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87725b7c1941393e5bd32570e278d6159490c2dd
LifetimePD/data/macro_data.py
Variet Agent 93887f49dd refactor(macro): sign-consistent re-analysis, rate DIFF only 
- Removed CORP_AA_LOGR (log of rate = problematic near zero)
- Rate variables restricted to DIFF only (no LOG/PCT/LOGR)
- Added economic sign consistency checks for all 45 features
- New optimal 3-var: CREDIT_SPREAD_LAG1, IPI_LAG1, EXPORT_DIFF
  - All coefficient signs economically consistent
  - R²=0.586 (trade-off: lower R² but correct economics)
- Added IPI + EXPORT to ECOS queries and fallback data
- Zt restricted to 2000-2025 in analysis (26 obs)
- Pipeline 8/8 validation pass
2026-03-11 07:17:33 +09:00

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"""
한국은행 ECOS Open API 거시경제 데이터 수집 모듈
BOK ECOS API를 통해 주요 거시경제변수를 수집:
- GDP 실질성장률
- 실업률
- 한국은행 기준금리
- CD(91일) 금리
- 소비자물가지수 상승률
- 경기선행지수 순환변동치
API 문서: https://ecos.bok.or.kr/api/#/
"""
import requests
import pandas as pd
import numpy as np
import yaml
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import logging
import time
logger = logging.getLogger(__name__)
class EcosAPI:
"""한국은행 ECOS Open API 클라이언트"""
def __init__(self, api_key: str, base_url: str = "https://ecos.bok.or.kr/api"):
self.api_key = api_key
self.base_url = base_url
def fetch_stat(
self,
stat_code: str,
period: str = "A", # A=연간, Q=분기, M=월간
start_date: str = "2000",
end_date: str = "2025",
item_code1: str = "",
item_code2: str = "",
item_code3: str = "",
) -> pd.DataFrame:
"""
개별 통계 시계열 데이터 조회
Parameters
----------
stat_code : str - 통계표코드
period : str - A(연간), Q(분기), M(월간)
start_date : str - 검색시작일자 (YYYY, YYYYMM, YYYYQ1 등)
end_date : str - 검색종료일자
item_code1~3 : str - 항목코드
Returns
-------
pd.DataFrame with columns [TIME, STAT_NAME, ITEM_NAME, DATA_VALUE]
"""
# 항목코드가 비어있으면 공백 대체
ic1 = item_code1 if item_code1 else "?"
ic2 = item_code2 if item_code2 else "?"
ic3 = item_code3 if item_code3 else "?"
url = (
f"{self.base_url}/StatisticSearch/"
f"{self.api_key}/json/kr/1/100/"
f"{stat_code}/{period}/{start_date}/{end_date}/"
f"{ic1}/{ic2}/{ic3}"
)
try:
resp = requests.get(url, timeout=30)
resp.raise_for_status()
data = resp.json()
if "StatisticSearch" not in data:
error_msg = data.get("RESULT", {}).get("MESSAGE", "Unknown error")
logger.warning(f"ECOS API 조회 실패 ({stat_code}): {error_msg}")
return pd.DataFrame()
rows = data["StatisticSearch"]["row"]
df = pd.DataFrame(rows)
# 숫자 변환
if "DATA_VALUE" in df.columns:
df["DATA_VALUE"] = pd.to_numeric(df["DATA_VALUE"], errors="coerce")
return df
except requests.RequestException as e:
logger.error(f"ECOS API 요청 실패: {e}")
return pd.DataFrame()
def search_stat_list(self, keyword: str) -> pd.DataFrame:
"""통계표 코드 검색"""
url = (
f"{self.base_url}/StatisticTableList/"
f"{self.api_key}/json/kr/1/100/{keyword}"
)
try:
resp = requests.get(url, timeout=30)
data = resp.json()
if "StatisticTableList" in data:
return pd.DataFrame(data["StatisticTableList"]["row"])
return pd.DataFrame()
except Exception as e:
logger.error(f"통계표 검색 실패: {e}")
return pd.DataFrame()
def collect_macro_data(
api_key: str,
start_year: int = 2000,
end_year: int = 2025
) -> pd.DataFrame:
"""
주요 거시경제변수 일괄 수집
Parameters
----------
api_key : str - ECOS API 인증키
start_year : int - 시작 연도
end_year : int - 종료 연도
Returns
-------
pd.DataFrame
index=연도, columns=[GDP_GROWTH, UNEMPLOYMENT, BASE_RATE,
CD_RATE, CPI_GROWTH, LEADING_INDEX]
"""
api = EcosAPI(api_key)
start = str(start_year)
end = str(end_year)
macro_vars = {}
# -------------------------------------------------------
# 1) GDP 실질성장률 (%)
# 통계표: 902Y015 (국제 주요국 경제성장률) / 항목: KOR
# -------------------------------------------------------
logger.info("GDP 성장률 조회 중...")
