189 lines
7.0 KiB
Python
189 lines
7.0 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Created on 2025-06-18
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"""
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import logging
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import time
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from typing import Optional
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import sys
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import pandas as pd
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sys.path.extend(['../..', '.'])
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import kis_auth as ka
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# 로깅 설정
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logging.basicConfig(level=logging.INFO, format='%(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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##############################################################################################
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# [국내주식] ELW시세 - ELW 민감도 순위[국내주식-170]
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##############################################################################################
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# 상수 정의
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API_URL = "/uapi/elw/v1/ranking/sensitivity"
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def sensitivity(
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fid_cond_mrkt_div_code: str, # 조건시장분류코드
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fid_cond_scr_div_code: str, # 조건화면분류코드
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fid_unas_input_iscd: str, # 기초자산입력종목코드
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fid_input_iscd: str, # 입력종목코드
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fid_div_cls_code: str, # 콜풋구분코드
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fid_input_price_1: str, # 가격(이상)
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fid_input_price_2: str, # 가격(이하)
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fid_input_vol_1: str, # 거래량(이상)
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fid_input_vol_2: str, # 거래량(이하)
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fid_rank_sort_cls_code: str, # 순위정렬구분코드
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fid_input_rmnn_dynu_1: str, # 잔존일수(이상)
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fid_input_date_1: str, # 조회기준일
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fid_blng_cls_code: str, # 결재방법
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tr_cont: str = "", # 연속 거래 여부
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dataframe: Optional[pd.DataFrame] = None, # 누적 데이터프레임
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depth: int = 0, # 현재 재귀 깊이
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max_depth: int = 10 # 최대 재귀 깊이
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) -> Optional[pd.DataFrame]:
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"""
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[국내주식] ELW시세
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ELW 민감도 순위[국내주식-170]
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ELW 민감도 순위 API를 호출하여 DataFrame으로 반환합니다.
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Args:
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fid_cond_mrkt_div_code (str): 조건시장분류코드
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fid_cond_scr_div_code (str): 조건화면분류코드
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fid_unas_input_iscd (str): 기초자산입력종목코드
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fid_input_iscd (str): 입력종목코드
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fid_div_cls_code (str): 콜풋구분코드
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fid_input_price_1 (str): 가격(이상)
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fid_input_price_2 (str): 가격(이하)
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fid_input_vol_1 (str): 거래량(이상)
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fid_input_vol_2 (str): 거래량(이하)
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fid_rank_sort_cls_code (str): 순위정렬구분코드
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fid_input_rmnn_dynu_1 (str): 잔존일수(이상)
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fid_input_date_1 (str): 조회기준일
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fid_blng_cls_code (str): 결재방법
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tr_cont (str): 연속 거래 여부
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dataframe (Optional[pd.DataFrame]): 누적 데이터프레임
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depth (int): 현재 재귀 깊이
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max_depth (int): 최대 재귀 깊이 (기본값: 10)
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Returns:
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Optional[pd.DataFrame]: ELW 민감도 순위 데이터
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Example:
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>>> df = sensitivity(
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fid_cond_mrkt_div_code='W',
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fid_cond_scr_div_code='20285',
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fid_unas_input_iscd='000000',
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fid_input_iscd='00000',
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fid_div_cls_code='0',
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fid_input_price_1='0',
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fid_input_price_2='100000',
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fid_input_vol_1='0',
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fid_input_vol_2='1000000',
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fid_rank_sort_cls_code='0',
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fid_input_rmnn_dynu_1='0',
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fid_input_date_1='20230101',
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fid_blng_cls_code='0'
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)
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>>> print(df)
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"""
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# 필수 파라미터 검증
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if not fid_cond_mrkt_div_code:
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logger.error("fid_cond_mrkt_div_code is required. (e.g. 'W')")
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raise ValueError("fid_cond_mrkt_div_code is required. (e.g. 'W')")
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if not fid_cond_scr_div_code:
