0", fid_div_cls_code="0", fid_input_price_1="", fid_input_price_2="", fid_input_vol_1="", fid_input_vol_2="", fid_input_date_1="", fid_rank_sort_cls_code="0", fid_blng_cls_code="0", fid_input_date_2="")
"""
try:
# pandas 출력 옵션 설정
pd.set_option('display.max_columns', None) # 모든 컬럼 표시
pd.set_option('display.width', None) # 출력 너비 제한 해제
pd.set_option('display.max_rows', None) # 모든 행 표시
# 토큰 발급
logger.info("토큰 발급 중...")
ka.auth()
logger.info("토큰 발급 완료")
# API 호출
logger.info("API 호출")
result = updown_rate(
fid_cond_mrkt_div_code="W", # 사용자권한정보
fid_cond_scr_div_code="20277", # 거래소코드
fid_unas_input_iscd="000000", # 상승율/하락율 구분
fid_input_iscd="00000", # N일자값
fid_input_rmnn_dynu_1="0", # 거래량조건
fid_div_cls_code="0", # NEXT KEY BUFF
fid_input_price_1="", # 사용자권한정보
fid_input_price_2="", # 거래소코드
fid_input_vol_1="", # 상승율/하락율 구분
fid_input_vol_2="", # N일자값
fid_input_date_1="1", # 거래량조건
fid_rank_sort_cls_code="0", # NEXT KEY BUFF
fid_blng_cls_code="0", # 사용자권한정보
fid_input_date_2="", # 거래소코드
)
if result is None or result.empty:
logger.warning("조회된 데이터가 없습니다.")
return
# 컬럼명 출력
logger.info("사용 가능한 컬럼 목록:")
logger.info(result.columns.tolist())
# 한글 컬럼명으로 변환
result = result.rename(columns=COLUMN_MAPPING)
# 숫자 컬럼 처리
for col in NUMERIC_COLUMNS:
if col in result.columns:
result[col] = pd.to_numeric(result[col], errors='coerce').round(2)
# 결과 출력
logger.info("=== ELW 상승률순위 결과 ===")
logger.info("조회된 데이터 건수: %d", len(result))
print(result)
except Exception as e:
logger.error("에러 발생: %s", str(e))
raise
if __name__ == "__main__":
main()

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한국투자증권(API)/examples_llm/elw/updown_rate/updown_rate.py Normal file
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# -*- coding: utf-8 -*-
"""
Created on 2025-06-18
"""
import logging
import time
from typing import Optional
import sys
import pandas as pd
sys.path.extend(['../..', '.'])
import kis_auth as ka
# 로깅 설정
logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
##############################################################################################
# [국내주식] ELW시세 - ELW 상승률순위[국내주식-167]
##############################################################################################
# 상수 정의
API_URL = "/uapi/elw/v1/ranking/updown-rate"
def updown_rate(
fid_cond_mrkt_div_code: str, # 사용자권한정보
fid_cond_scr_div_code: str, # 거래소코드
fid_unas_input_iscd: str, # 상승율/하락율 구분
fid_input_iscd: str, # N일자값
fid_input_rmnn_dynu_1: str, # 거래량조건
fid_div_cls_code: str, # NEXT KEY BUFF
fid_input_price_1: str, # 사용자권한정보
fid_input_price_2: str, # 거래소코드
fid_input_vol_1: str, # 상승율/하락율 구분
fid_input_vol_2: str, # N일자값
fid_input_date_1: str, # 거래량조건
fid_rank_sort_cls_code: str, # NEXT KEY BUFF
fid_blng_cls_code: str, # 사용자권한정보
fid_input_date_2: str, # 거래소코드
tr_cont: str = "",
dataframe: Optional[pd.DataFrame] = None,
depth: int = 0,
max_depth: int = 10
) -> Optional[pd.DataFrame]:
"""
[국내주식] ELW시세
ELW 상승률순위[국내주식-167]
ELW 상승률순위 API를 호출하여 DataFrame으로 반환합니다.