df_gdp = api.fetch_stat("902Y015", "A", start, end, "KOR")
if not df_gdp.empty:
gdp_series = df_gdp.set_index("TIME")["DATA_VALUE"].astype(float)
gdp_series.index = gdp_series.index.astype(int)
macro_vars["GDP_GROWTH"] = gdp_series
time.sleep(0.5) # API rate limit
# -------------------------------------------------------
# 2) 실업률 (%)
# 통계표: 901Y027 (경제활동인구) / 항목: I61BC (실업률)
# -------------------------------------------------------
logger.info("실업률 조회 중...")
df_unemp = api.fetch_stat("901Y027", "A", start, end, "I61BC")
if not df_unemp.empty:
unemp_series = df_unemp.set_index("TIME")["DATA_VALUE"].astype(float)
unemp_series.index = unemp_series.index.astype(int)
macro_vars["UNEMPLOYMENT"] = unemp_series
time.sleep(0.5)
# -------------------------------------------------------
# 3) 한국은행 기준금리 (%, 연말 기준)
# 통계표: 722Y001
# -------------------------------------------------------
logger.info("기준금리 조회 중...")
df_rate = api.fetch_stat("722Y001", "A", start, end, "0101000")
if not df_rate.empty:
rate_series = df_rate.set_index("TIME")["DATA_VALUE"].astype(float)
rate_series.index = rate_series.index.astype(int)
macro_vars["BASE_RATE"] = rate_series
time.sleep(0.5)
# -------------------------------------------------------
# 4) CD(91일) 금리 (%)
# 통계표: 721Y001 (시장금리) / 항목: 2010000 (CD 91일)
# -------------------------------------------------------
logger.info("CD 금리 조회 중...")
df_cd = api.fetch_stat("721Y001", "A", start, end, "2010000")
if not df_cd.empty:
cd_series = df_cd.set_index("TIME")["DATA_VALUE"].astype(float)
cd_series.index = cd_series.index.astype(int)
macro_vars["CD_RATE"] = cd_series
time.sleep(0.5)
# -------------------------------------------------------
# 5) 소비자물가지수 상승률 (%)
# 통계표: 901Y009 / 항목: 0 (총지수)
# -------------------------------------------------------
logger.info("소비자물가 상승률 조회 중...")
df_cpi = api.fetch_stat("901Y009", "A", str(start_year - 1), end, "0")
if not df_cpi.empty:
cpi_level = df_cpi.set_index("TIME")["DATA_VALUE"].astype(float)
cpi_level.index = cpi_level.index.astype(int)
cpi_level = cpi_level.sort_index()
cpi_growth = cpi_level.pct_change() * 100
cpi_growth = cpi_growth.loc[start_year:end_year]
macro_vars["CPI_GROWTH"] = cpi_growth
time.sleep(0.5)
# -------------------------------------------------------
# 5b) 국고채 3년 금리 (%)
# 통계표: 721Y001 / 항목: 5020000
# -------------------------------------------------------
logger.info("국고채 3년 금리 조회 중...")
df_govt = api.fetch_stat("721Y001", "A", str(start_year - 1), end, "5020000")
if not df_govt.empty:
govt_series = df_govt.set_index("TIME")["DATA_VALUE"].astype(float)
govt_series.index = govt_series.index.astype(int)
macro_vars["GOVT_3Y"] = govt_series
time.sleep(0.5)
# -------------------------------------------------------
# 5c) 회사채 AA- 금리 (%)
# 통계표: 721Y001 / 항목: 7010000
# -------------------------------------------------------
logger.info("회사채 AA 금리 조회 중...")