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logger.error("fid_cond_scr_div_code is required. (e.g. '20285')")
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raise ValueError("fid_cond_scr_div_code is required. (e.g. '20285')")
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if not fid_unas_input_iscd:
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logger.error("fid_unas_input_iscd is required. (e.g. '000000')")
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raise ValueError("fid_unas_input_iscd is required. (e.g. '000000')")
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if not fid_input_iscd:
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logger.error("fid_input_iscd is required. (e.g. '00000')")
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raise ValueError("fid_input_iscd is required. (e.g. '00000')")
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if not fid_div_cls_code:
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logger.error("fid_div_cls_code is required. (e.g. '0')")
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raise ValueError("fid_div_cls_code is required. (e.g. '0')")
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if not fid_rank_sort_cls_code:
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logger.error("fid_rank_sort_cls_code is required. (e.g. '0')")
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raise ValueError("fid_rank_sort_cls_code is required. (e.g. '0')")
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if not fid_blng_cls_code:
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logger.error("fid_blng_cls_code is required. (e.g. '0')")
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raise ValueError("fid_blng_cls_code is required. (e.g. '0')")
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# 최대 재귀 깊이 체크
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if depth >= max_depth:
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logger.warning("Maximum recursion depth (%d) reached. Stopping further requests.", max_depth)
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return dataframe if dataframe is not None else pd.DataFrame()
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tr_id = "FHPEW02850000"
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params = {
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"FID_COND_MRKT_DIV_CODE": fid_cond_mrkt_div_code,
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"FID_COND_SCR_DIV_CODE": fid_cond_scr_div_code,
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"FID_UNAS_INPUT_ISCD": fid_unas_input_iscd,
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"FID_INPUT_ISCD": fid_input_iscd,
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"FID_DIV_CLS_CODE": fid_div_cls_code,
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"FID_INPUT_PRICE_1": fid_input_price_1,
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"FID_INPUT_PRICE_2": fid_input_price_2,
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"FID_INPUT_VOL_1": fid_input_vol_1,
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"FID_INPUT_VOL_2": fid_input_vol_2,
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"FID_RANK_SORT_CLS_CODE": fid_rank_sort_cls_code,
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"FID_INPUT_RMNN_DYNU_1": fid_input_rmnn_dynu_1,
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"FID_INPUT_DATE_1": fid_input_date_1,
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"FID_BLNG_CLS_CODE": fid_blng_cls_code,
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}
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# API 호출
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res = ka._url_fetch(API_URL, tr_id, tr_cont, params)
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if res.isOK():
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if hasattr(res.getBody(), 'output'):
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output_data = res.getBody().output
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if not isinstance(output_data, list):
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output_data = [output_data]
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current_data = pd.DataFrame(output_data)
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else:
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current_data = pd.DataFrame()
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if dataframe is not None:
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dataframe = pd.concat([dataframe, current_data], ignore_index=True)
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else:
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dataframe = current_data
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tr_cont = res.getHeader().tr_cont
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if tr_cont == "M":
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logger.info("Calling next page...")
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ka.smart_sleep()
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return sensitivity(
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fid_cond_mrkt_div_code,
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fid_cond_scr_div_code,
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fid_unas_input_iscd,
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fid_input_iscd,
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fid_div_cls_code,
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fid_input_price_1,
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fid_input_price_2,
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fid_input_vol_1,
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fid_input_vol_2,
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fid_rank_sort_cls_code,
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fid_input_rmnn_dynu_1,
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fid_input_date_1,
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fid_blng_cls_code,
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"N", dataframe, depth + 1, max_depth
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)
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else:
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logger.info("Data fetch complete.")
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return dataframe
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else:
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logger.error("API call failed: %s - %s", res.getErrorCode(), res.getErrorMessage())
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res.printError(API_URL)
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return pd.DataFrame()
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