Args:
fid_cond_mrkt_div_code (str): 시장구분코드 (W)
fid_cond_scr_div_code (str): Unique key(20277)
fid_unas_input_iscd (str): '000000(전체), 2001(코스피200) , 3003(코스닥150), 005930(삼성전자) '
fid_input_iscd (str): '00000(전체), 00003(한국투자증권) , 00017(KB증권), 00005(미래에셋주식회사)'
fid_input_rmnn_dynu_1 (str): '0(전체), 1(1개월이하), 2(1개월~2개월), 3(2개월~3개월), 4(3개월~6개월), 5(6개월~9개월),6(9개월~12개월), 7(12개월이상)'
fid_div_cls_code (str): 0(전체), 1(콜), 2(풋)
fid_input_price_1 (str):
fid_input_price_2 (str):
fid_input_vol_1 (str):
fid_input_vol_2 (str):
fid_input_date_1 (str):
fid_rank_sort_cls_code (str): '0(상승율), 1(하락율), 2(시가대비상승율) , 3(시가대비하락율), 4(변동율)'
fid_blng_cls_code (str): 0(전체)
fid_input_date_2 (str):
tr_cont (str): 연속 거래 여부
dataframe (Optional[pd.DataFrame]): 누적 데이터프레임
depth (int): 현재 재귀 깊이
max_depth (int): 최대 재귀 깊이 (기본값: 10)
Returns:
Optional[pd.DataFrame]: ELW 상승률순위 데이터
Example:
>>> df = updown_rate(
... fid_cond_mrkt_div_code='W',
... fid_cond_scr_div_code='20277',
... fid_unas_input_iscd='000000',
... fid_input_iscd='00000',
... fid_input_rmnn_dynu_1='0',
... fid_div_cls_code='0',
... fid_input_price_1='',
... fid_input_price_2='',
... fid_input_vol_1='',
... fid_input_vol_2='',
... fid_input_date_1='1',
... fid_rank_sort_cls_code='0',
... fid_blng_cls_code='0',
... fid_input_date_2=''
... )
>>> print(df)
"""
# 로깅 설정
logger = logging.getLogger(__name__)
# 필수 파라미터 검증
if not fid_cond_mrkt_div_code:
logger.error("fid_cond_mrkt_div_code is required. (e.g. 'W')")
raise ValueError("fid_cond_mrkt_div_code is required. (e.g. 'W')")
if not fid_cond_scr_div_code:
logger.error("fid_cond_scr_div_code is required. (e.g. '20277')")
raise ValueError("fid_cond_scr_div_code is required. (e.g. '20277')")
if not fid_unas_input_iscd:
logger.error("fid_unas_input_iscd is required. (e.g. '000000')")
raise ValueError("fid_unas_input_iscd is required. (e.g. '000000')")
if not fid_input_iscd:
logger.error("fid_input_iscd is required. (e.g. '00000')")
raise ValueError("fid_input_iscd is required. (e.g. '00000')")
if not fid_input_rmnn_dynu_1:
logger.error("fid_input_rmnn_dynu_1 is required. (e.g. '0')")
raise ValueError("fid_input_rmnn_dynu_1 is required. (e.g. '0')")
if not fid_div_cls_code:
logger.error("fid_div_cls_code is required. (e.g. '0')")
raise ValueError("fid_div_cls_code is required. (e.g. '0')")
if not fid_rank_sort_cls_code:
logger.error("fid_rank_sort_cls_code is required. (e.g. '0')")
raise ValueError("fid_rank_sort_cls_code is required. (e.g. '0')")
if not fid_blng_cls_code:
logger.error("fid_blng_cls_code is required. (e.g. '0')")
raise ValueError("fid_blng_cls_code is required. (e.g. '0')")
# 최대 재귀 깊이 체크
if depth >= max_depth:
logger.warning("Maximum recursion depth (%d) reached. Stopping further requests.", max_depth)
return dataframe if dataframe is not None else pd.DataFrame()
tr_id = "FHPEW02770000"
params = {
"FID_COND_MRKT_DIV_CODE": fid_cond_mrkt_div_code,
"FID_COND_SCR_DIV_CODE": fid_cond_scr_div_code,
"FID_UNAS_INPUT_ISCD": fid_unas_input_iscd,
"FID_INPUT_ISCD": fid_input_iscd,
"FID_INPUT_RMNN_DYNU_1": fid_input_rmnn_dynu_1,
"FID_DIV_CLS_CODE": fid_div_cls_code,
"FID_INPUT_PRICE_1": fid_input_price_1,
"FID_INPUT_PRICE_2": fid_input_price_2,
"FID_INPUT_VOL_1": fid_input_vol_1,
"FID_INPUT_VOL_2": fid_input_vol_2,
"FID_INPUT_DATE_1": fid_input_date_1,
"FID_RANK_SORT_CLS_CODE": fid_rank_sort_cls_code,
"FID_BLNG_CLS_CODE": fid_blng_cls_code,
"FID_INPUT_DATE_2": fid_input_date_2,
}
# API 호출
res = ka._url_fetch(API_URL, tr_id, tr_cont, params)
if res.isOK():
if hasattr(res.getBody(), 'output'):
output_data = res.getBody().output
if not isinstance(output_data, list):
output_data = [output_data]
current_data = pd.DataFrame(output_data)
else:
current_data = pd.DataFrame()
if dataframe is not None:
dataframe = pd.concat([dataframe, current_data], ignore_index=True)
else:
dataframe = current_data
tr_cont = res.getHeader().tr_cont
if tr_cont == "M":
logger.info("Calling next page...")
ka.smart_sleep()
return updown_rate(
fid_cond_mrkt_div_code,
fid_cond_scr_div_code,
fid_unas_input_iscd,
fid_input_iscd,
fid_input_rmnn_dynu_1,
fid_div_cls_code,
fid_input_price_1,
fid_input_price_2,
fid_input_vol_1,
fid_input_vol_2,
fid_input_date_1,
fid_rank_sort_cls_code,
fid_blng_cls_code,
fid_input_date_2,
"N", dataframe, depth + 1, max_depth
)
else:
logger.info("Data fetch complete.")
return dataframe
else:
logger.error("API call failed: %s - %s", res.getErrorCode(), res.getErrorMessage())
res.printError(API_URL)
return pd.DataFrame()