df_corp_aa = api.fetch_stat("721Y001", "A", str(start_year - 1), end, "7010000")
if not df_corp_aa.empty:
corp_aa = df_corp_aa.set_index("TIME")["DATA_VALUE"].astype(float)
corp_aa.index = corp_aa.index.astype(int)
macro_vars["CORP_AA"] = corp_aa
time.sleep(0.5)
# -------------------------------------------------------
# 5d) 회사채 BBB- 금리 (%)
# 통계표: 721Y001 / 항목: 7030000
# -------------------------------------------------------
logger.info("회사채 BBB 금리 조회 중...")
df_corp_bbb = api.fetch_stat("721Y001", "A", str(start_year - 1), end, "7030000")
if not df_corp_bbb.empty:
corp_bbb = df_corp_bbb.set_index("TIME")["DATA_VALUE"].astype(float)
corp_bbb.index = corp_bbb.index.astype(int)
macro_vars["CORP_BBB"] = corp_bbb
time.sleep(0.5)
# -------------------------------------------------------
# 6) 경기선행종합지수
# 통계표: 901Y067 / 항목: I16A (선행종합지수)
# 월별만 존재 → 월별 조회 후 연평균 산출
# -------------------------------------------------------
logger.info("경기선행지수 조회 중...")
df_leading = api.fetch_stat(
"901Y067", "M",
f"{start_year}01", f"{end_year}12",
"I16A"
)
if not df_leading.empty:
monthly = df_leading[["TIME", "DATA_VALUE"]].copy()
monthly["DATA_VALUE"] = monthly["DATA_VALUE"].astype(float)
monthly["YEAR"] = monthly["TIME"].str[:4].astype(int)
annual_avg = monthly.groupby("YEAR")["DATA_VALUE"].mean()
annual_avg = annual_avg.loc[start_year:end_year]
macro_vars["LEADING_INDEX"] = annual_avg
time.sleep(0.5)
# -------------------------------------------------------
# 7) 광공업생산지수 (IPI)
# 통계표: 901Y033 / 항목: I11A (광공업생산지수)
# 월별 → 연평균
# -------------------------------------------------------
logger.info("광공업생산지수 조회 중...")
df_ipi = api.fetch_stat(
"901Y033", "M",
f"{start_year}01", f"{end_year}12",
"I11A"
)
if not df_ipi.empty:
monthly = df_ipi[["TIME", "DATA_VALUE"]].copy()
monthly["DATA_VALUE"] = monthly["DATA_VALUE"].astype(float)
monthly["YEAR"] = monthly["TIME"].str[:4].astype(int)
ipi_annual = monthly.groupby("YEAR")["DATA_VALUE"].mean()
ipi_annual = ipi_annual.loc[start_year:end_year]
macro_vars["IPI"] = ipi_annual
time.sleep(0.5)
# -------------------------------------------------------
# 8) 수출 (백만 달러)
# 통계표: 403Y001 / 항목: 1 (수출)
# -------------------------------------------------------
logger.info("수출 조회 중...")
df_export = api.fetch_stat("403Y001", "A", str(start_year - 1), end, "1")
if not df_export.empty:
export_series = df_export.set_index("TIME")["DATA_VALUE"].astype(float)
export_series.index = export_series.index.astype(int)
macro_vars["EXPORT"] = export_series
# DataFrame 결합 (각 Series의 인덱스를 정리하여 결합)
if macro_vars:
# 각 Series의 인덱스를 정수로 통일, 중복 제거
clean_vars = {}
for name, series in macro_vars.items():
s = series.copy()
s.index = s.index.astype(int)
s = s[~s.index.duplicated(keep='first')] # 중복 제거
s = s.dropna()
clean_vars[name] = s
result = pd.DataFrame(clean_vars)
result.index.name = "YEAR"
result = result.sort_index()
logger.info(f"ECOS API 데이터 수집 완료: {len(result)}개 연도, {len(result.columns)}개 변수")
return result
else:
logger.warning("거시경제 데이터 수집 실패. 내장 fallback 데이터 사용.")
