# [국내주식] 종목정보 - 국내주식 증권사별 투자의견 # Generated by KIS API Generator (Single API Mode) # -*- coding: utf-8 -*- """ Created on 2025-06-17 """ import logging import sys import time from typing import Optional 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] 종목정보 > 국내주식 증권사별 투자의견[국내주식-189] ############################################################################################## # 상수 정의 API_URL = "/uapi/domestic-stock/v1/quotations/invest-opbysec" def invest_opbysec( fid_cond_mrkt_div_code: str, # 조건시장분류코드 fid_cond_scr_div_code: str, # 조건화면분류코드 fid_input_iscd: str, # 입력종목코드 fid_div_cls_code: str, # 분류구분코드 fid_input_date_1: str, # 입력날짜1 fid_input_date_2: str, # 입력날짜2 tr_cont: str = "", # 연속 거래 여부 dataframe: Optional[pd.DataFrame] = None, # 누적 데이터프레임 depth: int = 0, # 현재 재귀 깊이 max_depth: int = 10 # 최대 재귀 깊이 ) -> Optional[pd.DataFrame]: """ [국내주식] 종목정보 국내주식 증권사별 투자의견[국내주식-189] 국내주식 증권사별 투자의견 API를 호출하여 DataFrame으로 반환합니다. Args: fid_cond_mrkt_div_code (str): J(시장 구분 코드) fid_cond_scr_div_code (str): 16634(Primary key) fid_input_iscd (str): 회원사코드 (kis developers 포탈 사이트 포럼-> FAQ -> 종목정보 다운로드(국내) 참조) fid_div_cls_code (str): 전체(0) 매수(1) 중립(2) 매도(3) fid_input_date_1 (str): 이후 ~ fid_input_date_2 (str): ~ 이전 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]: 국내주식 증권사별 투자의견 데이터 Example: >>> df = invest_opbysec( ... fid_cond_mrkt_div_code="J", ... fid_cond_scr_div_code="16634", ... fid_input_iscd="005930", ... fid_div_cls_code="0", ... fid_input_date_1="20230101", ... fid_input_date_2="20231231" ... ) >>> 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. 'J')") raise ValueError("fid_cond_mrkt_div_code is required. (e.g. 'J')") if not fid_cond_scr_div_code: logger.error("fid_cond_scr_div_code is required. (e.g. '16634')") raise ValueError("fid_cond_scr_div_code is required. (e.g. '16634')") if not fid_input_iscd: logger.error("fid_input_iscd is required. (e.g. '005930')") raise ValueError("fid_input_iscd is required. (e.g. '005930')") 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_input_date_1: logger.error("fid_input_date_1 is required. (e.g. '20230101')") raise ValueError("fid_input_date_1 is required. (e.g. '20230101')") if not fid_input_date_2: logger.error("fid_input_date_2 is required. (e.g. '20231231')") raise ValueError("fid_input_date_2 is required. (e.g. '20231231')") # 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() # API 호출 URL 및 거래 ID 설정 tr_id = "FHKST663400C0" # API 요청 파라미터 설정 params = { "FID_COND_MRKT_DIV_CODE": fid_cond_mrkt_div_code, "FID_COND_SCR_DIV_CODE": fid_cond_scr_div_code, "FID_INPUT_ISCD": fid_input_iscd, "FID_DIV_CLS_CODE": fid_div_cls_code, "FID_INPUT_DATE_1": fid_input_date_1, "FID_INPUT_DATE_2": fid_input_date_2, } # API 호출 res = ka._url_fetch(API_URL, tr_id, tr_cont, params) # API 응답 처리 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 invest_opbysec( fid_cond_mrkt_div_code, fid_cond_scr_div_code, fid_input_iscd, fid_div_cls_code, fid_input_date_1, 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()