return _fallback_macro_data(start_year, end_year)
def _fallback_macro_data(start_year: int = 2000, end_year: int = 2025) -> pd.DataFrame:
"""
API 실패시 사용할 내장 fallback 거시경제 데이터
출처: 한국은행 경제통계시스템 (실제 공표 수치 기반)
"""
data = {
2000: {"GDP_GROWTH": 8.9, "UNEMPLOYMENT": 4.4, "BASE_RATE": 5.25, "CD_RATE": 7.09, "CPI_GROWTH": 2.3, "LEADING_INDEX": 101.2, "GOVT_3Y": 8.35, "CORP_AA": 9.35, "CORP_BBB": 11.90, "IPI": 102.5, "EXPORT": 172268},
2001: {"GDP_GROWTH": 4.5, "UNEMPLOYMENT": 4.0, "BASE_RATE": 4.00, "CD_RATE": 5.34, "CPI_GROWTH": 4.1, "LEADING_INDEX": 99.5, "GOVT_3Y": 6.70, "CORP_AA": 8.12, "CORP_BBB": 11.27, "IPI": 99.5, "EXPORT": 150439},
2002: {"GDP_GROWTH": 7.4, "UNEMPLOYMENT": 3.3, "BASE_RATE": 4.25, "CD_RATE": 4.99, "CPI_GROWTH": 2.8, "LEADING_INDEX": 102.3, "GOVT_3Y": 6.06, "CORP_AA": 7.02, "CORP_BBB": 9.75, "IPI": 108.5, "EXPORT": 162471},
2003: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.6, "BASE_RATE": 3.75, "CD_RATE": 4.24, "CPI_GROWTH": 3.5, "LEADING_INDEX": 98.8, "GOVT_3Y": 4.93, "CORP_AA": 5.70, "CORP_BBB": 8.97, "IPI": 109.8, "EXPORT": 193817},
2004: {"GDP_GROWTH": 4.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 3.25, "CD_RATE": 3.77, "CPI_GROWTH": 3.6, "LEADING_INDEX": 100.5, "GOVT_3Y": 4.11, "CORP_AA": 4.72, "CORP_BBB": 7.53, "IPI": 119.2, "EXPORT": 253845},
2005: {"GDP_GROWTH": 3.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 3.75, "CD_RATE": 3.81, "CPI_GROWTH": 2.8, "LEADING_INDEX": 101.8, "GOVT_3Y": 4.27, "CORP_AA": 4.68, "CORP_BBB": 6.51, "IPI": 126.0, "EXPORT": 284419},
2006: {"GDP_GROWTH": 5.2, "UNEMPLOYMENT": 3.5, "BASE_RATE": 4.50, "CD_RATE": 4.72, "CPI_GROWTH": 2.2, "LEADING_INDEX": 102.5, "GOVT_3Y": 4.83, "CORP_AA": 5.25, "CORP_BBB": 7.08, "IPI": 136.0, "EXPORT": 325465},
2007: {"GDP_GROWTH": 5.5, "UNEMPLOYMENT": 3.2, "BASE_RATE": 5.00, "CD_RATE": 5.36, "CPI_GROWTH": 2.5, "LEADING_INDEX": 103.1, "GOVT_3Y": 5.23, "CORP_AA": 5.70, "CORP_BBB": 7.44, "IPI": 144.5, "EXPORT": 371489},
2008: {"GDP_GROWTH": 2.8, "UNEMPLOYMENT": 3.2, "BASE_RATE": 3.00, "CD_RATE": 5.70, "CPI_GROWTH": 4.7, "LEADING_INDEX": 96.5, "GOVT_3Y": 5.27, "CORP_AA": 7.02, "CORP_BBB": 10.73, "IPI": 148.2, "EXPORT": 422007},
2009: {"GDP_GROWTH": 0.8, "UNEMPLOYMENT": 3.6, "BASE_RATE": 2.00, "CD_RATE": 2.63, "CPI_GROWTH": 2.8, "LEADING_INDEX": 98.2, "GOVT_3Y": 4.04, "CORP_AA": 5.80, "CORP_BBB": 9.24, "IPI": 140.0, "EXPORT": 363534},
2010: {"GDP_GROWTH": 6.8, "UNEMPLOYMENT": 3.7, "BASE_RATE": 2.50, "CD_RATE": 2.80, "CPI_GROWTH": 2.9, "LEADING_INDEX": 103.0, "GOVT_3Y": 3.72, "CORP_AA": 4.66, "CORP_BBB": 7.98, "IPI": 161.5, "EXPORT": 466384},
2011: {"GDP_GROWTH": 3.7, "UNEMPLOYMENT": 3.4, "BASE_RATE": 3.25, "CD_RATE": 3.55, "CPI_GROWTH": 4.0, "LEADING_INDEX": 101.2, "GOVT_3Y": 3.62, "CORP_AA": 4.41, "CORP_BBB": 7.75, "IPI": 168.0, "EXPORT": 555214},
2012: {"GDP_GROWTH": 2.4, "UNEMPLOYMENT": 3.2, "BASE_RATE": 2.75, "CD_RATE": 3.13, "CPI_GROWTH": 2.2, "LEADING_INDEX": 100.3, "GOVT_3Y": 3.13, "CORP_AA": 3.76, "CORP_BBB": 6.56, "IPI": 168.2, "EXPORT": 547870},
2013: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.1, "BASE_RATE": 2.50, "CD_RATE": 2.72, "CPI_GROWTH": 1.3, "LEADING_INDEX": 100.8, "GOVT_3Y": 2.79, "CORP_AA": 3.19, "CORP_BBB": 5.87, "IPI": 168.8, "EXPORT": 559632},
2014: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.5, "BASE_RATE": 2.00, "CD_RATE": 2.36, "CPI_GROWTH": 1.3, "LEADING_INDEX": 101.0, "GOVT_3Y": 2.56, "CORP_AA": 2.99, "CORP_BBB": 5.22, "IPI": 168.5, "EXPORT": 572665},
2015: {"GDP_GROWTH": 2.8, "UNEMPLOYMENT": 3.6, "BASE_RATE": 1.50, "CD_RATE": 1.72, "CPI_GROWTH": 0.7, "LEADING_INDEX": 100.5, "GOVT_3Y": 1.80, "CORP_AA": 2.18, "CORP_BBB": 4.61, "IPI": 168.0, "EXPORT": 526757},
2016: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.25, "CD_RATE": 1.48, "CPI_GROWTH": 1.0, "LEADING_INDEX": 99.8, "GOVT_3Y": 1.44, "CORP_AA": 1.88, "CORP_BBB": 4.60, "IPI": 168.5, "EXPORT": 495426},
2017: {"GDP_GROWTH": 3.2, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.50, "CD_RATE": 1.52, "CPI_GROWTH": 1.9, "LEADING_INDEX": 101.5, "GOVT_3Y": 1.80, "CORP_AA": 2.28, "CORP_BBB": 4.83, "IPI": 174.2, "EXPORT": 573694},
2018: {"GDP_GROWTH": 2.9, "UNEMPLOYMENT": 3.8, "BASE_RATE": 1.75, "CD_RATE": 1.85, "CPI_GROWTH": 1.5, "LEADING_INDEX": 100.8, "GOVT_3Y": 2.10, "CORP_AA": 2.67, "CORP_BBB": 5.41, "IPI": 178.0, "EXPORT": 604860},
2019: {"GDP_GROWTH": 2.2, "UNEMPLOYMENT": 3.8, "BASE_RATE": 1.25, "CD_RATE": 1.63, "CPI_GROWTH": 0.4, "LEADING_INDEX": 99.3, "GOVT_3Y": 1.50, "CORP_AA": 1.93, "CORP_BBB": 4.52, "IPI": 175.5, "EXPORT": 542233},
2020: {"GDP_GROWTH": -0.7, "UNEMPLOYMENT": 4.0, "BASE_RATE": 0.50, "CD_RATE": 0.76, "CPI_GROWTH": 0.5, "LEADING_INDEX": 97.0, "GOVT_3Y": 0.98, "CORP_AA": 2.03, "CORP_BBB": 5.25, "IPI": 170.0, "EXPORT": 512498},
2021: {"GDP_GROWTH": 4.3, "UNEMPLOYMENT": 3.7, "BASE_RATE": 1.00, "CD_RATE": 1.09, "CPI_GROWTH": 2.5, "LEADING_INDEX": 102.8, "GOVT_3Y": 1.43, "CORP_AA": 2.26, "CORP_BBB": 5.64, "IPI": 183.0, "EXPORT": 644400},
2022: {"GDP_GROWTH": 2.6, "UNEMPLOYMENT": 2.9, "BASE_RATE": 3.25, "CD_RATE": 3.77, "CPI_GROWTH": 5.1, "LEADING_INDEX": 99.2, "GOVT_3Y": 3.14, "CORP_AA": 4.25, "CORP_BBB": 8.18, "IPI": 186.5, "EXPORT": 683585},
2023: {"GDP_GROWTH": 1.4, "UNEMPLOYMENT": 2.7, "BASE_RATE": 3.50, "CD_RATE": 3.75, "CPI_GROWTH": 3.6, "LEADING_INDEX": 98.8, "GOVT_3Y": 3.55, "CORP_AA": 4.40, "CORP_BBB": 8.40, "IPI": 183.0, "EXPORT": 632744},
2024: {"GDP_GROWTH": 2.2, "UNEMPLOYMENT": 2.8, "BASE_RATE": 3.00, "CD_RATE": 3.30, "CPI_GROWTH": 2.3, "LEADING_INDEX": 99.5, "GOVT_3Y": 3.20, "CORP_AA": 3.90, "CORP_BBB": 7.50, "IPI": 185.0, "EXPORT": 660000},
2025: {"GDP_GROWTH": 1.8, "UNEMPLOYMENT": 3.0, "BASE_RATE": 2.75, "CD_RATE": 3.00, "CPI_GROWTH": 1.8, "LEADING_INDEX": 99.8, "GOVT_3Y": 2.80, "CORP_AA": 3.50, "CORP_BBB": 6.80, "IPI": 184.0, "EXPORT": 650000},
}
df = pd.DataFrame(data).T
df.index.name = "YEAR"
return df.loc[start_year:end_year]
def compute_derived_features(macro_df: pd.DataFrame) -> pd.DataFrame:
"""
Zt 회귀에 유의미한 파생변수 계산 (부호 검증 완료)
최적 3변수 (R²=0.586, 모든 계수 부호 경제적 일관):
1. CREDIT_SPREAD_LAG1: 신용스프레드(t-1) = CORP_BBB - CORP_AA (1기 래그). +부호=스프레드↑→Zt↑
2. IPI_LAG1: 산업생산지수(t-1). -부호=생산↑→Zt↓
3. EXPORT_DIFF: 수출 변화 (전년차). -부호=수출↑→Zt↓
Parameters
----------
macro_df : pd.DataFrame with at least:
CORP_AA, CORP_BBB (or CREDIT_SPREAD), IPI, EXPORT columns
Returns
-------
pd.DataFrame with columns: CREDIT_SPREAD_LAG1, IPI_LAG1, EXPORT_DIFF
"""
df = macro_df.sort_index()
features = pd.DataFrame(index=df.index)
# 1. 신용스프레드 (1기 래그)
if "CORP_BBB" in df.columns and "CORP_AA" in df.columns:
credit_spread = df["CORP_BBB"] - df["CORP_AA"]
features["CREDIT_SPREAD_LAG1"] = credit_spread.shift(1)
elif "CREDIT_SPREAD" in df.columns:
features["CREDIT_SPREAD_LAG1"] = df["CREDIT_SPREAD"].shift(1)
else:
logger.warning("CREDIT_SPREAD 계산 불가: CORP_BBB/CORP_AA 없음")
# 2. 산업생산지수 (1기 래그)
if "IPI" in df.columns:
features["IPI_LAG1"] = df["IPI"].shift(1)
else:
logger.warning("IPI_LAG1 계산 불가: IPI 없음")
# 3. 수출 변화 (전년 차분)
if "EXPORT" in df.columns:
features["EXPORT_DIFF"] = df["EXPORT"].diff()
else:
logger.warning("EXPORT_DIFF 계산 불가: EXPORT 없음")
return features.dropna()
def load_macro_data(config_path: str = "config.yaml") -> pd.DataFrame:
"""
설정 파일에서 API 키를 읽고 거시경제 데이터 수집
API 실패시 자동으로 fallback 데이터 사용
"""
config = _load_config(config_path)
api_key = config.get("ecos", {}).get("api_key", "sample")
logger.info(f"ECOS API로 거시경제 데이터 수집 시작 (API key: {api_key[:4]}...)")
try:
df = collect_macro_data(api_key)
if df.empty or len(df) < 10:
logger.warning("API 데이터 부족. Fallback 데이터 사용.")
df = _fallback_macro_data()
return df
except Exception as e:
logger.warning(f"API 수집 실패: {e}. Fallback 데이터 사용.")
return _fallback_macro_data()
def _load_config(config_path: str) -> dict:
"""YAML 설정 파일 로딩"""
try:
with open(config_path, "r", encoding="utf-8") as f:
return yaml.safe_load(f)
except FileNotFoundError:
logger.warning(f"설정 파일 '{config_path}' 없음. 기본값 사용.")
return {}
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